pax_global_header00006660000000000000000000000064152303550250014512gustar00rootroot0000000000000052 comment=bc022b91aa1c3b1fb771ac70931ae0009dbf9bad python-mistral-common-1.11.7/000077500000000000000000000000001523035502500160615ustar00rootroot00000000000000python-mistral-common-1.11.7/.github/000077500000000000000000000000001523035502500174215ustar00rootroot00000000000000python-mistral-common-1.11.7/.github/ISSUE_TEMPLATE/000077500000000000000000000000001523035502500216045ustar00rootroot00000000000000python-mistral-common-1.11.7/.github/ISSUE_TEMPLATE/bug_report.yml000066400000000000000000000033041523035502500244770ustar00rootroot00000000000000name: Bug report related to mistral-common description: Submit a bug report that's related to mistral-common title: '[BUG: ' labels: ['bug', 'triage'] body: - type: markdown attributes: value: | Thanks for taking the time to fill out this bug report! - type: textarea id: python-vv attributes: label: Python -VV description: Run `python -VV` from your virtual environment placeholder: Copy-paste the output (no need for backticks, will be formatted into code automatically) render: shell validations: required: true - type: textarea id: pip-freeze attributes: label: Pip Freeze description: Run `pip freeze` from your virtual environment placeholder: Copy-paste the output (no need for backticks, will be formatted into code automatically) render: shell validations: required: true - type: textarea id: reproduction-steps attributes: label: Reproduction Steps description: Provide a clear and concise description of the steps that lead to your issue. placeholder: | 1. 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Step 2... ... validations: required: true - type: textarea id: expected-behavior attributes: label: Expected Behavior description: Explain briefly what you expected to happen. validations: required: true - type: textarea id: additional-context attributes: label: Additional Context description: Add any context about your problem that you deem relevant. - type: textarea id: suggested-solutions attributes: label: Suggested Solutions description: Please list any solutions you recommend we consider. python-mistral-common-1.11.7/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000004171523035502500235760ustar00rootroot00000000000000blank_issues_enabled: false contact_links: - name: Documentation url: https://docs.mistral.ai about: Developer documentation for the Mistral AI platform - name: Discord url: https://discord.com/invite/mistralai) about: Chat with the Mistral community python-mistral-common-1.11.7/.github/workflows/000077500000000000000000000000001523035502500214565ustar00rootroot00000000000000python-mistral-common-1.11.7/.github/workflows/docs.yaml000066400000000000000000000022531523035502500232740ustar00rootroot00000000000000name: Mistral Common CI on: push: branches: - main permissions: contents: write jobs: deploy_docs: name: Publish runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4 - name: Configure Git credentials run: | git config user.name github-actions[bot] git config user.email 41898282+github-actions[bot]@users.noreply.github.com - name: Install uv uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5 with: python-version: 3.14 - name: Cache env run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - name: Cache dependencies uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4 with: key: mkdocs-material-${{ env.cache_id }} path: .cache restore-keys: | mkdocs-material- - name: Install dependencies run: | uv sync --all-extras --group docs uv pip install -e . --no-deps - name: Publish documentation website run: mkdocs gh-deploy --forcepython-mistral-common-1.11.7/.github/workflows/lint_build_test.yaml000066400000000000000000000060551523035502500255340ustar00rootroot00000000000000name: Mistral Common CI permissions: contents: read on: push: branches: - main pull_request: types: - opened - synchronize - reopened - ready_for_review # we cancel all runs of previous commits in PR. On main we run the CI on all commits concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || 'no-pr' }} cancel-in-progress: ${{ github.event.pull_request.number != null }} jobs: build_lint_test: if: ${{ github.event.pull_request.draft != true }} runs-on: ubuntu-latest strategy: matrix: # Support all LTS versions of Python python-version: [ "3.10", "3.11", "3.12", "3.13", "3.14", ] steps: - name: Checkout uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4 - name: Install uv uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5 with: python-version: ${{ matrix.python-version }} - name: Check uv lock run: | uv lock --check # Install - name: Install Dependencies run: | uv sync --frozen --all-extras --group dev # Ruff Linter - name: Ruff Linter run: | uv run ruff check . # Ruff Format - name: Ruff Format run: | uv run ruff format . --check # Mypy - name: Mypy Check run: | uv run mypy . # PyTest - name: Tests run: | uv run pytest --cov=mistral_common tests/ --ignore=tests/integrations --cov-report "xml:coverage.xml" # Doctests - name: Doctests run: | uv run pytest --doctest-modules ./src build_docs: if: ${{ github.event.pull_request.draft != true }} runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4 - name: Install uv uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5 with: python-version: 3.14 - name: Install Dependencies run: | uv sync --frozen --all-extras --group docs - name: Build Docs run: | uv run mkdocs build --strict integration_tests: if: ${{ github.event.pull_request.draft != true }} runs-on: ubuntu-latest strategy: matrix: python-version: [ "3.10", "3.11", "3.12", "3.13", "3.14", ] steps: - name: Checkout uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4 - name: Install uv uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5 with: python-version: ${{ matrix.python-version }} - name: Install Dependencies run: | uv sync --frozen --all-extras --group dev --group test-integrations - name: Integration Tests run: | uv run --no-sync pytest tests/integrations/ -v python-mistral-common-1.11.7/.gitignore000066400000000000000000000060561523035502500200600ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # VS Code .vscode/ # MAC OS .DS_Store # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ python-mistral-common-1.11.7/.pre-commit-config.yaml000066400000000000000000000006661523035502500223520ustar00rootroot00000000000000repos: - repo: https://github.com/rhysd/actionlint rev: v1.7.7 hooks: - id: actionlint entry: actionlint -shellcheck='' - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.12.7 hooks: - id: ruff-check args: [ --fix, --exit-non-zero-on-fix, --unsafe-fixes] - id: ruff-format - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.11.14 hooks: - id: uv-lock python-mistral-common-1.11.7/AGENTS.md000066400000000000000000000156611523035502500173750ustar00rootroot00000000000000# AGENTS.md ## Project overview Mistral-Common is a preprocessing library for Mistral's Large Language Models (LLMs). It encodes requests for Instruct, Transcription or Fill-In-The-Middle (FIM) tasks to tokens and optionally processed images or audios. - Language: Python 3.10 to 3.14 - Package Manager: uv - Testing: pytest - Formatting and Linting: Ruff - Type checker: mypy - CI: GitHub Actions ## Project Structure ``` mistral-common/ ├── src/ │ └── mistral_common/ │ ├── guidance/ │ ├── integrations/ │ ├── protocol/ │ ├── tokens/ │ └── ... ├── scripts/ ├── tests/ ├── docs/ ├── .github/ ├── .pre-commit-config.yaml ├── pyproject.toml └── README.md ``` ### Root-level files in src/mistral_common/ - `audio.py`: Audio processing utilities including Audio class and mel-scale conversions - `base.py`: Base Pydantic model configuration - `deprecation.py`: Deprecation utilities (`deprecated_import`, `warn_once`) for emitting one-shot warnings on moved or removed symbols - `exceptions.py`: Custom exception classes for the library - `image.py`: Image processing utilities including download and serialization - `imports.py`: Import utilities and dependency checks - `multimodal.py`: Multimodal processing utilities deprecated in favor to `image.py` ### Protocol - `src/mistral_common/protocol/`: Protocol handling - `instruct/`: Instruct protocol - `chunk.py`: Chunks content used by messages - `converters.py`: Converter helpers between `ChatCompletionRequest` and `Openai` requests - `messages.py`: Instruct messages definition - `normalize.py`: Normalizers for `ChatCompletionRequest` - `request.py`: Definition of `ChatCompletionRequest`. This is the entry point of user queries for Instruct requests - `tool_calls.py`: Tool calling logic - `validator.py`: Validators for `ChatCompletionRequest` - `fim/`: Fill-in-the-middle protocol - `request.py`: Definition of `FIMRequest`. This is the entry point of user queries for FIM requests - `transcription/`: Transcription protocol - `request.py`: Definition of `TranscriptionRequest`. This is the entry point of user queries for Transcription requests - `speech/`: Speech protocol - `request.py`: Definition of `SpeechRequest`. This is the entry point of user queries for Speech requests - `base.py`: Definition of `BaseCompletionRequest` subclassed by FIM and Instruct requests - `utils.py`: Utility functions ### Tokenization - `src/mistral_common/tokens/`: Tokenization - `tokenizers/`: Tokenizer implementations - `audio.py`: Audio processing - `base.py`: Base Tokenizer implementation - `image.py`: Image processing - `instruct.py`: Instruct Tokenizer that encodes requests via Tekken tokenizer - `mistral.py`: Mistral Tokenizer that normalizes and validates requests to pass them to an instruct tokenizer - `model_settings_builder.py`: Builders (`FieldBuilder`, `EnumBuilder`, `ModelSettingsBuilder`) for validating and constructing model settings - `multimodal.py`: deprecated in favor of `image.py` - `sentencepiece.py`: Sentence Piece tokenizer (deprecated) - `tekken.py`: Tekken tokenizer used by all recent models - `utils.py`: Utility functions for the tokenizers - `instruct/`: deprecated in favor of `src/mistral_common/protocol/instruct/request.py` ### Guidance (Grammar) - `src/mistral_common/guidance/`: Creates Lark grammars for tool calls, JSON schema and reasoning using llguidance - `grammar_factory.py`: `GrammarFactory` that builds and renders Lark grammars from Jinja templates - `tokenizer.py`: Adapts Tekken tokenizer for llguidance - `data/`: Jinja-templated Lark grammar files for base, thinking (special tokens) and thinking (plain text) modes ### Integrations - `src/mistral_common/integrations/`: Third-party framework integrations - `chat_templates/`: Chat template generation for HuggingFace Transformers - `chat_templates.py`: Public API for generating chat templates (`generate_chat_template`) - `template_generator.py`: Core template generation engine with `TemplateConfig` and `build_chat_template` ### Scripts - `scripts/generate_chat_template.py`: CLI for generating and saving chat templates ## Experimental - `src/mistral_common/experimental/`: Experimental features - `utils.py`: Utility functions - `tools.py`: Tool calls parser - `think.py`: Thinking parser - `app/`: FastAPI application - `routers.py`: API routers - `main.py`: Application entry point - `models.py`: Pydantic models ## Data - `src/mistral_common/data/`: Data files for tokenizers ### Other files and directories - `tests/`: Test suite - `docs/`: Documentation - `.github/workflows/`: CI/CD workflows - `.pre-commit-config.yaml`: Pre-commit hooks - `pyproject.toml`: Project configuration ## Code Style Guidelines ### Style - Respect ruff and mypy rules - Naming: snake_case for functions/variables, PascalCase for classes - Use Python functionalities supported by Python 3.10 - Call function arguments explicitly by keyword, not implicitly by position (e.g. `fn(x=1, y=2)`, not `fn(1, 2)`) ### Imports - Use absolute imports for modules within the project - Do NOT use wildcard imports - Do NOT add import inside `__init__` - Use `TYPE_CHECKING` blocks for type-only imports - Do NOT use `from __future__ import annotations` ### Type Hints (Required) - Use Python's type hints extensively - Use modern (Python 3.10+) typing module types ### Error Handling - Use custom exceptions from `mistral_common.exceptions` - Provide meaningful error messages ### Docstrings (Required) - Use Google-style docstrings - One-liner for simple functions - Multi-line with Args/Returns for complex ones - Use `r"""` for raw docstrings - Document all parameters and return values. - Do NOT put types in the docstring for parameters and return values. - For the returns sections, only describe the returned value and do not write its name - Include examples where appropriate that can be tested via `doctest` ## Development Workflow 1. Set up using uv ```bash uv sync --frozen --all-extras --group dev --python 3.12 source .venv/bin/activate uv run pre-commit install ``` 2. Make Changes - Follow code style guidelines - Write tests for new functionality - Update documentation - When adding dependencies, modify root `pyproject.toml`, then run `uv lock` followed by `uv sync --frozen` - Backward Compatibility: Don't break existing functionality 3. Run linter, formatter (Ruff), type checker (mypy) and tests (pytest), including doctests ### Commit - After adding your changes before committing ensure pre-commit is installed or run it manually. - Use imperative grammar, start with a verb and be concise. ## Additional Resources - [Pydantic Documentation](https://docs.pydantic.dev/latest/) - [Ruff Documentation](https://docs.astral.sh/ruff/) - [Mypy Documentation](https://mypy.readthedocs.io/) - [Pytest Documentation](https://docs.pytest.org/) python-mistral-common-1.11.7/LICENCE000066400000000000000000000261131523035502500170510ustar00rootroot00000000000000 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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Mistral AI

# Mistral-common [![PyPI version](https://img.shields.io/pypi/v/mistral-common?label=release&logo=pypi&logoColor=white)](https://pypi.org/project/mistral-common/) [![Tests](https://img.shields.io/github/actions/workflow/status/mistralai/mistral-common/lint_build_test.yaml?label=tests&branch=main)](https://github.com/mistralai/mistral-common/actions/workflows/lint_build_test.yaml) [![Documentation](https://img.shields.io/website?url=https%3A%2F%2Fmistralai.github.io%2Fmistral-common%2F&up_message=online&down_message=offline&label=docs)](https://mistralai.github.io/mistral-common/) [![Python version](https://img.shields.io/badge/dynamic/json?query=info.requires_python&label=python&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fmistral-common%2Fjson)](https://www.python.org/downloads/) [![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](./LICENCE)
## What is it? **mistral-common** is a set of tools to help you work with [Mistral AI](https://mistral.ai/) models. We open-source the tokenizers, validation and normalization code that can be used with our models. This ensures that you can take full advantage of our models for the following features: - **tokenization** of text, images and tools calls. - **validation and normalization** of requests, messages, tool calls, and responses. This is built on top of the [Pydantic](https://docs.pydantic.dev/latest/) library. We also version our tokenizers to guarantee backward compatibility for the models that we release. ## For who ? This library is for you if you want to: - use our models in your own application. - build your own models and want to use the same tokenization and validation code as we do. ## How to use it ? You can install the library using pip: ```sh pip install mistral-common ``` We propose different dependencies to install depending on your needs: - `image`: to use the image tokenizers. - `audio`: to use the audio tokenizers. - `hf-hub`: to download the tokenizers from the Hugging Face Hub. - `sentencepiece`: to allow the use of SentencePiece tokenizers. This is now optional as we only release `Tekken` tokenizers for recent models. - \[Experimental\] `server`: to use our tokenizers in a server mode. Each dependency is optional and can be installed separately or all together using the following commands: ```sh pip install "mistral-common[image]" pip install "mistral-common[audio]" pip install "mistral-common[hf-hub]" pip install "mistral-common[sentencepiece]" pip install "mistral-common[server]" pip install "mistral-common[image,audio,hf-hub,sentencepiece,server]" ``` For more information, please refer to the [documentation](https://mistralai.github.io/mistral-common/). ## How to contribute ? We welcome contributions to this library. All of our features are tested to ensure best usage. But if you encounter a bug, find difficulties in using `mistral-common`, please open an issue on our [GitHub repository](https://github.com/mistralai/mistral-common/issues). If you want to contribute to the code, please fork the repository and open a pull request. We will review your changes and merge them if they are relevant to the library. ```sh # Clone the repository git clone https://github.com//mistral-common.git cd mistral-common # Create a virtual environment and install the dependencies uv venv source .venv/bin/activate uv sync --frozen --all-extras --group dev # --group docs if you want to build the documentation # Install the pre-commit hooks uv run pre-commit install ``` ## License This library is licensed under the Apache 2.0 License. See the [LICENCE](./LICENCE) file for more information. *You must not use this library or our models in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.* python-mistral-common-1.11.7/docs/000077500000000000000000000000001523035502500170115ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/assets/000077500000000000000000000000001523035502500203135ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/assets/extra.css000066400000000000000000000020701523035502500221470ustar00rootroot00000000000000[data-md-color-media="(prefers-color-scheme: light)"] { --md-primary-fg-color: #be906e; --md-primary-fg-color--light: #c7a083; --md-primary-fg-color--dark: #b58059; --md-code-bg-color: #f5e6dd; --md-accent-fg-color: hsl(0, 0%, 4%); --md-primary-bg-color: hsl(0, 0%, 30%); .md-header { background-color: #f5e6dd; }; .md-tabs { background-color: #f5e6dd; }; .md-tabs__item { color: hsl(0, 0%, 30%) } #logo-light { display: block } #logo-dark { display: none }; } [data-md-color-media="(prefers-color-scheme: dark)"] { --md-primary-fg-color: #ef9743; --md-primary-fg-color--light: #f1a75f; --md-primary-fg-color--dark: #ed8727; --md-code-bg-color: #131313; --md-accent-fg-color: hsl(0, 0%, 94%); --md-primary-bg-color: hsl(0, 0%, 90%); .md-header { background-color: #131313; }; .md-tabs { background-color: #131313; }; .md-tabs__item { color: hsl(0, 0%, 90%) } #logo-light { display: none } #logo-dark { display: block } }python-mistral-common-1.11.7/docs/assets/logo-dark.svg000066400000000000000000000053611523035502500227200ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/assets/logo.svg000066400000000000000000000054371523035502500220050ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/assets/logo_favicon.png000066400000000000000000000044351523035502500234740ustar00rootroot00000000000000PNG  IHDR - pHYs!8!8E1`sRGBgAMA aIDATx݉]Uog&A{Am@$hJ2T \^h r!qt+^zXk7w޵ Qp2?s37wi.lGl·q3C<|feǹGÝuo"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H !2"H q>}msy~.gm93p?.fe9&y"2<jݾVt$D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bdt,m{js,yx=]d $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@b:;{,x}<=Oy: W__~: lηYym~p=:q9vֲn_bY:7Z`wws @Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd $D@Bd9y8l}|7ǹXݶ4ook`H #IENDB`python-mistral-common-1.11.7/docs/examples/000077500000000000000000000000001523035502500206275ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/examples/inference.md000066400000000000000000000156611523035502500231200ustar00rootroot00000000000000# Inference We have a few examples of how to use the library with our models: - [Chat Completion](#chat-completion) - [Text only](#text-only) - [Image](#image) - [Function calling](#function-calling) - [Audio](#audio) - [Fill-in-the-middle (FIM) Completion](#fim) - [Audio Transcription](#audio-transcription) ## Chat Completion ### Text-only ```python from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import ( AssistantMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer repo_id = "mistralai/Mistral-Large-Instruct-2411" tokenizer = MistralTokenizer.from_hf_hub(repo_id) messages = [ UserMessage(content="What is the capital of France?"), AssistantMessage(content="The capital of France is Paris."), UserMessage(content="And the capital of Spain?"), ] request = ChatCompletionRequest(messages=messages) tokenized = tokenizer.encode_chat_completion(request) # pass tokenized.tokens to your favorite model print(tokenized.tokens) # print text to visually see tokens print(tokenized.text) ``` ### Image ```python from mistral_common.protocol.instruct.chunk import ( ImageURLChunk, TextChunk ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer repo_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" tokenizer = MistralTokenizer.from_hf_hub(repo_id) system_prompt = """You are Mistral Small 3.2, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris. You power an AI assistant called Le Chat. Your knowledge base was last updated on 2023-10-01. The current date is 03-06-2025. You have the ability to process images.""" messages = [ SystemMessage(content=system_prompt), UserMessage(content="What is the capital of France?"), AssistantMessage(content="The capital of France is Paris."), UserMessage(content=[TextChunk(text="Does this photo comes from there ?")]), UserMessage( content=[ ImageURLChunk( image_url="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4b/La_Tour_Eiffel_vue_de_la_Tour_Saint-Jacques%2C_Paris_ao%C3%BBt_2014_%282%29.jpg/1280px-La_Tour_Eiffel_vue_de_la_Tour_Saint-Jacques%2C_Paris_ao%C3%BBt_2014_%282%29.jpg" ) ] ), ] request = ChatCompletionRequest(messages=messages) tokenized = tokenizer.encode_chat_completion(request) # pass tokenized.tokens to your favorite image model print(tokenized.tokens) print(tokenized.images) # print text to visually see tokens print(tokenized.text) ``` ### Function calling ```python from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import ( AssistantMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.tool_calls import Function, Tool repo_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" tokenizer = MistralTokenizer.from_hf_hub(repo_id) messages = [UserMessage(content="What is the weather in France like?")] tool = Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) request = ChatCompletionRequest(messages=messages, tools=[tool]) tokenized = tokenizer.encode_chat_completion(request) # pass tokenized.tokens to your favorite agent model print(tokenized.tokens) # print text to visually see tokens print(tokenized.text) ``` ### Audio ```py from mistral_common.protocol.instruct.chunk import TextChunk, AudioChunk from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.audio import Audio from huggingface_hub import hf_hub_download repo_id = "mistralai/Voxtral-Mini-3B-2507" tokenizer = MistralTokenizer.from_hf_hub(repo_id) obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset") bcn_file = hf_hub_download("patrickvonplaten/audio_samples", "bcn_weather.mp3", repo_type="dataset") def file_to_chunk(file: str) -> AudioChunk: audio = Audio.from_file(file, strict=False) return AudioChunk.from_audio(audio) text_chunk = TextChunk(text="Which speaker do you prefer between the two? Why? How are they different from each other?") user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai() request = ChatCompletionRequest(messages=[user_msg]) tokenized = tokenizer.encode_chat_completion(request) # pass tokenized.tokens to your favorite audio model print(tokenized.tokens) print(tokenized.audios) # print text to visually see tokens print(tokenized.text) ``` ## FIM ```python from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.fim.request import FIMRequest tokenizer = MistralTokenizer.from_hf_hub("mistralai/Codestral-22B-v0.1") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokenized = tokenizer.encode_fim(request) # pass tokenized.tokens to your favorite model print(tokenized.tokens) # print text to visually see tokens print(tokenized.text) ``` ## Audio Transcription ```python from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from huggingface_hub import hf_hub_download repo_id = "mistralai/Voxtral-Mini-3B-2507" tokenizer = MistralTokenizer.from_hf_hub(repo_id) obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset") audio = Audio.from_file(obama_file, strict=False) request = TranscriptionRequest(model=repo_id, audio=audio.to_base64(audio.format), language="en") tokenized = tokenizer.encode_transcription(request) # pass tokenized.tokens to your favorite audio model print(tokenized.tokens) print(tokenized.audios) # print text to visually see tokens print(tokenized.text) ``` python-mistral-common-1.11.7/docs/gen_api_pages.py000066400000000000000000000031301523035502500221410ustar00rootroot00000000000000"""Generate the code API reference pages Refer to https://mkdocstrings.github.io/recipes/ for more details. """ from pathlib import Path import mkdocs_gen_files SRC_PATH = "./src/" REF_PATH = "code_reference" IGNORED_FILES = ["__init__.py", "multimodal.py"] nav = mkdocs_gen_files.Nav() for path in sorted(Path(SRC_PATH).rglob("*.py")): module_path = path.relative_to(SRC_PATH).with_suffix("") doc_path = path.relative_to(SRC_PATH).with_suffix(".md") full_doc_path = Path(REF_PATH, doc_path) parts = list(module_path.parts) if parts[-1] == "__init__": parts = parts[:-1] elif parts[-1] == "__main__": continue # Create root index file if not parts: doc_path = doc_path.with_name("index.md") full_doc_path = full_doc_path.with_name("index.md") nav["index"] = doc_path.as_posix() with open(f"./docs/{REF_PATH}/index.md", "r", encoding="utf-8") as index_file: lines = index_file.readlines() with mkdocs_gen_files.open(full_doc_path, "w") as fd: fd.writelines(lines) mkdocs_gen_files.set_edit_path(full_doc_path, path) # Add all python files that are not in the ignore list elif not any(ignored in path.name for ignored in IGNORED_FILES): nav[parts] = doc_path.as_posix() with mkdocs_gen_files.open(full_doc_path, "w") as fd: print("::: " + ".".join(parts), file=fd) mkdocs_gen_files.set_edit_path(full_doc_path, Path(path)) with mkdocs_gen_files.open(f"{REF_PATH}/SUMMARY.md", "w") as nav_file: nav_file.writelines(nav.build_literate_nav()) python-mistral-common-1.11.7/docs/index.md000066400000000000000000000023761523035502500204520ustar00rootroot00000000000000# Introduction ## What is it? **mistral-common** is a set of tools to help you work with Mistral models. We open-source the tokenizers, validation and normalization code that can be used with our models. This ensures that you can take full advantage of our models for the following features: - **tokenization** of text, images and tools calls. - **validation and normalization** of requests, messages, tool calls, and responses. This is built on top of the [Pydantic](https://docs.pydantic.dev/latest/) library. We also version our tokenizers to guarantee backward compatibility for the models that we release. ## For who ? This library is for you if you want to: - use our models in your own application. - build your own models and want to use the same tokenization and validation code as we do. ## Table of contents Explore the following sections: - [Quickstart](./examples/inference.md) for simple code snippets to use the library for different tasks. - [Usage](./usage/index.md) section to install and use the library. - [Code Reference][mistral_common.base] section to see the code documentation. ## Launch the documentation locally To launch the documentation locally, simply run the following command at the root of the repository: ```sh mkdocs serve ``` python-mistral-common-1.11.7/docs/overrides/000077500000000000000000000000001523035502500210135ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/overrides/partials/000077500000000000000000000000001523035502500226325ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/overrides/partials/copyright.html000066400000000000000000000002351523035502500255300ustar00rootroot00000000000000
{% if config.copyright %}
{{ config.copyright }}
{% endif %}
python-mistral-common-1.11.7/docs/overrides/partials/logo.html000066400000000000000000000005451523035502500244640ustar00rootroot00000000000000{% if config.theme.logo %} =Mistral AI Mistral AI {% else %} {% set icon = config.theme.icon.logo or "material/library" %} {% include ".icons/" ~ icon ~ ".svg" %} {% endif %}python-mistral-common-1.11.7/docs/usage/000077500000000000000000000000001523035502500201155ustar00rootroot00000000000000python-mistral-common-1.11.7/docs/usage/experimental.md000066400000000000000000000203631523035502500231400ustar00rootroot00000000000000# Mistral-common Experimental API ## Context The `experimental` module provides access to a FastAPI server designed to handle tokenization and detokenization operations through a REST API. This server features: 1. **Tokenization**: Converting chat completion requests into token sequences 2. **Detokenization**: Converting token sequences back into human-readable formats to an [AssistantMessage][mistral_common.protocol.instruct.messages.AssistantMessage] object or a raw string. 3. **Chat Completion**: Generating an [AssistantMessage][mistral_common.protocol.instruct.messages.AssistantMessage] from a [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest] using an engine backend. This API serves as a bridge between different providers and tokenization needs regardless of the provider programming language. ## Features 1. **Tokenizer Version Support**: Handles all our tokenizer versions automatically 2. **Tool Call Parsing**: Automatically extracts tool calls from token sequences 3. **Think Chunk Support**: Handles think chunks for v13+ tokenizers 5. **OpenAI Compatibility**: Accepts OpenAI-formatted requests for easier integration ## Error Handling The API provides detailed error messages for: - Invalid token sequences - Empty token lists - Missing tokenizer configuration - Validation errors in requests - Tool call parsing failures ## Usage ### Installation To use the experimental API server, you need to install mistral-common with the appropriate dependencies: ```bash pip install mistral-common[server] ``` ### Launching the Server You can launch the server using the follwing CLI command: ```bash mistral_common serve mistralai/Magistral-Small-2507 [validation_mode] \ --host 127.0.0.1 --port 8000 \ --engine-url http://127.0.0.1:8080 --engine-backend llama_cpp \ --timeout 60 ``` #### Command Line Options - `tokenizer_path`: Path to the tokenizer. Can be a HuggingFace model ID or a local path. - `validation_mode`: Validation mode to use, choices in: "test", "finetuning", "serving" (Optional, defaults to `"test"`) - `--host`: API host (default: `127.0.0.1`) - `--port`: API port (default: `0` - auto-selects available port) - `--engine-url`: URL of the engine (default: `http://127.0.0.1:8080`) - `--engine-backend`: Engine backend to use (default: `llama_cpp`) - `--timeout`: Timeout for the engine (default: `60`) ## Available Routes ### Documentation - **Route**: `/` or `/docs` - **Method**: GET - **Description**: The Swagger documentation The Swagger UI provides interactive documentation for all available endpoints. ### Tokenization #### Tokenize Request - **Route**: `/v1/tokenize/` - **Method**: POST - **Description**: Tokenizes a chat completion request - **Request Body**: - Chat completion request in either: - Mistral-common format ([ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest]) - OpenAI-compatible format ([OpenAIChatCompletionRequest][mistral_common.experimental.app.models.OpenAIChatCompletionRequest]). Streaming is not supported. - **Response**: List of token IDs Example requests: ```python import requests from fastapi.encoders import jsonable_encoder from mistral_common.protocol.instruct.chunk import TextChunk, ThinkChunk from mistral_common.protocol.instruct.messages import SystemMessage, UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest # Simple request response = requests.post("http://localhost:8000/v1/tokenize/", json={ "messages": [ {"role": "user", "content": "Hello, how are you?"} ] }) print(response.json()) # [1, 3, 22177, 1044, 2606, 1584, 1636, 1063, 4] # Complex request with tools and think chunks system_message = SystemMessage( content=[ TextChunk( text=( "First draft your thinking process (inner monologue) until you arrive at a response. Format your " "response using Markdown, and use LaTeX for any mathematical equations. Write both your thoughts and " "the response in the same language as the input.\n\nYour thinking process must follow the template " "below:" ) ), ThinkChunk( thinking=( "Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and " "as long as you want until you are confident to generate the response. Use the same language as the " "input." ), closed=True, ), TextChunk(text="Here, provide a self-contained response."), ], ) response = requests.post( "http://localhost:8000/v1/tokenize/", json=jsonable_encoder( ChatCompletionRequest(messages=[ system_message, UserMessage(content="How many 'r' are there in strawberry ?") ]) ), ) print(response.json()) # [1, 17, 10107, 19564, 2143, ..., 33681, 3082, 4] ``` ### Detokenization #### Detokenize to Assistant Message - **Route**: `/v1/detokenize/` - **Method**: POST - **Description**: Converts tokens to an assistant message with tool call parsing - **Request Body**: - List of token IDs - **Response**: [AssistantMessage][mistral_common.protocol.instruct.messages.AssistantMessage] object with parsed content and tool calls Example requests: ```python import requests response = requests.post( "http://localhost:8000/v1/detokenize/", json=[1, 3, 22177, 1044, 2606, 1584, 1636, 1063, 4] ) print(response.json()) # {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Hello, how are you?'}], 'tool_calls': None, 'prefix': True} # With think chunks response = requests.post( "http://localhost:8000/v1/detokenize/", json=[12598, 1639, 3648, 2314, 1494, 1046, 34, 6958, 1584, 1032, 1051, 1576, 1114, 1039, 1294, 51567, 33681, 3082, 35, 19587, 1051, 19587, 2] ) print(response.json()) # {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Let me think about it.'}, {'type': 'thinking', 'thinking': "There are 3 'r' in strawberry ?", 'closed': True}, {'type': 'text', 'text': '$$3$$'}], 'tool_calls': None, 'prefix': False} # With tool calls response = requests.post( "http://localhost:8000/v1/detokenize/", json=[9, 12296, 1095, 99571, 32, 38985, 1039, 3494, 4550, 1576, 1314, 3416, 33681, 2096, 1576, 27965, 4550, 1576, 1114, 1039, 27024, 2] ) print(response.json()) # {'role': 'assistant', 'content': None, 'tool_calls': [{'id': 'null', 'type': 'function', 'function': {'name': 'count_letters', 'arguments': "{'word': 'strawberry', 'letter': 'r'}"}}], 'prefix': False} ``` #### Detokenize to String - **Route**: `/v1/detokenize/string` - **Method**: POST - **Description**: Converts tokens to a raw string - **Request Body**: - `tokens`: List of token IDs - `special_token_policy`: Policy for handling special tokens. See [SpecialTokenPolicy][mistral_common.tokens.tokenizers.base.SpecialTokenPolicy] - **Response**: Detokenized string Example request: ```python import requests response = requests.post("http://localhost:8000/v1/detokenize/string", json={ "tokens":[1, 3, 22177, 1044, 2606, 1584, 1636, 1063, 4], "special_token_policy": 1, }) print(response.json()) # [INST]Hello, how are you?[/INST] ``` ### Chat Completions - **Route**: `/v1/chat/completions` - **Method**: POST - **Description**: Generates text from a chat completion request. This endpoint forwards the request to the engine server. - **Request Body**: - Chat completion request in either: - Mistral-common format ([ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest]) - OpenAI-compatible format ([OpenAIChatCompletionRequest][mistral_common.experimental.app.models.OpenAIChatCompletionRequest]). Streaming is not supported. - **Response**: Chat completion response in OpenAI-compatible format Example request: ```python import requests response = requests.post("http://localhost:8000/v1/chat/completions", json={ "messages": [ {"role": "user", "content": "Hello, how are you?"} ] }) print(response.json()) # {'role': 'assistant', 'content': 'Hello! I am doing well, thank you for asking.'} ``` ## Issues and feedback If you encounter any issues or have feedback, please open an issue or discussion on the [GitHub repository](https://github.com/mistralai/mistral-common). python-mistral-common-1.11.7/docs/usage/images.md000066400000000000000000000106011523035502500217020ustar00rootroot00000000000000# Images Most of the recently released [Mistral models](https://huggingface.co/mistralai/models) support image inputs. Images are represented as [BaseContentChunk][mistral_common.protocol.instruct.chunk.BaseContentChunk] objects within the `messages` field of the [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest]. Encoding an image via a [ImageEncoder][mistral_common.tokens.tokenizers.image.ImageEncoder] will return: - a sequence of special tokens representing the image. - the image normalized as a numpy array. ## Supported image formats Mistral Image encoders use Pillow to decode images and OpenCV to encode. Hence, the supported formats are the same as Pillow's. The images can be provided as: - an [ImageURLChunk][mistral_common.protocol.instruct.chunk.ImageURLChunk]: a pydantic model containing an image URL from which the image will be downloaded. - an [ImageChunk][mistral_common.protocol.instruct.chunk.ImageChunk]: a pydantic model containing a serialized image that can be either a base64 string or a pillow image. ## Use an Image encoder with our tokenizer Our tokenizers can an [ImageEncoder][mistral_common.tokens.tokenizers.image.ImageEncoder] that is configured with [ImageConfig][mistral_common.tokens.tokenizers.image.ImageConfig]. The attributes of the [ImageConfig][mistral_common.tokens.tokenizers.image.ImageConfig] configure how the images will be patched into tokens: - `image_patch_size`: the square size of a patch in pixels to form one token. E.g if the image is 224x224 and the patch size is 14, then the image will be divided into 16x16 patches. - `max_image_size`: the maximum size of the image in pixels. If the image is larger, it will be resized to this size. - `spatial_merge_size`: the number of patches to merge into one token. This is useful to reduce the number of redundant tokens in the image. E.g if the image is 224x224 and the patch size is 14, then the image will be divided into 16x16 patches. If the spatial merge size is 2, then the image will be divided into 8x8 patches. ```python from mistral_common.protocol.instruct.chunk import ImageURLChunk from mistral_common.tokens.tokenizers.image import ImageEncoder, ImageConfig, SpecialImageIDs special_ids = SpecialImageIDs(img=10, img_break=11, img_end=12) # These are normally automatically set by the tokenizer config = ImageConfig(image_patch_size=14, max_image_size=224, spatial_merge_size=2) image = ImageURLChunk(image_url="https://live.staticflickr.com/7250/7534338696_b33e941b7d_b.jpg") encoder = ImageEncoder(config, special_ids) encoder(image) ``` ## Tokenize an image Let's load the tekken tokenizer used for [Mistral Small 3.1's](https://mistral.ai/news/mistral-small-3-1) tokenizer to encode and tokenize an image. ```python from huggingface_hub import hf_hub_download from mistral_common.protocol.instruct.chunk import ImageURLChunk, TextChunk from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer model_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" tokenizer = MistralTokenizer.from_hf_hub(repo_id=model_id, token="your_hf_token") tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ ImageURLChunk(image_url="https://live.staticflickr.com/7250/7534338696_b33e941b7d_b.jpg"), TextChunk(text="What is displayed in this image?"), ] ) ], ) ) # Tokenized(tokens=[1, 3, 10, 10, ...], text='[INST][IMG][IMG][IMG][IMG]...', prefix_ids=None, images=[array[[(0.95238595, 0.95238795, 0.95224484, ...,)]]]) ``` The output contains: - the **text**: the string equivalent of the tokens. The image is represented into a sequence of special `[IMG]` tokens with `[IMG_BREAK]` at regular intervals and `[IMG_END]` at the end. An image is a grid and each `[IMG]` represent a patch, the `IMG_BREAK` tokens the end of a row. The `IMG_END` token is used to mark the end of the image. - the **tokens**: identifier used by the model for the text. The special tokens of images are not directly used by the model, but replaced by the features of an image encoder. - the **prefix_ids**: Used for FIM (Fill-In-the-Middle) tasks, here it is `None`. - the **images**: the images normalized as a numpy array. python-mistral-common-1.11.7/docs/usage/index.md000066400000000000000000000012301523035502500215420ustar00rootroot00000000000000# Usage ## Installation See [installation instructions](./install.md). ## Getting Started `mistral-common` is a collection of common utilities for Mistral to construct valid requests and call tokenizers. To take full advantage of the features of our models, you will need to use text, images, and tools. Building a valid request can be challenging, so we've created a set of tools to help you get started. See the following sections for more details: - The [requests](./requests.md) and their different types. - The [images](./images.md). - The [tools](./tools.md). - The [tokenizers](./tokenizers.md) and their different layer of abstractions that we use.python-mistral-common-1.11.7/docs/usage/install.md000066400000000000000000000030061523035502500221040ustar00rootroot00000000000000# Install ## Pip You can install the library using pip: ```sh pip install mistral-common ``` We propose different dependencies to install depending on your needs: - `image`: to use the image tokenizers. - `audio`: to use the audio tokenizers. - `hf-hub`: to download the tokenizers from the Hugging Face Hub. - `sentencepiece`: to allow the use of SentencePiece tokenizers. This is now optional as we only release `Tekken` tokenizers for recent models. - `guidance`: to use the grammar-based guided generation feature. - \[Experimental\] `server`: to use our tokenizers in a server mode. Each dependency is optional and can be installed separately or all together using the following commands: ```sh pip install "mistral-common[image]" pip install "mistral-common[audio]" pip install "mistral-common[hf-hub]" pip install "mistral-common[sentencepiece]" pip install "mistral-common[guidance]" pip install "mistral-common[server]" pip install "mistral-common[image,audio,hf-hub,sentencepiece,guidance,server]" ``` ## From source To build it for source, you can clone the repository and install it using [uv](https://github.com/astral-sh/uv) or pip. We recommend using uv for faster and more reliable dependency resolution: ```sh git clone https://github.com/mistralai/mistral-common.git cd mistral-common uv sync --frozen --extra image # or --all-extras to install all dependencies. ``` For development, you can install the `dev` group and/or the `docs` groups: ```sh uv sync --frozen --all-extras --group dev # and/or --group docs. ``` python-mistral-common-1.11.7/docs/usage/requests.md000066400000000000000000000136171523035502500223220ustar00rootroot00000000000000# Requests To query an AI assistant like [Mistral's LeChat](https://chat.mistral.ai/chat) or ChatGPT you need to provide the following: - The history of the conversation between the user, the assistant and the tool calls. - The tools available to the assistant. - The context of the request. In `mistral-common`, we currently support the following requests types: - Instruct requests: - [Chat completion requests](#chat-completion). - [Fill-In-the-Middle completion](#fim). - [Transcription requests](#transcription) Every instruct requests should be encoded with its corresponding `encode_function` function by the tokenizers. ## Chat completion Chat completion consists in a conversation between a user and an assistant. The assistant can call tools to enrich its response. Some of our tokenizers also support the use of images (see the [images](./images.md)) Every chat completion request are defined via [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest]. To perform actual task every requests should follow the following structure: 1. validate the request via a [MistralRequestValidator][mistral_common.protocol.instruct.validator.MistralRequestValidator] 2. normalize the requests via the [InstructRequestNormalizer][mistral_common.protocol.instruct.normalize.InstructRequestNormalizer]. 3. encode the request. Using the [MistralTokenizer.encode_chat_completion][mistral_common.tokens.tokenizers.mistral.MistralTokenizer.encode_chat_completion] method will perform all these steps for you. Following this design ensures minimizing unexpected behavior from the user. ### Conversation A conversation with a model is a sequence of messages. Each message can be a user message, an assistant message, a tool message or a system message. To ease the creation of these messages, we provide a set of Pydantic classes that you can use to create them: - [UserMessage][mistral_common.protocol.instruct.messages.UserMessage]: a message from the user. Users are the ones that interact with the model. - [AssistantMessage][mistral_common.protocol.instruct.messages.AssistantMessage]: a message from the assistant. The assistant is the model itself. - [ToolMessage][mistral_common.protocol.instruct.messages.ToolMessage]: a message from a tool. Tools are functions that the model can call to get information to answer the user's question. - [SystemMessage][mistral_common.protocol.instruct.messages.SystemMessage]: a message from the system. Also called `System Prompt`, it is a set of instructions that the model should follow to answer the user's question. This allows you to customize the behavior of the model. ### Tools Tools are functions that the model can call to get information to answer the user's question. See the [Tools](./tools.md) section for more information. ### Example Here is an example of a request where a user asks for the weather in Paris. The model is also given access to a `get_current_weather` tool to get the weather: ```python from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import Function, Tool from mistral_common.tokens.tokenizers.mistral import MistralTokenizer request = ChatCompletionRequest( tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) ], messages=[ UserMessage(content="What's the weather like today in Paris"), ], ) tokenizer = MistralTokenizer.v3() tokenizer.encode_chat_completion(request) ``` ## FIM Fill In the Middle (FIM) is a task where the model is given a prefix and a suffix and is asked to fill in the middle. This is useful for code completion, where the model is given a prefix of code and is asked to complete the code. A pydantic class [FIMRequest][mistral_common.protocol.fim.request.FIMRequest] is defined to ease the creation of these requests. ```python from mistral_common.protocol.fim.request import FIMRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer request = FIMRequest( prompt="def hello_world():\n print('Hello, world!')", suffix="\n\nhello_world()", ) tokenizer = MistralTokenizer.v3() tokenizer.encode_fim(request) ``` ## Transcription Transcription is a task where the model is given an audio file and returns the text corresponding to the speech contained in the audio. A pydantic class [TranscriptionRequest][mistral_common.protocol.transcription.request.TranscriptionRequest] is defined to ease the creation of these requests. ```python from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from huggingface_hub import hf_hub_download repo_id = "mistralai/voxtral-mini" tokenizer = MistralTokenizer.from_hf_hub(repo_id) obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset") audio = Audio.from_file(obama_file, strict=False) request = TranscriptionRequest(model=repo_id, audio=audio.to_base64(audio.format), language="en") tokenizer.encode_transcription(request) ``` python-mistral-common-1.11.7/docs/usage/tokenizers.md000066400000000000000000000105071523035502500226370ustar00rootroot00000000000000# Tokenizers ## How do we perform tokenization ? Tokenization is the process of converting a sequence of characters into a sequence of tokens. To dive deeper into the tokenization process, you can read the [tokenization guide](https://docs.mistral.ai/guides/tokenization/) on our website. Historically, we based our tokenizers on the following libraries: - [**SentencePiece**](https://github.com/google/sentencepiece): we moved from this library to the one below. - [**tiktoken**](https://github.com/openai/tiktoken): we use this library for our **tekken** tokenizers. It is more efficient for multilingual tokenization. ## Tokenizer For our releases, we provide a tokenizer file that you can use to load a tokenizer. If the tokenizer is based on the `tiktoken` library, the file is generally named `tekken.json`. Here is how you could load the [Mistral Small 3.1 Instruct](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)'s tokenizer: ```python from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer tokenizer = MistralTokenizer.from_hf_hub("mistralai/Mistral-Small-3.1-24B-Instruct-2503", token="your_hf_token") chat_completion = ChatCompletionRequest( messages=[ UserMessage(role="user", content="Hello, how are you?"), ] ) tokenizer.encode_chat_completion(chat_completion).tokens # [1, 3, 22177, 1044, 2606, 1584, 1636, 1063, 4] ``` We provide three layer of abstraction for our tokenizers: - [Tekkenizer][mistral_common.tokens.tokenizers.tekken.Tekkenizer] or [SentencePieceTokenizer][mistral_common.tokens.tokenizers.sentencepiece.SentencePieceTokenizer]: raw tokenizers to handle raw data to convert them into ids and vice versa. - [InstructTokenizer][mistral_common.tokens.tokenizers.base.InstructTokenizer]: instruct tokenizers that wrap the raw tokenizers to add several helper methods for the different tasks (chat completion or FIM). They are versioned. - [MistralTokenizer][mistral_common.tokens.tokenizers.mistral.MistralTokenizer]: mistral tokenizers that validate the requests, see [requests section](./requests.md), and call the instruct tokenizers. For instance, you can directly load the [Tekkenizer][mistral_common.tokens.tokenizers.tekken.Tekkenizer]: ```python from huggingface_hub import hf_hub_download from mistral_common.tokens.tokenizers.tekken import Tekkenizer model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json", token="your_hf_token") tokenizer = Tekkenizer.from_file(tekken_file) tokenizer.encode("Plz tekkenize me", bos=True, eos=True) # [1, 4444, 1122, 16058, 3569, 2033, 1639, 2] ``` See the [Examples section](../examples/inference.md) for examples on how to use the tokenizers with our models. ## Special tokens Special tokens are tokens that have a special meaning for the model. They are used to mark the beginning or end of a sequence, an image, tools, etc. For example, in the [Mistral Small 3.1 Instruct](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) tokenizer, some special tokens include: - ``: Beginning of a sequence - ``: End of a sequence - `[INST]`: Beginning of an instruction - `[/INST]`: End of an instruction - `[TOOL_CALLS]`: Beginning of a tool call - `[IMG]`: Content of an image - `[IMG_BREAK]`: End of a row in an image - `[IMG_END]`: End of an image - ... These tokens are defined in the tokenizer configuration file (recommended) or at instantiation for the [Tekkenizer][mistral_common.tokens.tokenizers.tekken.Tekkenizer] (deprecated). In `mistral-common`, special tokens are never encoded directly. This means that: ```python tokenizer.encode("") ``` will not return the ID of the `` token. Instead, it will return a list of IDs corresponding to the tokenization of the string `""`. The special token IDs are added directly to the sequence of IDs when encoding the [requests](requests.md). To add new tokens to the tokenizer and use them correctly, you need to: 1. Add the tokens to the tokenizer configuration file. 2. Include the special token IDs in the input IDs when encoding requests. We are open to suggestions for improvement. Please open an [issue](https://github.com/mistralai/mistral-common/issues) if you have any feedback. python-mistral-common-1.11.7/docs/usage/tools.md000066400000000000000000000134201523035502500215770ustar00rootroot00000000000000# Tools To execute external tools, like searching the web, LLMs need to be given the ability to call such tools whenever they see fit. This is usually achieved by adding all available tools that the LLM can call into the context of the [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest] and representing tool calls as well as tool message as its own objects. ## Define tools To define tools in `mistral-common`, you use the [Tool][mistral_common.protocol.instruct.tool_calls.Tool] class. Let's define a simple tool that fetches the current weather for a given city. The example function below is not a real call that calls the weather, but it very well be an call to a weather API. We hardcode random numbers here just for demonstrative purposes. ```python from mistral_common.protocol.instruct.tool_calls import Tool, Function def get_current_weather(location: str, format: str) -> int: """Get the current weather in a given location""" if "tokyo" in location.lower(): return 22 if format == "celsius" else 72 elif "san francisco" in location.lower(): return 14 if format == "celsius" else 57 elif "paris" in location.lower(): return 18 if format == "celsius" else 64 else: return 20 if format == "celsius" else 68 weather_tool = Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) ``` ## Tool calling and messages When a model decides to call a tool, it will add a [ToolCall][mistral_common.protocol.instruct.tool_calls.ToolCall] to the [AssistantMessage][mistral_common.protocol.instruct.messages.AssistantMessage]. The tool call will have an `id` and a [FunctionCall][mistral_common.protocol.instruct.tool_calls.FunctionCall] with the name of the function and the arguments to pass to it. ```python from mistral_common.protocol.instruct.messages import AssistantMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import ( Function, FunctionCall, Tool, ToolCall, ) AssistantMessage( content=None, tool_calls=[ ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) ], ) ``` Then you can execute the function and return the result in a [ToolMessage][mistral_common.protocol.instruct.messages.ToolMessage]. The [ToolMessage][mistral_common.protocol.instruct.messages.ToolMessage] must have the same `id` as the [ToolCall][mistral_common.protocol.instruct.tool_calls.ToolCall] it's responding to. ```python from mistral_common.protocol.instruct.messages import ToolMessage ToolMessage(tool_call_id="VvvODy9mT", name="get_current_weather", content="22") ``` ## Putting it all together Now we can put it all together and create a [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest] with the tools and the messages. We can then use the [MistralTokenizer][mistral_common.tokens.tokenizers.mistral.MistralTokenizer] to encode the request. ```python from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import ( Function, FunctionCall, Tool, ToolCall, ) from mistral_common.tokens.tokenizers.mistral import MistralTokenizer request = ChatCompletionRequest( messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What's the weather like in Paris?"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) ], ), ToolMessage(tool_call_id="VvvODy9mT", name="get_current_weather", content="22"), ], tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) ], ) tokenizer = MistralTokenizer.v3() tokens = tokenizer.encode_chat_completion(request).tokens ``` Now the model can use the tool result to answer the user's question. python-mistral-common-1.11.7/mkdocs.yml000066400000000000000000000061201523035502500200630ustar00rootroot00000000000000site_name: Mistral-common site_url: https://mistralai.github.io/mistral-common/ site_description: Mistral-common is a library of common utilities for Mistral AI. theme: name: material custom_dir: ./docs/overrides logo: assets/logo.svg logo_dark: assets/logo-dark.svg favicon: assets/logo_favicon.png icon: repo: fontawesome/brands/github palette: - media: "(prefers-color-scheme: light)" primary: custom scheme: default toggle: icon: material/brightness-7 name: Switch to dark mode - media: "(prefers-color-scheme: dark)" primary: custom scheme: slate toggle: icon: material/brightness-4 name: Switch to light mode font: text: Poppins features: - navigation.instant - navigation.tabs - navigation.tabs.sticky - navigation.tracking - navigation.top - navigation.indexes - toc.follow - toc.integrate - content.code.copy - content.action.edit - content.action.view repo_url: https://github.com/mistralai/mistral-common repo_name: mistralai/mistral-common edit_uri: blob/main/docs/ copyright: Mistral AI 2025. extra: homepage: https://mistral.ai/ social: - icon: material/email-outline link: https://mistral.ai/contact name: Contact us - icon: material/web link: https://mistral.ai/ name: Mistral AI - icon: fontawesome/brands/github link: https://github.com/mistralai/mistral-common name: GitHub extra_css: - assets/extra.css plugins: - search - autorefs - gen-files: scripts: - docs/gen_api_pages.py - literate-nav: nav_file: SUMMARY.md - mkdocstrings: enable_inventory: true handlers: python: paths: ["src"] inventories: - https://docs.python.org/3.12/objects.inv - https://numpy.org/doc/1.23/objects.inv - https://pillow.readthedocs.io/en/stable/objects.inv - https://docs.pydantic.dev/latest/objects.inv - https://pytorch.org/docs/stable/objects.inv options: docstring_style: google docstring_options: returns_named_value: false ignore_init_summary: yes show_submodules: true docstring_section_style: table members_order: alphabetical merge_init_into_class: yes show_root_heading: true show_source: true heading_level: 1 preload_modules: - pydantic show_docstring_examples: true markdown_extensions: - pymdownx.highlight: anchor_linenums: true line_spans: __span pygments_lang_class: true - pymdownx.inlinehilite - pymdownx.snippets - pymdownx.superfences nav: - "Home": index.md - "Quickstart": examples/ - "Usage": - usage/index.md - "Install": usage/install.md - "Requests": usage/requests.md - "Tools": usage/tools.md - "Images": usage/images.md - "Tokenizers": usage/tokenizers.md - "REST API (experimental)": usage/experimental.md - "Code reference": code_reference/ python-mistral-common-1.11.7/pyproject.toml000066400000000000000000000057721523035502500210100ustar00rootroot00000000000000[build-system] requires = ["setuptools>=42", "wheel", "build"] build-backend = "setuptools.build_meta" [project] name = "mistral_common" version = "1.11.7" description = "Mistral-common is a library of common utilities for Mistral AI." authors = [{name = "Mistral AI", email = "oss@mistral.ai"}] license = "Apache-2.0" license-files = ["LICENSE"] readme = "README.md" requires-python = ">=3.10.0,<3.15" dependencies = [ "pydantic>=2.7,<3.0; python_version <= '3.13'", "pydantic>=2.12,<3.0; python_version >= '3.14'", "jsonschema>=4.21.1", "typing-extensions>=4.11.0", "tiktoken>=0.7.0; python_version <= '3.13'", "tiktoken>=0.12.0; python_version >= '3.14'", "pillow>=10.3.0", "requests>=2.0.0", "numpy>=1.25,<2.4; python_version <= '3.12'", "numpy>=1.25; python_version > '3.12'", "pydantic-extra-types[pycountry]>=2.10.5", ] [project.optional-dependencies] opencv = ["opencv-python-headless>=4.0.0"] sentencepiece = ["sentencepiece>=0.2.0"] soundfile = ["soundfile>=0.12.1"] soxr = ["soxr>=0.5.0"] audio = ["mistral_common[soundfile]", "mistral_common[soxr]"] image = ["mistral_common[opencv]"] guidance = ["llguidance>=1.3.0,<1.4.0", "jinja2>=3.1.0"] hf-hub = ["huggingface-hub>=1.0"] server = [ "fastapi[standard]>=0.118.3", "pydantic-settings >= 2.9.1", "click>=8.1.0", "uvloop>=0.22.1; python_version >= '3.14'" ] all = [ "mistral_common[opencv,sentencepiece,audio,image,guidance,hf-hub,server]" ] [project.scripts] mistral_common = "mistral_common.experimental.app.main:cli" [dependency-groups] dev = [ "types-jsonschema>=4.21.0.20240118", "types-protobuf>=4.24.0.20240129", "mypy-protobuf>=3.5.0", "pytest>=7.4.4", "ruff>=0.2.2", "mypy>=1.8.0", "pytest-cov>=4.1.0", "diff-cover>=8.0.3", "types-Pillow>=10.2.0", "types-requests>=2.32", "coverage[toml]>=7.4.4", "openai>=1.84.0", "pre-commit>=4.0.0", ] docs = [ "mkdocs", "mkdocstrings", "mkdocstrings-python", "mkdocs-material", "mkdocs-gen-files", "mkdocs-literate-nav", "pyyaml>=6.0.3" ] test-integrations = [ "transformers>=4.0.0", "regex>=2025.10.22", ] [tool.uv] required-version = "==0.11.14" [tool.ruff] lint.select = ["E", "F", "W", "Q", "I"] lint.ignore = ["E203"] lint.fixable = ["ALL"] lint.unfixable = [] line-length = 120 exclude = ["*.md", "docs", "build"] [tool.mypy] disallow_untyped_defs = true show_error_codes = true no_implicit_optional = true warn_return_any = true warn_unused_ignores = true exclude = ["docs", "tools", "build"] [[tool.mypy.overrides]] module = ["sentencepiece.*", "cv2", "cv2.*","soxr", "soundfile", "llguidance", "llguidance.*", "jinja2", "jinja2.*", "tokenizers", "tokenizers.*", "transformers", "transformers.*"] ignore_missing_imports = true [tool.pytest.ini_options] testpaths = ["./tests"] [tool.coverage.run] omit = ["tests", "*src/mistral_common/data*"] [tool.coverage.report] skip_covered = true [tool.setuptools] include-package-data = true [tool.setuptools.packages.find] where = ["src"] python-mistral-common-1.11.7/scripts/000077500000000000000000000000001523035502500175505ustar00rootroot00000000000000python-mistral-common-1.11.7/scripts/generate_chat_template.py000066400000000000000000000062051523035502500246110ustar00rootroot00000000000000import argparse from mistral_common.integrations.chat_templates.chat_templates import ( convert_tokenizer_to_chat_template, generate_chat_template, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion def main() -> None: r"""Generate a chat template and save it to a file.""" parser = argparse.ArgumentParser(description="Generate a Mistral chat template") parser.add_argument( "--tokenizer_file", type=str, default=None, help="Path to a tokenizer file for auto-detect mode", ) parser.add_argument("--version", type=str, default=None, help="Tokenizer version (e.g., v1, v3, v7, v15)") parser.add_argument("--spm", action="store_true", help="Use SentencePiece tokenizer") parser.add_argument("--image", action="store_true", help="Enable image support") parser.add_argument("--audio", action="store_true", help="Enable audio support") parser.add_argument("--thinking", action="store_true", help="Enable thinking support (special tokens)") parser.add_argument("--plain_thinking", action="store_true", help="Enable plain text thinking ( tags)") parser.add_argument("--default_system_prompt", type=str, default=None, help="Default system prompt to embed") parser.add_argument("--saving_path", type=str, default="./chat_template.jinja", help="Output path for the template") parser.add_argument( "--no_special_token_variables", action="store_true", help="Embed literal BOS/EOS values instead of using bos_token/eos_token variables", ) args = parser.parse_args() if args.tokenizer_file is not None: manual_flag_checks: list[tuple[bool, str]] = [ (args.version is not None, "--version"), (args.spm, "--spm"), (args.image, "--image"), (args.audio, "--audio"), (args.thinking, "--thinking"), (args.plain_thinking, "--plain_thinking"), ] conflicting = [flag for is_set, flag in manual_flag_checks if is_set] if conflicting: parser.error(f"--tokenizer_file cannot be combined with manual capability flags: {', '.join(conflicting)}") template = convert_tokenizer_to_chat_template( tokenizer_file=args.tokenizer_file, system_prompt=args.default_system_prompt, use_special_token_variables=not args.no_special_token_variables, ) else: if args.version is None: parser.error("--version is required when --tokenizer_file is not provided") template = generate_chat_template( spm=args.spm, tokenizer_version=TokenizerVersion(args.version), image_support=args.image, audio_support=args.audio, thinking_support=args.thinking, default_system_prompt=args.default_system_prompt, plain_thinking_support=args.plain_thinking, use_special_token_variables=not args.no_special_token_variables, ) with open(args.saving_path, "w", encoding="utf-8") as f: f.write(template) print(f"Template saved to {args.saving_path}") if __name__ == "__main__": main() python-mistral-common-1.11.7/src/000077500000000000000000000000001523035502500166505ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/000077500000000000000000000000001523035502500216735ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/__init__.py000066400000000000000000000001261523035502500240030ustar00rootroot00000000000000import importlib.metadata __version__ = importlib.metadata.version("mistral-common") python-mistral-common-1.11.7/src/mistral_common/audio.py000066400000000000000000000114141523035502500233470ustar00rootroot00000000000000import logging from functools import cache import numpy as np from mistral_common.deprecation import deprecated_import logger = logging.getLogger(__name__) def hertz_to_mel(freq: float | np.ndarray) -> float | np.ndarray: r"""Convert frequency from hertz to mels using the "slaney" mel-scale. Args: freq: The frequency, or multiple frequencies, in hertz (Hz). Returns: The frequencies on the mel scale. """ min_log_hertz = 1000.0 min_log_mel = 15.0 logstep = 27.0 / np.log(6.4) mels = 3.0 * freq / 200.0 if isinstance(freq, np.ndarray): assert isinstance(mels, np.ndarray), type(mels) log_region = freq >= min_log_hertz mels[log_region] = min_log_mel + np.log(freq[log_region] / min_log_hertz) * logstep elif freq >= min_log_hertz: mels = min_log_mel + np.log(freq / min_log_hertz) * logstep return mels def mel_to_hertz(mels: np.ndarray) -> np.ndarray: r"""Convert frequency from mels to hertz using the "slaney" mel-scale. Args: mels: The frequency, or multiple frequencies, in mels. Returns: The frequencies in hertz. """ min_log_hertz = 1000.0 min_log_mel = 15.0 logstep = np.log(6.4) / 27.0 freq = 200.0 * mels / 3.0 log_region = mels >= min_log_mel freq[log_region] = min_log_hertz * np.exp(logstep * (mels[log_region] - min_log_mel)) return freq def _create_triangular_filter_bank(fft_freqs: np.ndarray, filter_freqs: np.ndarray) -> np.ndarray: r"""Creates a triangular filter bank. Adapted from *torchaudio* and *librosa*. Args: fft_freqs: Discrete frequencies of the FFT bins in Hz. filter_freqs: Center frequencies of the triangular filters to create, in Hz. Returns: array of shape `(num_frequency_bins, num_mel_filters)` """ filter_diff = np.diff(filter_freqs) slopes = np.expand_dims(filter_freqs, 0) - np.expand_dims(fft_freqs, 1) down_slopes = -slopes[:, :-2] / filter_diff[:-1] up_slopes = slopes[:, 2:] / filter_diff[1:] filter_bank: np.ndarray = np.maximum(np.zeros(1), np.minimum(down_slopes, up_slopes)) return filter_bank @cache def mel_filter_bank( num_frequency_bins: int, num_mel_bins: int, min_frequency: float, max_frequency: float, sampling_rate: int, ) -> np.ndarray: r"""Create a Mel filter bank matrix for converting frequency bins to the Mel scale. This function generates a filter bank matrix that can be used to transform a spectrum represented in frequency bins to the Mel scale. The Mel scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another. Args: num_frequency_bins: The number of frequency bins in the input spectrum. num_mel_bins: The number of desired Mel bins in the output. min_frequency: The minimum frequency (in Hz) to consider. max_frequency: The maximum frequency (in Hz) to consider. sampling_rate: The sampling rate of the audio signal. Returns: A filter bank matrix of shape (num_mel_bins, num_frequency_bins) that can be used to project frequency bin energies onto Mel bins. """ if num_frequency_bins < 2: raise ValueError(f"Require num_frequency_bins: {num_frequency_bins} >= 2") if min_frequency > max_frequency: raise ValueError(f"Require min_frequency: {min_frequency} <= max_frequency: {max_frequency}") # center points of the triangular mel filters mel_min = hertz_to_mel(min_frequency) mel_max = hertz_to_mel(max_frequency) mel_freqs = np.linspace(mel_min, mel_max, num_mel_bins + 2) filter_freqs = mel_to_hertz(mel_freqs) # frequencies of FFT bins in Hz fft_freqs = np.linspace(0, sampling_rate // 2, num_frequency_bins) mel_filters = _create_triangular_filter_bank(fft_freqs, filter_freqs) # Slaney-style mel is scaled to be approx constant energy per channel enorm = 2.0 / (filter_freqs[2 : num_mel_bins + 2] - filter_freqs[:num_mel_bins]) mel_filters *= np.expand_dims(enorm, 0) if (mel_filters.max(axis=0) == 0.0).any(): raise ValueError( "At least one mel filter has all zero values. " f"The value for `num_mel_filters` ({num_mel_bins}) " "may be set too high. " "Or, the value for `num_frequency_bins` " f"({num_frequency_bins}) may be set too low." ) return mel_filters def __getattr__(name: str) -> object: if name in ("Audio", "AudioFormat", "EXPECTED_FORMAT_VALUES"): return deprecated_import( old_path="mistral_common.audio", new_module="mistral_common.tokens.tokenizers.audio", name=name, version="1.13.0", ) raise AttributeError(f"module {__name__!r} has no attribute {name!r}") python-mistral-common-1.11.7/src/mistral_common/base.py000066400000000000000000000015341523035502500231620ustar00rootroot00000000000000from typing import Any from pydantic import BaseModel, ConfigDict from typing_extensions import Self class MistralBase(BaseModel): r"""Base class for all Mistral Pydantic models. Forbids extra attributes, validates default values and use enum values. """ model_config = ConfigDict(extra="forbid", validate_default=True, use_enum_values=True) @classmethod def _filter_cls_fields(cls, data: dict[str, Any]) -> dict[str, Any]: r"""Filter a dictionary to only include keys that are valid model fields.""" return {k: v for k, v in data.items() if k in cls.model_fields} @classmethod def model_validate_ignore_extra(cls, data: dict[str, Any]) -> Self: r"""Build the model from the data after filtering out keys not in the model fields.""" return cls.model_validate(cls._filter_cls_fields(data)) python-mistral-common-1.11.7/src/mistral_common/data/000077500000000000000000000000001523035502500226045ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/data/mistral_instruct_tokenizer_240216.model.v2000066400000000000000000021732141523035502500325040ustar00rootroot00000000000000      [INST]  [/INST]  [TOOL_CALLS]  [AVAILABLE_TOOLS]  [/AVAILABLE_TOOLS]  [TOOL_RESULTS]  [/TOOL_RESULTS]  [control_8]  [control_9]  [control_10]  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ψ ボ š 各  진  追 句 警 Φ ѣ ḍ 词 男 글 식  隐" 복$ 盘& Ì( 申* 议, ザ. 近0 능2 য4 東6 這8 ர: 距< 院> 德@ ǐB 针D ▀F ↔H 房J 青L 政N 😅P 递R পT 波V ソX 绑Z ビ\ ễ^ 포` b ửd 등f 환h 士j তl Θn 초p 境r 差t 采v 디x ĩz 升| 背~ 배 龙 街 ್ ṛ ু 弹 魔 객 ‰ ⌁ ἐ 禁 ผ қ 島 ா ♭ 百 ứ ネ 专 來 刷 필 յ ắ 华 Β श ¸ 屏 死 遍 검 Χ 것 八 览 택 唯 ∙ ¤ 페 让 锁 무 思 隔 Ô  ṃ ワ 低 션 半 较 ត 享 积 ˆ 😊 典 ǔ 六 便 ɐ 简 继 仅  尾  ‹ வ կ ƒ 영 火 湖 書 발 ハ  循" 术$ 結& ļ( 乐* 滤, 종. ถ0 ὶ2 满4 ╝6 わ8 ど: ็< 형> 國@ ựB 線D 블F 封H 確J 依L սN 永P 색R 歌T 數V 福X 삭Z 実\ 레^ ſ` 千b d 母f 더h 임j տl ےn 几p 双r 노t ณv 掉x Ρz ἀ| 標~ 長 档 태 ペ 본 Œ 底 终 請 კ ̯ 예 ▬ 報 ピ ๏ 暂 李 Υ   替 운 射  매  🏼 票 附 ノ ũ 压 阿 Ò 테 ∼ 万 մ 후 普 截 속 括 😀 ை ▶ 까 ট 曲 师 钱 栏 Ы 走 ữ ‬ 归 점 🔥 었 連 私 청 刘 免  奖 見 ֹ  ☺  ケ 역 际 받 望 帝 减 두 领 „  钟" ガ$ 架& 든( ல* 松, □. 越0 答2 ɕ4 ῦ6 染8 : 质< 顺> 气@ ╗B 計D ქF 亮H 🤦J ̂L ٹN 座P ˌR 均T  V 官X 适Z 护\ 久^ 春` 曹b 皇d 脚f 池h 延j 키l 품n 現p 檔r ばt ⴰv 希x 玩z 固| 黄~  ☽ 银  ┃ 👏 불 攻 へ 决 ⊙ 宁 च 機 義 ɲ  했 ẩ 愛 矩 패 ặ 郎 Ь 绘 负 ổ ய 汉 編 ێ ് じ 카 似 ں や 認  過 통 ▪ 约 香 买 住 ╚ 😁 扩 静 려 학 钥 증 ỉ 她 食 往 點 偏 康  į 준  ฟ ♣ 戏 ʂ  井  军 爱 ٱ 七 차 币 ♠ 哈 阅 介  观" 區$ ˜& ً( 又* 冲, 朝. 姓0 课2 龍4 각6 ∈8 米: ƒ< 喜> 夜@ 团B ⇒D 远F H ὐJ 承L ಿN 室P ʀR ងT अV 罗X 🙏Z 软\ 🟡^ 건` ؟b းd ᴇf ユh 토j 策l ̄n 국p ֶr 协t 营v 関x 吉z 💀| 奇~ 滚 轴 処 土 划 ड 临 ֵ 航 浏 ゴ 別 寺 於 進 ὸ 風 ன 班 ◼ 九 ̥ 號 류 础 般 ︙ ̈ 番 ✨ 😎 ো 😍 單 帧 授 赋 巴 占 假 ṅ 透 項 ħ 馬 🟢 Ľ լ 券 같 類 對 월 激  戦 独 訊 ិ 套 ʷ 跟 ở 渲 顯 降 ာ 尼 血 언  牛  將 ศ 拍 刻 ზ ╔ 藤 ్ ῶ 🟠  良" 김$ দ& Ṣ( 録* 伊, 落. 雄0 雪2 映4 著6 른8 ფ: 対< 智> 译@ ┬B 抽D ῖF 酒H ЋJ 股L ់N 순P 직R भT 谷V 물X ǒZ ⠄\ 热^ 終` 夹b 干d 彩f 敗h ќj ♯l ̣n վp 轮r 阵t 夏v 幕x 吧z 港| 益~ 儿 액 售 兵 惠 欢 › 零 學 ž 員 ỗ 玉 逻 ᥀ 吗 沒 ≠ 너 ச  夫 წ 堂 電 ≡ 陆 져 研 荐 健 碼 练 検 송 ै 哪 圆 Ա ↩ 托 ̪ ू 缀 네 沙 兴 病  ល ừ Ἀ 강 항  換 温 帖 ទ 込 削 알 征 习 법 栈 绝  ڕ 圖  苏  発 ု 町 互 ় ც 守 새 侧 草  ས" 扫$ ‒& 恢( ң* ण, ற. 째0 ්2 拟4 派6 🏽8 呼: Š< 演> 究@ 교B ɣD एF ីH ףJ 富L 駅N ずP ♪R 😆T 접V ғX ▓Z 존\ ಾ^ 旋` ゃb 补d ץf 門h ចj 날l ภn གp 傳r ∆t †v ׁx 缺z 頭| 怪~ 組 별 Ъ 發 雷 ರ ซ び 翻 ھ პ 題 居 집 🌍 ˚ 避 줄 ុ 滑 故 ญ 〜 ನ 양 완 ள 倍 宗 択 브 ɴ 効 尺 視 ẽ 覆 ध 骨 달 ᴛ 蓝 關 額 Õ ∗ 卷 갑 르 众 ᴀ 態 ٰ 暗 君 錯 ɒ យ ḫ ῆ 亚 ♡ 割 鼠 ̶ Ë 読 격 ゲ 眼  Ý  ژ 雨 宮 쪽 ष 複 剩 早 杂 焦  贝" 突$ 워& 另( 摄* , ‭. 府0 외2 盖4 6 ษ8 佛: 概< 與> 經@ －B һD 問F ುH ἰJ 話L 倒N 葛P べR ろT V ।X ေZ ᴏ\ 训^ 體` 👌b 內d ကf 企h 약j 찾l ོn 破p 輸r 림t 塔v 턴x 杀z 』| 味~ 浮 ┆ ġ 郡 ┐ 『 阶 雅 ┈ 园 ． 吃 남   ར 帮 毛 耗 举 ర 拿 밀 ご 够 礼 ព ね ‰ 兰 ❌ 折 십 💎 業 诸 孙 བ 😳 種 Ï ึ ⁣ 医 拼 ↵ ⅓  မ 叫 জ 予 寸 梅 醒 津 န ి 厂 屋 ख 師 👀 ỏ ヤ ὰ  ◆ ដ 材 ホ  張  洞 餐 천 হ 達 們 斗 横 백 ំ  ۆ" 말$ গ& 佳( 랜* 仁, 陈. 飞0 极2 4 및6 仓8 ⬛: 昌< 錢> 殊@ ┴B ○D 길F 泉H 甲J 활L ひN শP ንR ŤT ღV 皮X 強Z 赛\ ా^ 預` ငb 튼d 플f ყh ⋆j քl ાn 尚p 또r բt ┌v 節x 森z आ| 办~ 園 牙 庆 隆 😔 叉 գ 피 ギ 啊 続 灵 ヒ 忽 ʌ 량 油 讯 ⵉ 릭 刚 氏 ိ Ī 誤 齐 末 🙌 ̞ 圈 念 숫 毫 當 規 판 ు 旧 卖 ฉ 幸 署 근 ই 岛 դ 觉 害 毕 ฐ 威 育 呢 峰 职 陽 ි 亞 ұ ₃ 따 施 泰 載 … 笑 華 迎 됩 豆  嘉  🤡 ĕ 庄 級 Ψ ི 気 责 հ អ  乱" 休$ 約& ฆ( ∑* 察, 온. 😬0 ড2 乘4 람6 इ8 Ά: ந< ើ> 亲@ េB 委D 赤F 됨H 勝J 怎L 감N 宋P 調R 짜T ীV 难X 못Z 티\ 備^ 塞` វb 险d 旅f 虚h ↳j 笔l 馆n Қp ⚡r ೆt ※v 唐x 律z 稍| 散~ ર ヴ 副 尽 挂 県 ⚠ 洋 鬼 암 孩 ℃ 並 ց ូ ℓ ⵏ 扣 铁 闻 ˆ 戳 む 秀 細 ပ 御 拖 좌 ؤ 绍 ỹ 참 향 Ď 끝 민 ძ 贵 纪 秋 ಕ ӏ 網 铺 恋 fl 兼 羽 창 啟 弟 년 慢 효 許 硬 잘 템 ્ න 術 ڈ 溪  暴 混 夢 랑 আ  還  探 祖 织 軍 թ 務 艺 ད ት ṁ  應" 擇$ 🥰& ķ( 渡* 葉, 령. 決0 刀2 從4 變6 올8 💪: 灣< ር> 평@ 衣B 😄D ിF ჩH ὁJ ほL ÛN চP රR 製T 隊V ₱X 纳Z 赖\ 农^ 桥` ỳb 🏾d 阻f ជh 秘j 박l 伤n 稿p ంr 拦t 넣v 💕x ₁z 宿| 錄~ 镜 채 Ə ང ⇔ ☼ ུ 党 급 洲 ղ 說 ĭ 尝 담 फ 哥 圣 萨 😏 ʏ ெ 丁 虎 권 善 岩 커 ◦ 抛 석 Έ 宣 拳 팅 枚 洛 証 陵 佐 館 누 돌 ₄ 稱 聊 車 루 ״ ಠ 庫 མ 統 련 ़ ṯ ക 旗 励 紀 忠 າ 杨 丹 Ù ฝ 却 舞 轉 တ  丽  借 ා ょ 옵 편 蒙 衡 ʋ 叶 ̇  ⬜" 🇺$ Հ& 谢( Ą* ே, ằ. 既0 济2 ≯4 準6 답8 ಲ: 残< 虑> ̆@ ┘B 急D 招F 막H ≮J 產L ṬN 😢P 垂R 親T ģV ־X 猫Z ʟ\ ☃^ ✪` 刪b 胡d ☉f 晚h 군j 승l నn ὴp 曾r 論t ɯv తx 戰z 鱼| ǧ~ 寶 특 💯 崎 甘 該 링 😡 उ ែ 頁 큰 ➤ 총 💰 ∂ 毁 聖 麻 ʐ 敏 運 될 쓰 ಸ စ ✦ 젝 復 寻 茶 ਾ 竹 遇 順 며 累 ĝ ˇ 覧 এ 株 취 ስ 争 势 宇 橋 Ӏ 堆 ⵙ 丶 棋 肉 የ  ❶ 季 ል 殿 優 試 첫 Ό 戶 ண 羅 桃 립 浪  脑  😛 弃 炮 轻 울  ヘ 奥 💜 忘  遠" 飛$ 魏& Ē( 汇* 央, 逆. 露0 須2 ѐ4 ḷ6 ದ8 ✭: 寄< 盟> 财@ 際B ἔD ǫF थH ാJ 宫L 巨N 途P ʹR ಗT 帐V ‪X 拒Z 药\ 🙃^ ŕ` 亡b 壁d ምf 參h 😩j շl ವn ណp 丰r 獲t 莉v 좋x ရz ₦| 겠~ 👉 吴 岡 诉 읽 🥺 爆 🇸 ভ 迭 엔 ἄ 捷 納 邀 ಯ 爾 船 赞 胜 므 သ 構 磁 冰 딩 ે 媒 繁 ☠ ❒ 仪 렬 昭 珠 離 ན ల ತ 拷 粉 벤 ⇽ 乌 拥 ҳ ය ེ 仙 塊 幅 🎉 Մ 跨 ٔ 恩 损 养 奈 ǀ 严 卫 迟 様 裡 난 았 ͜ Ζ ਰ  պ  ং 丢 伝 컨 ව ြ 冷 遗 銀 ̌  ᴜ" 瑞$ ฌ& ❍( ふ* 聚, 碎. 衛0 অ2 ញ4 퍼6 Ս8 ນ: ẓ< ✌> 孝@ 陳B 히D කF 黒H 💖J ḩL 応N 饰P ∪R 宜T 樂V 則X 勇Z 徐\ ⵓ^ 權` 鲁b ‟d 庭f 苗h 🔴j 闲l 독n ɹp ҽr ថt 宏v 尊x 總z 裝| ම~ ▸ 測 ಮ አ 轩 兄 剑 ન 朱 ǝ Ḩ 担 灰 讲 롤 ︎ 😤 ោ 애 였 질 振 灯 ĉ ස 閉 램 ಂ げ ̧ 狂 融 仍 實 楽 範 ٌ వ 嵌 摩 袁 ষ 乎 규 岗 糊 క 雲 심 ई འ ἡ 丝 Ħ ٍ ٓ အ 執 벨 ゼ 梦` some_data.txtdummy 0:textU?X_p=}@? [INST][/INST] [TOOL_CALLS][AVAILABLE_TOOLS][/AVAILABLE_TOOLS][TOOL_RESULTS][/TOOL_RESULTS] [control_8] [control_9] [control_10] [control_11] [control_12] [control_13] [control_14] [control_15] [control_16] [control_17] [control_18] [control_19] [control_20] [control_21] [control_22] [control_23] [control_24] [control_25] [control_26] [control_27] [control_28] [control_29] [control_30] [control_31] [control_32] [control_33] [control_34] [control_35] [control_36] [control_37] [control_38] [control_39] [control_40] [control_41] [control_42] [control_43] [control_44] [control_45] [control_46] [control_47] [control_48] [control_49] [control_50] [control_51] [control_52] [control_53] [control_54] [control_55] [control_56] [control_57] [control_58] [control_59] [control_60] [control_61] [control_62] [control_63] [control_64] [control_65] [control_66] [control_67] [control_68] [control_69] [control_70] [control_71] [control_72] [control_73] [control_74] [control_75] [control_76] [control_77] [control_78] [control_79] [control_80] [control_81] [control_82] [control_83] [control_84] [control_85] [control_86] [control_87] [control_88] [control_89] [control_90] [control_91] [control_92] [control_93] [control_94] [control_95] [control_96] [control_97] [control_98] [control_99] [control_100] [control_101] [control_102] [control_103] [control_104] [control_105] [control_106] [control_107] [control_108] [control_109] [control_110] [control_111] [control_112] [control_113] [control_114] [control_115] [control_116] [control_117] [control_118] [control_119] [control_120] [control_121] [control_122] [control_123] [control_124] [control_125] [control_126] [control_127] [control_128] [control_129] [control_130] [control_131] [control_132] [control_133] [control_134] [control_135] [control_136] [control_137] [control_138] [control_139] [control_140] [control_141] [control_142] [control_143] [control_144] [control_145] [control_146] [control_147] [control_148] [control_149] [control_150] [control_151] [control_152] [control_153] [control_154] [control_155] [control_156] [control_157] [control_158] [control_159] [control_160] [control_161] [control_162] [control_163] [control_164] [control_165] [control_166] [control_167] [control_168] [control_169] [control_170] [control_171] [control_172] [control_173] [control_174] [control_175] [control_176] [control_177] [control_178] [control_179] [control_180] [control_181] [control_182] [control_183] [control_184] [control_185] [control_186] [control_187] [control_188] [control_189] [control_190] [control_191] [control_192] [control_193] [control_194] [control_195] [control_196] [control_197] [control_198] [control_199] [control_200] [control_201] [control_202] [control_203] [control_204] [control_205] [control_206] [control_207] [control_208] [control_209] [control_210] [control_211] [control_212] [control_213] [control_214] [control_215] [control_216] [control_217] [control_218] [control_219] [control_220] [control_221] [control_222] [control_223] [control_224] [control_225] [control_226] [control_227] [control_228] [control_229] [control_230] [control_231] [control_232] [control_233] [control_234] [control_235] [control_236] [control_237] [control_238] [control_239] [control_240] [control_241] [control_242] [control_243] [control_244] [control_245] [control_246] [control_247] [control_248] [control_249] [control_250] [control_251] [control_252] [control_253] [control_254] [control_255] [control_256] [control_257] [control_258] [control_259] [control_260] [control_261] [control_262] [control_263] [control_264] [control_265] [control_266] [control_267] [control_268] [control_269] [control_270] [control_271] [control_272] [control_273] [control_274] [control_275] [control_276] [control_277] [control_278] [control_279] [control_280] [control_281] [control_282] [control_283] [control_284] [control_285] [control_286] [control_287] [control_288] [control_289] [control_290] [control_291] [control_292] [control_293] [control_294] [control_295] [control_296] [control_297] [control_298] [control_299] [control_300] [control_301] [control_302] [control_303] [control_304] [control_305] [control_306] [control_307] [control_308] [control_309] [control_310] [control_311] [control_312] [control_313] [control_314] [control_315] [control_316] [control_317] [control_318] [control_319] [control_320] [control_321] [control_322] [control_323] [control_324] [control_325] [control_326] [control_327] [control_328] [control_329] [control_330] [control_331] [control_332] [control_333] [control_334] [control_335] [control_336] [control_337] [control_338] [control_339] [control_340] [control_341] [control_342] [control_343] [control_344] [control_345] [control_346] [control_347] [control_348] [control_349] [control_350] [control_351] [control_352] [control_353] [control_354] [control_355] [control_356] [control_357] [control_358] [control_359] [control_360] [control_361] [control_362] [control_363] [control_364] [control_365] [control_366] [control_367] [control_368] [control_369] [control_370] [control_371] [control_372] [control_373] [control_374] [control_375] [control_376] [control_377] [control_378] [control_379] [control_380] [control_381] [control_382] [control_383] [control_384] [control_385] [control_386] [control_387] [control_388] [control_389] [control_390] [control_391] [control_392] [control_393] [control_394] [control_395] [control_396] [control_397] [control_398] [control_399] [control_400] [control_401] [control_402] [control_403] [control_404] [control_405] [control_406] [control_407] [control_408] [control_409] [control_410] [control_411] [control_412] [control_413] [control_414] [control_415] [control_416] [control_417] [control_418] [control_419] [control_420] [control_421] [control_422] [control_423] [control_424] [control_425] [control_426] [control_427] [control_428] [control_429] [control_430] [control_431] [control_432] [control_433] [control_434] [control_435] [control_436] [control_437] [control_438] [control_439] [control_440] [control_441] [control_442] [control_443] [control_444] [control_445] [control_446] [control_447] [control_448] [control_449] [control_450] [control_451] [control_452] [control_453] [control_454] [control_455] [control_456] [control_457] [control_458] [control_459] [control_460] [control_461] [control_462] [control_463] [control_464] [control_465] [control_466] [control_467] [control_468] [control_469] [control_470] [control_471] [control_472] [control_473] [control_474] [control_475] [control_476] [control_477] [control_478] [control_479] [control_480] [control_481] [control_482] [control_483] [control_484] [control_485] [control_486] [control_487] [control_488] [control_489] [control_490] [control_491] [control_492] [control_493] [control_494] [control_495] [control_496] [control_497] [control_498] [control_499] [control_500] [control_501] [control_502] [control_503] [control_504] [control_505] [control_506] [control_507] [control_508] [control_509] [control_510] [control_511] [control_512] [control_513] [control_514] [control_515] [control_516] [control_517] [control_518] [control_519] [control_520] [control_521] [control_522] [control_523] [control_524] [control_525] [control_526] [control_527] [control_528] [control_529] [control_530] [control_531] [control_532] [control_533] [control_534] [control_535] [control_536] [control_537] [control_538] [control_539] [control_540] [control_541] [control_542] [control_543] [control_544] [control_545] [control_546] [control_547] [control_548] [control_549] [control_550] [control_551] [control_552] [control_553] [control_554] [control_555] [control_556] [control_557] [control_558] [control_559] [control_560] [control_561] [control_562] [control_563] [control_564] [control_565] [control_566] [control_567] [control_568] [control_569] [control_570] [control_571] [control_572] [control_573] [control_574] [control_575] [control_576] [control_577] [control_578] [control_579] [control_580] [control_581] [control_582] [control_583] [control_584] [control_585] [control_586] [control_587] [control_588] [control_589] [control_590] [control_591] [control_592] [control_593] [control_594] [control_595] [control_596] [control_597] [control_598] [control_599] [control_600] [control_601] [control_602] [control_603] [control_604] [control_605] [control_606] [control_607] [control_608] [control_609] [control_610] [control_611] [control_612] [control_613] [control_614] [control_615] [control_616] [control_617] [control_618] [control_619] [control_620] [control_621] [control_622] [control_623] [control_624] [control_625] [control_626] [control_627] [control_628] [control_629] [control_630] [control_631] [control_632] [control_633] [control_634] [control_635] [control_636] [control_637] [control_638] [control_639] [control_640] [control_641] [control_642] [control_643] [control_644] [control_645] [control_646] [control_647] [control_648] [control_649] [control_650] [control_651] [control_652] [control_653] [control_654] [control_655] [control_656] [control_657] [control_658] [control_659] [control_660] [control_661] [control_662] [control_663] [control_664] [control_665] [control_666] [control_667] [control_668] [control_669] [control_670] [control_671] [control_672] [control_673] [control_674] [control_675] [control_676] [control_677] [control_678] [control_679] [control_680] [control_681] [control_682] [control_683] [control_684] [control_685] [control_686] [control_687] [control_688] [control_689] [control_690] [control_691] [control_692] [control_693] [control_694] [control_695] [control_696] [control_697] [control_698] [control_699] [control_700] [control_701] [control_702] [control_703] [control_704] [control_705] [control_706] [control_707] [control_708] [control_709] [control_710] [control_711] [control_712] [control_713] [control_714] [control_715] [control_716] [control_717] [control_718] [control_719] [control_720] [control_721] [control_722] [control_723] [control_724] [control_725] [control_726] [control_727] [control_728] [control_729] [control_730] [control_731] [control_732] [control_733] [control_734] [control_735] [control_736] [control_737] [control_738] [control_739] [control_740] [control_741] [control_742] [control_743] [control_744] [control_745] [control_746] [control_747] [control_748] [control_749] [control_750] [control_751] [control_752] [control_753] [control_754] [control_755] [control_756] [control_757] [control_758] [control_759] [control_760] [control_761] [control_762] [control_763] [control_764] [control_765] [control_766] [control_767] [control_768] ⁇  identity 2python-mistral-common-1.11.7/src/mistral_common/data/mistral_instruct_tokenizer_240323.model.v3000066400000000000000000021735071523035502500325110ustar00rootroot00000000000000      [INST]  [/INST]  [TOOL_CALLS]  [AVAILABLE_TOOLS]  [/AVAILABLE_TOOLS]  [TOOL_RESULTS]  [/TOOL_RESULTS]  [IMG]  [PREFIX]  [MIDDLE]  [SUFFIX]  [control_12]  [control_13]  [control_14]  [control_15]  [control_16]  [control_17]  [control_18]  [control_19]  [control_20]  [control_21]  [control_22]  [control_23]  [control_24]  [control_25]  [control_26]  [control_27]  [control_28]  [control_29]  [control_30]  [control_31]  [control_32]  [control_33]  [control_34]  [control_35]  [control_36]  [control_37]  [control_38]  [control_39]  [control_40]  [control_41]  [control_42]  [control_43]  [control_44]  [control_45]  [control_46]  [control_47]  [control_48]  [control_49]  [control_50]  [control_51]  [control_52]  [control_53]  [control_54]  [control_55]  [control_56]  [control_57]  [control_58]  [control_59]  [control_60]  [control_61]  [control_62]  [control_63]  [control_64]  [control_65]  [control_66]  [control_67]  [control_68]  [control_69]  [control_70]  [control_71]  [control_72]  [control_73]  [control_74]  [control_75]  [control_76]  [control_77]  [control_78]  [control_79]  [control_80]  [control_81]  [control_82]  [control_83]  [control_84]  [control_85]  [control_86]  [control_87]  [control_88]  [control_89]  [control_90]  [control_91]  [control_92]  [control_93]  [control_94]  [control_95]  [control_96]  [control_97]  [control_98]  [control_99]  [control_100]  [control_101]  [control_102]  [control_103]  [control_104]  [control_105]  [control_106]  [control_107]  [control_108]  [control_109]  [control_110]  [control_111]  [control_112]  [control_113]  [control_114]  [control_115]  [control_116]  [control_117]  [control_118]  [control_119]  [control_120]  [control_121]  [control_122]  [control_123]  [control_124]  [control_125]  [control_126]  [control_127]  [control_128]  [control_129]  [control_130]  [control_131]  [control_132]  [control_133]  [control_134]  [control_135]  [control_136]  [control_137]  [control_138]  [control_139]  [control_140]  [control_141]  [control_142]  [control_143]  [control_144]  [control_145]  [control_146]  [control_147]  [control_148]  [control_149]  [control_150]  [control_151]  [control_152]  [control_153]  [control_154]  [control_155]  [control_156]  [control_157]  [control_158]  [control_159]  [control_160]  [control_161]  [control_162]  [control_163]  [control_164]  [control_165]  [control_166]  [control_167]  [control_168]  [control_169]  [control_170]  [control_171]  [control_172]  [control_173]  [control_174]  [control_175]  [control_176]  [control_177]  [control_178]  [control_179]  [control_180]  [control_181]  [control_182]  [control_183]  [control_184]  [control_185]  [control_186]  [control_187]  [control_188]  [control_189]  [control_190]  [control_191]  [control_192]  [control_193]  [control_194]  [control_195]  [control_196]  [control_197]  [control_198]  [control_199]  [control_200]  [control_201]  [control_202]  [control_203]  [control_204]  [control_205]  [control_206]  [control_207]  [control_208]  [control_209]  [control_210]  [control_211]  [control_212]  [control_213]  [control_214]  [control_215]  [control_216]  [control_217]  [control_218]  [control_219]  [control_220]  [control_221]  [control_222]  [control_223]  [control_224]  [control_225]  [control_226]  [control_227]  [control_228]  [control_229]  [control_230]  [control_231]  [control_232]  [control_233]  [control_234]  [control_235]  [control_236]  [control_237]  [control_238]  [control_239]  [control_240]  [control_241]  [control_242]  [control_243]  [control_244]  [control_245]  [control_246]  [control_247]  [control_248]  [control_249]  [control_250]  [control_251]  [control_252]  [control_253]  [control_254]  [control_255]  [control_256]  [control_257]  [control_258]  [control_259]  [control_260]  [control_261]  [control_262]  [control_263]  [control_264]  [control_265]  [control_266]  [control_267]  [control_268]  [control_269]  [control_270]  [control_271]  [control_272]  [control_273]  [control_274]  [control_275]  [control_276]  [control_277]  [control_278]  [control_279]  [control_280]  [control_281]  [control_282]  [control_283]  [control_284]  [control_285]  [control_286]  [control_287]  [control_288]  [control_289]  [control_290]  [control_291]  [control_292]  [control_293]  [control_294]  [control_295]  [control_296]  [control_297]  [control_298]  [control_299]  [control_300]  [control_301]  [control_302]  [control_303]  [control_304]  [control_305]  [control_306]  [control_307]  [control_308]  [control_309]  [control_310]  [control_311]  [control_312]  [control_313]  [control_314]  [control_315]  [control_316]  [control_317]  [control_318]  [control_319]  [control_320]  [control_321]  [control_322]  [control_323]  [control_324]  [control_325]  [control_326]  [control_327]  [control_328]  [control_329]  [control_330]  [control_331]  [control_332]  [control_333]  [control_334]  [control_335]  [control_336]  [control_337]  [control_338]  [control_339]  [control_340]  [control_341]  [control_342]  [control_343]  [control_344]  [control_345]  [control_346]  [control_347]  [control_348]  [control_349]  [control_350]  [control_351]  [control_352]  [control_353]  [control_354]  [control_355]  [control_356]  [control_357]  [control_358]  [control_359]  [control_360]  [control_361]  [control_362]  [control_363]  [control_364]  [control_365]  [control_366]  [control_367]  [control_368]  [control_369]  [control_370]  [control_371]  [control_372]  [control_373]  [control_374]  [control_375]  [control_376]  [control_377]  [control_378]  [control_379]  [control_380]  [control_381]  [control_382]  [control_383]  [control_384]  [control_385]  [control_386]  [control_387]  [control_388]  [control_389]  [control_390]  [control_391]  [control_392]  [control_393]  [control_394]  [control_395]  [control_396]  [control_397]  [control_398]  [control_399]  [control_400]  [control_401]  [control_402]  [control_403]  [control_404]  [control_405]  [control_406]  [control_407]  [control_408]  [control_409]  [control_410]  [control_411]  [control_412]  [control_413]  [control_414]  [control_415]  [control_416]  [control_417]  [control_418]  [control_419]  [control_420]  [control_421]  [control_422]  [control_423]  [control_424]  [control_425]  [control_426]  [control_427]  [control_428]  [control_429]  [control_430]  [control_431]  [control_432]  [control_433]  [control_434]  [control_435]  [control_436]  [control_437]  [control_438]  [control_439]  [control_440]  [control_441]  [control_442]  [control_443]  [control_444]  [control_445]  [control_446]  [control_447]  [control_448]  [control_449]  [control_450]  [control_451]  [control_452]  [control_453]  [control_454]  [control_455]  [control_456]  [control_457]  [control_458]  [control_459]  [control_460]  [control_461]  [control_462]  [control_463]  [control_464]  [control_465]  [control_466]  [control_467]  [control_468]  [control_469]  [control_470]  [control_471]  [control_472]  [control_473]  [control_474]  [control_475]  [control_476]  [control_477]  [control_478]  [control_479]  [control_480]  [control_481]  [control_482]  [control_483]  [control_484]  [control_485]  [control_486]  [control_487]  [control_488]  [control_489]  [control_490]  [control_491]  [control_492]  [control_493]  [control_494]  [control_495]  [control_496]  [control_497]  [control_498]  [control_499]  [control_500]  [control_501]  [control_502]  [control_503]  [control_504]  [control_505]  [control_506]  [control_507]  [control_508]  [control_509]  [control_510]  [control_511]  [control_512]  [control_513]  [control_514]  [control_515]  [control_516]  [control_517]  [control_518]  [control_519]  [control_520]  [control_521]  [control_522]  [control_523]  [control_524]  [control_525]  [control_526]  [control_527]  [control_528]  [control_529]  [control_530]  [control_531]  [control_532]  [control_533]  [control_534]  [control_535]  [control_536]  [control_537]  [control_538]  [control_539]  [control_540]  [control_541]  [control_542]  [control_543]  [control_544]  [control_545]  [control_546]  [control_547]  [control_548]  [control_549]  [control_550]  [control_551]  [control_552]  [control_553]  [control_554]  [control_555]  [control_556]  [control_557]  [control_558]  [control_559]  [control_560]  [control_561]  [control_562]  [control_563]  [control_564]  [control_565]  [control_566]  [control_567]  [control_568]  [control_569]  [control_570]  [control_571]  [control_572]  [control_573]  [control_574]  [control_575]  [control_576]  [control_577]  [control_578]  [control_579]  [control_580]  [control_581]  [control_582]  [control_583]  [control_584]  [control_585]  [control_586]  [control_587]  [control_588]  [control_589]  [control_590]  [control_591]  [control_592]  [control_593]  [control_594]  [control_595]  [control_596]  [control_597]  [control_598]  [control_599]  [control_600]  [control_601]  [control_602]  [control_603]  [control_604]  [control_605]  [control_606]  [control_607]  [control_608]  [control_609]  [control_610]  [control_611]  [control_612]  [control_613]  [control_614]  [control_615]  [control_616]  [control_617]  [control_618]  [control_619]  [control_620]  [control_621]  [control_622]  [control_623]  [control_624]  [control_625]  [control_626]  [control_627]  [control_628]  [control_629]  [control_630]  [control_631]  [control_632]  [control_633]  [control_634]  [control_635]  [control_636]  [control_637]  [control_638]  [control_639]  [control_640]  [control_641]  [control_642]  [control_643]  [control_644]  [control_645]  [control_646]  [control_647]  [control_648]  [control_649]  [control_650]  [control_651]  [control_652]  [control_653]  [control_654]  [control_655]  [control_656]  [control_657]  [control_658]  [control_659]  [control_660]  [control_661]  [control_662]  [control_663]  [control_664]  [control_665]  [control_666]  [control_667]  [control_668]  [control_669]  [control_670]  [control_671]  [control_672]  [control_673]  [control_674]  [control_675]  [control_676]  [control_677]  [control_678]  [control_679]  [control_680]  [control_681]  [control_682]  [control_683]  [control_684]  [control_685]  [control_686]  [control_687]  [control_688]  [control_689]  [control_690]  [control_691]  [control_692]  [control_693]  [control_694]  [control_695]  [control_696]  [control_697]  [control_698]  [control_699]  [control_700]  [control_701]  [control_702]  [control_703]  [control_704]  [control_705]  [control_706]  [control_707]  [control_708]  [control_709]  [control_710]  [control_711]  [control_712]  [control_713]  [control_714]  [control_715]  [control_716]  [control_717]  [control_718]  [control_719]  [control_720]  [control_721]  [control_722]  [control_723]  [control_724]  [control_725]  [control_726]  [control_727]  [control_728]  [control_729]  [control_730]  [control_731]  [control_732]  [control_733]  [control_734]  [control_735]  [control_736]  [control_737]  [control_738]  [control_739]  [control_740]  [control_741]  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承L ಿN 室P ʀR ងT अV 罗X 🙏Z 软\ 🟡^ 건` ؟b းd ᴇf ユh 토j 策l ̄n 국p ֶr 协t 营v 関x 吉z 💀| 奇~ 滚 轴 処 土 划 ड 临 ֵ 航 浏 ゴ 別 寺 於 進 ὸ 風 ன 班 ◼ 九 ̥ 號 류 础 般 ︙ ̈ 番 ✨ 😎 ো 😍 單 帧 授 赋 巴 占 假 ṅ 透 項 ħ 馬 🟢 Ľ լ 券 같 類 對 월 激  戦 独 訊 ិ 套 ʷ 跟 ở 渲 顯 降 ာ 尼 血 언  牛  將 ศ 拍 刻 ზ ╔ 藤 ్ ῶ 🟠  良" 김$ দ& Ṣ( 録* 伊, 落. 雄0 雪2 映4 著6 른8 ფ: 対< 智> 译@ ┬B 抽D ῖF 酒H ЋJ 股L ់N 순P 직R भT 谷V 물X ǒZ ⠄\ 热^ 終` 夹b 干d 彩f 敗h ќj ♯l ̣n վp 轮r 阵t 夏v 幕x 吧z 港| 益~ 儿 액 售 兵 惠 欢 › 零 學 ž 員 ỗ 玉 逻 ᥀ 吗 沒 ≠ 너 ச  夫 წ 堂 電 ≡ 陆 져 研 荐 健 碼 练 検 송 ै 哪 圆 Ա ↩ 托 ̪ ू 缀 네 沙 兴 病  ល ừ Ἀ 강 항  換 温 帖 ទ 込 削 알 征 习 법 栈 绝  ڕ 圖  苏  発 ု 町 互 ় ც 守 새 侧 草  ས" 扫$ ‒& 恢( ң* ण, ற. 째0 ්2 拟4 派6 🏽8 呼: Š< 演> 究@ 교B ɣD एF ីH ףJ 富L 駅N ずP ♪R 😆T 접V ғX ▓Z 존\ ಾ^ 旋` ゃb 补d ץf 門h ចj 날l ภn གp 傳r ∆t †v ׁx 缺z 頭| 怪~ 組 별 Ъ 發 雷 ರ ซ び 翻 ھ პ 題 居 집 🌍 ˚ 避 줄 ុ 滑 故 ญ 〜 ನ 양 완 ள 倍 宗 択 브 ɴ 効 尺 視 ẽ 覆 ध 骨 달 ᴛ 蓝 關 額 Õ ∗ 卷 갑 르 众 ᴀ 態 ٰ 暗 君 錯 ɒ យ ḫ ῆ 亚 ♡ 割 鼠 ̶ Ë 読 격 ゲ 眼  Ý  ژ 雨 宮 쪽 ष 複 剩 早 杂 焦  贝" 突$ 워& 另( 摄* , ‭. 府0 외2 盖4 6 ษ8 佛: 概< 與> 經@ －B һD 問F ುH ἰJ 話L 倒N 葛P べR ろT V ।X ေZ ᴏ\ 训^ 體` 👌b 內d ကf 企h 약j 찾l ོn 破p 輸r 림t 塔v 턴x 杀z 』| 味~ 浮 ┆ ġ 郡 ┐ 『 阶 雅 ┈ 园 ． 吃 남   ར 帮 毛 耗 举 ర 拿 밀 ご 够 礼 ព ね ‰ 兰 ❌ 折 십 💎 業 诸 孙 བ 😳 種 Ï ึ ⁣ 医 拼 ↵ ⅓  မ 叫 জ 予 寸 梅 醒 津 န ి 厂 屋 ख 師 👀 ỏ ヤ ὰ  ◆ ដ 材 ホ  張  洞 餐 천 হ 達 們 斗 横 백 ំ  ۆ" 말$ গ& 佳( 랜* 仁, 陈. 飞0 极2 4 및6 仓8 ⬛: 昌< 錢> 殊@ ┴B ○D 길F 泉H 甲J 활L ひN শP ንR ŤT ღV 皮X 強Z 赛\ ా^ 預` ငb 튼d 플f ყh ⋆j քl ાn 尚p 또r բt ┌v 節x 森z आ| 办~ 園 牙 庆 隆 😔 叉 գ 피 ギ 啊 続 灵 ヒ 忽 ʌ 량 油 讯 ⵉ 릭 刚 氏 ိ Ī 誤 齐 末 🙌 ̞ 圈 念 숫 毫 當 規 판 ు 旧 卖 ฉ 幸 署 근 ই 岛 դ 觉 害 毕 ฐ 威 育 呢 峰 职 陽 ි 亞 ұ ₃ 따 施 泰 載 … 笑 華 迎 됩 豆  嘉  🤡 ĕ 庄 級 Ψ ི 気 责 հ អ  乱" 休$ 約& ฆ( ∑* 察, 온. 😬0 ড2 乘4 람6 इ8 Ά: ந< ើ> 亲@ េB 委D 赤F 됨H 勝J 怎L 감N 宋P 調R 짜T ীV 难X 못Z 티\ 備^ 塞` វb 险d 旅f 虚h ↳j 笔l 馆n Қp ⚡r ೆt ※v 唐x 律z 稍| 散~ ર ヴ 副 尽 挂 県 ⚠ 洋 鬼 암 孩 ℃ 並 ց ូ ℓ ⵏ 扣 铁 闻 ˆ 戳 む 秀 細 ပ 御 拖 좌 ؤ 绍 ỹ 참 향 Ď 끝 민 ძ 贵 纪 秋 ಕ ӏ 網 铺 恋 fl 兼 羽 창 啟 弟 년 慢 효 許 硬 잘 템 ્ න 術 ڈ 溪  暴 混 夢 랑 আ  還  探 祖 织 軍 թ 務 艺 ད ት ṁ  應" 擇$ 🥰& ķ( 渡* 葉, 령. 決0 刀2 從4 變6 올8 💪: 灣< ር> 평@ 衣B 😄D ിF ჩH ὁJ ほL ÛN চP රR 製T 隊V ₱X 纳Z 赖\ 农^ 桥` ỳb 🏾d 阻f ជh 秘j 박l 伤n 稿p ంr 拦t 넣v 💕x ₁z 宿| 錄~ 镜 채 Ə ང ⇔ ☼ ུ 党 급 洲 ղ 說 ĭ 尝 담 फ 哥 圣 萨 😏 ʏ ெ 丁 虎 권 善 岩 커 ◦ 抛 석 Έ 宣 拳 팅 枚 洛 証 陵 佐 館 누 돌 ₄ 稱 聊 車 루 ״ ಠ 庫 མ 統 련 ़ ṯ ക 旗 励 紀 忠 າ 杨 丹 Ù ฝ 却 舞 轉 တ  丽  借 ා ょ 옵 편 蒙 衡 ʋ 叶 ̇  ⬜" 🇺$ Հ& 谢( Ą* ே, ằ. 既0 济2 ≯4 準6 답8 ಲ: 残< 虑> ̆@ ┘B 急D 招F 막H ≮J 產L ṬN 😢P 垂R 親T ģV ־X 猫Z ʟ\ ☃^ ✪` 刪b 胡d ☉f 晚h 군j 승l నn ὴp 曾r 論t ɯv తx 戰z 鱼| ǧ~ 寶 특 💯 崎 甘 該 링 😡 उ ែ 頁 큰 ➤ 총 💰 ∂ 毁 聖 麻 ʐ 敏 運 될 쓰 ಸ စ ✦ 젝 復 寻 茶 ਾ 竹 遇 順 며 累 ĝ ˇ 覧 এ 株 취 ስ 争 势 宇 橋 Ӏ 堆 ⵙ 丶 棋 肉 የ  ❶ 季 ል 殿 優 試 첫 Ό 戶 ண 羅 桃 립 浪  脑  😛 弃 炮 轻 울  ヘ 奥 💜 忘  遠" 飛$ 魏& Ē( 汇* 央, 逆. 露0 須2 ѐ4 ḷ6 ದ8 ✭: 寄< 盟> 财@ 際B ἔD ǫF थH ാJ 宫L 巨N 途P ʹR ಗT 帐V ‪X 拒Z 药\ 🙃^ ŕ` 亡b 壁d ምf 參h 😩j շl ವn ណp 丰r 獲t 莉v 좋x ရz ₦| 겠~ 👉 吴 岡 诉 읽 🥺 爆 🇸 ভ 迭 엔 ἄ 捷 納 邀 ಯ 爾 船 赞 胜 므 သ 構 磁 冰 딩 ે 媒 繁 ☠ ❒ 仪 렬 昭 珠 離 ན ల ತ 拷 粉 벤 ⇽ 乌 拥 ҳ ය ེ 仙 塊 幅 🎉 Մ 跨 ٔ 恩 损 养 奈 ǀ 严 卫 迟 様 裡 난 았 ͜ Ζ ਰ  պ  ং 丢 伝 컨 ව ြ 冷 遗 銀 ̌  ᴜ" 瑞$ ฌ& ❍( ふ* 聚, 碎. 衛0 অ2 ញ4 퍼6 Ս8 ນ: ẓ< ✌> 孝@ 陳B 히D කF 黒H 💖J ḩL 応N 饰P ∪R 宜T 樂V 則X 勇Z 徐\ ⵓ^ 權` 鲁b ‟d 庭f 苗h 🔴j 闲l 독n ɹp ҽr ថt 宏v 尊x 總z 裝| ම~ ▸ 測 ಮ አ 轩 兄 剑 ન 朱 ǝ Ḩ 担 灰 讲 롤 ︎ 😤 ោ 애 였 질 振 灯 ĉ ස 閉 램 ಂ げ ̧ 狂 融 仍 實 楽 範 ٌ వ 嵌 摩 袁 ষ 乎 규 岗 糊 క 雲 심 ई འ ἡ 丝 Ħ ٍ ٓ အ 執 벨 ゼ 梦a 6/mnt/vast/shared/asablayrolles/tokenizer/some_data.txtdummy 0:textU?X_p=}@? [INST][/INST] [TOOL_CALLS][AVAILABLE_TOOLS][/AVAILABLE_TOOLS][TOOL_RESULTS][/TOOL_RESULTS][IMG][PREFIX][MIDDLE][SUFFIX] [control_12] [control_13] [control_14] [control_15] [control_16] [control_17] [control_18] [control_19] [control_20] [control_21] [control_22] [control_23] [control_24] [control_25] [control_26] [control_27] [control_28] [control_29] [control_30] [control_31] [control_32] [control_33] [control_34] [control_35] [control_36] [control_37] [control_38] [control_39] [control_40] [control_41] [control_42] [control_43] [control_44] [control_45] [control_46] [control_47] [control_48] [control_49] [control_50] [control_51] [control_52] [control_53] [control_54] [control_55] [control_56] [control_57] [control_58] [control_59] [control_60] [control_61] [control_62] [control_63] [control_64] [control_65] [control_66] [control_67] [control_68] [control_69] [control_70] [control_71] [control_72] [control_73] [control_74] [control_75] [control_76] [control_77] [control_78] 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[control_649] [control_650] [control_651] [control_652] [control_653] [control_654] [control_655] [control_656] [control_657] [control_658] [control_659] [control_660] [control_661] [control_662] [control_663] [control_664] [control_665] [control_666] [control_667] [control_668] [control_669] [control_670] [control_671] [control_672] [control_673] [control_674] [control_675] [control_676] [control_677] [control_678] [control_679] [control_680] [control_681] [control_682] [control_683] [control_684] [control_685] [control_686] [control_687] [control_688] [control_689] [control_690] [control_691] [control_692] [control_693] [control_694] [control_695] [control_696] [control_697] [control_698] [control_699] [control_700] [control_701] [control_702] [control_703] [control_704] [control_705] [control_706] [control_707] [control_708] [control_709] [control_710] [control_711] [control_712] [control_713] [control_714] [control_715] [control_716] [control_717] [control_718] [control_719] [control_720] [control_721] [control_722] [control_723] [control_724] [control_725] [control_726] [control_727] [control_728] [control_729] [control_730] [control_731] [control_732] [control_733] [control_734] [control_735] [control_736] [control_737] [control_738] [control_739] [control_740] [control_741] [control_742] [control_743] [control_744] [control_745] [control_746] [control_747] [control_748][REFERENCE_DOC_19][REFERENCE_DOC_18][REFERENCE_DOC_17][REFERENCE_DOC_16][REFERENCE_DOC_15][REFERENCE_DOC_14][REFERENCE_DOC_13][REFERENCE_DOC_12][REFERENCE_DOC_11][REFERENCE_DOC_10][REFERENCE_DOC_9][REFERENCE_DOC_8][REFERENCE_DOC_7][REFERENCE_DOC_6][REFERENCE_DOC_5][REFERENCE_DOC_4][REFERENCE_DOC_3][REFERENCE_DOC_2][REFERENCE_DOC_1][REFERENCE_DOC_0] ⁇  identity 2python-mistral-common-1.11.7/src/mistral_common/data/mistral_instruct_tokenizer_241114.model.v7000066400000000000000000021734521523035502500325130ustar00rootroot00000000000000      [INST]  [/INST]  [TOOL_CALLS]  [AVAILABLE_TOOLS]  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[control_76]  [control_77]  [control_78]  [control_79]  [control_80]  [control_81]  [control_82]  [control_83]  [control_84]  [control_85]  [control_86]  [control_87]  [control_88]  [control_89]  [control_90]  [control_91]  [control_92]  [control_93]  [control_94]  [control_95]  [control_96]  [control_97]  [control_98]  [control_99]  [control_100]  [control_101]  [control_102]  [control_103]  [control_104]  [control_105]  [control_106]  [control_107]  [control_108]  [control_109]  [control_110]  [control_111]  [control_112]  [control_113]  [control_114]  [control_115]  [control_116]  [control_117]  [control_118]  [control_119]  [control_120]  [control_121]  [control_122]  [control_123]  [control_124]  [control_125]  [control_126]  [control_127]  [control_128]  [control_129]  [control_130]  [control_131]  [control_132]  [control_133]  [control_134]  [control_135]  [control_136]  [control_137]  [control_138]  [control_139]  [control_140]  [control_141]  [control_142]  [control_143]  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[control_738]  [control_739]  [control_740]  [control_741]  [control_742]  [control_743]  [control_744]  [control_745]  [control_746]  [/REF]  [REF]  [REFERENCE_DOC_19]  [REFERENCE_DOC_18]  [REFERENCE_DOC_17]  [REFERENCE_DOC_16]  [REFERENCE_DOC_15]  [REFERENCE_DOC_14]  [REFERENCE_DOC_13]  [REFERENCE_DOC_12]  [REFERENCE_DOC_11]  [REFERENCE_DOC_10]  [REFERENCE_DOC_9]  [REFERENCE_DOC_8]  [REFERENCE_DOC_7]  [REFERENCE_DOC_6]  [REFERENCE_DOC_5]  [REFERENCE_DOC_4]  [REFERENCE_DOC_3]  [REFERENCE_DOC_2]  [REFERENCE_DOC_1]  [REFERENCE_DOC_0]  <0x00>  <0x01>  <0x02>  <0x03>  <0x04>  <0x05>  <0x06>  <0x07>  <0x08>  <0x09>  <0x0A>  <0x0B>  <0x0C>  <0x0D>  <0x0E>  <0x0F>  <0x10>  <0x11>  <0x12>  <0x13>  <0x14>  <0x15>  <0x16>  <0x17>  <0x18>  <0x19>  <0x1A>  <0x1B>  <0x1C>  <0x1D>  <0x1E>  <0x1F>  <0x20>  <0x21>  <0x22>  <0x23>  <0x24>  <0x25>  <0x26>  <0x27>  <0x28>  <0x29>  <0x2A>  <0x2B>  <0x2C>  <0x2D>  <0x2E>  <0x2F>  <0x30>  <0x31>  <0x32>  <0x33>  <0x34>  <0x35>  <0x36>  <0x37>  <0x38>  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״ ಠ 庫 མ 統 련 ़ ṯ ക 旗 励 紀 忠 າ 杨 丹 Ù ฝ 却 舞 轉 တ  丽  借 ා ょ 옵 편 蒙 衡 ʋ 叶 ̇  ⬜" 🇺$ Հ& 谢( Ą* ே, ằ. 既0 济2 ≯4 準6 답8 ಲ: 残< 虑> ̆@ ┘B 急D 招F 막H ≮J 產L ṬN 😢P 垂R 親T ģV ־X 猫Z ʟ\ ☃^ ✪` 刪b 胡d ☉f 晚h 군j 승l నn ὴp 曾r 論t ɯv తx 戰z 鱼| ǧ~ 寶 특 💯 崎 甘 該 링 😡 उ ែ 頁 큰 ➤ 총 💰 ∂ 毁 聖 麻 ʐ 敏 運 될 쓰 ಸ စ ✦ 젝 復 寻 茶 ਾ 竹 遇 順 며 累 ĝ ˇ 覧 এ 株 취 ስ 争 势 宇 橋 Ӏ 堆 ⵙ 丶 棋 肉 የ  ❶ 季 ል 殿 優 試 첫 Ό 戶 ண 羅 桃 립 浪  脑  😛 弃 炮 轻 울  ヘ 奥 💜 忘  遠" 飛$ 魏& Ē( 汇* 央, 逆. 露0 須2 ѐ4 ḷ6 ದ8 ✭: 寄< 盟> 财@ 際B ἔD ǫF थH ാJ 宫L 巨N 途P ʹR ಗT 帐V ‪X 拒Z 药\ 🙃^ ŕ` 亡b 壁d ምf 參h 😩j շl ವn ណp 丰r 獲t 莉v 좋x ရz ₦| 겠~ 👉 吴 岡 诉 읽 🥺 爆 🇸 ভ 迭 엔 ἄ 捷 納 邀 ಯ 爾 船 赞 胜 므 သ 構 磁 冰 딩 ે 媒 繁 ☠ ❒ 仪 렬 昭 珠 離 ན ల ತ 拷 粉 벤 ⇽ 乌 拥 ҳ ය ེ 仙 塊 幅 🎉 Մ 跨 ٔ 恩 损 养 奈 ǀ 严 卫 迟 様 裡 난 았 ͜ Ζ ਰ  պ  ং 丢 伝 컨 ව ြ 冷 遗 銀 ̌  ᴜ" 瑞$ ฌ& ❍( ふ* 聚, 碎. 衛0 অ2 ញ4 퍼6 Ս8 ນ: ẓ< ✌> 孝@ 陳B 히D කF 黒H 💖J ḩL 応N 饰P ∪R 宜T 樂V 則X 勇Z 徐\ ⵓ^ 權` 鲁b ‟d 庭f 苗h 🔴j 闲l 독n ɹp ҽr ថt 宏v 尊x 總z 裝| ම~ ▸ 測 ಮ አ 轩 兄 剑 ન 朱 ǝ Ḩ 担 灰 讲 롤 ︎ 😤 ោ 애 였 질 振 灯 ĉ ස 閉 램 ಂ げ ̧ 狂 融 仍 實 楽 範 ٌ వ 嵌 摩 袁 ষ 乎 규 岗 糊 క 雲 심 ई འ ἡ 丝 Ħ ٍ ٓ အ 執 벨 ゼ 梦a -/mnt/vast/portal-las1/tokenizer/some_data.txtdummy 0:textU?X_p=}@? [INST][/INST] [TOOL_CALLS][AVAILABLE_TOOLS][/AVAILABLE_TOOLS][TOOL_RESULTS][/TOOL_RESULTS][IMG][PREFIX][MIDDLE][SUFFIX] [IMG_BREAK] [IMG_END][SYSTEM_PROMPT][/SYSTEM_PROMPT][TOOL_CONTENT] [control_17] [control_18] [control_19] [control_20] [control_21] [control_22] [control_23] [control_24] [control_25] [control_26] [control_27] [control_28] [control_29] [control_30] [control_31] [control_32] [control_33] [control_34] [control_35] [control_36] [control_37] [control_38] [control_39] [control_40] [control_41] [control_42] [control_43] [control_44] [control_45] [control_46] [control_47] [control_48] [control_49] [control_50] [control_51] [control_52] [control_53] [control_54] [control_55] [control_56] [control_57] [control_58] [control_59] [control_60] [control_61] [control_62] [control_63] [control_64] [control_65] [control_66] [control_67] [control_68] [control_69] [control_70] [control_71] [control_72] [control_73] [control_74] [control_75] [control_76] [control_77] [control_78] [control_79] [control_80] [control_81] [control_82] [control_83] [control_84] [control_85] [control_86] [control_87] [control_88] [control_89] [control_90] [control_91] [control_92] [control_93] [control_94] [control_95] [control_96] [control_97] [control_98] [control_99] [control_100] [control_101] [control_102] [control_103] [control_104] [control_105] [control_106] [control_107] [control_108] [control_109] [control_110] [control_111] [control_112] [control_113] [control_114] [control_115] [control_116] [control_117] [control_118] [control_119] [control_120] [control_121] [control_122] [control_123] [control_124] [control_125] [control_126] [control_127] [control_128] [control_129] [control_130] [control_131] [control_132] [control_133] [control_134] [control_135] [control_136] [control_137] [control_138] [control_139] [control_140] [control_141] [control_142] [control_143] [control_144] [control_145] [control_146] [control_147] [control_148] [control_149] [control_150] [control_151] [control_152] [control_153] [control_154] [control_155] [control_156] [control_157] [control_158] [control_159] [control_160] [control_161] [control_162] [control_163] [control_164] [control_165] [control_166] [control_167] [control_168] [control_169] [control_170] [control_171] [control_172] [control_173] [control_174] [control_175] [control_176] [control_177] [control_178] [control_179] [control_180] [control_181] [control_182] [control_183] [control_184] [control_185] [control_186] [control_187] [control_188] [control_189] [control_190] [control_191] [control_192] [control_193] [control_194] [control_195] [control_196] [control_197] [control_198] [control_199] [control_200] [control_201] [control_202] [control_203] [control_204] [control_205] [control_206] [control_207] [control_208] [control_209] [control_210] [control_211] [control_212] [control_213] [control_214] [control_215] [control_216] [control_217] [control_218] [control_219] [control_220] [control_221] [control_222] [control_223] [control_224] [control_225] [control_226] [control_227] [control_228] [control_229] [control_230] [control_231] [control_232] [control_233] [control_234] [control_235] [control_236] [control_237] [control_238] [control_239] [control_240] [control_241] [control_242] [control_243] [control_244] [control_245] [control_246] [control_247] [control_248] [control_249] [control_250] [control_251] [control_252] [control_253] [control_254] [control_255] [control_256] [control_257] [control_258] [control_259] [control_260] [control_261] [control_262] [control_263] [control_264] [control_265] [control_266] [control_267] [control_268] [control_269] [control_270] [control_271] [control_272] [control_273] [control_274] [control_275] [control_276] [control_277] [control_278] [control_279] [control_280] [control_281] [control_282] [control_283] [control_284] [control_285] [control_286] [control_287] [control_288] [control_289] [control_290] [control_291] [control_292] [control_293] [control_294] [control_295] [control_296] [control_297] [control_298] [control_299] [control_300] [control_301] [control_302] [control_303] [control_304] [control_305] [control_306] [control_307] [control_308] [control_309] [control_310] [control_311] [control_312] [control_313] [control_314] [control_315] [control_316] [control_317] [control_318] [control_319] [control_320] [control_321] [control_322] [control_323] [control_324] [control_325] [control_326] [control_327] [control_328] [control_329] [control_330] [control_331] [control_332] [control_333] [control_334] [control_335] [control_336] [control_337] [control_338] [control_339] [control_340] [control_341] [control_342] [control_343] [control_344] [control_345] [control_346] [control_347] [control_348] [control_349] [control_350] [control_351] [control_352] [control_353] [control_354] [control_355] [control_356] [control_357] [control_358] [control_359] [control_360] [control_361] [control_362] [control_363] [control_364] [control_365] [control_366] [control_367] [control_368] [control_369] [control_370] [control_371] [control_372] [control_373] [control_374] [control_375] [control_376] [control_377] [control_378] [control_379] [control_380] [control_381] [control_382] [control_383] [control_384] [control_385] [control_386] [control_387] [control_388] [control_389] [control_390] [control_391] [control_392] [control_393] [control_394] [control_395] [control_396] [control_397] [control_398] [control_399] [control_400] [control_401] [control_402] [control_403] [control_404] [control_405] [control_406] [control_407] [control_408] [control_409] [control_410] [control_411] [control_412] [control_413] [control_414] [control_415] [control_416] [control_417] [control_418] [control_419] [control_420] [control_421] [control_422] [control_423] [control_424] [control_425] [control_426] [control_427] [control_428] [control_429] [control_430] [control_431] [control_432] [control_433] [control_434] [control_435] [control_436] [control_437] [control_438] [control_439] [control_440] [control_441] [control_442] [control_443] [control_444] [control_445] [control_446] [control_447] [control_448] [control_449] [control_450] [control_451] [control_452] [control_453] [control_454] [control_455] [control_456] [control_457] [control_458] [control_459] [control_460] [control_461] [control_462] [control_463] [control_464] [control_465] [control_466] [control_467] [control_468] [control_469] [control_470] [control_471] [control_472] [control_473] [control_474] [control_475] [control_476] [control_477] [control_478] [control_479] [control_480] [control_481] [control_482] [control_483] [control_484] [control_485] [control_486] [control_487] [control_488] [control_489] [control_490] [control_491] [control_492] [control_493] [control_494] [control_495] [control_496] [control_497] [control_498] [control_499] [control_500] [control_501] [control_502] [control_503] [control_504] [control_505] [control_506] [control_507] [control_508] [control_509] [control_510] [control_511] [control_512] [control_513] [control_514] [control_515] [control_516] [control_517] [control_518] [control_519] [control_520] [control_521] [control_522] [control_523] [control_524] [control_525] [control_526] [control_527] [control_528] [control_529] [control_530] [control_531] [control_532] [control_533] [control_534] [control_535] [control_536] [control_537] [control_538] [control_539] [control_540] [control_541] [control_542] [control_543] [control_544] [control_545] [control_546] [control_547] [control_548] [control_549] [control_550] [control_551] [control_552] [control_553] [control_554] [control_555] [control_556] [control_557] [control_558] [control_559] [control_560] [control_561] [control_562] [control_563] [control_564] [control_565] [control_566] [control_567] [control_568] [control_569] [control_570] [control_571] [control_572] [control_573] [control_574] [control_575] [control_576] [control_577] [control_578] [control_579] [control_580] [control_581] [control_582] [control_583] [control_584] [control_585] [control_586] [control_587] [control_588] [control_589] [control_590] [control_591] [control_592] [control_593] [control_594] [control_595] [control_596] [control_597] [control_598] [control_599] [control_600] [control_601] [control_602] [control_603] [control_604] [control_605] [control_606] [control_607] [control_608] [control_609] [control_610] [control_611] [control_612] [control_613] [control_614] [control_615] [control_616] [control_617] [control_618] [control_619] [control_620] [control_621] [control_622] [control_623] [control_624] [control_625] [control_626] [control_627] [control_628] [control_629] [control_630] [control_631] [control_632] [control_633] [control_634] [control_635] [control_636] [control_637] [control_638] [control_639] [control_640] [control_641] [control_642] [control_643] [control_644] [control_645] [control_646] [control_647] [control_648] [control_649] [control_650] [control_651] [control_652] [control_653] [control_654] [control_655] [control_656] [control_657] [control_658] [control_659] [control_660] [control_661] [control_662] [control_663] [control_664] [control_665] [control_666] [control_667] [control_668] [control_669] [control_670] [control_671] [control_672] [control_673] [control_674] [control_675] [control_676] [control_677] [control_678] [control_679] [control_680] [control_681] [control_682] [control_683] [control_684] [control_685] [control_686] [control_687] [control_688] [control_689] [control_690] [control_691] [control_692] [control_693] [control_694] [control_695] [control_696] [control_697] [control_698] [control_699] [control_700] [control_701] [control_702] [control_703] [control_704] [control_705] [control_706] [control_707] [control_708] [control_709] [control_710] [control_711] [control_712] [control_713] [control_714] [control_715] [control_716] [control_717] [control_718] [control_719] [control_720] [control_721] [control_722] [control_723] [control_724] [control_725] [control_726] [control_727] [control_728] [control_729] [control_730] [control_731] [control_732] [control_733] [control_734] [control_735] [control_736] [control_737] [control_738] [control_739] [control_740] [control_741] [control_742] [control_743] [control_744] [control_745] [control_746][/REF][REF][REFERENCE_DOC_19][REFERENCE_DOC_18][REFERENCE_DOC_17][REFERENCE_DOC_16][REFERENCE_DOC_15][REFERENCE_DOC_14][REFERENCE_DOC_13][REFERENCE_DOC_12][REFERENCE_DOC_11][REFERENCE_DOC_10][REFERENCE_DOC_9][REFERENCE_DOC_8][REFERENCE_DOC_7][REFERENCE_DOC_6][REFERENCE_DOC_5][REFERENCE_DOC_4][REFERENCE_DOC_3][REFERENCE_DOC_2][REFERENCE_DOC_1][REFERENCE_DOC_0] ⁇  identity 2python-mistral-common-1.11.7/src/mistral_common/data/mistral_instruct_tokenizer_241114.model.v7m1000066400000000000000000021734521523035502500327510ustar00rootroot00000000000000      [INST]  [/INST]  [TOOL_CALLS]  [AVAILABLE_TOOLS]  [/AVAILABLE_TOOLS]  [TOOL_RESULTS]  [/TOOL_RESULTS]  [IMG]  [PREFIX]  [MIDDLE]  [SUFFIX]  [IMG_BREAK]  [IMG_END]  [SYSTEM_PROMPT]  [/SYSTEM_PROMPT]  [TOOL_CONTENT]  [control_17]  [control_18]  [control_19]  [control_20]  [control_21]  [control_22]  [control_23]  [control_24]  [control_25]  [control_26]  [control_27]  [control_28]  [control_29]  [control_30]  [control_31]  [control_32]  [control_33]  [control_34]  [control_35]  [control_36]  [control_37]  [control_38]  [control_39]  [control_40]  [control_41]  [control_42]  [control_43]  [control_44]  [control_45]  [control_46]  [control_47]  [control_48]  [control_49]  [control_50]  [control_51]  [control_52]  [control_53]  [control_54]  [control_55]  [control_56]  [control_57]  [control_58]  [control_59]  [control_60]  [control_61]  [control_62]  [control_63]  [control_64]  [control_65]  [control_66]  [control_67]  [control_68]  [control_69]  [control_70]  [control_71]  [control_72]  [control_73]  [control_74]  [control_75]  [control_76]  [control_77]  [control_78]  [control_79]  [control_80]  [control_81]  [control_82]  [control_83]  [control_84]  [control_85]  [control_86]  [control_87]  [control_88]  [control_89]  [control_90]  [control_91]  [control_92]  [control_93]  [control_94]  [control_95]  [control_96]  [control_97]  [control_98]  [control_99]  [control_100]  [control_101]  [control_102]  [control_103]  [control_104]  [control_105]  [control_106]  [control_107]  [control_108]  [control_109]  [control_110]  [control_111]  [control_112]  [control_113]  [control_114]  [control_115]  [control_116]  [control_117]  [control_118]  [control_119]  [control_120]  [control_121]  [control_122]  [control_123]  [control_124]  [control_125]  [control_126]  [control_127]  [control_128]  [control_129]  [control_130]  [control_131]  [control_132]  [control_133]  [control_134]  [control_135]  [control_136]  [control_137]  [control_138]  [control_139]  [control_140]  [control_141]  [control_142]  [control_143]  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[control_672]  [control_673]  [control_674]  [control_675]  [control_676]  [control_677]  [control_678]  [control_679]  [control_680]  [control_681]  [control_682]  [control_683]  [control_684]  [control_685]  [control_686]  [control_687]  [control_688]  [control_689]  [control_690]  [control_691]  [control_692]  [control_693]  [control_694]  [control_695]  [control_696]  [control_697]  [control_698]  [control_699]  [control_700]  [control_701]  [control_702]  [control_703]  [control_704]  [control_705]  [control_706]  [control_707]  [control_708]  [control_709]  [control_710]  [control_711]  [control_712]  [control_713]  [control_714]  [control_715]  [control_716]  [control_717]  [control_718]  [control_719]  [control_720]  [control_721]  [control_722]  [control_723]  [control_724]  [control_725]  [control_726]  [control_727]  [control_728]  [control_729]  [control_730]  [control_731]  [control_732]  [control_733]  [control_734]  [control_735]  [control_736]  [control_737]  [control_738]  [control_739]  [control_740]  [control_741]  [control_742]  [control_743]  [control_744]  [control_745]  [control_746]  [/REF]  [REF]  [REFERENCE_DOC_19]  [REFERENCE_DOC_18]  [REFERENCE_DOC_17]  [REFERENCE_DOC_16]  [REFERENCE_DOC_15]  [REFERENCE_DOC_14]  [REFERENCE_DOC_13]  [REFERENCE_DOC_12]  [REFERENCE_DOC_11]  [REFERENCE_DOC_10]  [REFERENCE_DOC_9]  [REFERENCE_DOC_8]  [REFERENCE_DOC_7]  [REFERENCE_DOC_6]  [REFERENCE_DOC_5]  [REFERENCE_DOC_4]  [REFERENCE_DOC_3]  [REFERENCE_DOC_2]  [REFERENCE_DOC_1]  [REFERENCE_DOC_0]  <0x00>  <0x01>  <0x02>  <0x03>  <0x04>  <0x05>  <0x06>  <0x07>  <0x08>  <0x09>  <0x0A>  <0x0B>  <0x0C>  <0x0D>  <0x0E>  <0x0F>  <0x10>  <0x11>  <0x12>  <0x13>  <0x14>  <0x15>  <0x16>  <0x17>  <0x18>  <0x19>  <0x1A>  <0x1B>  <0x1C>  <0x1D>  <0x1E>  <0x1F>  <0x20>  <0x21>  <0x22>  <0x23>  <0x24>  <0x25>  <0x26>  <0x27>  <0x28>  <0x29>  <0x2A>  <0x2B>  <0x2C>  <0x2D>  <0x2E>  <0x2F>  <0x30>  <0x31>  <0x32>  <0x33>  <0x34>  <0x35>  <0x36>  <0x37>  <0x38>  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״ ಠ 庫 མ 統 련 ़ ṯ ക 旗 励 紀 忠 າ 杨 丹 Ù ฝ 却 舞 轉 တ  丽  借 ා ょ 옵 편 蒙 衡 ʋ 叶 ̇  ⬜" 🇺$ Հ& 谢( Ą* ே, ằ. 既0 济2 ≯4 準6 답8 ಲ: 残< 虑> ̆@ ┘B 急D 招F 막H ≮J 產L ṬN 😢P 垂R 親T ģV ־X 猫Z ʟ\ ☃^ ✪` 刪b 胡d ☉f 晚h 군j 승l నn ὴp 曾r 論t ɯv తx 戰z 鱼| ǧ~ 寶 특 💯 崎 甘 該 링 😡 उ ែ 頁 큰 ➤ 총 💰 ∂ 毁 聖 麻 ʐ 敏 運 될 쓰 ಸ စ ✦ 젝 復 寻 茶 ਾ 竹 遇 順 며 累 ĝ ˇ 覧 এ 株 취 ስ 争 势 宇 橋 Ӏ 堆 ⵙ 丶 棋 肉 የ  ❶ 季 ል 殿 優 試 첫 Ό 戶 ண 羅 桃 립 浪  脑  😛 弃 炮 轻 울  ヘ 奥 💜 忘  遠" 飛$ 魏& Ē( 汇* 央, 逆. 露0 須2 ѐ4 ḷ6 ದ8 ✭: 寄< 盟> 财@ 際B ἔD ǫF थH ാJ 宫L 巨N 途P ʹR ಗT 帐V ‪X 拒Z 药\ 🙃^ ŕ` 亡b 壁d ምf 參h 😩j շl ವn ណp 丰r 獲t 莉v 좋x ရz ₦| 겠~ 👉 吴 岡 诉 읽 🥺 爆 🇸 ভ 迭 엔 ἄ 捷 納 邀 ಯ 爾 船 赞 胜 므 သ 構 磁 冰 딩 ે 媒 繁 ☠ ❒ 仪 렬 昭 珠 離 ན ల ತ 拷 粉 벤 ⇽ 乌 拥 ҳ ය ེ 仙 塊 幅 🎉 Մ 跨 ٔ 恩 损 养 奈 ǀ 严 卫 迟 様 裡 난 았 ͜ Ζ ਰ  պ  ং 丢 伝 컨 ව ြ 冷 遗 銀 ̌  ᴜ" 瑞$ ฌ& ❍( ふ* 聚, 碎. 衛0 অ2 ញ4 퍼6 Ս8 ນ: ẓ< ✌> 孝@ 陳B 히D කF 黒H 💖J ḩL 応N 饰P ∪R 宜T 樂V 則X 勇Z 徐\ ⵓ^ 權` 鲁b ‟d 庭f 苗h 🔴j 闲l 독n ɹp ҽr ថt 宏v 尊x 總z 裝| ම~ ▸ 測 ಮ አ 轩 兄 剑 ન 朱 ǝ Ḩ 担 灰 讲 롤 ︎ 😤 ោ 애 였 질 振 灯 ĉ ස 閉 램 ಂ げ ̧ 狂 融 仍 實 楽 範 ٌ వ 嵌 摩 袁 ষ 乎 규 岗 糊 క 雲 심 ई འ ἡ 丝 Ħ ٍ ٓ အ 執 벨 ゼ 梦a -/mnt/vast/portal-las1/tokenizer/some_data.txtdummy 0:textU?X_p=}@? [INST][/INST] [TOOL_CALLS][AVAILABLE_TOOLS][/AVAILABLE_TOOLS][TOOL_RESULTS][/TOOL_RESULTS][IMG][PREFIX][MIDDLE][SUFFIX] [IMG_BREAK] [IMG_END][SYSTEM_PROMPT][/SYSTEM_PROMPT][TOOL_CONTENT] [control_17] [control_18] [control_19] [control_20] [control_21] [control_22] [control_23] [control_24] [control_25] [control_26] [control_27] [control_28] [control_29] [control_30] [control_31] [control_32] [control_33] [control_34] [control_35] [control_36] [control_37] [control_38] [control_39] [control_40] [control_41] [control_42] [control_43] [control_44] [control_45] [control_46] [control_47] [control_48] [control_49] [control_50] [control_51] [control_52] [control_53] [control_54] [control_55] [control_56] [control_57] [control_58] [control_59] [control_60] [control_61] [control_62] [control_63] [control_64] [control_65] [control_66] [control_67] [control_68] [control_69] [control_70] [control_71] [control_72] [control_73] [control_74] [control_75] [control_76] [control_77] [control_78] 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[control_649] [control_650] [control_651] [control_652] [control_653] [control_654] [control_655] [control_656] [control_657] [control_658] [control_659] [control_660] [control_661] [control_662] [control_663] [control_664] [control_665] [control_666] [control_667] [control_668] [control_669] [control_670] [control_671] [control_672] [control_673] [control_674] [control_675] [control_676] [control_677] [control_678] [control_679] [control_680] [control_681] [control_682] [control_683] [control_684] [control_685] [control_686] [control_687] [control_688] [control_689] [control_690] [control_691] [control_692] [control_693] [control_694] [control_695] [control_696] [control_697] [control_698] [control_699] [control_700] [control_701] [control_702] [control_703] [control_704] [control_705] [control_706] [control_707] [control_708] [control_709] [control_710] [control_711] [control_712] [control_713] [control_714] [control_715] [control_716] [control_717] [control_718] [control_719] [control_720] [control_721] [control_722] [control_723] [control_724] [control_725] [control_726] [control_727] [control_728] [control_729] [control_730] [control_731] [control_732] [control_733] [control_734] [control_735] [control_736] [control_737] [control_738] [control_739] [control_740] [control_741] [control_742] [control_743] [control_744] [control_745] [control_746][/REF][REF][REFERENCE_DOC_19][REFERENCE_DOC_18][REFERENCE_DOC_17][REFERENCE_DOC_16][REFERENCE_DOC_15][REFERENCE_DOC_14][REFERENCE_DOC_13][REFERENCE_DOC_12][REFERENCE_DOC_11][REFERENCE_DOC_10][REFERENCE_DOC_9][REFERENCE_DOC_8][REFERENCE_DOC_7][REFERENCE_DOC_6][REFERENCE_DOC_5][REFERENCE_DOC_4][REFERENCE_DOC_3][REFERENCE_DOC_2][REFERENCE_DOC_1][REFERENCE_DOC_0] ⁇  identity 2python-mistral-common-1.11.7/src/mistral_common/data/tekken_240718.json000066400000000000000000703545071523035502500256320ustar00rootroot00000000000000{ "config": { "pattern": "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", "num_vocab_tokens": 150000, "default_vocab_size": 131072, "default_num_special_tokens": 1000, "version": "v3" }, "vocab": [ { "rank": 0, "token_bytes": "AA==", "token_str": "\u0000" }, { "rank": 1, "token_bytes": "AQ==", "token_str": "\u0001" }, { "rank": 2, "token_bytes": "Ag==", "token_str": "\u0002" }, { "rank": 3, "token_bytes": "Aw==", "token_str": "\u0003" }, { "rank": 4, "token_bytes": "BA==", "token_str": "\u0004" }, { "rank": 5, "token_bytes": "BQ==", "token_str": "\u0005" }, { "rank": 6, "token_bytes": "Bg==", "token_str": "\u0006" }, { "rank": 7, "token_bytes": "Bw==", "token_str": "\u0007" }, { "rank": 8, "token_bytes": "CA==", "token_str": "\b" }, { "rank": 9, "token_bytes": "CQ==", "token_str": "\t" }, { "rank": 10, 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@/mnt/test/datasets/tokenizer_training/8T_train_data/shuffled.txttok_v0 0:textU?X_p=}@?P  ⁇  identity 2python-mistral-common-1.11.7/src/mistral_common/deprecation.py000066400000000000000000000027271523035502500245520ustar00rootroot00000000000000import importlib import warnings _warned_keys: set[str] = set() def deprecated_import(old_path: str, new_module: str, name: str, version: str) -> object: r"""Warn once and lazily import a symbol that moved to a new module. Args: old_path: The old module path (e.g. `"mistral_common.audio"`). new_module: The new module path (e.g. `"mistral_common.tokens.tokenizers.audio"`). name: The symbol name (e.g. `"Audio"`). version: The version in which the symbol will be removed. Returns: The imported symbol from the new module. """ key = f"import:{old_path}.{name}" if key not in _warned_keys: _warned_keys.add(key) warnings.warn( f"Importing {name} from {old_path} is deprecated. " f"Use {new_module}.{name} instead. " f"Will be removed in {version}.", DeprecationWarning, stacklevel=3, ) mod = importlib.import_module(new_module) return getattr(mod, name) def warn_once(key: str, message: str, category: type[Warning], stacklevel: int) -> None: r"""Emit a warning only on the first call for a given key. Args: key: Unique identifier for this warning. message: The warning message. category: The warning category class. stacklevel: Stack level for the warning. """ if key not in _warned_keys: _warned_keys.add(key) warnings.warn(message, category, stacklevel=stacklevel) python-mistral-common-1.11.7/src/mistral_common/exceptions.py000066400000000000000000000110101523035502500244170ustar00rootroot00000000000000class MistralCommonException(Exception): r"""Base class for all Mistral exceptions. Attributes: message: A human-readable message describing the error. """ message: str = "Internal server error" def __init__( self, message: str | None = None, ) -> None: r"""Initialize the `MistralCommonException` with an optional message. If no message is provided, the default message is used. Args: message: A human-readable message describing the error. """ if message: self.message = message class TokenizerException(MistralCommonException): r"""Exception raised for errors in the tokenizer.""" def __init__(self, message: str) -> None: r"""Initialize the `TokenizerException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class UnsupportedTokenizerFeatureException(MistralCommonException): r"""Exception raised for unsupported features in the tokenizer.""" def __init__(self, message: str) -> None: r"""Initialize the `UnsupportedTokenizerFeatureException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidRequestException(MistralCommonException): r"""Exception raised for invalid requests.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidRequestException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidSystemPromptException(MistralCommonException): r"""Exception raised for invalid system prompts.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidSystemPromptException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidMessageStructureException(MistralCommonException): r"""Exception raised for invalid message structures.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidMessageStructureException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidAssistantMessageException(MistralCommonException): r"""Exception raised for invalid assistant messages.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidAssistantMessageException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidToolMessageException(MistralCommonException): r"""Exception raised for invalid tool messages.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidToolMessageException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidToolSchemaException(MistralCommonException): r"""Exception raised for invalid tool schemas.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidToolSchemaException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidUserMessageException(MistralCommonException): r"""Exception raised for invalid user messages.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidUserMessageException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidFunctionCallException(MistralCommonException): r"""Exception raised for invalid function calls.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidFunctionCallException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) class InvalidToolException(MistralCommonException): r"""Exception raised for invalid tools.""" def __init__(self, message: str) -> None: r"""Initialize the `InvalidToolException` with a message. Args: message: A human-readable message describing the error. """ super().__init__(message) python-mistral-common-1.11.7/src/mistral_common/experimental/000077500000000000000000000000001523035502500243705ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/experimental/__init__.py000066400000000000000000000000001523035502500264670ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/experimental/app/000077500000000000000000000000001523035502500251505ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/experimental/app/__init__.py000066400000000000000000000000001523035502500272470ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/experimental/app/main.py000066400000000000000000000071511523035502500264520ustar00rootroot00000000000000from functools import lru_cache from pathlib import Path import click import uvicorn from fastapi import FastAPI from mistral_common.experimental.app.models import ( EngineBackend, Settings, get_settings, ) from mistral_common.experimental.app.routers import ( decode_router, main_router, tokenize_router, ) from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.mistral import MistralTokenizer def create_app( tokenizer: str | Path | MistralTokenizer, validation_mode: ValidationMode = ValidationMode.test, engine_url: str = "127.0.0.1", engine_backend: EngineBackend = EngineBackend.llama_cpp, timeout: int = 60, ) -> FastAPI: r"""Create a Mistral-common FastAPI app with the given tokenizer and validation mode. Args: tokenizer: The tokenizer path or a MistralTokenizer instance. validation_mode: The validation mode to use. engine_url: The URL of the engine API. timeout: The timeout of the engine API. Returns: The Mistral-common FastAPI app. """ if not isinstance(tokenizer, (MistralTokenizer, str, Path)): raise ValueError("Tokenizer must be a path or a MistralTokenizer instance.") app = FastAPI() app.include_router(tokenize_router) app.include_router(decode_router) app.include_router(main_router) @lru_cache def get_settings_override() -> Settings: settings = Settings( engine_url=engine_url, engine_backend=engine_backend, timeout=timeout, ) if isinstance(tokenizer, MistralTokenizer): settings.tokenizer = tokenizer else: settings._load_tokenizer(tokenizer, validation_mode) return settings get_settings_override() app.dependency_overrides[get_settings] = get_settings_override return app @click.group() def cli() -> None: r"""Mistral-common CLI.""" pass @cli.command(name="serve", context_settings={"auto_envvar_prefix": "UVICORN"}) @click.argument("tokenizer_path", type=str) @click.argument( "validation_mode", type=click.Choice([mode.value for mode in ValidationMode], case_sensitive=False), default=ValidationMode.test.value, ) @click.option( "--host", type=str, default="127.0.0.1", help="Mistral-common API host", show_default=True, ) @click.option( "--port", type=int, default=0, help="Mistral-common API port", show_default=True, ) @click.option( "--engine-url", type=str, default="http://127.0.0.1:8080", help="Engine URL", show_default=True, ) @click.option( "--engine-backend", type=click.Choice([mode.value for mode in EngineBackend], case_sensitive=False), default=EngineBackend.llama_cpp.value, help="Engine API backend", show_default=True, ) @click.option( "--timeout", type=int, default=60, help="Timeout", show_default=True, ) def serve( tokenizer_path: str | Path, validation_mode: ValidationMode | str = ValidationMode.test, host: str = "127.0.0.1", port: int = 0, engine_url: str = "http://127.0.0.1:8080", engine_backend: str = EngineBackend.llama_cpp.value, timeout: int = 60, ) -> None: r"""Serve the Mistral-common API with the given tokenizer path and validation mode.""" app = create_app( tokenizer=tokenizer_path, validation_mode=ValidationMode(validation_mode), engine_url=engine_url, engine_backend=EngineBackend(engine_backend), timeout=timeout, ) uvicorn.run(app, host=host, port=port) if __name__ == "__main__": cli() python-mistral-common-1.11.7/src/mistral_common/experimental/app/models.py000066400000000000000000000105161523035502500270100ustar00rootroot00000000000000import importlib.metadata from enum import Enum from pathlib import Path from typing import Any from pydantic import BaseModel, ConfigDict, field_validator from pydantic_settings import BaseSettings from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.mistral import MistralTokenizer class OpenAIChatCompletionRequest(BaseModel): r"""OpenAI chat completion request. This class is used to parse the request body for the OpenAI chat completion endpoint. Attributes: messages: The messages to use for the chat completion. tools: The tools to use for the chat completion. Note: This class accepts extra fields that are not validated. """ messages: list[dict[str, str | list[dict[str, str | dict[str, Any]]]]] tools: list[dict[str, Any]] | None = None # Allow extra fields as the `from_openai` method will handle them. # We never validate the input, so we don't need to worry about the extra fields. model_config = ConfigDict(extra="allow") def drop_extra_fields(self) -> dict[str, Any]: r"""Drop extra fields from the model. This method is used to drop extra fields from the model, which are not defined in the model fields. Returns: The extra fields that were dropped from the model. """ extra_fields = { field: value for field, value in self.model_dump().items() if field not in type(self).model_fields } self.__dict__ = {k: v for k, v in self.__dict__.items() if k not in extra_fields} return extra_fields class EngineBackend(str, Enum): r"""The engine backend to use. Attributes: llama_cpp: The llama.cpp backend. """ llama_cpp = "llama_cpp" class Settings(BaseSettings): r"""Settings for the Mistral-common API. Attributes: app_name: The name of the application. app_version: The version of the application. engine_url: The URL of the engine. engine_backend: The backend to use for the engine. timeout: The timeout to use for the engine API. """ app_name: str = "Mistral-common API" app_version: str = importlib.metadata.version("mistral-common") engine_url: str = "127.0.0.1" engine_backend: EngineBackend = EngineBackend.llama_cpp api_key: str = "" timeout: int = 60 @field_validator("engine_url", mode="before") @classmethod def _validate_engine_url(cls, value: str) -> str: if isinstance(value, str) and value.endswith("/"): value = value[:-1] return value @field_validator("engine_backend", mode="before") @classmethod def _validate_backend(cls, value: str | EngineBackend) -> EngineBackend: if isinstance(value, str): value = EngineBackend(value) return value def model_post_init(self, context: Any) -> None: super().model_post_init(context) self._tokenizer: MistralTokenizer | None = None def _load_tokenizer(self, tokenizer_path: str | Path, validation_mode: ValidationMode) -> None: if tokenizer_path == "": raise ValueError("Tokenizer path must be set via the environment variable `TOKENIZER_PATH`.") elif self._tokenizer is not None: raise ValueError("Tokenizer has already been initialized.") if isinstance(tokenizer_path, str): candidate_tokenizer_path = Path(tokenizer_path) if candidate_tokenizer_path.exists(): tokenizer_path = candidate_tokenizer_path if isinstance(tokenizer_path, Path) and tokenizer_path.exists(): self._tokenizer = MistralTokenizer.from_file(tokenizer_path, mode=validation_mode) else: self._tokenizer = MistralTokenizer.from_hf_hub(str(tokenizer_path), mode=validation_mode) @property def tokenizer(self) -> MistralTokenizer: if self._tokenizer is None: raise ValueError("Tokenizer not initialized.") return self._tokenizer @tokenizer.setter def tokenizer(self, value: MistralTokenizer) -> None: if not isinstance(value, MistralTokenizer): raise ValueError("Tokenizer must be an instance of MistralTokenizer.") self._tokenizer = value def get_settings() -> Settings: r"""Get the settings for the Mistral-common API.""" return Settings() python-mistral-common-1.11.7/src/mistral_common/experimental/app/routers.py000066400000000000000000000173711523035502500272360ustar00rootroot00000000000000import json from typing import Annotated import requests from fastapi import APIRouter, Body, Depends, HTTPException from fastapi.responses import RedirectResponse from pydantic import ValidationError from mistral_common.experimental.app.models import ( EngineBackend, OpenAIChatCompletionRequest, Settings, get_settings, ) from mistral_common.experimental.think import _split_content_and_think_chunks from mistral_common.experimental.tools import _decode_tool_calls, _split_content_and_tool_calls from mistral_common.protocol.instruct.chunk import ContentChunk, TextChunk, ThinkChunk from mistral_common.protocol.instruct.messages import AssistantMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, Tokenized, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV13 main_router = APIRouter(tags=["app"]) tokenize_router = APIRouter(prefix="/v1/tokenize", tags=["tokenizer", "tokenize"]) decode_router = APIRouter(prefix="/v1/detokenize", tags=["tokenizer", "detokenize"]) @main_router.get("/") async def redirect_to_docs() -> RedirectResponse: r"""Redirect to the documentation.""" return RedirectResponse(url="docs") @tokenize_router.post("/") async def tokenize_request( request: ChatCompletionRequest | OpenAIChatCompletionRequest, settings: Annotated[Settings, Depends(get_settings)], ) -> list[int]: r"""Tokenize a chat completion request.""" if isinstance(request, OpenAIChatCompletionRequest): try: request.drop_extra_fields() request = ChatCompletionRequest.from_openai(**request.model_dump()) except (ValidationError, ValueError) as e: raise HTTPException(status_code=400, detail=str(e)) if request.messages == []: raise HTTPException(status_code=400, detail="Messages list cannot be empty.") tokenized = settings.tokenizer.encode_chat_completion(request) assert isinstance(tokenized, Tokenized), type(tokenized) return tokenized.tokens @decode_router.post("/string") async def detokenize_to_string( settings: Annotated[Settings, Depends(get_settings)], tokens: list[int] = Body(default_factory=list), special_token_policy: SpecialTokenPolicy = Body(default=SpecialTokenPolicy.IGNORE), ) -> str: r"""Detokenize a list of tokens to a string. Args: tokens: The tokens to detokenize. special_token_policy: The policy to use for special tokens. Returns: The detokenized string or assistant message. """ if len(tokens) == 0: raise HTTPException(status_code=400, detail="Tokens list cannot be empty.") try: return settings.tokenizer.decode(tokens, special_token_policy=special_token_policy) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) @decode_router.post("/") async def detokenize_to_assistant_message( settings: Annotated[Settings, Depends(get_settings)], tokens: list[int] = Body(default_factory=list), ) -> AssistantMessage: r"""Detokenize a list of tokens to an assistant message. Parse tool calls from the tokens and extract content before the first tool call. Args: tokens: The tokens to detokenize. Returns: The detokenized assistant message. """ if len(tokens) == 0: raise HTTPException(status_code=400, detail="Tokens list cannot be empty.") if settings.tokenizer.instruct_tokenizer.tokenizer.version > TokenizerVersion.v1: content_tokens, tool_calls_tokens = _split_content_and_tool_calls( tokens, settings.tokenizer.instruct_tokenizer.tokenizer.get_special_token("[TOOL_CALLS]") ) else: content_tokens, tool_calls_tokens = tokens, () content: str | list[ContentChunk] | None = None if settings.tokenizer.instruct_tokenizer.tokenizer.version >= TokenizerVersion.v13: assert isinstance(settings.tokenizer.instruct_tokenizer, InstructTokenizerV13) begin_think = settings.tokenizer.instruct_tokenizer.BEGIN_THINK end_think = settings.tokenizer.instruct_tokenizer.END_THINK else: begin_think = end_think = None if begin_think is not None and end_think is not None: try: content_or_think_tokens = _split_content_and_think_chunks(content_tokens, begin_think, end_think) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) eos = settings.tokenizer.instruct_tokenizer.tokenizer.eos_id if content_or_think_tokens: content = [ TextChunk(text=settings.tokenizer.decode(chunk, special_token_policy=SpecialTokenPolicy.IGNORE)) if not is_think else ThinkChunk( thinking=settings.tokenizer.decode(chunk, special_token_policy=SpecialTokenPolicy.IGNORE), closed=chunk[-1] == end_think, ) for chunk, is_think in content_or_think_tokens if chunk != [eos] # Don't add a TextChunk with just the EOS token ] if len(content) == 1 and isinstance(content[0], TextChunk): content = content[0].text elif content_tokens: content = settings.tokenizer.decode(content_tokens, special_token_policy=SpecialTokenPolicy.IGNORE) if tool_calls_tokens: try: tool_calls = _decode_tool_calls(tool_calls_tokens, settings.tokenizer.instruct_tokenizer.tokenizer) except (ValueError, json.JSONDecodeError) as e: raise HTTPException(status_code=400, detail=str(e)) else: tool_calls = None has_eos = tokens[-1] == settings.tokenizer.instruct_tokenizer.tokenizer.eos_id return AssistantMessage(content=content, tool_calls=tool_calls, prefix=not has_eos) @main_router.post("/v1/chat/completions", tags=["chat", "completions"]) async def generate( request: ChatCompletionRequest | OpenAIChatCompletionRequest, settings: Annotated[Settings, Depends(get_settings)], ) -> AssistantMessage: r"""Generate a chat completion. Args: request: The chat completion request. settings: The settings for the Mistral-common API. Returns: The generated chat completion. """ if isinstance(request, OpenAIChatCompletionRequest): extra_fields = request.drop_extra_fields() request = ChatCompletionRequest.from_openai(**request.model_dump()) else: extra_fields = {} tokens_ids = await tokenize_request(request, settings) exclude_fields = {"messages", "tools"} request_json = request.model_dump() request_json.update(extra_fields) request_json = {k: v for k, v in request_json.items() if k not in exclude_fields} if request_json.get("stream", False): raise HTTPException(status_code=400, detail="Streaming is not supported.") try: if settings.engine_backend == EngineBackend.llama_cpp: response = requests.post( f"{settings.engine_url}/completions", json={ "prompt": tokens_ids, "return_tokens": True, **request_json, }, timeout=settings.timeout, ) else: raise ValueError(f"Unsupported engine backend: {settings.engine_backend}") except requests.exceptions.Timeout: raise HTTPException(status_code=504, detail="Timeout") except requests.exceptions.RequestException as e: raise HTTPException(status_code=500, detail=str(e)) if response.status_code != 200: raise HTTPException(status_code=response.status_code, detail=response.text) response_json = response.json() return await detokenize_to_assistant_message(settings, response_json["tokens"]) python-mistral-common-1.11.7/src/mistral_common/experimental/think.py000066400000000000000000000031161523035502500260600ustar00rootroot00000000000000def _split_content_and_think_chunks( tokens: list[int], begin_think_token_id: int, end_think_token_id: int ) -> list[tuple[list[int], bool]]: r"""Split the content and think chunks from a list of tokens. Args: tokens: list of tokens. begin_think_token_id: The token id for the begin think token. end_think_token_id: The token id for the end think token. Returns: list of tuples, where each tuple contains a list of tokens and a boolean indicating if the chunk is a think chunk. """ if not tokens: return [] content_chunks: list[tuple[list[int], bool]] = [] current_content: list[int] = [] in_think_chunk = False for token in tokens: if token == begin_think_token_id and in_think_chunk: raise ValueError("Nested think chunks are not allowed.") elif token == begin_think_token_id: if current_content: content_chunks.append((current_content, False)) current_content = [] in_think_chunk = True current_content.append(token) elif token == end_think_token_id: if not in_think_chunk: raise ValueError("End think token found without a begin think token.") current_content.append(token) content_chunks.append((current_content, True)) current_content = [] in_think_chunk = False else: current_content.append(token) if current_content: content_chunks.append((current_content, in_think_chunk)) return content_chunks python-mistral-common-1.11.7/src/mistral_common/experimental/tools.py000066400000000000000000000145011523035502500261030ustar00rootroot00000000000000import json from typing import Sequence from mistral_common.experimental.utils import ( _split_integer_list_by_value, _split_tokens_by_one_occurrence_control_token, ) from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, Tokenizer, TokenizerVersion class InvalidToolCallError(ValueError): pass class InvalidArgsToolCallError(InvalidToolCallError): pass def _split_content_and_tool_calls( tokens: list[int], tool_call_token_id: int ) -> tuple[list[int], tuple[list[int], ...]]: r"""Split the content and tool calls from a list of tokens. The content is the first sequence of tokens that does not start with the tool call token ID. The tool calls are the remaining sequences of tokens that start with the tool call token ID. Args: tokens: The list of tokens. tool_call_token_id: The token ID that indicates the start of a tool call. Returns: A tuple containing the content and tool calls. """ if not tokens: return [], () maybe_content_and_tools_calls = _split_integer_list_by_value(tokens, tool_call_token_id) has_content = maybe_content_and_tools_calls[0][0] != tool_call_token_id if has_content: content_tokens = maybe_content_and_tools_calls[0] tools_calls_tokens = maybe_content_and_tools_calls[1:] else: content_tokens = [] tools_calls_tokens = maybe_content_and_tools_calls return content_tokens, tools_calls_tokens def _decode_tool_calls_v2_up_to_v7(tool_call_tokens: list[int], tokenizer: Tokenizer) -> list[ToolCall]: r"""Decode a list of tool call tokens into a list of tool calls for tokenizer versions v2 to v7. Note: Expects the tool call tokens to be in the format: `[TOOL_CALLS][{"id": "call_id", "name": "name", "arguments": {"arg1": "value1", "arg2": "value2"}}, ...]` or `[TOOL_CALLS][{"name": "name", "arguments": {"arg1": "value1", "arg2": "value2"}}, ...]` """ tool_calls_list_string = tokenizer.decode(tool_call_tokens, special_token_policy=SpecialTokenPolicy.IGNORE) try: tool_calls_decoded_list = json.loads(tool_calls_list_string) for tool_call in tool_calls_decoded_list: # Check that the tool call arguments are dicts. if "arguments" not in tool_call or not isinstance(tool_call["arguments"], dict): raise InvalidArgsToolCallError("Invalid tool call arguments tokenization. Expected a dict.") except json.JSONDecodeError as e: raise InvalidToolCallError( "Invalid tool call tokenization. Expected a JSON list of tool calls.", ) from e if not isinstance(tool_calls_decoded_list, list): raise InvalidToolCallError("Invalid tool call tokenization. Expected a list of tool calls.") return [ ToolCall( id=tool_call.get("id", "null"), function=FunctionCall(name=tool_call["name"], arguments=tool_call["arguments"]), ) for tool_call in tool_calls_decoded_list ] def _decode_tool_call_v11_with_call_id(tool_call_tokens: list[int], tokenizer: Tokenizer) -> ToolCall: r"""Decode a list of tool call tokens into a tool call for tokenizer version v11 with call ID. Note: Expects the tool call tokens to be in the format: `[TOOL_CALLS]name[CALL_ID]call_id[ARGS]{"arg1": "value1", "arg2": "value2"}` """ name, call_id_and_args = _split_tokens_by_one_occurrence_control_token(tool_call_tokens, tokenizer, "[CALL_ID]") call_id, args = _split_tokens_by_one_occurrence_control_token(call_id_and_args, tokenizer, "[ARGS]") try: tool_call = ToolCall( id=tokenizer.decode(call_id), function=FunctionCall( name=tokenizer.decode(name), arguments=json.loads(tokenizer.decode(args, special_token_policy=SpecialTokenPolicy.IGNORE)), ), ) except json.JSONDecodeError as e: raise InvalidArgsToolCallError("Invalid tokenized tool call arguments.") from e return tool_call def _decode_tool_call_v11(tool_call_tokens: list[int], tokenizer: Tokenizer) -> ToolCall: r"""Decode a list of tool call tokens into a tool call for tokenizer version v11 without call ID. Note: Expects the tool call tokens to be in the format: `[TOOL_CALLS]name[ARGS]{"arg1": "value1", "arg2": "value2"}` """ name, args = _split_tokens_by_one_occurrence_control_token(tool_call_tokens, tokenizer, "[ARGS]") try: tool_call = ToolCall( function=FunctionCall( name=tokenizer.decode(name, special_token_policy=SpecialTokenPolicy.IGNORE), arguments=json.loads(tokenizer.decode(args, special_token_policy=SpecialTokenPolicy.IGNORE)), ), ) except json.JSONDecodeError as e: raise InvalidArgsToolCallError("Invalid tokenized tool call arguments.") from e return tool_call def _decode_tool_calls(tool_call_tokens: Sequence[list[int]], tokenizer: Tokenizer) -> list[ToolCall]: r"""Decode a list of tool call tokens into a list of tool calls. Note: Each list of tool call tokens are expected to be in the format: - v2 to v7: `[TOOL_CALLS][{"name": "name", "arguments": {"arg1": "value1", "arg2": "value2"}}, ...]` - v11+ without call ID: `[TOOL_CALLS]name[ARGS]{"arg1": "value1", "arg2": "value2"}` - v11+ with call ID: `[TOOL_CALLS]name[CALL_ID]call_id[ARGS]{"arg1": "value1", "arg2": "value2"}` Args: tool_call_tokens: A list of lists of tokens. tokenizer: The tokenizer to use for decoding. Returns: The list of decoded tool calls. """ tools_calls = [] for tool_call in tool_call_tokens: if tokenizer.version == TokenizerVersion.v1: raise ValueError("Tool calls are not supported for tokenizer version v1.") elif tokenizer.version <= TokenizerVersion.v7: tools_calls.extend(_decode_tool_calls_v2_up_to_v7(tool_call, tokenizer)) elif tokenizer.version == TokenizerVersion.v11 and tokenizer.get_special_token("[CALL_ID]") in tool_call: tools_calls.append(_decode_tool_call_v11_with_call_id(tool_call, tokenizer)) else: tools_calls.append(_decode_tool_call_v11(tool_call, tokenizer)) return tools_calls python-mistral-common-1.11.7/src/mistral_common/experimental/utils.py000066400000000000000000000032521523035502500261040ustar00rootroot00000000000000from mistral_common.tokens.tokenizers.base import Tokenizer def _split_integer_list_by_value(list_: list[int], value: int) -> tuple[list[int], ...]: r"""Split a list of integers by a given value. Args: list_: The list to split. value: The value to split the list by. Returns: A tuple of lists of integers. Examples: >>> _split_integer_list_by_value([1, 2, 3, 4, 5], 3) ([1, 2], [3, 4, 5]) >>> _split_integer_list_by_value([1, 2, 3, 4, 5], 6) ([1, 2, 3, 4, 5],) >>> _split_integer_list_by_value([1, 2, 3, 4, 5], 1) ([1, 2, 3, 4, 5],) >>> _split_integer_list_by_value([1, 2, 3, 4, 5, 3, 5, 6, 7], 3) ([1, 2], [3, 4, 5], [3, 5, 6, 7]) """ result = [list_[0]] for i, item in enumerate(list_[1:]): if item == value: return (result, *_split_integer_list_by_value(list_[1 + i :], value)) result.append(item) return (result,) def _split_tokens_by_one_occurrence_control_token( list_: list[int], tokenizer: Tokenizer, control_token: str ) -> tuple[list[int], list[int]]: r"""Split a list of integers by a given control token. Raises: ValueError: If the control token is not found in the list or if it is found more than once. """ control_token_id = tokenizer.get_special_token(control_token) first, *rest = _split_integer_list_by_value(list_, control_token_id) if len(rest) == 0: raise ValueError(f"Control token {control_token} not found in the list of tokens.") if len(rest) > 1: raise ValueError(f"Control token {control_token} found more than once in the list of tokens.") return first, rest[0] python-mistral-common-1.11.7/src/mistral_common/guidance/000077500000000000000000000000001523035502500234525ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/guidance/__init__.py000066400000000000000000000000001523035502500255510ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/guidance/data/000077500000000000000000000000001523035502500243635ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/guidance/data/base_grammar.lark.jinja000066400000000000000000000007501523035502500307520ustar00rootroot00000000000000{% if json_schema_str != None -%} {% if json_only -%} start: SAFE_WS? %json {{ json_schema_str }} {% else -%} start: body | SAFE_WS? %json {{ json_schema_str }} {% endif -%} {% else -%} start: body {% endif -%} {% if mode == "auto" -%} body: content | (content? fcalls) {% elif mode == "any" -%} body: fcalls {% elif mode == "required" -%} body: content? fcalls {% elif mode == "none" -%} body: content {% endif -%} fcalls: {{ fcall }} content: (/(.|\n)+/)+ SAFE_WS: /[ \t\r\n]{1,8}/ python-mistral-common-1.11.7/src/mistral_common/guidance/data/plain_text_think_grammar.lark.jinja000066400000000000000000000012211523035502500333760ustar00rootroot00000000000000{% if json_schema_str != None -%} {% if json_only -%} start: SAFE_WS? %json {{ json_schema_str }} {% else -%} start: body | SAFE_WS? %json {{ json_schema_str }} {% endif -%} {% else -%} start: body {% endif -%} {% if mode == "auto" -%} body: think (content | fcalls) {% elif mode == "any" or mode == "required" -%} body: think fcalls {% elif mode == "none" -%} body: think content {% endif -%} fcalls: {{ fcall }} NO_THINK: /(.|\n)+/ & ~/(?s:.*)(<\/think>|)(?s:.*)/ end_think[capture, lazy]: /(.|\n)*<\/think>/ text_first_optional: (NO_THINK)* content: NO_THINK think: SAFE_WS? // text_first_optional end_think SAFE_WS: /[ \t\r\n]{1,8}/ python-mistral-common-1.11.7/src/mistral_common/guidance/data/think_grammar.lark.jinja000066400000000000000000000012151523035502500311520ustar00rootroot00000000000000{% if json_schema_str != None -%} {% if json_only -%} start: json_grammar {% else -%} start: body | json_grammar {% endif -%} {% if think_with_json -%} json_grammar: think? SAFE_WS? %json {{ json_schema_str }} {% else -%} json_grammar: SAFE_WS? %json {{ json_schema_str }} {% endif -%} {% else -%} start: body {% endif -%} {% if mode == "auto" -%} body: think? (content | fcalls) {% elif mode == "any" or mode == "required" -%} body: think? fcalls {% elif mode == "none" -%} body: think? content {% endif -%} fcalls: content? {{ fcall }} content: (/(.|\n)+/)+ think: {{ begin_think_token }} content {{ end_think_token }} SAFE_WS: /[ \t\r\n]{1,8}/ python-mistral-common-1.11.7/src/mistral_common/guidance/grammar_factory.py000066400000000000000000000266751523035502500272210ustar00rootroot00000000000000import json from enum import Enum from functools import lru_cache from pathlib import Path from typing import Any, Callable from mistral_common.deprecation import warn_once from mistral_common.guidance.tokenizer import from_mistral_tokenizer from mistral_common.imports import ( assert_jinja2_installed, assert_llguidance_installed, is_jinja2_installed, is_llguidance_installed, ) from mistral_common.protocol.instruct.tool_calls import NamedToolChoice, Tool, ToolChoice, ToolChoiceEnum from mistral_common.tokens.tokenizers.base import SpecialTokens, TokenizerVersion from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import is_tekkenizer if is_llguidance_installed(): import llguidance as llg if is_jinja2_installed(): from jinja2 import Template JINJA_DIR = Path(__file__).parent / "data" def _validate_mode_and_tools(mode: ToolChoice, tools: list[Tool] | None) -> None: if isinstance(mode, NamedToolChoice) and all(mode.function.name != tool.function.name for tool in (tools or [])): raise ValueError( f"Tool choice requires the {mode.function.name} tool but no tools with this name has been passed." ) elif mode in [ToolChoiceEnum.any, ToolChoiceEnum.required] and not tools: raise ValueError(f"When {mode=} please ensure to pass tools, got {tools=}.") @lru_cache() def _cached_get_jinja_template(tokenizer_version: TokenizerVersion, has_think_tokens: bool) -> str: if tokenizer_version >= TokenizerVersion.v13 and has_think_tokens: jinja_key = _GrammarVariant.think else: jinja_key = _GrammarVariant.base return JINJA_PATHS[jinja_key].read_text(encoding="utf-8") @lru_cache() def _cached_get_lark_from_jinja( template: str, mode: str, fcall: str, json_schema_str: str | None, parallel_tool_calls: bool, json_only: bool, think_with_json: bool, begin_think_token: str | None, end_think_token: str | None, ) -> str: jinja_template = Template(template) lark_grammar = jinja_template.render( mode=mode, fcall=fcall, json_schema_str=json_schema_str, parallel_tool_calls=parallel_tool_calls, json_only=json_only, think_with_json=think_with_json, begin_think_token=begin_think_token, end_think_token=end_think_token, ) return lark_grammar class _GrammarVariant(str, Enum): base = "base" plain_think = "plain_think" think = "think" JINJA_PATHS = { _GrammarVariant.base: JINJA_DIR / "base_grammar.lark.jinja", _GrammarVariant.plain_think: JINJA_DIR / "plain_text_think_grammar.lark.jinja", _GrammarVariant.think: JINJA_DIR / "think_grammar.lark.jinja", } _TOOL_CALL_GRAMMAR = "{tool_calls_token} SAFE_WS? {tool_name} {args_token} SAFE_WS? %json {args_json} SAFE_WS?" def _get_tool_args_json(tool: Tool) -> dict[str, Any]: r"""Returns the JSON schema for a tool's arguments.""" args = tool.function.parameters if tool.function.strict else {"type": "object"} return args or {"type": "object", "properties": {}, "additionalProperties": False} def _convert_tool_calls( tools: list[Tool] | None, mode: ToolChoice, parallel_tool_calls: bool, get_special_token_id: Callable[[str], str], ) -> str: r"""Converts tool calls to a lark grammar string. Args: tools: The list of tools available. mode: The tool choice mode. parallel_tool_calls: Whether parallel tool calls are allowed. get_special_token_id: Callable that maps a special token name to its lark grammar syntax. Returns: The lark grammar string for tool calls. """ if mode == ToolChoiceEnum.none: return "" tool_calls_token = get_special_token_id(SpecialTokens.tool_calls.value) args_token = get_special_token_id(SpecialTokens.args.value) any_strict_true = any(tool.function.strict for tool in tools) if tools else False if not tools or not any_strict_true: tool_name = f'"{mode.function.name}"' if isinstance(mode, NamedToolChoice) else "/.+/" tool_entries = [(tool_name, '{"type": "object"}')] else: filtered_tools = ( [next(tool for tool in tools if tool.function.name == mode.function.name)] if isinstance(mode, NamedToolChoice) else tools ) tool_entries = [ (f'"{tool.function.name}"', json.dumps(_get_tool_args_json(tool), ensure_ascii=False)) for tool in filtered_tools ] grammar_parts = [ _TOOL_CALL_GRAMMAR.format( tool_calls_token=tool_calls_token, args_token=args_token, tool_name=name, args_json=args_json, ) for name, args_json in tool_entries ] grammar_tool_call = ( " | ".join(f"({part})" for part in grammar_parts) if len(grammar_parts) > 1 else grammar_parts[0] ) return f"({grammar_tool_call})+" if parallel_tool_calls else grammar_tool_call class GrammarFactory: r"""Generates grammars for a given tokenizer.""" @staticmethod def is_supported(tokenizer: MistralTokenizer) -> bool: r"""Checks whether the given tokenizer is supported by guidance. Guidance requires a Tekken tokenizer with version >= v11. Args: tokenizer: The Mistral tokenizer to check. Returns: Whether the tokenizer is supported. """ inner = tokenizer.instruct_tokenizer.tokenizer return is_tekkenizer(inner) and not inner.version < TokenizerVersion.v11 def __init__(self, tokenizer: MistralTokenizer) -> None: r"""Initialize the grammar factory. Args: tokenizer: The Mistral tokenizer to generate grammars for. Raises: ValueError: If the tokenizer is not supported (see [`is_supported`][mistral_common.guidance.grammar_factory.GrammarFactory.is_supported]). """ assert_llguidance_installed() assert_jinja2_installed() self._tokenizer = tokenizer.instruct_tokenizer.tokenizer if not self.is_supported(tokenizer): raise ValueError( f"Guidance requires a Tekken tokenizer with version >= v11, " f"got {type(self._tokenizer).__name__} {self._tokenizer.version.value}" ) self._llg_tokenizer = from_mistral_tokenizer(tokenizer) self._special_token_map = self._build_special_token_map() def _build_special_token_map(self) -> dict[str, str]: r"""Build a mapping from special token names to their grammar syntax.""" return {self._tokenizer.id_to_piece(i): f"<[{i}]>" for i in range(self._tokenizer.num_special_tokens)} def _special_token_lark(self, token_name: str) -> str: r"""Convert special token name to lark grammar syntax.""" assert token_name in self._special_token_map, f"Unknown special token: {token_name}" return self._special_token_map[token_name] def _get_optional_special_token_lark(self, token_name: str) -> str | None: r"""Returns lark grammar syntax for a special token, or `None` if absent.""" return self._special_token_map.get(token_name) @property def llg_tokenizer(self) -> "llg.LLTokenizer": return self._llg_tokenizer def select_jinja_template(self, reasoning: bool | None = None) -> str: r"""Selects and returns the appropriate jinja template content. Selection derives from the tokenizer version and presence of think tokens: - Returns the `think` template when the tokenizer version is >= v13 and both `[THINK]` and `[/THINK]` special tokens are registered. - Returns the `base` template in all other cases. Args: reasoning: Deprecated and ignored. Template selection is determined solely by the tokenizer version and presence of think tokens. Will be removed in 1.13.0. Returns: The jinja template content as a string. """ if reasoning is not None: warn_once( "select_jinja_template.reasoning", "The reasoning parameter of select_jinja_template is deprecated, " "no longer has any effect, and will be removed in 1.13.0.", DeprecationWarning, stacklevel=2, ) has_begin_think = SpecialTokens.begin_think.value in self._special_token_map has_end_think = SpecialTokens.end_think.value in self._special_token_map assert has_begin_think == has_end_think, ( f"both {SpecialTokens.begin_think.value} and {SpecialTokens.end_think.value} " "should be defined or none of them." ) return _cached_get_jinja_template(tokenizer_version=self._tokenizer.version, has_think_tokens=has_begin_think) def get_lark_from_jinja( self, template: str, mode: ToolChoice, tools: list[Tool] | None, json_schema: dict[str, Any] | None, parallel_tool_calls: bool, json_only: bool = False, ) -> str: r"""Renders a lark grammar from a jinja template. Args: template: Jinja template to render as a string. mode: The function calling mode (auto, any, none). tools: The list of tools available. json_schema: JSON schema to additionally allow, unioned with the grammar. parallel_tool_calls: Whether parallel tool calls are allowed. json_only: If True, generates only JSON schema grammar without text/tool call alternatives. Returns: The rendered lark grammar string. """ # Verifies that the NamedToolChoice has a valid tool and "any", "required" has tools. _validate_mode_and_tools(mode=mode, tools=tools) fcall = _convert_tool_calls(tools, mode, parallel_tool_calls, self._special_token_lark) json_schema_str = json.dumps(json_schema, ensure_ascii=False) if json_schema else None # NamedToolChoice forces a specific tool, which maps to "required" grammar. template_mode = ToolChoiceEnum.required if isinstance(mode, NamedToolChoice) else ToolChoiceEnum(mode) think_with_json = self._tokenizer.version.supports_model_settings begin_think_token = self._get_optional_special_token_lark(SpecialTokens.begin_think.value) end_think_token = self._get_optional_special_token_lark(SpecialTokens.end_think.value) return _cached_get_lark_from_jinja( template=template, mode=template_mode.value, fcall=fcall, json_schema_str=json_schema_str, parallel_tool_calls=parallel_tool_calls, json_only=json_only, think_with_json=think_with_json, begin_think_token=begin_think_token, end_think_token=end_think_token, ) def get_lark_for_json_schema(self, template: str, json_schema: dict[str, Any]) -> str: r"""Returns a lark grammar that only accepts JSON objects matching the given schema. Args: template: Jinja template to render as a string. json_schema: The JSON schema to validate against. Returns: The rendered lark grammar string that only matches the given JSON schema. """ return self.get_lark_from_jinja( template=template, mode=ToolChoiceEnum.none, tools=None, json_schema=json_schema, parallel_tool_calls=True, json_only=True, ) python-mistral-common-1.11.7/src/mistral_common/guidance/tokenizer.py000066400000000000000000000076161523035502500260500ustar00rootroot00000000000000import re from mistral_common.imports import assert_llguidance_installed, is_llguidance_installed from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, Tokenizer from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import is_tekkenizer if is_llguidance_installed(): import llguidance as llg class MistralLLGTokenizer: r"""Wraps a Tekken tokenizer for use with llguidance.""" @property def bos_token_id(self) -> int: r"""The beginning of string token id.""" return self._tokenizer.bos_id @property def eos_token_id(self) -> int: r"""The end of string token id.""" return self._tokenizer.eos_id @property def tokens(self) -> list[bytes]: r"""The list of token byte representations.""" return self._tokens @property def special_token_ids(self) -> list[int]: r"""The list of special token ids.""" return self._special_token_ids def __init__(self, tokenizer: Tokenizer) -> None: r"""Initialize the wrapper. Args: tokenizer: The Tekken tokenizer to wrap for llguidance compatibility. Raises: TypeError: If the tokenizer is not a Tekkenizer. ValueError: If a special token has an invalid format. """ assert_llguidance_installed() if not is_tekkenizer(tokenizer): raise TypeError(f"Guidance only supports Tekken tokenizers, got {type(tokenizer)}") self._tokenizer = tokenizer self._tokens: list[bytes] = [] self._special_token_ids: list[int] = [] seen_special_tokens: set[str] = set() for i in range(self._tokenizer.n_words): # Convert square brackets to angle brackets for special tokens, # since llg only recognizes the latter. if i < self._tokenizer.num_special_tokens: token_rep = self._tokenizer.id_to_piece(i) if match := re.fullmatch(r"\[(.*)\]", token_rep): token_rep_llg = f"<{match.group(1)}>" else: token_rep_llg = token_rep if not re.fullmatch(r"<.*>", token_rep_llg): raise ValueError(f"Invalid special token: {token_rep_llg} ({token_rep})") if token_rep_llg in seen_special_tokens: raise ValueError(f"Duplicate special token: {token_rep_llg} (already seen: {seen_special_tokens})") seen_special_tokens.add(token_rep_llg) self._special_token_ids.append(i) self._tokens.append(token_rep_llg.encode("utf-8")) else: token_bytes = self._tokenizer.id_to_byte_piece(i, SpecialTokenPolicy.RAISE) self._tokens.append(token_bytes) if len(self._special_token_ids) != self._tokenizer.num_special_tokens: raise ValueError( f"Expected {self._tokenizer.num_special_tokens} special tokens, but found " f"{len(self._special_token_ids)}" ) def __call__(self, s: str) -> list[int]: r"""Tokenizes a string into token ids. Args: s: The string to tokenize. Returns: The list of token ids. """ return self._tokenizer.encode(s, bos=False, eos=False) def from_mistral_tokenizer(tokenizer: MistralTokenizer) -> "llg.LLTokenizer": r"""Creates an llguidance tokenizer from a Mistral tokenizer. Args: tokenizer: The Mistral tokenizer to convert. Must wrap a Tekkenizer. Returns: The llguidance tokenizer. Raises: TypeError: If the underlying tokenizer is not a Tekkenizer. """ assert_llguidance_installed() inner_tokenizer = tokenizer.instruct_tokenizer.tokenizer tokenizer_data = MistralLLGTokenizer(inner_tokenizer) return llg.LLTokenizer(llg.TokenizerWrapper(tokenizer_data)) python-mistral-common-1.11.7/src/mistral_common/image.py000066400000000000000000000062211523035502500233300ustar00rootroot00000000000000import base64 import io import requests from PIL import Image from pydantic import BeforeValidator, PlainSerializer, SerializationInfo from typing_extensions import Annotated from mistral_common import __version__ def download_image(url: str) -> Image.Image: r"""Download an image from a URL and return it as a PIL Image. Args: url: The URL of the image to download. Returns: The downloaded image as a PIL Image object. """ headers = {"User-Agent": f"mistral-common/{__version__}"} try: # Make a request to download the image response = requests.get(url, headers=headers) response.raise_for_status() # Raise an error for bad responses (4xx, 5xx) # Convert the image content to a PIL Image img = Image.open(io.BytesIO(response.content)) return img except requests.exceptions.RequestException as e: raise RuntimeError(f"Error downloading the image from {url}: {e}.") except Exception as e: raise RuntimeError(f"Error converting to PIL image: {e}") def maybe_load_image_from_str_or_bytes(x: Image.Image | str | bytes) -> Image.Image: r"""Load an image from a string or bytes. If the input is already a PIL Image, return it as is. Args: x: The input to load the image from. Can be a PIL Image, a string, or bytes. If it's a string, it's assumed to be a base64 encoded string of bytes. Returns: The loaded image as a PIL Image object. """ if isinstance(x, Image.Image): return x if isinstance(x, bytes): try: return Image.open(io.BytesIO(x)) except Exception: raise RuntimeError("Encountered an error when loading image from bytes.") try: image = Image.open(io.BytesIO(base64.b64decode(x.encode("ascii")))) return image except Exception as e: raise RuntimeError( f"Encountered an error when loading image from bytes starting " f"with '{x[:20]}'. Expected either a PIL.Image.Image or a base64 " f"encoded string of bytes." ) from e def serialize_image_to_byte_str(im: Image.Image, info: SerializationInfo) -> str: r"""Serialize an image to a base64 encoded string of bytes. Args: im: The image to serialize. info: The serialization info. Returns: The serialized image as a base64 encoded string of bytes. """ if hasattr(info, "context"): context = info.context or {} else: context = {} stream = io.BytesIO() im_format = im.format or "PNG" im.save(stream, format=im_format) im_b64 = base64.b64encode(stream.getvalue()).decode("ascii") if context and (max_image_b64_len := context.get("max_image_b64_len")): return im_b64[:max_image_b64_len] + "..." if context and context.get("add_format_prefix"): im_b64 = f"data:image/{im_format.lower()};base64," + im_b64 return im_b64 SerializableImage = Annotated[ Image.Image, BeforeValidator(maybe_load_image_from_str_or_bytes), PlainSerializer(serialize_image_to_byte_str), "A normal PIL image that supports serialization to b64 bytes string.", ] python-mistral-common-1.11.7/src/mistral_common/imports.py000066400000000000000000000060541523035502500237470ustar00rootroot00000000000000import importlib.util import logging from functools import lru_cache logger = logging.getLogger(__name__) def _get_dependency_error_message(package_name: str, dependency_group: str) -> str: return f"`{package_name}` is not installed. Please install it with `pip install mistral-common[{dependency_group}]`" def is_package_installed(package_name: str) -> bool: return importlib.util.find_spec(package_name) is not None def assert_package_installed(package_name: str, error_message: str | None = None) -> None: if not is_package_installed(package_name): error_message = error_message or f"Package '{package_name}' is required but not installed." raise ImportError(error_message) @lru_cache() def is_hf_hub_installed() -> bool: return is_package_installed("huggingface_hub") @lru_cache() def is_jinja2_installed() -> bool: return is_package_installed("jinja2") @lru_cache() def is_llguidance_installed() -> bool: return is_package_installed("llguidance") @lru_cache() def is_opencv_installed() -> bool: try: import cv2 # noqa: F401 except ImportError: _cv2_available = False except Exception as e: # cv2 has lots of import problems: https://github.com/opencv/opencv-python/issues/884 # for better UX, let's simply skip all errors that might arise from import for now _cv2_available = False logger.warning( f"Warning: Your installation of OpenCV appears to be broken: {e}." "Please follow the instructions at https://github.com/opencv/opencv-python/issues/884 " "to correct your environment. The import of cv2 has been skipped." ) else: _cv2_available = True return _cv2_available @lru_cache() def is_sentencepiece_installed() -> bool: return is_package_installed("sentencepiece") @lru_cache() def is_soundfile_installed() -> bool: return is_package_installed("soundfile") @lru_cache() def is_soxr_installed() -> bool: return is_package_installed("soxr") @lru_cache() def assert_hf_hub_installed() -> None: assert_package_installed("huggingface_hub", _get_dependency_error_message("huggingface_hub", "hf-hub")) @lru_cache() def assert_jinja2_installed() -> None: assert_package_installed("jinja2", _get_dependency_error_message("jinja2", "guidance")) @lru_cache() def assert_llguidance_installed() -> None: assert_package_installed("llguidance", _get_dependency_error_message("llguidance", "guidance")) @lru_cache() def assert_opencv_installed() -> None: assert_package_installed("cv2", _get_dependency_error_message("opencv", "opencv")) @lru_cache() def assert_sentencepiece_installed() -> None: assert_package_installed("sentencepiece", _get_dependency_error_message("sentencepiece", "sentencepiece")) @lru_cache() def assert_soundfile_installed() -> None: assert_package_installed("soundfile", _get_dependency_error_message("soundfile", "soundfile")) @lru_cache() def assert_soxr_installed() -> None: assert_package_installed("soxr", _get_dependency_error_message("soxr", "soxr")) python-mistral-common-1.11.7/src/mistral_common/integrations/000077500000000000000000000000001523035502500244015ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/integrations/__init__.py000066400000000000000000000000001523035502500265000ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/integrations/chat_templates/000077500000000000000000000000001523035502500273765ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/integrations/chat_templates/__init__.py000066400000000000000000000000001523035502500314750ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/integrations/chat_templates/chat_templates.py000066400000000000000000000124671523035502500327570ustar00rootroot00000000000000from pathlib import Path from mistral_common.integrations.chat_templates.template_generator import ( TemplateConfig, ) from mistral_common.integrations.chat_templates.template_generator import ( build_chat_template as _build_chat_template, ) from mistral_common.tokens.tokenizers.base import SpecialTokens, TokenizerVersion from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.sentencepiece import SentencePieceTokenizer def generate_chat_template( spm: bool, tokenizer_version: TokenizerVersion, image_support: bool, audio_support: bool, thinking_support: bool, default_system_prompt: str | None, plain_thinking_support: bool, use_special_token_variables: bool, ) -> str: r"""Generate a chat template based on configuration. Programmatically generates a Jinja2 chat template string that formats conversation messages for Mistral models. The generated template handles message roles, special tokens, tool calls, and multimodal content. Args: spm: Whether to use SentencePiece tokenizer. tokenizer_version: The tokenizer version. image_support: Whether to support image chunks. audio_support: Whether to support audio chunks. thinking_support: Whether to support thinking chunks with special tokens. default_system_prompt: Optional default system prompt to embed. plain_thinking_support: Whether to support thinking chunks with plain ``/`` text tags. Only available for v11. Mutually exclusive with `thinking_support`. use_special_token_variables: Whether to emit BOS/EOS as Jinja variable references (`bos_token`/`eos_token`) or as literal values. Returns: The generated Jinja2 template as a string. """ config = TemplateConfig( version=tokenizer_version, spm=spm, image_support=image_support, audio_support=audio_support, thinking_support=thinking_support, plain_thinking_support=plain_thinking_support, use_special_token_variables=use_special_token_variables, ) template = _build_chat_template(config) if default_system_prompt is not None: # Thinking chunks are not a concern here because default system prompts # are plain text strings, not structured content with chunk types. escaped_prompt = default_system_prompt.replace("\\", "\\\\").replace("'", "\\'") template = template.replace( "{%- set default_system_message = '' %}", "{%- set default_system_message = '" + escaped_prompt + "' %}", ) return template def convert_tokenizer_to_chat_template( tokenizer_file: str | Path, system_prompt: str | None = None, use_special_token_variables: bool = True, ) -> str: r"""Load a tokenizer file and auto-detect its capabilities to generate a matching chat template. Loads the tokenizer via `MistralTokenizer.from_file`, inspects the resulting instruct tokenizer to determine version, backend (SentencePiece vs Tekken), and supported modalities, then delegates to `generate_chat_template` with the detected flags. The `plain_thinking_support` flag is set heuristically: a v11 tokenizer without an audio encoder uses plain ``/`` text tags instead of special `[THINK]`/`[/THINK]` tokens. When audio is present on a v11 tokenizer, `plain_thinking_support` is set to `False` because the two are mutually exclusive. `thinking_support` (special-token thinking) is detected by checking whether both `begin_think` and `end_think` special tokens are registered in the underlying tokenizer via its public `is_special` method. Args: tokenizer_file: Path to the tokenizer file (tekken JSON or SentencePiece `.model.vX`). system_prompt: Optional default system prompt to embed in the template. When not `None`, maps to `generate_chat_template`'s `default_system_prompt`. use_special_token_variables: Whether to emit BOS/EOS as Jinja variable references (`bos_token`/`eos_token`) or as literal string values. Returns: The generated Jinja2 chat template string matching the tokenizer's capabilities. Raises: TokenizerException: If the tokenizer file is not recognized or invalid. """ mistral_tokenizer = MistralTokenizer.from_file(tokenizer_file) instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer version = mistral_tokenizer.version spm = isinstance(tokenizer, SentencePieceTokenizer) image_support = instruct_tokenizer.image_encoder is not None audio_support = instruct_tokenizer.audio_encoder is not None thinking_support = tokenizer.is_special(SpecialTokens.begin_think.value) and tokenizer.is_special( SpecialTokens.end_think.value ) plain_thinking_support = version == TokenizerVersion.v11 and not audio_support return generate_chat_template( spm=spm, tokenizer_version=version, image_support=image_support, audio_support=audio_support, thinking_support=thinking_support, default_system_prompt=system_prompt, plain_thinking_support=plain_thinking_support, use_special_token_variables=use_special_token_variables, ) python-mistral-common-1.11.7/src/mistral_common/integrations/chat_templates/template_generator.py000066400000000000000000002410661523035502500336420ustar00rootroot00000000000000from dataclasses import dataclass from mistral_common.tokens.tokenizers.base import SpecialTokens, TokenizerVersion # Module-level constants derived from SpecialTokens enum _BOS = SpecialTokens.bos.value _EOS = SpecialTokens.eos.value _BEGIN_INST = SpecialTokens.begin_inst.value _END_INST = SpecialTokens.end_inst.value _BEGIN_TOOLS = SpecialTokens.begin_tools.value _END_TOOLS = SpecialTokens.end_tools.value _BEGIN_TOOL_RESULTS = SpecialTokens.begin_tool_results.value _END_TOOL_RESULTS = SpecialTokens.end_tool_results.value _TOOL_CALLS = SpecialTokens.tool_calls.value _IMG = SpecialTokens.img.value _AUDIO = SpecialTokens.audio.value _BEGIN_SYSTEM = SpecialTokens.begin_system.value _END_SYSTEM = SpecialTokens.end_system.value _BEGIN_TOOL_CONTENT = SpecialTokens.begin_tool_content.value _ARGS = SpecialTokens.args.value _CALL_ID = SpecialTokens.call_id.value _BEGIN_THINK = SpecialTokens.begin_think.value _END_THINK = SpecialTokens.end_think.value _BEGIN_MODEL_SETTINGS = SpecialTokens.begin_model_settings.value _END_MODEL_SETTINGS = SpecialTokens.end_model_settings.value @dataclass class TemplateConfig: r"""Configuration for generating a chat template. This class encapsulates all the configuration options required to generate a Jinja2 chat template that formats conversation messages for Mistral models. The template handles message roles, special tokens, tool calls, and multimodal content. Attributes: version: The tokenizer version (e.g., v1, v2, v3, v7, v11, v13, v15). Determines special token formatting, tool call syntax, and available features. spm: Whether to use SentencePiece tokenizer formatting. When True, adds spaces after special tokens. Not supported for versions v11+ or with audio. image_support: Whether to enable image chunk processing in user messages. Adds [IMG] token support. Requires version v3+. Mutually exclusive with audio. audio_support: Whether to enable audio chunk processing in user messages. Adds [AUDIO] token support. Requires version v7+. Mutually exclusive with image. thinking_support: Whether to enable thinking chunks in system and assistant messages. Adds [THINK]/[/THINK] token support. Requires version v13+. plain_thinking_support: Whether to enable plain text thinking chunks using ``/`` tags instead of special tokens. Only available for v11. Mutually exclusive with `thinking_support`. use_special_token_variables: Whether to emit BOS/EOS as Jinja variable references (`bos_token`/`eos_token`) or as literal string values (`''`/`''`). When True, the template expects `bos_token` and `eos_token` to be passed as render kwargs. Raises: ValueError: If the configuration is invalid (e.g., conflicting options like spm with v11+, image and audio together, or version requirements not met). Examples: >>> config = TemplateConfig(version=TokenizerVersion.v3, image_support=True) >>> config.image_support True >>> config.version == TokenizerVersion.v3 True """ version: TokenizerVersion spm: bool = False image_support: bool = False audio_support: bool = False thinking_support: bool = False plain_thinking_support: bool = False use_special_token_variables: bool = False def __post_init__(self) -> None: if self.plain_thinking_support and self.thinking_support: raise ValueError("Plain thinking support and thinking support are mutually exclusive") if self.spm and (self.version >= TokenizerVersion.v11 or self.audio_support): raise ValueError("SPM tokenizer is not supported for tokenizer versions v11 and above or audio") if self.image_support and self.audio_support: raise ValueError("Image and audio support are mutually exclusive") if self.image_support and self.version < TokenizerVersion.v3: raise ValueError("Image support is only available for tokenizer versions v3 and above") if self.audio_support and self.version < TokenizerVersion.v7: raise ValueError("Audio support is only available for tokenizer versions v7 and above") if self.thinking_support and self.version < TokenizerVersion.v13: raise ValueError("Thinking support is only available for tokenizer versions v13 and above") if self.audio_support and self.thinking_support: raise ValueError("Audio and thinking support are mutually exclusive") if self.plain_thinking_support and self.version != TokenizerVersion.v11: raise ValueError("Plain thinking support is only available for tokenizer version v11") if self.audio_support and self.plain_thinking_support: raise ValueError("Audio and plain thinking support are mutually exclusive") @property def bos_expr(self) -> str: r"""Jinja expression for the BOS token.""" if self.use_special_token_variables: return "bos_token" return f"'{_BOS}'" @property def eos_expr(self) -> str: r"""Jinja expression for the EOS token.""" if self.use_special_token_variables: return "eos_token" return f"'{_EOS}'" @property def any_thinking_support(self) -> bool: r"""Whether any form of thinking support is enabled.""" return self.thinking_support or self.plain_thinking_support @property def has_tools(self) -> bool: r"""Whether tool definitions are supported.""" return self.version >= TokenizerVersion.v2 @property def uses_system_prompt_tokens(self) -> bool: r"""Whether to use [SYSTEM_PROMPT] tokens vs inline system message.""" return self.version >= TokenizerVersion.v7 @property def tracks_max_idx_user(self) -> bool: r"""Whether to track max user index for tools definition placement.""" return self.has_tools and self.version < TokenizerVersion.v13 @property def tools_at_beginning(self) -> bool: r"""Whether tools definition is emitted at the beginning.""" return self.version >= TokenizerVersion.v13 @property def uses_v13_tool_format(self) -> bool: r"""Whether to use v13-style tool calls (name[ARGS]arguments).""" return self.version >= TokenizerVersion.v13 @property def uses_json_tool_results(self) -> bool: r"""Whether tool results use JSON format with content/call_id.""" return self.version == TokenizerVersion.v3 and self.spm @property def uses_tool_content_format(self) -> bool: r"""Whether tool results use [TOOL_RESULTS]id[TOOL_CONTENT]content format.""" return self.version >= TokenizerVersion.v7 and self.version < TokenizerVersion.v13 @property def uses_simple_tool_results(self) -> bool: r"""Whether tool results use simple [TOOL_RESULTS]content format.""" return self.version >= TokenizerVersion.v13 @property def uses_v2_spm_tool_format(self) -> bool: r"""Whether to use v2_spm tool format (no ID, uses name in results).""" return self.version == TokenizerVersion.v2 and self.spm @property def uses_v2_tool_format(self) -> bool: r"""Whether to use v2 tool format (no ID, uses name in results, elif branch).""" return self.version == TokenizerVersion.v2 @property def uses_spm_space_tracking(self) -> bool: r"""Whether to track add_space for SPM formatting.""" return self.spm and self.version >= TokenizerVersion.v2 @property def uses_spm_prev_img_tracking(self) -> bool: r"""Whether to track prev_img for SPM image formatting.""" return self.spm and self.image_support @property def spm_system_prompt_has_space(self) -> bool: r"""Whether SPM system prompt tokens have trailing space.""" return self.spm and self.version >= TokenizerVersion.v7 @property def uses_call_id_in_tool_calls(self) -> bool: r"""Whether to include [CALL_ID] in tool calls.""" return self.version == TokenizerVersion.v11 @property def tracks_has_sp_for_audio(self) -> bool: r"""Whether to track has_sp for audio constraint. V15+ allows audio with system prompts.""" return self.audio_support and self.version < TokenizerVersion.v15 @property def supports_model_settings(self) -> bool: r"""Whether model settings (reasoning_effort) are supported. V15+.""" return self.version >= TokenizerVersion.v15 @property def is_v1(self) -> bool: r"""Whether this is a v1 template with minimal features.""" return self.version == TokenizerVersion.v1 @property def system_supports_thinking(self) -> bool: r"""Whether system messages support thinking chunks. Pre-v15 only.""" return self.any_thinking_support and self.version < TokenizerVersion.v15 @property def uses_v2_v3spm_tool_branch(self) -> bool: r"""Whether assistant tool calls use the v2/v3-SPM inline elif branch.""" return self.version == TokenizerVersion.v2 or (self.spm and self.version == TokenizerVersion.v3) @property def forbids_assistant_content_with_tools(self) -> bool: r"""Whether assistant messages cannot have both content and tool calls.""" return self.version in [TokenizerVersion.v2, TokenizerVersion.v3] @property def validates_assistant_non_empty(self) -> bool: r"""Whether to validate that assistant messages have non-empty content or tool calls.""" return self.version >= TokenizerVersion.v7 or (self.version >= TokenizerVersion.v3 and not self.spm) @property def tool_supports_multimodal(self) -> bool: r"""Whether tool messages can contain non-text content chunks. V15+.""" return self.version >= TokenizerVersion.v15 @property def system_supports_audio(self) -> bool: r"""Whether system messages can contain audio. V15+ with audio support.""" return self.audio_support and self.version >= TokenizerVersion.v15 def _join_types_desc(parts: list[str]) -> str: r"""Join type names into a human-readable description string. Args: parts: List of type names (e.g. ["text", "thinking", "image"]). Returns: Formatted string like "text", "text and thinking", or "text, thinking and image". """ if len(parts) == 1: return parts[0] return ", ".join(parts[:-1]) + " and " + parts[-1] def _render_content_call( config: TemplateConfig, content_expr: str, context_name: str, *, supported_types_desc: str, support_thinking: bool, support_images: bool, support_audio: bool, initial_prev_img: str = "false", ) -> str: r"""Build a `render_content(...)` Jinja call string with all args emitted explicitly. Every argument that the `render_content` macro declares for `config` is included as a keyword argument in macro-declared order, so no call site relies on Jinja implicit `undefined`. Arguments for features absent from `config` are omitted because the macro does not declare them. Args: config: Template configuration controlling which macro parameters exist. content_expr: Raw Jinja expression for the `content` argument (e.g. `message['content']`). context_name: Human-readable name for the calling context (used in macro error messages). supported_types_desc: Description of supported chunk types for error messages. Only emitted when the macro declares this parameter (i.e. when `config` has any extra-type support enabled). support_thinking: Whether thinking chunks are allowed at this call site. Only emitted when `config.any_thinking_support` is True. support_images: Whether image/image_url chunks are allowed at this call site. Only emitted when `config.image_support` is True. support_audio: Whether audio/audio_url chunks are allowed at this call site. Only emitted when `config.audio_support` is True. initial_prev_img: Initial value for SPM prev-image tracking namespace. Only emitted when `config.uses_spm_prev_img_tracking` is True. Returns: A Jinja call string of the form `render_content()` with all macro-declared arguments emitted as keyword arguments in declaration order. No surrounding `{{- -}}` tags are included. Examples: >>> from mistral_common.tokens.tokenizers.base import TokenizerVersion >>> config = TemplateConfig(version=TokenizerVersion.v7) >>> _render_content_call( ... config=config, ... content_expr="message['content']", ... context_name="user message content", ... supported_types_desc="text", ... support_thinking=False, ... support_images=False, ... support_audio=False, ... ) "render_content(content=message['content'], context_name='user message content')" """ args = [f"content={content_expr}", f"context_name='{context_name}'"] # IMPORTANT: argument emission order below must stay in sync with the # `render_content` macro parameter build order in `_generate_macros` # (the `params` list starting with ["content", "context_name"]). # Any reorder here must be mirrored there, and vice-versa. if config.any_thinking_support or config.image_support or config.audio_support: args.append(f"supported_types_desc='{supported_types_desc}'") if config.any_thinking_support: args.append(f"support_thinking={'true' if support_thinking else 'false'}") if config.image_support: args.append(f"support_images={'true' if support_images else 'false'}") if config.audio_support: args.append(f"support_audio={'true' if support_audio else 'false'}") if config.uses_spm_prev_img_tracking: args.append(f"initial_prev_img={initial_prev_img}") return "render_content(" + ", ".join(args) + ")" def _generate_header(config: TemplateConfig) -> str: r"""Generate template header with default system message. Args: config: Template configuration. Returns: Jinja2 header block with default system message variable. """ return ( """{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- """ + config.bos_expr + """ }} """ ) def _generate_system_prompt_handling(config: TemplateConfig) -> str: r"""Generate system prompt handling section. For pre-v7: Extract ALL system messages from anywhere in the conversation, merge their text content with `"\\n\\n"`, and filter them out of `loop_messages`. For v7+: Keep system messages in `loop_messages` so they are handled individually in the message processing loop. Only emit default system prompt if no system message is at position 0. Args: config: The template configuration. Returns: The system prompt handling section of the chat template. """ lines = [ "", "{#- Handle system prompt if it exists. #}", ] if config.uses_system_prompt_tokens: lines.extend(_generate_system_prompt_handling_v7_plus(config)) else: lines.extend(_generate_system_prompt_handling_pre_v7(config)) return "\n".join(lines) def _generate_system_prompt_handling_pre_v7(config: TemplateConfig) -> list[str]: r"""Generate pre-v7 system prompt handling that extracts all system messages. Loops through all messages to collect system content and filters them out of `loop_messages`. Args: config: The template configuration. Returns: Lines of Jinja2 template code for pre-v7 system prompt handling. """ lines = [ "{#- Extract all system messages and merge into system prompt. #}", "{%- set ns_sys = namespace(system_parts=[], filtered=[]) %}", "{%- for message in messages %}", " {%- if message['role'] == 'system' %}", " {%- if message['content'] is string %}", " {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %}", " {%- else %}", " {%- for block in message['content'] %}", " {%- if block['type'] == 'text' %}", " {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %}", " {%- else %}", " {{- raise_exception('Only text chunks are supported in system message content.') }}", " {%- endif %}", " {%- endfor %}", " {%- endif %}", " {%- else %}", " {%- set ns_sys.filtered = ns_sys.filtered + [message] %}", " {%- endif %}", "{%- endfor %}", "{%- if ns_sys.system_parts | length > 0 %}", " {%- set system_message = ns_sys.system_parts | join('\\n\\n') %}", "{%- else %}", " {%- set system_message = default_system_message %}", "{%- endif %}", "{%- set loop_messages = ns_sys.filtered %}", ] return lines def _generate_system_prompt_handling_v7_plus(config: TemplateConfig) -> list[str]: r"""Generate v7+ system prompt handling. For v7+, system messages stay in `loop_messages` and are handled by the aggregation pre-pass (which coalesces their `TextChunks`) and the message processing loop (which emits `[SYSTEM_PROMPT]...[/SYSTEM_PROMPT]`). Sets `loop_messages = messages` (no filtering needed since system messages are kept in place) and emits the default system prompt if the first message is not a system message. Args: config: The template configuration. Returns: Lines of Jinja2 template code for v7+ system prompt handling. """ lines = [ "{%- set loop_messages = messages %}", "{%- if messages[0]['role'] != 'system' and default_system_message != '' %}", " {{- '" + _BEGIN_SYSTEM + "' + default_system_message + '" + _END_SYSTEM + "' }}", "{%- endif %}", ] if config.tracks_has_sp_for_audio: lines.extend( [ "{%- if messages[0]['role'] == 'system' or default_system_message != '' %}", " {%- set has_sp = true %}", "{%- else %}", " {%- set has_sp = false %}", "{%- endif %}", ] ) return lines def _generate_available_tools_definition(config: TemplateConfig) -> str: r"""Generate available tools and model settings definition. Builds an `available_tools` string variable that contains `[AVAILABLE_TOOLS]...[/AVAILABLE_TOOLS]` (if tools are provided). For v15+, also builds a separate `model_settings` variable containing `[MODEL_SETTINGS]...[/MODEL_SETTINGS]` which is always emitted (defaults to `reasoning_effort="none"` when not specified). Both variables are emitted later in the message loop at the appropriate user message position. Args: config: The template configuration. Returns: The tools and settings definition section of the chat template. """ if config.supports_model_settings: comment = "{#- Tools and model settings definition #}" else: comment = "{#- Tools definition #}" lines = [ "", "", comment, "{%- set available_tools = '' %}", "{%- set has_tools = false %}", ] if config.has_tools: lines.append("{%- if tools is defined and tools is not none and tools|length > 0 %}") lines.append(" {%- set has_tools = true %}") if config.spm: lines.append( " {%- set available_tools = '" + _BEGIN_TOOLS + " ' + (tools| tojson) + '" + _END_TOOLS + "' %}" ) else: lines.append( " {%- set available_tools = '" + _BEGIN_TOOLS + "' + (tools| tojson) + '" + _END_TOOLS + "' %}" ) lines.append("{%- endif %}") if config.supports_model_settings: lines.extend( [ "{%- if reasoning_effort is not defined or reasoning_effort is none %}", " {%- set reasoning_effort = 'none' %}", "{%- endif %}", "{%- if reasoning_effort not in ['none', 'high'] %}", ' {{- raise_exception(\'reasoning_effort must be either "none" or "high"\') }}', "{%- endif %}", "{%- set model_settings = '" + _BEGIN_MODEL_SETTINGS + '{"reasoning_effort": "\' + reasoning_effort + \'"}' + _END_MODEL_SETTINGS + "' %}", # noqa: E501 ] ) return "\n".join(lines) def _generate_macros(config: TemplateConfig) -> str: r"""Generate Jinja2 macro definitions for the chat template. Defines a unified `render_content` macro that handles content rendering for all message roles. The macro accepts content as either a plain string or a list of blocks, iterating blocks and dispatching by type based on boolean flags. Only the branches relevant to the current template config are emitted. For configs with SPM image tracking (`uses_spm_prev_img_tracking`), an `initial_prev_img` parameter enables inter-block spacing logic. Args: config: The template configuration. Returns: The macro definitions section of the chat template. Returns empty string only for v1 templates. """ if config.is_v1: return "" has_extra_types = config.any_thinking_support or config.image_support or config.audio_support # --- Build parameter list --- params = ["content", "context_name"] if has_extra_types: params.append("supported_types_desc") if config.any_thinking_support: params.append("support_thinking") if config.image_support: params.append("support_images") if config.audio_support: params.append("support_audio") if config.uses_spm_prev_img_tracking: params.append("initial_prev_img") lines = [ "", "{#- Macros #}", "{%- macro render_content(" + ", ".join(params) + ") -%}", " {%- if content is string -%}", " {{- content -}}", " {%- elif content -%}", ] # SPM prev_img tracking: create local namespace if config.uses_spm_prev_img_tracking: lines.append(" {%- set _ns = namespace(prev_img=initial_prev_img) -%}") lines.append(" {%- for block in content -%}") # SPM prev_img: add space before text if previous block was image if config.uses_spm_prev_img_tracking: lines.append(" {%- if _ns.prev_img and block['type'] == 'text' -%}") lines.append(" {{- ' ' -}}") lines.append(" {%- endif -%}") lines.extend( [ " {%- if block['type'] == 'text' -%}", " {{- block['text'] -}}", ] ) # SPM prev_img: reset after text block if config.uses_spm_prev_img_tracking: lines.append(" {%- set _ns.prev_img = false -%}") # Thinking branch if config.any_thinking_support: begin_tag = "" if config.plain_thinking_support else _BEGIN_THINK end_tag = "" if config.plain_thinking_support else _END_THINK lines.extend( [ " {%- elif support_thinking and block['type'] == 'thinking' -%}", " {{- '" + begin_tag + "' + block['thinking'] -}}", " {%- if block.get('closed', true) -%}{{- '" + end_tag + "' -}}{%- endif -%}", ] ) # Image branch if config.image_support: lines.append(" {%- elif support_images and block['type'] in ['image', 'image_url'] -%}") lines.append(" {{- '[IMG]' -}}") if config.uses_spm_prev_img_tracking: lines.append(" {%- set _ns.prev_img = true -%}") # Audio branch if config.audio_support: lines.append(" {%- elif support_audio and block['type'] in ['input_audio', 'audio_url'] -%}") if config.tracks_has_sp_for_audio: lines.append(" {%- if has_sp -%}") lines.append( " {{- raise_exception('Audio chunks are not supported in user message content when system prompt is provided.') -}}" # noqa: E501 ) lines.append(" {%- endif -%}") lines.append(" {{- '[AUDIO]' -}}") # Error branch if has_extra_types: lines.append(" {%- else -%}") lines.append( " {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}}" # noqa: E501 ) else: lines.append(" {%- else -%}") lines.append( " {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}}" ) lines.extend( [ " {%- endif -%}", " {%- endfor -%}", " {%- else -%}", " {{- raise_exception(context_name + ' must have non-empty content.') -}}", " {%- endif -%}", "{%- endmacro -%}", ] ) return "\n".join(lines) def _emit_int_float_parsing(indent: str) -> list[str]: r"""Generate the Jinja2 block for parsing `message['content']` as int or float. Attempts int parsing first, then float, falling back to the string representation. Used in tool message handling to preserve numeric types in JSON-serialized tool results. Args: indent: Whitespace prefix for each emitted line. Returns: Lines of Jinja2 template code for int/float content parsing. """ return [ f"{indent}{{# Try to parse 'content' as int or float if possible #}}", f"{indent}{{%- set tool_content = message['content']|string %}}", f"{indent}{{# Try to parse as int #}}", f"{indent}{{%- set parsed_int = message['content']|int %}}", f"{indent}{{% if parsed_int|string == message['content'] %}}", f"{indent} {{%- set tool_content = parsed_int %}}", f"{indent}{{# If int fails, try to parse as float #}}", f"{indent}{{%- else %}}", f"{indent} {{%- set parsed_float = message['content']|float %}}", f"{indent} {{%- if parsed_float|string == message['content'] %}}", f"{indent} {{%- set tool_content = parsed_float %}}", f"{indent} {{%- endif %}}", f"{indent}{{%- endif %}}", ] def _emit_call_id_resolution(indent: str) -> list[str]: r"""Generate the Jinja2 block for extracting `tool_id` from a tool message. Checks `message['call_id']` first, then `message['tool_call_id']`, requiring exactly 9 characters. Raises a template exception if neither field is present or valid. Args: indent: Whitespace prefix for each emitted line. Returns: Lines of Jinja2 template code for call ID resolution. """ return [ f"{indent}{{%- if message['call_id'] is not undefined and message['call_id']|length == 9 %}}", f"{indent} {{%- set tool_id = message['call_id'] %}}", f"{indent}{{%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %}}", f"{indent} {{%- set tool_id = message['tool_call_id'] %}}", f"{indent}{{%- else %}}", f"{indent} {{{{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }}}}", # noqa: E501 f"{indent}{{%- endif %}}", ] def _emit_argument_normalization(indent: str) -> list[str]: r"""Generate the Jinja2 block for normalizing tool call `arguments`. Converts non-string arguments to JSON via `tojson|safe`, and replaces empty-string arguments with `'{}'`. Args: indent: Whitespace prefix for each emitted line. Returns: Lines of Jinja2 template code for argument normalization. """ return [ f"{indent}{{%- if arguments is not string %}}", f"{indent} {{%- set arguments = arguments|tojson|safe %}}", f"{indent}{{%- elif arguments == '' %}}", f"{indent} {{%- set arguments = '{{}}' %}}", f"{indent}{{%- endif %}}", ] def _emit_call_id_validation(indent: str) -> list[str]: r"""Generate the Jinja2 block for validating `tool['id']` in tool calls. Checks that `tool['id']` is defined and has exactly 9 characters, raising a template exception otherwise. Args: indent: Whitespace prefix for each emitted line. Returns: Lines of Jinja2 template code for call ID validation. """ return [ f"{indent}{{%- set id = tool['id']%}}", f"{indent}{{%- if id is not defined or id|length != 9 %}}", f"{indent} {{{{- raise_exception('Tool call must have an id of 9 characters or numbers.') }}}}", f"{indent}{{%- endif %}}", ] def _generate_reasoning_to_thinking_inline() -> list[str]: r"""Generate inline reasoning-to-thinking conversion inside the aggregation loop. Emitted inside the `for msg in ns_agg.current_group` loop when `config.any_thinking_support` is enabled. For each assistant message, converts a top-level `reasoning_content` or `reasoning` field into a leading `{"type": "thinking", "thinking": ...}` chunk prepended to the content, so that reasoning traces from third-party APIs (OpenAI, DeepSeek, vLLM, …) are aggregated uniformly with inline `ThinkChunk`\s. `reasoning_content` takes precedence over `reasoning` when both are present. Raises at template render time if the message already contains thinking chunks in its content alongside a `reasoning` or `reasoning_content` field. Returns: Lines of Jinja2 template code for the inline conversion. """ return [ " {#- Convert reasoning / reasoning_content to a leading thinking chunk. #}", " {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %}", " {%- if reasoning is not none and reasoning != '' %}", " {%- if msg['content'] is not none and msg['content'] is not string %}", " {%- for block in msg['content'] %}", " {%- if block['type'] == 'thinking' %}", " {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }}", # noqa: E501 " {%- endif %}", " {%- endfor %}", " {%- endif %}", " {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %}", " {%- if msg['content'] is string and msg['content'] != '' %}", " {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %}", " {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %}", # noqa: E501 " {%- set new_content = [think_chunk] + msg['content'] | list %}", " {%- else %}", " {%- set new_content = [think_chunk] %}", " {%- endif %}", " {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %}", " {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %}", # noqa: E501 " {%- else %}", " {%- set msg = {'role': msg['role'], 'content': new_content} %}", " {%- endif %}", " {%- endif %}", ] def _generate_message_aggregation(config: TemplateConfig) -> str: r"""Generate message aggregation pre-processing block. Aggregates consecutive messages with the same role before the alternation check to match the behavior of the mistral-common normalizer: - **User messages**: consecutive messages merged into one, text content joined with `"\\n\\n"`, non-text chunks (image, audio) preserved in order. - **Assistant messages**: consecutive messages merged into one, text content joined with `"\\n\\n"`, `tool_calls` lists concatenated. - **System messages**: each message coalesced individually (adjacent `TextChunks` joined with `"\\n\\n"`), but NOT merged across consecutive messages. - **Tool messages**: passed through as-is. Non-text chunk type validation is deferred to the message rendering loop. The grouping logic ensures system and tool messages always form single-message groups (they break any ongoing group), so the flush logic is role-agnostic: it always coalesces the group into one output message. Args: config: The template configuration. Returns: The message aggregation section of the chat template. """ lines = [ "", "{#- Aggregate consecutive messages with the same role except system and tool. #}", "{#- A sentinel message is appended so the last group gets flushed inside the loop. #}", "{%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %}", "{%- for message in loop_messages + [{'role': '__sentinel__'}] %}", " {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %}", # noqa: E501 ] lines.extend(_generate_flush_logic(config)) lines.extend( [ " {%- if message['role'] != '__sentinel__' %}", " {%- set ns_agg.current_group = [message] %}", " {%- set ns_agg.current_role = message['role'] %}", " {%- endif %}", " {%- else %}", " {%- set ns_agg.current_group = ns_agg.current_group + [message] %}", " {%- endif %}", "{%- endfor %}", "{%- set loop_messages = ns_agg.messages %}", ] ) return "\n".join(lines) def _generate_flush_logic(config: TemplateConfig) -> list[str]: r"""Generate the flush logic for aggregating a group of same-role messages. Called when the role changes (or at end of messages) to coalesce all messages in the current group into a single output message. Adjacent `TextChunk`\s are joined with `"\\n\\n"`, non-text chunks are preserved as barriers, and `tool_calls` from all messages in the group are concatenated. Chunk type validation is deferred to the message rendering loop. When `config.any_thinking_support` is enabled, `reasoning_content` / `reasoning` fields on assistant messages are converted to a leading thinking chunk prepended to the message content **inside** the aggregation loop, so that reasoning traces are aggregated uniformly with inline `ThinkChunk`\s. The logic is role-agnostic for user, assistant, and system: system messages always form single-message groups (enforced by the grouping logic), so they are effectively coalesced individually. Tool messages pass through as-is to preserve extra fields like `tool_call_id` and `name`. Args: config: The template configuration. Returns: Lines of Jinja2 template code for flushing the current aggregation group. """ lines = [ " {%- if ns_agg.current_role == 'tool' %}", " {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %}", " {%- elif ns_agg.current_role is not none %}", " {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %}", " {%- for msg in ns_agg.current_group %}", ] if config.any_thinking_support: lines.extend(_generate_reasoning_to_thinking_inline()) # Build list-content processing block: v15+ joins intra-message TextChunks with "" # (no separator), pre-v15 joins all TextChunks with "\n\n" (same as inter-message). if config.version >= TokenizerVersion.v15: list_content_lines = [ " {%- elif msg['content'] is not none %}", " {%- set ns_msg = namespace(msg_text_parts=[]) %}", " {%- for block in msg['content'] %}", " {%- if block['type'] == 'text' %}", " {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %}", " {%- else %}", " {%- if ns_msg.msg_text_parts | length > 0 %}", " {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %}", # noqa: E501 " {%- set ns_msg.msg_text_parts = [] %}", " {%- endif %}", " {%- if ns_c.text_parts | length > 0 %}", " {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\\n\\n')}] %}", # noqa: E501 " {%- set ns_c.text_parts = [] %}", " {%- endif %}", " {%- set ns_c.chunks = ns_c.chunks + [block] %}", " {%- set ns_c.has_non_text = true %}", " {%- endif %}", " {%- endfor %}", " {%- if ns_msg.msg_text_parts | length > 0 %}", " {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %}", " {%- endif %}", " {%- endif %}", ] else: list_content_lines = [ " {%- elif msg['content'] is not none %}", " {%- for block in msg['content'] %}", " {%- if block['type'] == 'text' %}", " {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %}", " {%- else %}", " {%- if ns_c.text_parts | length > 0 %}", " {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\\n\\n')}] %}", # noqa: E501 " {%- set ns_c.text_parts = [] %}", " {%- endif %}", " {%- set ns_c.chunks = ns_c.chunks + [block] %}", " {%- set ns_c.has_non_text = true %}", " {%- endif %}", " {%- endfor %}", " {%- endif %}", ] lines.extend( [ " {%- if msg['content'] is string %}", " {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %}", *list_content_lines, " {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %}", " {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %}", " {%- endif %}", " {%- endfor %}", # Finalize: flush remaining text, build merged_content " {%- if ns_c.has_non_text %}", " {%- if ns_c.text_parts | length > 0 %}", " {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\\n\\n')}] %}", # noqa: E501 " {%- endif %}", " {%- set merged_content = ns_c.chunks %}", " {%- else %}", " {%- set merged_content = ns_c.text_parts | join('\\n\\n') %}", " {%- endif %}", # Emit the merged message with role and optional tool_calls " {%- if ns_c.tool_calls | length > 0 %}", " {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %}", # noqa: E501 " {%- else %}", " {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %}", # noqa: E501 " {%- endif %}", " {%- endif %}", ] ) return lines def _generate_system_message_handling(config: TemplateConfig) -> str: r"""Generate system message handling in the message loop for v7+. Emits `[SYSTEM_PROMPT]...[/SYSTEM_PROMPT]` for each system message encountered during message processing. Args: config: The template configuration. Returns: The system message handling section for the message loop, or empty string for pre-v7. """ if not config.uses_system_prompt_tokens: return "" lines = [ "", " {#- System messages. #}", " {%- elif message['role'] == 'system' %}", ] if config.spm_system_prompt_has_space: lines.append(" {{- '" + _BEGIN_SYSTEM + " ' -}}") else: lines.append(" {{- '" + _BEGIN_SYSTEM + "' -}}") if config.system_supports_thinking: sys_types_desc = "text and thinking" elif config.system_supports_audio: sys_types_desc = "text and audio" else: sys_types_desc = "text" rc = _render_content_call( config=config, content_expr="message['content']", context_name="system message contents", supported_types_desc=sys_types_desc, support_thinking=config.system_supports_thinking, support_images=False, support_audio=config.system_supports_audio, initial_prev_img="false", ) lines.append(" {{- " + rc + " -}}") lines.append(" {{- '" + _END_SYSTEM + "' -}}") return "\n".join(lines) def _generate_alternation_check(config: TemplateConfig) -> str: r"""Generate message ordering validation using a role transition table. Validates that message roles follow a valid ordering, matching the rules from `mistral_common.protocol.instruct.validator._validate_message_order`. The transition table defines which roles can follow each previous role: - After `system`: `user`, `assistant`, `system` - After `user`: `assistant`, `system`, `user` - After `assistant`: `assistant`, `user`, `tool` - After `tool`: `assistant`, `tool`, `user` For pre-v7 templates, system messages are extracted before this check, so only `user`, `assistant`, and `tool` roles are seen. Args: config: The template configuration. Returns: The message ordering validation section of the chat template. """ lines = [ "", "{#- Validates message ordering. #}", ] ns_vars: list[str] = [] if config.tracks_max_idx_user: ns_vars.append("max_idx_user=-1") if config.uses_spm_space_tracking: ns_vars.append("add_space=false") if config.uses_v2_spm_tool_format: ns_vars.append("prev_tool=false") if config.tools_at_beginning: emitted_var = ( "available_tools_and_settings_emitted" if config.supports_model_settings else "available_tools_emitted" ) ns_vars.append(f"{emitted_var}=false") lines.append("{%- set ns = namespace(" + ", ".join(ns_vars) + ") %}") # First-message constraint if config.uses_system_prompt_tokens: # v7+: system messages remain in loop_messages, so first message can be user or system first_msg_cond = "{%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %}" first_msg_err = " {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }}" # noqa: E501 else: # pre-v7: system messages are extracted, so first message must be user first_msg_cond = "{%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %}" first_msg_err = " {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }}" # noqa: E501 lines.append(first_msg_cond) lines.append(first_msg_err) lines.append("{%- endif %}") # Transition table validation lines.append("{%- set ns_order = namespace(previous_role=none) %}") lines.append("{%- for message in loop_messages %}") lines.append(" {%- set current_role = message['role'] %}") lines.append(" {%- if ns_order.previous_role is not none %}") transition_error = " {{- raise_exception('Unexpected role \\'' + current_role + '\\' after role \\'' + ns_order.previous_role + '\\'') }}" # noqa: E501 if config.uses_system_prompt_tokens: # v7+: full transition table including system lines.append(" {%- if ns_order.previous_role == 'system' %}") lines.append(" {%- if current_role not in ['user', 'assistant', 'system'] %}") lines.append(transition_error) lines.append(" {%- endif %}") lines.append(" {%- elif ns_order.previous_role == 'user' %}") lines.append(" {%- if current_role not in ['assistant', 'system', 'user'] %}") else: # pre-v7: no system in loop_messages lines.append(" {%- if ns_order.previous_role == 'user' %}") lines.append(" {%- if current_role not in ['assistant', 'user'] %}") lines.append(transition_error) lines.append(" {%- endif %}") lines.append(" {%- elif ns_order.previous_role == 'assistant' %}") lines.append(" {%- if current_role not in ['assistant', 'user', 'tool'] %}") lines.append(transition_error) lines.append(" {%- endif %}") lines.append(" {%- elif ns_order.previous_role == 'tool' %}") lines.append(" {%- if current_role not in ['assistant', 'tool', 'user'] %}") lines.append(transition_error) lines.append(" {%- endif %}") lines.append(" {%- endif %}") lines.append(" {%- endif %}") # Track max_idx_user in the same loop if config.tracks_max_idx_user: lines.append(" {%- if message.role == 'user' %}") lines.append(" {%- set ns.max_idx_user = ns.max_idx_user + 1 %}") lines.append(" {%- endif %}") lines.append(" {%- set ns_order.previous_role = current_role %}") lines.append("{%- endfor %}") return "\n".join(lines) def _generate_user_message_handling(config: TemplateConfig) -> str: r"""Generate user message handling section. Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for user message processing. """ lines = [] chunk_types = ["text"] if config.image_support: chunk_types.extend(["image", "image_url"]) if config.audio_support: chunk_types.extend(["input_audio", "audio_url"]) chunk_desc = ", ".join(chunk_types[:-1]) + " and " + chunk_types[-1] if len(chunk_types) > 1 else chunk_types[0] comment = f"{{#- User messages supports {chunk_desc} content. #}}" lines.append(f" {comment}") lines.append(" {%- if message['role'] == 'user' %}") # Tools and model settings placement if config.tracks_max_idx_user: # Pre-v13: emit before the last user message lines.append(" {%- if (ns.index == ns.max_idx_user) and has_tools %}") lines.append(" {{- available_tools }}") lines.append(" {%- endif %}") elif config.tools_at_beginning: # v13+: emit tools and settings before the first user message emitted_var = ( "available_tools_and_settings_emitted" if config.supports_model_settings else "available_tools_emitted" ) lines.append(f" {{%- if not ns.{emitted_var} %}}") lines.append(" {{- available_tools }}") if config.supports_model_settings: lines.append(" {{- model_settings }}") lines.append(f" {{%- set ns.{emitted_var} = true %}}") lines.append(" {%- endif %}") if config.spm and not config.uses_spm_prev_img_tracking: inst_open = _BEGIN_INST + " " inst_close = _END_INST else: inst_open = _BEGIN_INST inst_close = _END_INST if config.image_support: user_types_desc = "text, image and image_url" elif config.audio_support: user_types_desc = "text, input_audio and audio_url" else: user_types_desc = "text" if config.uses_system_prompt_tokens: # ================================================================= # v7+ path: self-contained, no tag/render_content duplication # ================================================================= needs_content_prep = config.image_support or ( config.version >= TokenizerVersion.v13 and not config.audio_support ) if needs_content_prep: # --- Content preparation: sort/reorder list content --- lines.append(" {%- if message['content'] is not string and message['content'] %}") if config.image_support: lines.append( " {#- When content has exactly one image and one text block, put image first. #}" ) lines.append( " {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %}" # noqa: E501 ) lines.append(" {%- set blocks = [message['content'][1], message['content'][0]] %}") lines.append(" {%- else %}") lines.append(" {%- set blocks = message['content'] %}") lines.append(" {%- endif %}") lines.append(" {%- set user_content = blocks %}") else: # v13+ non-audio: sort blocks by type lines.append(" {%- set user_content = message['content'] | sort(attribute='type') %}") if config.uses_spm_prev_img_tracking: # SPM + prev_img: list content gets [INST] without space lines.append(" {%- set inst_tag = '" + _BEGIN_INST + "' %}") lines.append(" {%- else %}") lines.append(" {%- set user_content = message['content'] %}") if config.uses_spm_prev_img_tracking: # SPM + prev_img: string content gets [INST] with space lines.append(" {%- set inst_tag = '" + _BEGIN_INST + " ' %}") lines.append(" {%- endif %}") # --- Emit [INST] tag --- if config.uses_spm_prev_img_tracking: lines.append(" {{- inst_tag -}}") elif config.spm: lines.append(" {{- '" + _BEGIN_INST + " ' -}}") else: lines.append(" {{- '" + _BEGIN_INST + "' -}}") # --- Build unified render_content call (v7+ call site) --- content_var = "user_content" if needs_content_prep else "message['content']" rc = _render_content_call( config=config, content_expr=content_var, context_name="user message content", supported_types_desc=user_types_desc, support_thinking=False, support_images=config.image_support, support_audio=config.audio_support, initial_prev_img="not added_sp", ) # --- Emit render_content + [/INST] (once each) --- lines.append(" {{- " + rc + " -}}") lines.append(" {{- '" + inst_close + "' }}") else: # ================================================================= # Pre-v7 paths: uses_spm_prev_img_tracking (v3_image_spm) or plain # Keep existing structure with shared image/sorting code # ================================================================= if config.uses_spm_prev_img_tracking: lines.append(f" {{{{- '{inst_open}' }}}}") lines.append(" {%- set added_sp = false %}") lines.append(" {%- if (ns.index == ns.max_idx_user) and system_message != '' %}") lines.append(" {%- set added_sp = true %}") lines.append(" {{- ' ' + system_message + '\\n\\n' }}") lines.append(" {%- elif message['content'] is string %}") lines.append(" {{- ' ' }}") lines.append(" {%- endif %}") lines.append("") lines.append("") lines.append(" {%- if message['content'] is string %}") # Pre-v7 SPM+image string branch (call site 2): initial_prev_img irrelevant # for string content but added_sp is in scope so use "not added_sp" consistently. rc_str = _render_content_call( config=config, content_expr="message['content']", context_name="user message content", supported_types_desc=user_types_desc, support_thinking=False, support_images=config.image_support, support_audio=config.audio_support, initial_prev_img="not added_sp", ) lines.append(" {{- " + rc_str + " -}}") lines.append(" {%- elif message['content'] | length > 0 %}") else: lines.append(f" {{{{- '{inst_open}' }}}}") if config.is_v1: lines.append(" {%- if loop.index0 == 0 and system_message != '' %}") else: lines.append(" {%- if (ns.index == ns.max_idx_user) and system_message != '' %}") lines.append(" {{- system_message + '\\n\\n' }}") lines.append(" {%- endif %}") lines.append(" {%- if message['content'] is string %}") # Pre-v7 plain string branch (call site 2 for non-SPM): simple text-only rc_str = _render_content_call( config=config, content_expr="message['content']", context_name="user message content", supported_types_desc=user_types_desc, support_thinking=False, support_images=config.image_support, support_audio=config.audio_support, ) lines.append(" {{- " + rc_str + " -}}") lines.append(" {%- elif message['content'] | length > 0 %}") # Shared: image sorting + render_content for list case (pre-v7 only, call site 3) if config.image_support: lines.append(" {#- When content has exactly one image and one text block, put image first. #}") lines.append( " {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %}" # noqa: E501 ) lines.append(" {%- set blocks = [message['content'][1], message['content'][0]] %}") lines.append(" {%- else %}") lines.append(" {%- set blocks = message['content'] %}") lines.append(" {%- endif %}") block_var = "blocks" else: block_var = "message['content']" rc_list = _render_content_call( config=config, content_expr=block_var, context_name="user message content", supported_types_desc=user_types_desc, support_thinking=False, support_images=config.image_support, support_audio=config.audio_support, initial_prev_img="not added_sp", ) lines.append(" {{- " + rc_list + " -}}") # Closing lines.append(" {%- else %}") lines.append( " {{- raise_exception('User message must have a string or a list of chunks in content') }}" ) lines.append(" {%- endif %}") lines.append(f" {{{{- '{inst_close}' }}}}") if config.uses_spm_space_tracking: lines.append(" {%- if loop.index < loop.length %}") lines.append(" {%- set ns.add_space=true %}") lines.append(" {%- endif %}") lines.append(" {%- set ns.prev_tool=false %}") return "\n".join(lines) def _generate_assistant_message_handling(config: TemplateConfig) -> str: r"""Generate assistant message handling section. Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for assistant message processing. """ lines = [] comment_parts = ["text"] if config.any_thinking_support: comment_parts.append("thinking") chunk_types = _join_types_desc(comment_parts) comment = f"{{#- Assistant messages supports {chunk_types} content. #}}" lines.append("") lines.append(f" {comment}") lines.append(" {%- elif message['role'] == 'assistant' %}") if config.forbids_assistant_content_with_tools: lines.append( " {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}" # noqa: E501 ) lines.append(" {{- raise_exception('Assistant message cannot have both content and tool calls.') }}") lines.append(" {%- endif %}") lines.append("") if config.validates_assistant_non_empty: lines.append( " {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}" # noqa: E501 ) lines.append( " {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }}" # noqa: E501 ) lines.append(" {%- endif %}") lines.append("") asst_desc_parts: list[str] = ["text"] if config.any_thinking_support: asst_desc_parts.append("thinking") asst_types_desc = _join_types_desc(asst_desc_parts) rc = _render_content_call( config=config, content_expr="message['content']", context_name="assistant message contents", supported_types_desc=asst_types_desc, support_thinking=True, support_images=False, support_audio=False, initial_prev_img="false", ) lines.append(" {%- if message['content'] %}") if config.uses_spm_space_tracking: lines.append(" {%- if ns.add_space %}") lines.append(" {{- ' ' }}") lines.append(" {%- set ns.add_space=false %}") lines.append(" {%- endif %}") lines.append(" {{- " + rc + " -}}") if config.uses_v2_tool_format: lines.append(" {{- " + config.eos_expr + " }}") if config.uses_v2_v3spm_tool_branch: lines.append(_generate_tool_calls_elif_v2_v3(config)) lines.append(" {%- else %}") lines.append( " {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }}" # noqa: E501 ) lines.append(" {%- endif %}") else: lines.append(" {%- endif %}") if config.has_tools and not config.uses_v2_v3spm_tool_branch: lines.append("") lines.append(_generate_tool_calls_block(config)) if not config.uses_v2_tool_format: lines.append("") lines.append(" {{- " + config.eos_expr + " }}") if config.uses_spm_space_tracking: lines.append(" {%- set ns.prev_tool=false %}") return "\n".join(lines) def _generate_tool_calls_elif_v2_v3(config: TemplateConfig) -> str: r"""Generate tool calls as elif branch for v2 (SPM and non-SPM) and v3_spm templates. Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for v2/v3 tool call formatting. """ lines = [] if config.uses_v2_tool_format: lines.append( " {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index > ns.max_idx_user %}" # noqa: E501 ) else: lines.append( " {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}" # noqa: E501 ) if config.spm: lines.append(" {%- if ns.add_space %}") lines.append(" {%- set ns.add_space=false %}") lines.append(" {%- endif %}") lines.append(" {{- '" + _TOOL_CALLS + " [' }}") else: lines.append(" {{- '" + _TOOL_CALLS + "[' }}") lines.append(" {%- for tool in message['tool_calls'] %}") lines.append(" {%- set name = tool['function']['name'] %}") lines.append(" {%- set arguments = tool['function']['arguments'] %}") if config.uses_v2_tool_format: # v2: no ID in tool calls lines.extend(_emit_argument_normalization(" ")) lines.append(" {{- '{\"name\": \"' + name + '\", \"arguments\": ' + arguments + '}' }}") else: # v3_spm: has ID in tool calls lines.extend(_emit_call_id_validation(" ")) lines.extend(_emit_argument_normalization(" ")) lines.append( ' {{- \'{"name": "\' + name + \'", "arguments": \' + arguments + \', "id": "\' + id + \'"}\' }}' # noqa: E501 ) lines.append(" {%- if loop.length > 1 and loop.index < loop.length %}") lines.append(" {{- ', ' }}") lines.append(" {%- endif %}") lines.append(" {%- endfor %}") lines.append(" {{- ']' }}") # v2 has EOS inside the elif, v3_spm has EOS after the endif if config.uses_v2_tool_format: lines.append(" {{- " + config.eos_expr + " }}") if config.uses_v2_tool_format: # v2: additional elif for tool calls during user messages (ignored) lines.append( " {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index <= ns.max_idx_user %}" # noqa: E501 ) return "\n".join(lines) def _generate_tool_calls_block(config: TemplateConfig) -> str: r"""Generate tool calls block (v7+ style). Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for v7+ tool call formatting. """ lines = [] lines.append( " {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}" # noqa: E501 ) if config.uses_spm_space_tracking: lines.append(" {%- if ns.add_space %}") lines.append(" {%- set ns.add_space=false %}") lines.append(" {%- endif %}") if config.uses_v13_tool_format: lines.append(" {%- for tool in message['tool_calls'] %}") lines.append(" {{- '" + _TOOL_CALLS + "' }}") lines.append(" {%- set name = tool['function']['name'] %}") lines.append(" {%- set arguments = tool['function']['arguments'] %}") lines.extend(_emit_argument_normalization(" ")) lines.append(" {{- name + '" + _ARGS + "' + arguments }}") lines.append(" {%- endfor %}") elif config.uses_call_id_in_tool_calls: lines.append(" {%- for tool in message['tool_calls'] %}") lines.append(" {{- '" + _TOOL_CALLS + "' }}") lines.append(" {%- set name = tool['function']['name'] %}") lines.append(" {%- set arguments = tool['function']['arguments'] %}") lines.extend(_emit_call_id_validation(" ")) lines.extend(_emit_argument_normalization(" ")) lines.append(" {{- name + '" + _CALL_ID + "' + id + '" + _ARGS + "' + arguments }}") lines.append(" {%- endfor %}") else: if config.spm: lines.append(" {{- '" + _TOOL_CALLS + " [' }}") else: lines.append(" {{- '" + _TOOL_CALLS + "[' }}") lines.append(" {%- for tool in message['tool_calls'] %}") lines.append(" {%- set name = tool['function']['name'] %}") lines.append(" {%- set arguments = tool['function']['arguments'] %}") lines.extend(_emit_call_id_validation(" ")) lines.extend(_emit_argument_normalization(" ")) lines.append( ' {{- \'{"name": "\' + name + \'", "arguments": \' + arguments + \', "id": "\' + id + \'"}\' }}' # noqa: E501 ) lines.append(" {%- if loop.length > 1 and loop.index < loop.length %}") lines.append(" {{- ', ' }}") lines.append(" {%- endif %}") lines.append(" {%- endfor %}") lines.append(" {{- ']' }}") lines.append(" {%- endif %}") return "\n".join(lines) def _generate_tool_message_handling(config: TemplateConfig) -> str: r"""Generate tool message handling section. Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for tool result message processing. """ if not config.has_tools: return "" lines = [] lines.append("") if config.uses_v2_tool_format: lines.append(" {#- Tool messages supports int, float or text content. #}") lines.append(" {%- elif message['role'] == 'tool' and ns.index > ns.max_idx_user %}") else: if config.tool_supports_multimodal: lines.append(" {#- Tool messages (multimodal). #}") else: lines.append(" {#- Tool messages only supports text content. #}") lines.append(" {%- elif message['role'] == 'tool' %}") if config.uses_spm_space_tracking: lines.append(" {%- if ns.add_space %}") lines.append(" {%- if not ns.prev_tool %}") lines.append(" {{- ' '}}") lines.append(" {%- endif %}") lines.append(" {%- set ns.add_space=false %}") lines.append(" {%- endif %}") if config.uses_v2_tool_format: lines.extend(_emit_int_float_parsing(" ")) lines.append(" ") lines.append(" {%- if message['name'] is undefined or message['name'] is none %}") lines.append(" {{- raise_exception('Tool message must have a name.') }}") lines.append(" {%- endif %}") lines.append("") lines.append(" {%- set tool_message = {'name': message['name'], 'content': tool_content} %}") lines.append(" ") if config.spm: lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + " [' + (tool_message|tojson) + ']" + _END_TOOL_RESULTS + "' }}" ) # noqa: E501 else: lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + "[' + (tool_message|tojson) + ']" + _END_TOOL_RESULTS + "' }}" ) # noqa: E501 elif config.uses_json_tool_results: lines.extend(_emit_int_float_parsing(" ")) lines.append(" ") lines.extend(_emit_call_id_resolution(" ")) lines.append("") lines.append(" {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %}") lines.append(" ") lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + " ' + (tool_message|tojson) + '" + _END_TOOL_RESULTS + "' }}" ) # noqa: E501 elif config.uses_tool_content_format: lines.extend(_emit_call_id_resolution(" ")) if config.spm: lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + " ' + tool_id + '" + _BEGIN_TOOL_CONTENT + " ' + message['content']|string + '" + _END_TOOL_RESULTS + "' }}" # noqa: E501 ) else: lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + "' + tool_id + '" + _BEGIN_TOOL_CONTENT + "' + message['content']|string + '" + _END_TOOL_RESULTS + "' }}" # noqa: E501 ) elif config.uses_simple_tool_results: if config.tool_supports_multimodal: tool_desc_parts: list[str] = ["text"] if config.image_support: tool_desc_parts.append("image") if config.audio_support: tool_desc_parts.append("audio") tool_types_desc = _join_types_desc(tool_desc_parts) tool_rc = _render_content_call( config=config, content_expr="message['content']", context_name="tool message contents", supported_types_desc=tool_types_desc, support_thinking=False, support_images=config.image_support, support_audio=config.audio_support, initial_prev_img="false", ) lines.append(" {{- '" + _BEGIN_TOOL_RESULTS + "' -}}") lines.append(" {{- " + tool_rc + " -}}") lines.append(" {{- '" + _END_TOOL_RESULTS + "' }}") else: lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + "' + message['content']|string + '" + _END_TOOL_RESULTS + "' }}" ) # noqa: E501 else: # v3 non-spm style lines.extend(_emit_int_float_parsing(" ")) lines.append(" ") lines.extend(_emit_call_id_resolution(" ")) lines.append("") lines.append(" {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %}") lines.append(" ") lines.append( " {{- '" + _BEGIN_TOOL_RESULTS + "' + (tool_message|tojson) + '" + _END_TOOL_RESULTS + "' }}" ) # noqa: E501 # SPM tracking if config.uses_spm_space_tracking: lines.append(" {%- set ns.prev_tool=true %}") lines.append(" {%- set ns.add_space=true %}") return "\n".join(lines) def _generate_else_role_block(config: TemplateConfig) -> str: r"""Generate else block for unsupported roles. Args: config: Template configuration specifying version and features. Returns: Jinja2 template lines for the fallback error on unknown roles. """ lines = [] lines.append("") lines.append(" {#- Raise exception for unsupported roles. #}") if config.uses_system_prompt_tokens: # v7+: system messages are handled in the loop, so 'system' is a valid role. # v7+ always has tools (has_tools is True for v2+), but we keep the branch # for clarity in case the property semantics change. lines.append(" {%- else %}") if config.has_tools: lines.append( " {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }}" # noqa: E501 ) else: lines.append( " {{- raise_exception('Only user, assistant and system roles are supported, got ' + message['role'] + '.') }}" # noqa: E501 ) else: # pre-v7: system messages are already filtered out if config.has_tools: if config.uses_v2_v3spm_tool_branch: lines.append(" {%- elif message['role'] != 'tool' or ns.index > ns.max_idx_user %}") else: lines.append(" {%- else %}") lines.append( " {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }}" # noqa: E501 ) else: lines.append(" {%- else %}") lines.append( " {{- raise_exception('Only user and assistant roles are supported, got ' + message['role'] + '.') }}" # noqa: E501 ) lines.append(" {%- endif %}") return "\n".join(lines) def _generate_message_loop(config: TemplateConfig) -> str: r"""Generate the main message processing loop. Args: config: The template configuration. Returns: A string representing the main message processing loop of the chat template. """ lines = [] if config.tracks_max_idx_user: lines.append("") lines.append("{%- set ns.index = 0 %}") lines.append("{#- Handle conversation messages. #}") lines.append("{%- for message in loop_messages %}") lines.append(_generate_user_message_handling(config)) lines.append(_generate_assistant_message_handling(config)) lines.append(_generate_tool_message_handling(config)) lines.append(_generate_system_message_handling(config)) lines.append(_generate_else_role_block(config)) # Index tracking for v3/v7 style if config.tracks_max_idx_user: lines.append(" {%- if message['role'] == 'user' %}") lines.append(" {%- set ns.index = ns.index + 1 %}") lines.append(" {%- endif %}") lines.append("{%- endfor %}") return "\n".join(lines) def _generate_v1_template(config: TemplateConfig) -> str: r"""Generate v1 template which has unique structure. Args: config: Template configuration specifying version and features. Returns: The complete Jinja2 template string for v1 tokenizer. """ lines = [] lines.append(_generate_header(config)) lines.append(_generate_system_prompt_handling(config)) lines.append(_generate_message_aggregation(config)) lines.append("") lines.append("{#- Validates message ordering. #}") lines.append("{%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %}") lines.append( " {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }}" # noqa: E501 ) lines.append("{%- endif %}") lines.append("{%- set ns_order = namespace(previous_role=none) %}") lines.append("{%- for message in loop_messages %}") lines.append(" {%- set current_role = message['role'] %}") lines.append(" {%- if ns_order.previous_role is not none %}") lines.append(" {%- if ns_order.previous_role == 'user' %}") lines.append(" {%- if current_role not in ['assistant', 'user'] %}") lines.append( " {{- raise_exception('Unexpected role \\'' + current_role + '\\' after role \\'' + ns_order.previous_role + '\\'') }}" # noqa: E501 ) lines.append(" {%- endif %}") lines.append(" {%- elif ns_order.previous_role == 'assistant' %}") lines.append(" {%- if current_role not in ['assistant', 'user'] %}") lines.append( " {{- raise_exception('Unexpected role \\'' + current_role + '\\' after role \\'' + ns_order.previous_role + '\\'') }}" # noqa: E501 ) lines.append(" {%- endif %}") lines.append(" {%- endif %}") lines.append(" {%- endif %}") lines.append(" {%- set ns_order.previous_role = current_role %}") lines.append("{%- endfor %}") lines.append("") lines.append("{#- Handle conversation messages. #}") lines.append("{%- for message in loop_messages %}") lines.append(" {#- User messages supports text content. #}") lines.append(" {%- if message['role'] == 'user' %}") if config.spm: lines.append(" {{- ' " + _BEGIN_INST + " ' }}") else: lines.append(" {{- '" + _BEGIN_INST + " ' }}") lines.append(" {%- if loop.index0 == 0 and system_message != '' %}") lines.append(" {{- system_message + '\\n\\n' }}") lines.append(" {%- endif %}") lines.append(" {%- if message['content'] is string %}") lines.append(" {{- message['content']}}") lines.append(" {%- elif message['content'] | length > 0 %}") lines.append(" {%- for block in message['content'] %}") lines.append(" {%- if block['type'] == 'text' %}") lines.append(" {{- block['text'] }}") lines.append(" {%- else %}") lines.append( " {{- raise_exception('Only text chunks are supported in user message content.') }}" ) lines.append(" {%- endif %}") lines.append(" {%- endfor %}") lines.append(" {%- else %}") lines.append(" {{- raise_exception('User message must have a string or a list of chunks in content') }}") lines.append(" {%- endif %}") lines.append(" {{- ' " + _END_INST + "' }}") if config.spm: lines.append(" {%- if loop.index < loop.length %}") lines.append(' {{- " " }}') lines.append(" {%- endif %}") lines.append("") lines.append(" {#- Assistant messages supports text content or text chunks. #}") lines.append(" {%- elif message['role'] == 'assistant' %}") lines.append(" {%- if message['content'] is string and message['content'] != '' %}") lines.append(" {{- message['content'] }}") lines.append(" {%- elif message['content'] | length > 0 %}") lines.append(" {%- for block in message['content'] %}") lines.append(" {%- if block['type'] == 'text' %}") lines.append(" {{- block['text'] }}") lines.append(" {%- else %}") lines.append( " {{- raise_exception('Only text chunks are supported in assistant message contents.') }}" ) lines.append(" {%- endif %}") lines.append(" {%- endfor %}") lines.append(" {#- End of sequence token for each assistant messages. #}") lines.append(" {%- else %}") lines.append(" {{- raise_exception('Assistant message content must be non-empty.') }}") lines.append(" {%- endif %}") lines.append(" {{- " + config.eos_expr + "}}") lines.append("") lines.append("") lines.append(" {#- Raise exception for unsupported roles. #}") lines.append(" {%- else %}") lines.append( " {{- raise_exception('Only user and assistant roles are supported, got ' + message['role'] + '.') }}" ) lines.append(" {%- endif %}") lines.append("{%- endfor %}") return "\n".join(lines) + "\n" def build_chat_template(config: TemplateConfig) -> str: r"""Generate a complete chat template based on configuration. Args: config: Template configuration specifying version and features. Returns: The complete Jinja2 template as a string. Examples: >>> config = TemplateConfig(version=TokenizerVersion.v3, image_support=True, use_special_token_variables=True) >>> template = build_chat_template(config) >>> "bos_token" in template True """ if config.is_v1: return _generate_v1_template(config) parts = [] parts.append(_generate_header(config)) parts.append(_generate_system_prompt_handling(config)) parts.append(_generate_available_tools_definition(config)) macros = _generate_macros(config) if macros: parts.append(macros) parts.append(_generate_message_aggregation(config)) parts.append(_generate_alternation_check(config)) parts.append("") parts.append(_generate_message_loop(config)) parts.append("") return "\n".join(parts) python-mistral-common-1.11.7/src/mistral_common/multimodal.py000066400000000000000000000003111523035502500244070ustar00rootroot00000000000000# This file is deprecated. TODO(Patrick) - as soon as vllm & transformers have adapted # to new naming, throw deprecation warning and remove this file. from mistral_common.image import * # noqa: F403 python-mistral-common-1.11.7/src/mistral_common/protocol/000077500000000000000000000000001523035502500235345ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/__init__.py000066400000000000000000000000001523035502500256330ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/base.py000066400000000000000000000016461523035502500250270ustar00rootroot00000000000000from pydantic import Field from mistral_common.base import MistralBase class BaseCompletionRequest(MistralBase): """Base class for completion requests. Attributes: temperature: Sampling temperature to use, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. top_p: Nucleus sampling parameter, top-p probability mass, between 0 and 1. max_tokens: Maximum number of tokens to generate. random_seed: Random seed for reproducibility. Examples: >>> request = BaseCompletionRequest(temperature=0.7, top_p=0.9, max_tokens=100, random_seed=42) """ temperature: float = Field(default=0.7, ge=0.0, le=1.0) top_p: float = Field(default=1.0, ge=0.0, le=1.0) max_tokens: int | None = Field(default=None, ge=0) random_seed: int | None = Field(default=None, ge=0) python-mistral-common-1.11.7/src/mistral_common/protocol/fim/000077500000000000000000000000001523035502500243075ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/fim/__init__.py000066400000000000000000000000001523035502500264060ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/fim/request.py000066400000000000000000000010131523035502500263440ustar00rootroot00000000000000from mistral_common.protocol.base import BaseCompletionRequest class FIMRequest(BaseCompletionRequest): r"""A valid Fill in the Middle completion request to be tokenized. Attributes: prompt: The prompt to be completed. suffix: The suffix of the prompt. If provided, the model will generate text between the prompt and the suffix. Examples: >>> request = FIMRequest(prompt="Hello, my name is", suffix=" and I live in New York.") """ prompt: str suffix: str | None = None python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/000077500000000000000000000000001523035502500254075ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/__init__.py000066400000000000000000000000001523035502500275060ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/chunk.py000066400000000000000000000360051523035502500270750ustar00rootroot00000000000000import base64 import io import re from enum import Enum from pathlib import Path from typing import TYPE_CHECKING, Any, Literal from urllib.parse import urlparse from pydantic import ConfigDict, Field, field_validator, model_validator from typing_extensions import Annotated from mistral_common.base import MistralBase from mistral_common.deprecation import warn_once from mistral_common.image import SerializableImage from mistral_common.imports import assert_soundfile_installed, is_soundfile_installed if is_soundfile_installed(): import soundfile as sf if TYPE_CHECKING: from mistral_common.tokens.tokenizers.audio import Audio def _strip_audio_data_url_prefix(data: str) -> str: r"""Remove the optional base64 audio data URL prefix.""" if re.match(r"^data:audio/\w+;base64,", data): return data.split(",", 1)[1] return data def _detect_audio_format(data: str | bytes) -> str: r"""Detect audio format from base64-encoded string or raw bytes. Uses soundfile to read only the file header, avoiding full audio decoding. Args: data: Base64-encoded audio string or raw audio bytes. Returns: The detected audio format as a lowercase string (e.g. "wav", "mp3"). Raises: ValueError: If the audio format cannot be detected. """ assert_soundfile_installed() if isinstance(data, str): audio_bytes = base64.b64decode(_strip_audio_data_url_prefix(data)) else: audio_bytes = data try: info = sf.info(io.BytesIO(audio_bytes)) except RuntimeError as e: raise ValueError("Failed to detect audio format. Verify that the given file is valid wav or mp3.") from e fmt: str = info.format.lower() return fmt class ChunkTypes(str, Enum): r"""Enum for the types of chunks that can be sent to the model. Attributes: text: A text chunk. image: An image chunk. image_url: An image url chunk. input_audio: An input audio chunk. audio_url: An audio url chunk. Examples: >>> from mistral_common.protocol.instruct.chunk import ChunkTypes >>> chunk_type = ChunkTypes.text """ text = "text" image = "image" image_url = "image_url" input_audio = "input_audio" audio_url = "audio_url" thinking = "thinking" class BaseContentChunk(MistralBase): r"""Base class for all content chunks. Content chunks are used to send different types of content to the model. Attributes: type: The type of the chunk. """ type: Literal[ ChunkTypes.text, ChunkTypes.image, ChunkTypes.image_url, ChunkTypes.input_audio, ChunkTypes.audio_url, ChunkTypes.thinking, ] def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format. Should be implemented by subclasses. """ raise NotImplementedError(f"to_openai method not implemented for {type(self).__name__}") @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "BaseContentChunk": r"""Converts the OpenAI chunk to the Mistral format. Should be implemented by subclasses. """ raise NotImplementedError(f"from_openai method not implemented for {cls.__name__}") class ImageChunk(BaseContentChunk): r"""Image chunk. Attributes: image: The image to be sent to the model. Examples: >>> from PIL import Image >>> image_chunk = ImageChunk(image=Image.new('RGB', (200, 200), color='blue')) """ type: Literal[ChunkTypes.image] = ChunkTypes.image image: SerializableImage model_config = ConfigDict(arbitrary_types_allowed=True) def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format.""" base64_image = self.model_dump(include={"image"}, context={"add_format_prefix": True})["image"] return {"type": "image_url", "image_url": {"url": base64_image}} @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "ImageChunk": r"""Converts the OpenAI chunk to the Mistral format.""" assert openai_chunk.get("type") == "image_url", openai_chunk image_url_dict = openai_chunk["image_url"] assert isinstance(image_url_dict, dict) and "url" in image_url_dict, image_url_dict url = image_url_dict["url"] if re.match(r"^data:image/\w+;base64,", url): # Remove the prefix if it exists url = url.split(",")[1] return cls.model_validate({"image": url}) class ImageURL(MistralBase): r"""Image URL or a base64 encoded image. Attributes: url: The URL of the image. detail: The detail of the image. Examples: >>> image_url = ImageURL(url="https://example.com/image.png") """ url: str detail: str | None = None class ImageURLChunk(BaseContentChunk): r"""Image URL chunk. Attributes: image_url: The URL of the image or a base64 encoded image to be sent to the model. Examples: >>> image_url_chunk = ImageURLChunk(image_url="data:image/png;base64,iVBORw0") """ type: Literal[ChunkTypes.image_url] = ChunkTypes.image_url image_url: ImageURL | str model_config = ConfigDict(arbitrary_types_allowed=True) def get_url(self) -> str: if isinstance(self.image_url, ImageURL): return self.image_url.url return self.image_url def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format.""" image_url_dict = {"url": self.get_url()} if isinstance(self.image_url, ImageURL) and self.image_url.detail is not None: image_url_dict["detail"] = self.image_url.detail out_dict: dict[str, Any] = { "type": "image_url", "image_url": image_url_dict, } return out_dict @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "ImageURLChunk": r"""Converts the OpenAI chunk to the Mistral format.""" return cls.model_validate({"image_url": openai_chunk["image_url"]}) class RawAudio(MistralBase): r"""Deprecated: Use `str | bytes` directly. Will be removed in 1.13.0.""" data: str | bytes format: str def model_post_init(self, __context: Any) -> None: warn_once( "RawAudio", "RawAudio is deprecated. Use str | bytes directly for audio data. Will be removed in 1.13.0.", DeprecationWarning, stacklevel=2, ) @classmethod def from_audio(cls, audio: "Audio") -> "RawAudio": r"""Create a RawAudio instance from an Audio object. Args: audio: An Audio object containing audio data, format, and duration. Returns: A RawAudio instance initialized with the audio data. """ format = audio.format data = audio.to_base64(format, False) return cls(data=data, format=format) @field_validator("format") def should_not_be_empty(cls, v: str) -> str: if not v.strip(): raise ValueError("`format` should not be empty") return v class AudioURL(MistralBase): r"""Audio URL. Attributes: url: The URL of the audio file. """ url: str class AudioURLType(str, Enum): r"""Enum for the types of audio URLs. Attributes: url: A URL. base64: A base64 encoded audio. Can be prefixed with `data:audio/;base64,`. file: A file path. file_uri: A file URI (eg. `file:///path/to/file`). """ url = "url" base64 = "base64" file = "file" file_uri = "file_uri" class AudioURLChunk(BaseContentChunk): r"""Audio URL chunk. Attributes: type: The type of the chunk, which is always `ChunkTypes.audio_url`. audio_url: The URL of the audio file. """ type: Literal[ChunkTypes.audio_url] = ChunkTypes.audio_url audio_url: str | AudioURL @property def url(self) -> str: if isinstance(self.audio_url, AudioURL): return self.audio_url.url return self.audio_url def get_url_type(self) -> AudioURLType: r"""Returns the type of the audio URL. Note: URLs should be either: - a valid URL (http:// or https://) - a valid file path (e.g. /path/to/file) - a valid file URI (e.g. file:///path/to/file) - a base64 encoded audio. It is assumed to be base64 encoded if it is not a valid URL or file path. Returns: The type of the audio URL. """ url_scheme = urlparse(self.url).scheme if url_scheme in {"http", "https"}: return AudioURLType.url elif url_scheme == "data": return AudioURLType.base64 elif url_scheme == "file": return AudioURLType.file_uri try: url_path = Path(self.url) exist_path = url_path.exists() except OSError: # File name too long exist_path = False if exist_path: return AudioURLType.file return AudioURLType.base64 def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format.""" if isinstance(self.audio_url, AudioURL): return self.model_dump() else: return {"type": self.type, "audio_url": {"url": self.audio_url}} @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "AudioURLChunk": r"""Converts the OpenAI chunk to the Mistral format.""" return cls.model_validate_ignore_extra(openai_chunk) class AudioChunk(BaseContentChunk): r"""Audio chunk containing raw audio data. Attributes: type: The type of the chunk, which is always ChunkTypes.input_audio. input_audio: The audio data as a base64-encoded string or raw bytes. Examples: >>> audio_chunk = AudioChunk(input_audio="base64_encoded_audio_data") """ type: Literal[ChunkTypes.input_audio] = ChunkTypes.input_audio input_audio: str | bytes @model_validator(mode="before") @classmethod def _flatten_audio_dict(cls, values: dict[str, Any]) -> dict[str, Any]: r"""Extract audio data from a nested dict or legacy RawAudio payload. Handles the OpenAI format where `input_audio` is a dict with a `data` key (e.g. `{"data": "...", "format": "wav"}`) as well as deprecated `RawAudio` instances, flattening them to a plain `str | bytes` value. """ if not isinstance(values, dict): return values raw = values.get("input_audio") if isinstance(raw, MistralBase): raw = raw.model_dump() if isinstance(raw, dict) and "data" in raw: values["input_audio"] = raw["data"] return values @field_validator("input_audio") @classmethod def should_not_be_empty(cls, v: str | bytes) -> str | bytes: r"""Validate that the audio data is not empty.""" if isinstance(v, str) and not v.strip(): raise ValueError("`input_audio` should not be empty.") if isinstance(v, bytes) and not v: raise ValueError("`input_audio` should not be empty.") return v @classmethod def from_audio(cls, audio: "Audio") -> "AudioChunk": r"""Create an AudioChunk instance from an Audio object. Args: audio: An Audio object containing audio data. Returns: An AudioChunk instance initialized with the audio data. """ return cls(input_audio=audio.to_base64(audio.format, False)) def to_openai(self) -> dict[str, Any]: r"""Convert the chunk to the OpenAI format. Returns: A dictionary representing the audio chunk in the OpenAI format. """ if isinstance(self.input_audio, bytes): content = base64.b64encode(self.input_audio).decode("utf-8") else: content = _strip_audio_data_url_prefix(self.input_audio) fmt = _detect_audio_format(self.input_audio) return { "type": self.type, "input_audio": { "data": content, "format": fmt, }, } @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "AudioChunk": r"""Convert the OpenAI chunk to the Mistral format. Args: openai_chunk: A dictionary representing the audio chunk in the OpenAI format. Returns: An AudioChunk instance initialized with the data from the OpenAI chunk. """ return cls.model_validate_ignore_extra(openai_chunk) class TextChunk(BaseContentChunk): r"""Text chunk. Attributes: text: The text to be sent to the model. Examples: >>> text_chunk = TextChunk(text="Hello, how can I help you?") """ type: Literal[ChunkTypes.text] = ChunkTypes.text text: str def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format.""" return self.model_dump() @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "TextChunk": r"""Converts the OpenAI chunk to the Mistral format.""" return cls.model_validate_ignore_extra(openai_chunk) class ThinkChunk(BaseContentChunk): r"""Thinking chunk. Attributes: type: The type of the chunk, which is always ChunkTypes.thinking. thinking: The list of text chunks of the thinking. closed: Whether the thinking chunk is closed or not. """ type: Literal[ChunkTypes.thinking] = ChunkTypes.thinking thinking: str closed: bool = Field(default=True, description="Whether the thinking chunk is closed or not.") def to_openai(self) -> dict[str, Any]: r"""Converts the chunk to the OpenAI format.""" return self.model_dump() @classmethod def from_openai(cls, openai_chunk: dict[str, Any]) -> "ThinkChunk": r"""Converts the OpenAI chunk to the Mistral format.""" return cls.model_validate_ignore_extra(openai_chunk) ContentChunk = Annotated[ TextChunk | ImageChunk | ImageURLChunk | AudioChunk | AudioURLChunk | ThinkChunk, Field(discriminator="type") ] def _convert_openai_content_chunks(openai_content_chunks: dict[str, Any]) -> ContentChunk: content_type_str = openai_content_chunks.get("type") if content_type_str is None: raise ValueError("Content chunk must have a type field.") content_type = ChunkTypes(content_type_str) if content_type == ChunkTypes.text: return TextChunk.from_openai(openai_content_chunks) elif content_type == ChunkTypes.image_url: return ImageURLChunk.from_openai(openai_content_chunks) elif content_type == ChunkTypes.image: return ImageChunk.from_openai(openai_content_chunks) elif content_type == ChunkTypes.input_audio: return AudioChunk.from_openai(openai_content_chunks) elif content_type == ChunkTypes.audio_url: return AudioURLChunk.from_openai(openai_content_chunks) elif content_type == ChunkTypes.thinking: return ThinkChunk.from_openai(openai_content_chunks) else: raise ValueError(f"Unknown content chunk type: {content_type}") python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/converters.py000066400000000000000000000030721523035502500301550ustar00rootroot00000000000000from typing import Any from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.tool_calls import Tool def convert_openai_messages( messages: list[dict[str, str | list[dict[str, str | dict[str, Any]]]]], ) -> list[ChatMessage]: r"""Convert OpenAI messages to Mistral messages. Args: messages: The OpenAI messages to convert. Returns: The Mistral messages. """ converted_messages: list[ChatMessage] = [] for openai_message in messages: message_role = openai_message.get("role") message: ChatMessage if message_role == "user": message = UserMessage.from_openai(openai_message) elif message_role == "assistant": message = AssistantMessage.from_openai(openai_message) elif message_role == "tool": message = ToolMessage.from_openai(openai_message) elif message_role == "system": message = SystemMessage.from_openai(openai_message) else: raise ValueError(f"Unknown message role: {message_role}") converted_messages.append(message) return converted_messages def convert_openai_tools( tools: list[dict[str, Any]], ) -> list[Tool]: r"""Convert OpenAI tools to Mistral tools. Args: tools: The OpenAI tools to convert. Returns: The Mistral tools. """ converted_tools = [Tool.from_openai(openai_tool) for openai_tool in tools] return converted_tools python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/messages.py000066400000000000000000000353111523035502500275730ustar00rootroot00000000000000import warnings from collections.abc import Sequence from enum import Enum from typing import Any, ClassVar, Literal, TypeVar from pydantic import Field, model_validator from typing_extensions import Annotated, TypeAlias, TypeGuard from mistral_common.base import MistralBase from mistral_common.exceptions import InvalidAssistantMessageException from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, BaseContentChunk, ContentChunk, ImageChunk, ImageURLChunk, TextChunk, ThinkChunk, _convert_openai_content_chunks, ) from mistral_common.protocol.instruct.tool_calls import ToolCall warnings.filterwarnings( action="once", category=FutureWarning, message=r".*`convert_thinking_format` defaults to 'thinking_chunks'.*", ) def _are_think_chunks(chunks: Sequence[ContentChunk]) -> TypeGuard[list[ThinkChunk]]: r"""Narrow a chunk list to ThinkChunk list.""" return all(isinstance(c, ThinkChunk) for c in chunks) def _are_text_chunks(chunks: Sequence[ContentChunk]) -> TypeGuard[list[TextChunk]]: r"""Narrow a chunk list to TextChunk list.""" return all(isinstance(c, TextChunk) for c in chunks) class ReasoningFieldFormat(str, Enum): r"""How to serialize leading `ThinkChunk` in `AssistantMessage.to_openai()`. Attributes: thinking_chunks: Use think chunks (Mistral convention). reasoning: Flat `reasoning` string (vLLM convention). reasoning_content: Flat `reasoning_content` string (SGLang convention). """ thinking_chunks = "thinking_chunks" reasoning = "reasoning" reasoning_content = "reasoning_content" class Roles(str, Enum): r"""Enum for the roles of the messages. Attributes: system: The system role. user: The user role. assistant: The assistant role. tool: The tool role. Examples: >>> role = Roles.user """ system = "system" user = "user" assistant = "assistant" tool = "tool" class BaseMessage(MistralBase): r"""Base class for all messages. Attributes: role: The role of the message. """ role: Literal[Roles.system, Roles.user, Roles.assistant, Roles.tool] # Allow-list of content chunk types accepted by this message. Must be set by each subclass. _allowed_content_chunks: ClassVar[tuple[type[BaseContentChunk], ...]] @model_validator(mode="after") def _validate_allowed_content_chunks(self) -> "BaseMessage": r"""Enforce the per-message content chunk allow-list.""" content = getattr(self, "content", None) if isinstance(content, list): for chunk in content: if not isinstance(chunk, self._allowed_content_chunks): raise ValueError(f"{type(chunk).__name__} cannot be used in {self.role} message.") return self @staticmethod def _content_to_openai( content: str | Sequence[ContentChunk] | None, ) -> str | list[dict[str, Any]] | None: r"""Serialize message content to OpenAI format. Args: content: String, list of content chunks, or None. Returns: String content as-is, list of chunks serialized via each chunk's to_openai(), or None. """ if content is None or isinstance(content, str): return content return [chunk.to_openai() for chunk in content] @staticmethod def _content_from_openai( raw: str | list[dict[str, Any]] | None, ) -> str | list[ContentChunk] | None: r"""Deserialize content from OpenAI format. Args: raw: Raw content from OpenAI message dict. Returns: String content as-is, list of deserialized content chunks, or None. Raises: ValueError: If content type is unrecognized. """ if raw is None or isinstance(raw, str): return raw if isinstance(raw, list): return [_convert_openai_content_chunks(chunk) for chunk in raw] raise ValueError(f"Unknown content type: {type(raw)}") def to_openai(self) -> dict[str, Any]: r"""Converts the message to the OpenAI format. Should be implemented by subclasses. """ raise NotImplementedError(f"to_openai method not implemented for {type(self).__name__}") @classmethod def from_openai(cls, openai_message: dict[str, Any]) -> "BaseMessage": r"""Converts the OpenAI message to the Mistral format. Should be implemented by subclasses. """ raise NotImplementedError(f"from_openai method not implemented for {cls.__name__}.") class UserMessage(BaseMessage): r"""User message. Attributes: content: The content of the message. Examples: >>> message = UserMessage(content="Can you help me to write a poem?") """ role: Literal[Roles.user] = Roles.user content: str | list[ContentChunk] _allowed_content_chunks: ClassVar[tuple[type[BaseContentChunk], ...]] = ( TextChunk, ImageChunk, ImageURLChunk, AudioChunk, AudioURLChunk, ) def to_openai(self) -> dict[str, Any]: r"""Converts the message to the OpenAI format.""" return {"role": self.role, "content": self._content_to_openai(self.content)} @classmethod def from_openai(cls, openai_message: dict[str, Any]) -> "UserMessage": r"""Converts the OpenAI message to the Mistral format.""" return cls.model_validate( {"role": openai_message["role"], "content": cls._content_from_openai(openai_message["content"])} ) class SystemMessage(BaseMessage): r"""System message. Attributes: content: The content of the message. Examples: >>> message = SystemMessage(content="You are a helpful assistant.") """ role: Literal[Roles.system] = Roles.system content: str | list[ContentChunk] _allowed_content_chunks: ClassVar[tuple[type[BaseContentChunk], ...]] = (TextChunk, AudioChunk, ThinkChunk) def to_openai(self) -> dict[str, Any]: r"""Converts the message to the OpenAI format.""" return {"role": self.role, "content": self._content_to_openai(self.content)} @classmethod def from_openai(cls, openai_message: dict[str, Any]) -> "SystemMessage": r"""Converts the OpenAI message to the Mistral format.""" return cls.model_validate( {"role": openai_message["role"], "content": cls._content_from_openai(openai_message["content"])} ) class AssistantMessage(BaseMessage): r"""Assistant message. Attributes: role: The role of the message. content: The content of the message. tool_calls: The tool calls of the message. prefix: Whether the message is a prefix. Examples: >>> message = AssistantMessage(content="Hello, how can I help you?") """ role: Literal[Roles.assistant] = Roles.assistant content: str | list[ContentChunk] | None = None _allowed_content_chunks: ClassVar[tuple[type[BaseContentChunk], ...]] = (TextChunk, ThinkChunk) tool_calls: list[ToolCall] | None = None prefix: bool = False def to_openai( self, reasoning_field_format: ReasoningFieldFormat | None = None, ) -> dict[str, Any]: r"""Converts the message to the OpenAI format. Args: reasoning_field_format: Format for converting thinking chunks. When `None`, defaults to `ReasoningFieldFormat.thinking_chunks` (chunks kept inline) but emits a `FutureWarning` if the content contains `ThinkChunk`. """ out_dict: dict[str, Any] = { "role": self.role, } if self.tool_calls is not None: out_dict["tool_calls"] = [tool_call.to_openai() for tool_call in self.tool_calls] if self.content is None: return out_dict if isinstance(self.content, str): out_dict["content"] = self.content return out_dict last_think_idx: int = -1 for i, chunk in enumerate(self.content): if isinstance(chunk, ThinkChunk): if (i - last_think_idx) > 1: raise InvalidAssistantMessageException( "ThinkChunks must be leading: all ThinkChunks must appear before any other content chunk." ) last_think_idx = i if reasoning_field_format is None and last_think_idx >= 0: warnings.warn( "`convert_thinking_format` defaults to 'thinking_chunks' but will change to 'reasoning' " "in 1.13.0. Pass `reasoning_field_format` explicitly to silence this warning.", FutureWarning, stacklevel=2, ) match reasoning_field_format: case None | ReasoningFieldFormat.thinking_chunks: out_dict["content"] = self._content_to_openai(self.content) case ReasoningFieldFormat.reasoning | ReasoningFieldFormat.reasoning_content: think_chunks, content_chunks = self.content[: last_think_idx + 1], self.content[last_think_idx + 1 :] if not _are_think_chunks(think_chunks) or not _are_text_chunks(content_chunks): raise RuntimeError("Impossible, only think or content chunks should have been present.") if len(think_chunks) > 0: out_dict[reasoning_field_format.value] = "\n".join(tc.thinking for tc in think_chunks) if len(content_chunks) == 1: out_dict["content"] = content_chunks[0].text elif content_chunks: out_dict["content"] = self._content_to_openai(content_chunks) case _: raise ValueError(f"{reasoning_field_format=} is not supported.") return out_dict @classmethod def from_openai(cls, openai_message: dict[str, Any]) -> "AssistantMessage": r"""Converts the OpenAI message to the Mistral format.""" openai_tool_calls = openai_message.get("tool_calls", None) if openai_tool_calls is None: tools_calls: list[ToolCall] | None = None elif isinstance(openai_tool_calls, list): tools_calls = [] for openai_tool_call in openai_tool_calls or []: tools_calls.append(ToolCall.from_openai(openai_tool_call)) else: raise ValueError(f"tool_calls must be a list, got {type(openai_tool_calls)}") content = cls._content_from_openai(openai_message.get("content")) reasoning_content: str | None = openai_message.get("reasoning_content") reasoning: str | None = openai_message.get("reasoning") match reasoning_content, reasoning: case None, None: openai_thinking = None case None, _: openai_thinking = reasoning case _, None: openai_thinking = reasoning_content case _, _: if reasoning_content != reasoning: raise ValueError("`reasoning_content` and `reasoning` should be equal.") openai_thinking = reasoning if openai_thinking is not None: has_thinking_chunks = isinstance(content, list) and any(isinstance(chunk, ThinkChunk) for chunk in content) if has_thinking_chunks: raise InvalidAssistantMessageException( "Message cannot have both thinking chunks in content and a top-level" " `reasoning` or `reasoning_content` field." ) reasoning_chunk = ThinkChunk(thinking=openai_thinking, closed=True) if isinstance(content, str): content = [reasoning_chunk, TextChunk(text=content)] elif content is None: content = [reasoning_chunk] else: content.insert(0, reasoning_chunk) return cls.model_validate( { "role": openai_message["role"], "content": content, "tool_calls": tools_calls, } ) class FinetuningAssistantMessage(AssistantMessage): r"""Assistant message for finetuning. Attributes: weight: The weight of the message to train on. Examples: >>> message = FinetuningAssistantMessage(content="Hello, how can I help you?", weight=0.5) """ weight: float | None = None class ToolMessage(BaseMessage): r"""Tool message. Attributes: content: The content of the message. tool_call_id: The tool call id of the message. name: The name of the tool. (Deprecated in V3 tokenization) Examples: >>> message = ToolMessage(content="Hello, how can I help you?", tool_call_id="123") """ content: str | list[ContentChunk] role: Literal[Roles.tool] = Roles.tool tool_call_id: str | None = None # Deprecated in V3 tokenization name: str | None = None # Tool messages do not accept thinking chunks. _allowed_content_chunks: ClassVar[tuple[type[BaseContentChunk], ...]] = ( TextChunk, ImageChunk, ImageURLChunk, AudioChunk, AudioURLChunk, ) def to_openai(self) -> dict[str, Any]: r"""Converts the message to the OpenAI format.""" assert self.tool_call_id is not None, "tool_call_id must be provided for tool messages." return { "role": self.role, "tool_call_id": self.tool_call_id, "content": self._content_to_openai(self.content), } @classmethod def from_openai(cls, openai_message: dict[str, Any]) -> "ToolMessage": r"""Converts the OpenAI message to the Mistral format.""" content = cls._content_from_openai(openai_message["content"]) tool_message = cls.model_validate( { "role": openai_message["role"], "tool_call_id": openai_message["tool_call_id"], "content": content, "name": openai_message.get("name"), } ) return tool_message ChatMessage = Annotated[SystemMessage | UserMessage | AssistantMessage | ToolMessage, Field(discriminator="role")] FinetuningMessage = Annotated[ SystemMessage | UserMessage | FinetuningAssistantMessage | ToolMessage, Field(discriminator="role"), ] ChatMessageType = TypeVar("ChatMessageType", bound=ChatMessage) # Used for type hinting in generic classes where we might override the message types UserMessageType = TypeVar("UserMessageType", bound=UserMessage) AssistantMessageType = TypeVar("AssistantMessageType", bound=AssistantMessage) ToolMessageType = TypeVar("ToolMessageType", bound=ToolMessage) SystemMessageType = TypeVar("SystemMessageType", bound=SystemMessage) UATS: TypeAlias = UserMessageType | AssistantMessageType | ToolMessageType | SystemMessageType python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/normalize.py000066400000000000000000000651771523035502500300010ustar00rootroot00000000000000import json import warnings from typing import Generic, Sequence from typing_extensions import assert_never from mistral_common.exceptions import InvalidRequestException from mistral_common.protocol.instruct.chunk import ( ContentChunk, TextChunk, ) from mistral_common.protocol.instruct.messages import ( UATS, AssistantMessage, AssistantMessageType, FinetuningAssistantMessage, Roles, SystemMessage, SystemMessageType, ToolMessage, ToolMessageType, UserMessage, UserMessageType, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest, InstructRequest, ModelSettings from mistral_common.protocol.instruct.tool_calls import FunctionCall, Tool, ToolCall from mistral_common.tokens.tokenizers.base import InstructRequestType, TokenizerVersion from mistral_common.tokens.tokenizers.model_settings_builder import ModelSettingsBuilder _DEFAULT_JOIN_STR = "\n\n" def _aggregate_content_chunks_impl( contents: list[list[ContentChunk] | str | None], msg_join_str: str, chunk_join_str: str, ) -> list[ContentChunk] | str: r"""Coalesce TextChunks within the same message and across different messages. Adjacent TextChunks within the same message are joined with `chunk_join_str`. Text from different messages is joined with `msg_join_str`. Args: contents: A list of message contents, where each element is either a string, a list of ContentChunks, or None. This is typically `[message.content for message in messages]`. msg_join_str: Separator inserted between text from different messages. chunk_join_str: Separator inserted between adjacent text chunks within the same message. Returns: A plain string if only text chunks were present, otherwise a list of ContentChunks with adjacent text coalesced. """ all_content: list[ContentChunk] = [] cur_text_parts: list[str] = [] def _flush_text() -> None: if cur_text_parts: all_content.append(TextChunk(text="".join(cur_text_parts))) cur_text_parts.clear() for content in contents: needs_new_msg_sep = bool(cur_text_parts) if not content: # skip None or empty string continue elif isinstance(content, str): join = msg_join_str if needs_new_msg_sep else "" cur_text_parts.append(join + content) else: # list[ContentChunk] for chunk in content: if isinstance(chunk, TextChunk): if not chunk.text: # skip empty text chunks continue if not cur_text_parts: join = "" elif needs_new_msg_sep: join = msg_join_str else: join = chunk_join_str cur_text_parts.append(join + chunk.text) needs_new_msg_sep = False else: _flush_text() all_content.append(chunk) if not all_content: # Only text encountered: return as str return "".join(cur_text_parts) _flush_text() return all_content class InstructRequestNormalizer( Generic[UserMessageType, AssistantMessageType, ToolMessageType, SystemMessageType, InstructRequestType] ): r"""Takes a [ChatCompletionRequest][mistral_common.protocol.instruct.request.ChatCompletionRequest] and normalizes it into an [InstructRequest][mistral_common.protocol.instruct.request.InstructRequest]. The normalization process does several things such as: - Aggregate consecutive messages of the same role - Aggregate system prompts - Normalize json content - Normalize tool calls Examples: >>> normalizer = InstructRequestNormalizer.normalizer() """ _system_prompt_in_begin: bool = False _allow_tool_call_and_content: bool = False _chunk_join_str: str = _DEFAULT_JOIN_STR _msg_join_str: str = _DEFAULT_JOIN_STR def __init__( self, user_message_class: type[UserMessageType], assistant_message_class: type[AssistantMessageType], tool_message_class: type[ToolMessageType], system_message_class: type[SystemMessageType], instruct_request_class: type[InstructRequestType], model_settings_builder: ModelSettingsBuilder | None, ): r"""Initializes the normalizer with the appropriate message classes. Args: user_message_class: The class for user messages. assistant_message_class: The class for assistant messages. tool_message_class: The class for tool messages. system_message_class: The class for system messages. instruct_request_class: The class for instruct requests. model_settings_builder: The builder for model settings, or None if unsupported. """ self._user_message_class = user_message_class self._assistant_message_class = assistant_message_class self._tool_message_class = tool_message_class self._instruct_request_class = instruct_request_class # this is unused but makes creation nicer self._system_message_class = system_message_class self._model_settings_builder = model_settings_builder @staticmethod def normalizer(model_settings_builder: ModelSettingsBuilder | None = None) -> "InstructRequestNormalizer": r"""Returns a normalizer for the default instruct request. Args: model_settings_builder: Must be None for this normalizer version. Returns: A normalizer for the default instruct request. Raises: ValueError: If model_settings_builder is not None. Examples: >>> normalizer = InstructRequestNormalizer.normalizer() """ if model_settings_builder is not None: raise ValueError( f"model_settings_builder must be None for InstructRequestNormalizer, got {model_settings_builder}" ) return InstructRequestNormalizer( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest[UATS, Tool], None ) def build_settings(self, request: ChatCompletionRequest) -> ModelSettings: r"""Build model settings from a chat completion request. For pre-v15 normalizers, model settings are all `None`. Args: request: The chat completion request. Returns: Returns `ModelSettings.none()`. """ if self._model_settings_builder is not None: raise InvalidRequestException( f"model_settings_builder should be None for {type(self).__name__}, got {self._model_settings_builder}" ) return ModelSettings.none() def _normalize_json_content(self, content: str | None) -> str: if content is None or len(content) == 0: return "{}" try: parsed_json = json.loads(content) normalized_content = json.dumps(parsed_json, ensure_ascii=False) except json.JSONDecodeError: normalized_content = content return normalized_content def _aggregate_content_chunks(self, messages: list[UATS]) -> list[ContentChunk] | str: """Coalesce neighboring blocks of ContentChunks across messages.""" return _aggregate_content_chunks_impl( [message.content for message in messages], msg_join_str=self._msg_join_str, chunk_join_str=self._chunk_join_str, ) def _aggregate_content_chunks_to_str_same_message(self, message: UATS) -> str: """Aggregate a single message's content chunks to a string. Args: message: A single message with role system or tool. Returns: The aggregated content as a string. """ assert message.role in (Roles.system, Roles.tool), message.role aggregated = self._aggregate_content_chunks([message]) assert isinstance(aggregated, str), aggregated return aggregated def _aggregate_system_prompts(self, messages: list[UATS]) -> str | None: system_prompt: list[str] = [] for message in messages: if message.role == Roles.system and message.content: system_prompt.append(self._aggregate_content_chunks_to_str_same_message(message)) return self._msg_join_str.join(system_prompt) if len(system_prompt) else None def _aggregate_tool_messages(self, messages: list[UATS], latest_call_ids: list[str]) -> list[ToolMessageType]: """Normalize tool messages without aggregation across messages. Each tool message's content is JSON-normalized; chunk types are guaranteed by the validator. """ tool_messages: list[ToolMessageType] = [] for message in messages: assert isinstance(message, self._tool_message_class), "Expected tool message" content = self._aggregate_content_chunks([message]) assert isinstance(content, str), ( f"Unexpected content chunk types in tool message: {[type(c).__name__ for c in content]}" ) normalized_content = self._normalize_json_content(content) tool_messages.append( self._tool_message_class( content=normalized_content, tool_call_id=message.tool_call_id, name=message.name ) ) return tool_messages def _normalize_tool_call(self, tool_call: ToolCall) -> ToolCall: normalized_function_aruments = self._normalize_json_content(tool_call.function.arguments) return ToolCall( function=FunctionCall(name=tool_call.function.name, arguments=normalized_function_aruments), id=tool_call.id, ) def _aggregate_system_messages(self, messages: list[UATS]) -> list[SystemMessageType]: return [] def _aggregate_assistant_messages(self, messages: list[UATS]) -> AssistantMessageType: tool_calls: list[ToolCall] = [] prefix: bool = False weight: float | None = None content = self._aggregate_content_chunks(messages) for message in messages: assert isinstance(message, self._assistant_message_class), "Expected assistant message" if not self._allow_tool_call_and_content and (message.tool_calls and message.content): raise ValueError(f"Tool calls and content cannot be used together in the same message. {message}") if message.tool_calls: for tool_call in message.tool_calls: normalized_tool_call = self._normalize_tool_call(tool_call) tool_calls.append(normalized_tool_call) prefix |= message.prefix if isinstance(message, FinetuningAssistantMessage): if weight is not None: assert weight == message.weight, ( "Expected weights of aggregated FinetuningAssistantMessage to be equal" ) weight = message.weight aggregated_message = self._assistant_message_class( content=content, tool_calls=tool_calls or None, prefix=prefix, ) if weight is not None and hasattr(aggregated_message, "weight"): aggregated_message.weight = weight return aggregated_message def _aggregate_user_messages(self, messages: list[UATS]) -> UserMessageType: """Coalesce neighboring blocks of ContentChunks in user messages.""" content = self._aggregate_content_chunks(messages) return self._user_message_class(content=content) def _aggregate_role(self, messages: list[UATS], role: Roles | None, latest_call_ids: list[str]) -> Sequence[UATS]: if role == Roles.tool: return self._aggregate_tool_messages(messages, latest_call_ids) elif role == Roles.assistant: return [self._aggregate_assistant_messages(messages)] elif role == Roles.user: return [self._aggregate_user_messages(messages)] else: # System messages are ignored return self._aggregate_system_messages(messages) def _aggregate_messages(self, messages: list[UATS]) -> list[UATS]: aggregated_messages: list[UATS] = [] messages_to_aggregate: list[UATS] = [] current_role: Roles | None = None current_weight: float | None = None latest_call_ids: list[str] = [] # Collect consecutive lists of messages with the same role and weight for message in messages: new_weight = getattr(message, "weight", None) if current_role != message.role or (new_weight != current_weight): aggregated_messages.extend(self._aggregate_role(messages_to_aggregate, current_role, latest_call_ids)) if current_role == Roles.assistant: assistant_message = aggregated_messages[-1] assert isinstance(aggregated_messages[-1], AssistantMessage) if assistant_message.tool_calls is not None: for tool_call in assistant_message.tool_calls: latest_call_ids.append(tool_call.id) elif current_role == Roles.tool: # reordering only depends on the latest aggregated assistant message latest_call_ids.clear() messages_to_aggregate.clear() current_weight = new_weight current_role = message.role messages_to_aggregate.append(message) # Add the last set of messages aggregated_messages.extend(self._aggregate_role(messages_to_aggregate, current_role, latest_call_ids)) # If the first message is not a user message, or we didn't aggregate # anything (all system messages) for example, add an empty user message if len(aggregated_messages) == 0 or ( not self._system_prompt_in_begin and aggregated_messages[0].role != Roles.user ): aggregated_messages.insert(0, self._user_message_class(content="")) return aggregated_messages def from_chat_completion_request(self, request: ChatCompletionRequest[UATS]) -> InstructRequestType: r"""Converts a chat completion request to an instruct request. Args: request: The chat completion request to convert. Returns: The converted instruct request. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage >>> request = ChatCompletionRequest( ... messages=[ ... UserMessage(content="Hello"), ... AssistantMessage(content="Hi"), ... ], ... ) >>> normalizer = InstructRequestNormalizer.normalizer() >>> instruct_request = normalizer.from_chat_completion_request(request) """ system_prompt = self._aggregate_system_prompts(request.messages) messages = self._aggregate_messages(request.messages) settings = self.build_settings(request) if settings != ModelSettings.none(): raise InvalidRequestException(f"Model settings are not supported for {type(self).__name__}, got {settings}") return self._instruct_request_class( messages=messages, system_prompt=system_prompt, available_tools=request.tools, continue_final_message=request.continue_final_message, settings=settings, ) class InstructRequestNormalizerV7(InstructRequestNormalizer): r"""Normalizer for the v7 tokenizer. Examples: >>> normalizer = InstructRequestNormalizerV7.normalizer() """ _system_prompt_in_begin: bool = True _allow_tool_call_and_content: bool = True @staticmethod def normalizer(model_settings_builder: ModelSettingsBuilder | None = None) -> "InstructRequestNormalizerV7": r"""Returns a normalizer for the default instruct request. Args: model_settings_builder: Must be None for this normalizer version. Returns: A normalizer for the V7 instruct request. Raises: ValueError: If model_settings_builder is not None. Examples: >>> normalizer = InstructRequestNormalizerV7.normalizer() """ if model_settings_builder is not None: raise ValueError( f"model_settings_builder must be None for InstructRequestNormalizerV7, got {model_settings_builder}" ) return InstructRequestNormalizerV7( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest[UATS, Tool], None ) def _aggregate_tool_messages(self, messages: list[UATS], latest_call_ids: list[str]) -> list[ToolMessageType]: """Normalize tool messages without JSON normalization. V7+ normalizers skip JSON content normalization for tool messages (chunk-type validation is handled by the validator). """ tool_messages: list[ToolMessageType] = [] for message in messages: assert isinstance(message, self._tool_message_class), "Expected tool message" content = self._aggregate_content_chunks([message]) assert isinstance(content, str), ( f"Unexpected content chunk types in tool message: {[type(c).__name__ for c in content]}" ) tool_messages.append( self._tool_message_class(content=content, tool_call_id=message.tool_call_id, name=message.name) ) return tool_messages def _aggregate_system_messages(self, messages: list[UATS]) -> list[SystemMessageType]: aggregated: list[SystemMessageType] = [] for message in messages: if isinstance(message, self._system_message_class): content = self._aggregate_content_chunks([message]) aggregated.append(self._system_message_class(content=content)) return aggregated def _aggregate_role(self, messages: list[UATS], role: Roles | None, latest_call_ids: list[str]) -> Sequence[UATS]: if role == Roles.tool: return self._aggregate_tool_messages(messages, latest_call_ids) elif role == Roles.assistant: return [self._aggregate_assistant_messages(messages)] elif role == Roles.user: return [self._aggregate_user_messages(messages)] elif role == Roles.system: return self._aggregate_system_messages(messages) else: assert role is None and len(messages) == 0 return [] def _aggregate_system_prompts(self, messages: list[UATS]) -> str | None: raise NotImplementedError("We should not aggregate system prompts") def from_chat_completion_request(self, request: ChatCompletionRequest[UATS]) -> InstructRequestType: # type: ignore[type-var, misc] r"""Converts a chat completion request to an instruct request. Args: request: The chat completion request to convert. Returns: The converted instruct request. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage >>> request = ChatCompletionRequest( ... messages=[ ... UserMessage(content="Hello"), ... AssistantMessage(content="Hi"), ... ], ... ) >>> normalizer = InstructRequestNormalizerV7.normalizer() >>> instruct_request = normalizer.from_chat_completion_request(request) """ messages = self._aggregate_messages(request.messages) settings = self.build_settings(request) if settings != ModelSettings.none(): raise InvalidRequestException(f"Model settings are not supported for {type(self).__name__}, got {settings}") return self._instruct_request_class( # type: ignore[no-any-return] messages=messages, system_prompt=None, available_tools=request.tools, continue_final_message=request.continue_final_message, settings=settings, ) class InstructRequestNormalizerV13(InstructRequestNormalizerV7): r"""Normalizer for the v13 tokenizer. It reorders tool messages based on the tool call order. Examples: >>> normalizer = InstructRequestNormalizerV13.normalizer() """ @staticmethod def normalizer(model_settings_builder: ModelSettingsBuilder | None = None) -> "InstructRequestNormalizerV13": r"""Returns a normalizer for the default instruct request. Args: model_settings_builder: Must be None for this normalizer version. Returns: A normalizer for the V13 instruct request. Raises: ValueError: If model_settings_builder is not None. """ if model_settings_builder is not None: raise ValueError( f"model_settings_builder must be None for InstructRequestNormalizerV13, got {model_settings_builder}" ) return InstructRequestNormalizerV13( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest[UATS, Tool], None ) @staticmethod def _inplace_sort_tool_messages(tool_messages: list[ToolMessageType], latest_call_ids: list[str]) -> None: id_to_tool_call_idx = {call_id: idx for idx, call_id in enumerate(latest_call_ids)} id_to_tool_result_idx = {message.tool_call_id: idx for idx, message in enumerate(tool_messages)} # First order by tool call idx and then by tool result idx tool_messages.sort( key=lambda msg: ( id_to_tool_call_idx.get(msg.tool_call_id or "null", float("inf")), id_to_tool_result_idx[msg.tool_call_id], ), ) def _aggregate_tool_messages(self, messages: list[UATS], latest_call_ids: list[str]) -> list[ToolMessageType]: tool_messages: list[ToolMessageType] = super()._aggregate_tool_messages(messages, latest_call_ids) self._inplace_sort_tool_messages(tool_messages=tool_messages, latest_call_ids=latest_call_ids) return tool_messages class InstructRequestNormalizerV15(InstructRequestNormalizerV13): r"""Normalizer for the v15 tokenizer. It reorders tool messages based on the tool call order and builds model settings. Examples: >>> normalizer = InstructRequestNormalizerV15.normalizer() """ _chunk_join_str: str = "" def _aggregate_tool_messages(self, messages: list[UATS], latest_call_ids: list[str]) -> list[ToolMessageType]: r"""V15 keeps all aggregated tool content (validation handled by the validator).""" tool_messages: list[ToolMessageType] = [] for message in messages: assert isinstance(message, self._tool_message_class), "Expected tool message" content = self._aggregate_content_chunks([message]) tool_messages.append( self._tool_message_class(content=content, tool_call_id=message.tool_call_id, name=message.name) ) self._inplace_sort_tool_messages(tool_messages=tool_messages, latest_call_ids=latest_call_ids) return tool_messages @staticmethod def normalizer(model_settings_builder: ModelSettingsBuilder | None = None) -> "InstructRequestNormalizerV15": r"""Returns a normalizer for the V15 instruct request. Args: model_settings_builder: The builder for model settings. Returns: A normalizer for the V15 instruct request. """ return InstructRequestNormalizerV15( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, model_settings_builder, ) def build_settings(self, request: ChatCompletionRequest) -> ModelSettings: r"""Build model settings using the configured model settings builder. Args: request: The chat completion request. Returns: The built model settings. Raises: InvalidRequestException: If no model settings builder is configured. """ if self._model_settings_builder is None: raise InvalidRequestException(f"model_settings_builder must not be None for {type(self).__name__}") return self._model_settings_builder.build_settings(request) def from_chat_completion_request(self, request: ChatCompletionRequest[UATS]) -> InstructRequestType: # type: ignore[type-var, misc] r"""Converts a chat completion request to an instruct request. Args: request: The chat completion request to convert. Returns: The converted instruct request. """ messages = self._aggregate_messages(request.messages) settings = self.build_settings(request) return self._instruct_request_class( # type: ignore[no-any-return] messages=messages, system_prompt=None, available_tools=request.tools, continue_final_message=request.continue_final_message, settings=settings, ) def normalizer_for_tokenizer_version( version: TokenizerVersion, model_settings_builder: ModelSettingsBuilder | None = None ) -> InstructRequestNormalizer: r"""Deprecated in favor to `get_normalizer`, will be removed in 1.12.0.""" warnings.warn( "`normalizer_for_tokenizer_version` is deprecated and will be removed in 1.12.0. " "Please call `get_normalizer` instead.", FutureWarning, ) return get_normalizer(version=version, model_settings_builder=model_settings_builder) def get_normalizer( version: TokenizerVersion, model_settings_builder: ModelSettingsBuilder | None = None ) -> InstructRequestNormalizer: r"""Gets the appropriate normalizer for the given tokenizer version. Args: version: The tokenizer version to get the normalizer for. model_settings_builder: The builder for model settings, or None if unsupported. Returns: The appropriate normalizer for the given tokenizer version. Examples: >>> normalizer = get_normalizer(TokenizerVersion.v1) """ match version: case TokenizerVersion.v1 | TokenizerVersion.v2 | TokenizerVersion.v3: normalizer_cls = InstructRequestNormalizer case TokenizerVersion.v7: normalizer_cls = InstructRequestNormalizerV7 case TokenizerVersion.v11 | TokenizerVersion.v13: normalizer_cls = InstructRequestNormalizerV13 case TokenizerVersion.v15: normalizer_cls = InstructRequestNormalizerV15 case _: assert_never(version) return normalizer_cls.normalizer(model_settings_builder=model_settings_builder) python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/request.py000066400000000000000000000261351523035502500274600ustar00rootroot00000000000000from enum import Enum from typing import Any, Generic from pydantic import Field from mistral_common.base import MistralBase from mistral_common.protocol.base import BaseCompletionRequest from mistral_common.protocol.instruct.converters import ( convert_openai_messages, convert_openai_tools, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, ChatMessageType, ReasoningFieldFormat, ) from mistral_common.protocol.instruct.tool_calls import Tool, ToolChoice, ToolChoiceEnum, ToolType class ResponseFormats(str, Enum): r"""Enum of the different formats of an instruct response. Attributes: text: The response is a plain text. json: The response is a JSON object. Examples: >>> response_format = ResponseFormats.text """ text = "text" json = "json_object" class ReasoningEffort(str, Enum): r"""Controls how much reasoning effort the model should apply. Attributes: none: No additional reasoning effort. high: High reasoning effort for complex tasks. """ none = "none" high = "high" class ModelSettings(MistralBase): r"""Model configuration settings for instruct requests. This class encapsulates various model configuration options that can be passed to the model during inference. Currently supports reasoning effort configuration, but can be extended with additional settings in the future. Attributes: reasoning_effort: Controls how much reasoning effort the model should apply when generating responses. Supported for tokenizer >= v15 and not supported for earlier versions. """ reasoning_effort: ReasoningEffort | None = None @staticmethod def none() -> "ModelSettings": r"""Create a ModelSettings instance with default (None) values.""" return ModelSettings() class ResponseFormat(MistralBase): r"""The format of the response. Attributes: type: The type of the response. Examples: >>> response_format = ResponseFormat(type=ResponseFormats.text) """ type: ResponseFormats = ResponseFormats.text class ChatCompletionRequest(BaseCompletionRequest, Generic[ChatMessageType]): r"""Request for a chat completion. Attributes: model: The model to use for the chat completion. messages: The messages to use for the chat completion. response_format: The format of the response. tools: The tools to use for the chat completion. tool_choice: The tool choice to use for the chat completion. truncate_for_context_length: Whether to truncate the messages for the context length. continue_final_message: Whether to continue the final message. reasoning_effort: Controls how much reasoning effort the model should apply. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage >>> from mistral_common.protocol.instruct.tool_calls import ToolTypes, Function >>> request = ChatCompletionRequest( ... messages=[ ... UserMessage(content="Hello!"), ... AssistantMessage(content="Hi! How can I help you?"), ... ], ... response_format=ResponseFormat(type=ResponseFormats.text), ... tools=[Tool(type=ToolTypes.function, function=Function(name="get_weather", parameters={}))], ... tool_choice=ToolChoiceEnum.auto, ... truncate_for_context_length=True, ... ) """ model: str | None = None messages: list[ChatMessageType] response_format: ResponseFormat = Field(default_factory=ResponseFormat) tools: list[Tool] | None = None tool_choice: ToolChoice = ToolChoiceEnum.auto truncate_for_context_length: bool = False continue_final_message: bool = False reasoning_effort: ReasoningEffort | None = None def to_openai( self, reasoning_field_format: ReasoningFieldFormat | None = None, **kwargs: Any, ) -> dict[str, Any]: r"""Convert the request messages and tools into the OpenAI format. Args: reasoning_field_format: Format for converting thinking chunks in assistant messages. See `AssistantMessage.to_openai` for details. kwargs: Additional parameters to be added to the request. Returns: The request in the OpenAI format. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage >>> from mistral_common.protocol.instruct.tool_calls import Tool, Function >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], temperature=0.15) >>> request.to_openai(stream=True) {'temperature': 0.15, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tool_choice': 'auto', 'stream': True} >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], tools=[ ... Tool(function=Function( ... name="get_current_weather", ... description="Get the current weather in a given location", ... parameters={ ... "type": "object", ... "properties": { ... "location": { ... "type": "string", ... "description": "The city and state, e.g. San Francisco, CA", ... }, ... "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, ... }, ... "required": ["location"], ... }, ... ), ... )]) >>> request.to_openai() {'temperature': 0.7, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}, 'strict': False}}], 'tool_choice': 'auto'} """ # noqa: E501 # Handle messages and tools separately. openai_request: dict[str, Any] = self.model_dump( exclude={"messages", "tools", "truncate_for_context_length", "tool_choice"}, exclude_none=True ) # Rename random_seed to seed. seed = openai_request.pop("random_seed", None) if seed is not None: openai_request["seed"] = seed if self.truncate_for_context_length: raise NotImplementedError("Truncating for context length is not implemented for OpenAI requests.") for kwarg in kwargs: if kwarg in openai_request: raise ValueError(f"Duplicate keyword argument: {kwarg}") elif kwarg in ChatCompletionRequest.model_fields: raise ValueError(f"Keyword argument {kwarg} is already set in the request.") openai_messages = [] for message in self.messages: if isinstance(message, AssistantMessage): openai_messages.append(message.to_openai(reasoning_field_format=reasoning_field_format)) else: openai_messages.append(message.to_openai()) openai_request["messages"] = openai_messages if self.tools is not None: openai_request["tools"] = [tool.to_openai() for tool in self.tools] openai_tool_choice: str | dict[str, Any] match self.tool_choice: case ToolChoiceEnum.auto | ToolChoiceEnum.none: openai_tool_choice = self.tool_choice case ToolChoiceEnum.required | ToolChoiceEnum.any: openai_tool_choice = ToolChoiceEnum.required.value case _: openai_tool_choice = self.tool_choice.model_dump() openai_request["tool_choice"] = openai_tool_choice openai_request.update(kwargs) return openai_request @classmethod def from_openai( cls, messages: list[dict[str, str | list[dict[str, str | dict[str, Any]]]]], tools: list[dict[str, Any]] | None = None, continue_final_message: bool = False, **kwargs: Any, ) -> "ChatCompletionRequest": r"""Create a chat completion request from the OpenAI format. Args: messages: The messages in the OpenAI format. tools: The tools in the OpenAI format. continue_final_message: Whether to continue the final message. **kwargs: Additional keyword arguments to pass to the constructor. These should be the same as the fields of the request class or the OpenAI API equivalent. Returns: The chat completion request. """ if "seed" in kwargs and "random_seed" in kwargs: raise ValueError("Cannot specify both `seed` and `random_seed`.") random_seed = kwargs.pop("seed", None) if random_seed is None: random_seed = kwargs.pop("random_seed", None) filtered_kwargs = cls._filter_cls_fields(kwargs) converted_messages: list[ChatMessage] = convert_openai_messages(messages) converted_tools = convert_openai_tools(tools) if tools is not None else None return cls( messages=converted_messages, # type: ignore[arg-type] tools=converted_tools, random_seed=random_seed, continue_final_message=continue_final_message, **filtered_kwargs, ) class InstructRequest(MistralBase, Generic[ChatMessageType, ToolType]): r"""A valid Instruct request to be tokenized. Note: This class is intended for internal use only. External users should use `ChatCompletionRequest` to build requests and to convert to and from the OpenAI format. Attributes: messages: The history of the conversation. system_prompt: The system prompt to be used for the conversation. available_tools: The tools available to the assistant. truncate_at_max_tokens: The maximum number of tokens to truncate the conversation at. continue_final_message: Whether to continue the final message. settings: Model configuration settings for the request. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, SystemMessage >>> request = InstructRequest( ... messages=[UserMessage(content="Hello, how are you?")], system_prompt="You are a helpful assistant." ... ) """ messages: list[ChatMessageType] system_prompt: str | None = None available_tools: list[ToolType] | None = None truncate_at_max_tokens: int | None = None continue_final_message: bool = False settings: ModelSettings = Field(default_factory=ModelSettings.none) python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/tool_calls.py000066400000000000000000000152231523035502500301170ustar00rootroot00000000000000import json from enum import Enum from typing import Any, TypeAlias, TypeVar from pydantic import field_validator from mistral_common.base import MistralBase class FunctionName(MistralBase): r"""A function identified by name. Attributes: name: The name of the function. Examples: >>> function_name = FunctionName(name="get_current_weather") """ name: str class Function(FunctionName): r"""Function definition for tools. Attributes: name: The name of the function. description: A description of what the function does. parameters: The parameters the functions accepts, described as a JSON Schema object. strict: Whether to enforce strict function calling. Examples: >>> function = Function( ... name="get_current_weather", ... description="Get the current weather in a given location", ... parameters={ ... "type": "object", ... "properties": { ... "location": { ... "type": "string", ... "description": "The city and state, e.g. San Francisco, CA", ... }, ... "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, ... }, ... "required": ["location"], ... }, ... ) """ description: str = "" parameters: dict[str, Any] strict: bool = False @classmethod def from_openai(cls, openai_function: dict[str, Any]) -> "Function": r"""Convert an OpenAI function definition to a Mistral `Function`. Filters out unknown fields and defaults missing `parameters` and `description`. Args: openai_function: The OpenAI function definition. Returns: The Mistral function. """ filtered = cls._filter_cls_fields(openai_function) if filtered.get("parameters") is None: filtered["parameters"] = {} if filtered.get("description") is None: filtered["description"] = "" return cls.model_validate(filtered) class ToolTypes(str, Enum): r"""Enum of tool types. Attributes: function: A function tool. Examples: >>> tool_type = ToolTypes.function """ function = "function" class ToolChoiceEnum(str, Enum): r"""Enum of tool choice types. Attributes: auto: Automatically choose the tool. none: Do not use any tools. any: Deprecated in favor of `required`. required: Require the model to call at least one tool. Examples: >>> tool_choice = ToolChoiceEnum.auto >>> isinstance(tool_choice, ToolChoice) True """ auto = "auto" none = "none" any = "any" # deprecated in favor of `required` required = "required" class NamedToolChoice(MistralBase): r"""Forces the model to call a specific function. Attributes: type: The type of the tool. function: The function the model should call. Examples: >>> named = NamedToolChoice(function=FunctionName(name="get_weather")) >>> isinstance(named, ToolChoice) True """ type: ToolTypes = ToolTypes.function function: FunctionName ToolChoice: TypeAlias = ToolChoiceEnum | NamedToolChoice r"""Tool choice are either a `ToolChoiceEnum` or a `NamedToolChoice`.""" class Tool(MistralBase): r"""Tool definition. Attributes: type: The type of the tool. function: The function definition. Examples: >>> tool = Tool( ... function=Function( ... name="get_current_weather", ... description="Get the current weather in a given location", ... parameters={ ... "type": "object", ... "properties": { ... "location": { ... "type": "string", ... "description": "The city and state, e.g. San Francisco, CA", ... }, ... "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, ... }, ... "required": ["location"], ... }, ... ), ... ) """ type: ToolTypes = ToolTypes.function function: Function def to_openai(self) -> dict[str, Any]: return self.model_dump() @classmethod def from_openai(cls, openai_tool: dict[str, Any]) -> "Tool": r"""Convert an OpenAI tool definition to a Mistral `Tool`. Delegates function parsing to `Function.from_openai`. Args: openai_tool: The OpenAI tool definition. Returns: The Mistral tool. """ openai_tool = openai_tool.copy() if function := openai_tool.get("function"): openai_tool["function"] = Function.from_openai(function) return cls.model_validate_ignore_extra(openai_tool) class FunctionCall(MistralBase): r"""Function call. Attributes: name: The name of the function to call. arguments: The arguments to pass to the function. Examples: >>> function_call = FunctionCall( ... name="get_current_weather", ... arguments={"location": "San Francisco, CA", "unit": "celsius"}, ... ) """ name: str arguments: str @field_validator("arguments", mode="before") def validate_arguments(cls, v: str | dict[str, Any] | None) -> str: r"""Convert arguments to a JSON string if they are a dictionary. Args: v: The arguments to validate. Returns: The arguments as a JSON string. """ if isinstance(v, dict): return json.dumps(v) elif v is None: return "{}" return v class ToolCall(MistralBase): r"""Tool call. Attributes: id: The ID of the tool call. Required for V3+ tokenization type: The type of the tool call. function: The function call. Examples: >>> tool_call = ToolCall( ... id="call_abc123", ... function=FunctionCall( ... name="get_current_weather", ... arguments={"location": "San Francisco, CA", "unit": "celsius"}, ... ), ... ) """ id: str = "null" type: ToolTypes = ToolTypes.function function: FunctionCall def to_openai(self) -> dict[str, Any]: return self.model_dump() @classmethod def from_openai(cls, tool_call: dict[str, Any]) -> "ToolCall": return cls.model_validate_ignore_extra(tool_call) ToolType = TypeVar("ToolType", bound=Tool) python-mistral-common-1.11.7/src/mistral_common/protocol/instruct/validator.py000066400000000000000000000637761523035502500277710ustar00rootroot00000000000000import re from collections.abc import Sequence from enum import Enum from typing import Generic from jsonschema import Draft7Validator, SchemaError from typing_extensions import assert_never from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidFunctionCallException, InvalidMessageStructureException, InvalidRequestException, InvalidSystemPromptException, InvalidToolException, InvalidToolMessageException, InvalidToolSchemaException, InvalidUserMessageException, MistralCommonException, ) from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, ContentChunk, ImageChunk, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( UATS, AssistantMessage, AssistantMessageType, FinetuningAssistantMessage, Roles, SystemMessage, SystemMessageType, ToolMessageType, UserMessage, UserMessageType, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import ( Function, FunctionCall, Tool, ToolCall, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion def _validate_content_chunk_types( content: str | Sequence[ContentChunk] | None, allowed: tuple[type[ContentChunk], ...], role: str, exception_cls: type[MistralCommonException], ) -> None: r"""Raise if any chunk in a list content is not an instance of `allowed`. String or None content is always accepted (covered elsewhere). """ if not isinstance(content, list): return invalid = sorted({type(chunk).__name__ for chunk in content if not isinstance(chunk, allowed)}) if invalid: raise exception_cls(f"Unexpected content chunk types in {role} message: {invalid}") class ValidationMode(str, Enum): r"""Enum for the validation mode. Attributes: serving: The serving mode. finetuning: The finetuning mode. test: The test mode. Examples: >>> mode = ValidationMode.serving """ serving = "serving" finetuning = "finetuning" test = "test" class MistralRequestValidator(Generic[UserMessageType, AssistantMessageType, ToolMessageType, SystemMessageType]): r"""Validator for Mistral requests. This class validates the structure and content of Mistral requests. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage >>> validator = MistralRequestValidator() >>> messages = [UserMessage(content="Hello how are you ?")] >>> validator.validate_messages(messages, False) """ _allow_tool_call_and_content: bool = False def __init__(self, mode: ValidationMode = ValidationMode.test): r"""Initializes the `MistralRequestValidator`. Args: mode: The validation mode. Defaults to ValidationMode.test. """ self._mode = mode @property def mode(self) -> ValidationMode: return self._mode def validate_messages(self, messages: list[UATS], continue_final_message: bool) -> None: r"""Validates the list of messages. Args: messages: The list of messages to validate. continue_final_message: Whether to continue the final message. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage >>> validator = MistralRequestValidator() >>> messages = [AssistantMessage(content="Hi"), UserMessage(content="Hello")] >>> validator.validate_messages(messages, False) """ self._validate_message_list_structure(messages, continue_final_message=continue_final_message) self._validate_message_list_content(messages) def validate_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest[UATS]: r"""Validates the request Args: request: The request to validate. Returns: The validated request. Examples: >>> from mistral_common.protocol.instruct.messages import UserMessage >>> validator = MistralRequestValidator() >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello")]) >>> validated_request = validator.validate_request(request) """ if self._mode == ValidationMode.serving: if request.model is None: raise InvalidRequestException("Model name parameter is required for serving mode") # Validate the messages self.validate_messages(request.messages, continue_final_message=request.continue_final_message) # Validate the tools self._validate_tools(request.tools or []) self._validate_model_settings(request) return request def _validate_function(self, function: Function) -> None: """ Checks: - That the function schema is valid """ try: Draft7Validator.check_schema(function.parameters) except SchemaError as e: raise InvalidToolSchemaException(f"Invalid tool schema: {e.message}") if not re.match(r"^[a-zA-Z0-9_-]{1,64}$", function.name): raise InvalidToolException( f"Function name was {function.name} but must be a-z, A-Z, 0-9, " "or contain underscores and dashes, with a maximum length of 64." ) def _validate_tools(self, tools: list[Tool]) -> None: """ Checks: - That the tool schemas are valid """ for tool in tools: self._validate_function(tool.function) def _validate_user_message(self, message: UserMessageType) -> None: self._validate_user_content_chunks(message.content) def _validate_user_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v1/v2 user messages accept text content only (image >= v3, audio >= v7).""" _validate_content_chunk_types(content, (TextChunk,), "user", InvalidUserMessageException) def _validate_assistant_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""Pre-v11 assistant messages accept text content only.""" _validate_content_chunk_types(content, (TextChunk,), "assistant", InvalidAssistantMessageException) def _validate_system_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v1-v3 system messages accept text content only.""" _validate_content_chunk_types(content, (TextChunk,), "system", InvalidSystemPromptException) def _validate_tool_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""Pre-v15 tool messages accept text content only.""" _validate_content_chunk_types(content, (TextChunk,), "tool", InvalidToolMessageException) def _validate_tool_message(self, message: ToolMessageType) -> None: """ Checks: - The tool name is valid """ self._validate_tool_content_chunks(message.content) if message.name is not None: if not re.match(r"^[a-zA-Z0-9_-]{1,64}$", message.name): raise InvalidToolMessageException( f"Function name was {message.name} but must be a-z, A-Z, 0-9, " "or contain underscores and dashes, with a maximum length of 64." ) def _validate_system_message(self, message: SystemMessageType) -> None: """ Checks: - That the system prompt has content """ if message.content is None: raise InvalidSystemPromptException("System prompt must have content") self._validate_system_content_chunks(message.content) def _validate_function_call(self, function_call: FunctionCall) -> None: """ Checks: - That the function call has a valid name """ if not re.match(r"^[a-zA-Z0-9_-]{1,64}$", function_call.name): raise InvalidFunctionCallException( f"Function name was {function_call.name} but must be a-z, A-Z, 0-9, " "or contain underscores and dashes, with a maximum length of 64." ) def _validate_tool_call(self, tool_call: ToolCall, is_last_message: bool) -> None: """ Checks: - That the tool call has a valid function """ self._validate_function_call(tool_call.function) def _validate_assistant_message(self, message: AssistantMessageType, is_last_message: bool = False) -> None: """ Checks: - That the assistant message has either text or tool_calls, but not both - That the tool calls are valid """ self._validate_assistant_content_chunks(message.content) # Validate that the message has either text or tool_calls # but not both and not neither. if (not self._allow_tool_call_and_content) and (bool(message.content) == bool(message.tool_calls)): raise InvalidAssistantMessageException( "Assistant message must have either content or tool_calls, but not both." ) # If we have tool calls, validate them if message.tool_calls is not None: # Validate that the tool calls are valid for tool_call in message.tool_calls: self._validate_tool_call(tool_call, is_last_message=is_last_message) if self._mode == ValidationMode.finetuning and isinstance(message, FinetuningAssistantMessage): if message.weight is not None and message.weight not in [0, 1]: raise InvalidAssistantMessageException("Assistant message weight must be either 0 or 1") if message.prefix: if not is_last_message: raise InvalidAssistantMessageException("Assistant message with prefix True must be last message") # note : we already validate that assistant message has content 3 lines up. def _validate_tool_calls_followed_by_tool_messages(self, messages: list[UATS]) -> None: """ Checks: - That the number of tool calls and tool messages are the same - That the tool calls are followed by tool messages """ prev_role = None expected_tool_messages = 0 for message in messages: if prev_role is None: prev_role = message.role continue if message.role == Roles.tool: expected_tool_messages -= 1 elif message.role == Roles.assistant: # if we have an assistant message and we have not received all the function calls # we need to raise an exception if expected_tool_messages != 0: raise InvalidMessageStructureException("Not the same number of function calls and responses") if message.tool_calls is not None: # Validate that the number of function calls and responses are the same expected_tool_messages = len(message.tool_calls) prev_role = message.role if expected_tool_messages != 0 and self._mode == ValidationMode.serving: raise InvalidMessageStructureException("Not the same number of function calls and responses") elif expected_tool_messages < 0 and self._mode == ValidationMode.finetuning: raise InvalidMessageStructureException("More tool responses than tool calls") def _validate_message_order(self, messages: list[UATS]) -> None: """ Validates the order of the messages, for example user -> assistant -> user -> assistant -> ... """ previous_role = None for message in messages: current_role = message.role if previous_role is not None: if previous_role == Roles.system: expected_roles = {Roles.user, Roles.assistant, Roles.system} elif previous_role == Roles.user: expected_roles = {Roles.assistant, Roles.system, Roles.user} elif previous_role == Roles.assistant: expected_roles = {Roles.assistant, Roles.user, Roles.tool} elif previous_role == Roles.tool: expected_roles = {Roles.assistant, Roles.tool, Roles.user} else: assert_never(previous_role) if current_role not in expected_roles: raise InvalidMessageStructureException( f"Unexpected role '{current_role}' after role '{previous_role}'" ) previous_role = current_role def _validate_last_message(self, message: UATS, continue_final_message: bool) -> None: # The last message must be a user or tool message in serving mode or an assistant message in finetuning mode last_message_role = message.role if self._mode == ValidationMode.finetuning: if last_message_role != Roles.assistant: raise InvalidMessageStructureException( f"Expected last role Assistant for finetuning but got {last_message_role}" ) if continue_final_message: raise InvalidMessageStructureException("Cannot continue final message in finetuning mode") else: bad_assistant = isinstance(message, AssistantMessage) and not message.prefix and not continue_final_message bad_role = message.role not in {Roles.user, Roles.tool} if bad_assistant and bad_role: raise InvalidMessageStructureException( f"Expected last role User or Tool (or Assistant with prefix or continue_final_message set to True) " f"for serving but got {last_message_role}" ) elif continue_final_message and (last_message_role != Roles.assistant or message.prefix): raise InvalidMessageStructureException( f"Expected last role Assistant with prefix False for serving with continue_final_message set to " f"True but got {last_message_role}" ) def _validate_message_list_structure(self, messages: list[UATS], continue_final_message: bool) -> None: """ Validates the structure of the list of messages For example the messages must be in the correct order of user/assistant/tool """ if len(messages) == 0: raise InvalidMessageStructureException("Conversation must have at least one message") # If we have one message it must be a user or a system message if len(messages) == 1: if messages[0].role not in {Roles.user, Roles.system}: raise InvalidMessageStructureException("Conversation must start with a user message or system message") # Always check the last message if in fine-tuning mode if self._mode == ValidationMode.finetuning or len(messages) > 1: self._validate_last_message(messages[-1], continue_final_message=continue_final_message) self._validate_message_order(messages) self._validate_tool_calls_followed_by_tool_messages(messages) def _validate_message_list_content(self, messages: list[UATS]) -> None: """ Validates the content of the messages """ for idx, message in enumerate(messages): if message.role == Roles.user: self._validate_user_message(message) elif message.role == Roles.assistant: self._validate_assistant_message(message, is_last_message=idx == len(messages) - 1) elif message.role == Roles.tool: self._validate_tool_message(message) elif message.role == Roles.system: self._validate_system_message(message) else: raise InvalidRequestException(f"Unsupported message type {type(message)}") def _validate_model_settings(self, request: ChatCompletionRequest) -> None: if (reasoning_effort := request.reasoning_effort) is not None: raise InvalidRequestException(f"{reasoning_effort=} is not supported for this model") class MistralRequestValidatorV3(MistralRequestValidator): r"""Validator for v3 Mistral requests. This validator adds additional validation for tool call IDs. Examples: >>> validator = MistralRequestValidatorV3() """ def _validate_user_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v3 user messages accept text and image chunks (audio >= v7).""" _validate_content_chunk_types( content, (TextChunk, ImageChunk, ImageURLChunk), "user", InvalidUserMessageException ) def _validate_tool_message(self, message: ToolMessageType) -> None: """ Checks: - The tool name is valid - Tool call id is valid """ super()._validate_tool_message(message) if message.tool_call_id is None: raise InvalidRequestException("Tool call id has to be defined.") if not re.match(r"^[a-zA-Z0-9]{9}$", message.tool_call_id): raise InvalidToolMessageException( f"Tool call id was {message.tool_call_id} but must be a-z, A-Z, 0-9, with a length of 9." ) def _validate_tool_call(self, tool_call: ToolCall, is_last_message: bool) -> None: """ Validate that the tool call has a valid ID """ if tool_call.id != "null": if not re.match(r"^[a-zA-Z0-9]{9}$", tool_call.id): raise InvalidFunctionCallException( f"Tool call id was {tool_call.id} but must be a-z, A-Z, 0-9, with a length of 9." ) if self._mode == ValidationMode.finetuning and not is_last_message and tool_call.id == "null": err_message = "Tool call id of assistant message that is not last has to be defined in finetuning mode." raise InvalidFunctionCallException(err_message) if self._mode == ValidationMode.serving and tool_call.id == "null": raise InvalidFunctionCallException("Tool call id has to be defined in serving mode.") self._validate_function_call(tool_call.function) def _validate_last_message(self, message: UATS, continue_final_message: bool) -> None: super()._validate_last_message(message, continue_final_message) if self._mode == ValidationMode.finetuning: # in finetuning mode it has to be an assistant message # as checked by parent `_validate_last_message` if message.tool_calls is not None: for tool_call in message.tool_calls: self._validate_tool_call(tool_call, is_last_message=True) class MistralRequestValidatorV5(MistralRequestValidatorV3): r"""Validator for v5 Mistral requests. This validator allows for both tool calls and content in the assistant message. Note: For requests containing audio, this validator ensures that no system prompt is present. Examples: >>> validator = MistralRequestValidatorV5() """ _allow_tool_call_and_content: bool = True def _validate_user_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v7+ user messages accept text, image and audio chunks.""" _validate_content_chunk_types( content, (TextChunk, ImageChunk, ImageURLChunk, AudioChunk, AudioURLChunk), "user", InvalidUserMessageException, ) def _validate_system_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v7+ system messages accept text, audio and thinking chunks.""" _validate_content_chunk_types( content, (TextChunk, AudioChunk, ThinkChunk), "system", InvalidSystemPromptException ) def _validate_system_prompt_and_audio(self, messages: list[UATS]) -> None: r"""Validates that system prompts and audio chunks are not used together in v5.""" def _is_system(message: UATS) -> bool: return isinstance(message, SystemMessage) def _has_audio(message: UATS) -> bool: return ( isinstance(message, UserMessage) and isinstance(message.content, list) and any(isinstance(chunk, (AudioChunk, AudioURLChunk)) for chunk in message.content) ) has_sp = any(_is_system(message=message) for message in messages) has_sp_and_audio = has_sp and any(_has_audio(message=message) for message in messages) if has_sp_and_audio: sp_indexes = [i for i, message in enumerate(messages) if _is_system(message=message)] audio_indexes = [i for i, message in enumerate(messages) if _has_audio(message=message)] raise ValueError( f"Found system messages at indexes {sp_indexes} and audio chunks in messages at indexes {audio_indexes}" ". This is not allowed prior to the tokenizer version 13." ) def _validate_message_list_structure(self, messages: list[UATS], continue_final_message: bool) -> None: super()._validate_message_list_structure(messages=messages, continue_final_message=continue_final_message) self._validate_system_prompt_and_audio(messages) class MistralRequestValidatorV13(MistralRequestValidatorV5): r"""Validator for v13 Mistral requests. This validator extends v5 functionality by: - Adding stricter tool call ID validation: they should be distinct and called. - Allowing system prompts with audio chunks """ def _validate_tool_calls_followed_by_tool_messages(self, messages: list[UATS]) -> None: """ Checks: - That the number and ids of tool calls and tool messages are the same - That the tool calls are followed by tool messages - That tool calls have distinct ids for a given assistant message """ prev_role = None expected_tool_ids: set[str] = set() observed_tool_ids: set[str] = set() for message in messages: if prev_role is None: prev_role = message.role continue if message.role == Roles.tool: tool_call_id = message.tool_call_id if tool_call_id in observed_tool_ids: raise InvalidMessageStructureException(f"Duplicate tool call id {tool_call_id} in tool results") if tool_call_id not in expected_tool_ids: raise InvalidMessageStructureException(f"Unexpected tool call id {tool_call_id} in tool results") observed_tool_ids.add(tool_call_id) elif message.role == Roles.assistant: # if we have an assistant message and we have not received all the function calls # we need to raise an exception if len(expected_tool_ids) != len(observed_tool_ids): raise InvalidMessageStructureException("Not the same number of function calls and responses") expected_tool_ids.clear() observed_tool_ids.clear() if message.tool_calls is not None: # Validate that the number of function calls and ids are the same for tool_call in message.tool_calls: if tool_call.id in expected_tool_ids: raise InvalidMessageStructureException( f"Duplicate tool call id {tool_call.id} in assistant message" ) expected_tool_ids.add(tool_call.id) prev_role = message.role if len(expected_tool_ids) != len(observed_tool_ids) and self._mode == ValidationMode.serving: raise InvalidMessageStructureException("Not the same number of function calls and responses") elif len(expected_tool_ids) < len(observed_tool_ids) and self._mode == ValidationMode.finetuning: raise InvalidMessageStructureException("More tool responses than tool calls") def _validate_assistant_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v11+ assistant messages accept text and thinking chunks.""" _validate_content_chunk_types(content, (TextChunk, ThinkChunk), "assistant", InvalidAssistantMessageException) def _validate_system_prompt_and_audio(self, messages: list[UATS]) -> None: r"""Allows system prompts and audio chunks to coexist in v13.""" return class MistralRequestValidatorV15(MistralRequestValidatorV13): def _validate_system_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v15 system messages accept text and audio but reject thinking chunks.""" _validate_content_chunk_types(content, (TextChunk, AudioChunk), "system", InvalidSystemPromptException) def _validate_tool_content_chunks(self, content: str | Sequence[ContentChunk] | None) -> None: r"""v15 tool messages accept all content chunk types except thinking (rejected at ToolMessage model level).""" return def _validate_model_settings(self, request: ChatCompletionRequest) -> None: pass def get_validator(version: TokenizerVersion, mode: ValidationMode) -> MistralRequestValidator: r"""Get the appropriate validator for a given tokenizer version and validation mode. Args: version: The tokenizer version. mode: The validation mode. Returns: The validator instance. """ validator: MistralRequestValidator match version: case TokenizerVersion.v1 | TokenizerVersion.v2: validator = MistralRequestValidator(mode=mode) case TokenizerVersion.v3: validator = MistralRequestValidatorV3(mode=mode) case TokenizerVersion.v7: validator = MistralRequestValidatorV5(mode=mode) case TokenizerVersion.v11 | TokenizerVersion.v13: validator = MistralRequestValidatorV13(mode=mode) case TokenizerVersion.v15: validator = MistralRequestValidatorV15(mode=mode) case _: assert_never(f"Unsupported tokenizer version: {version}") return validator python-mistral-common-1.11.7/src/mistral_common/protocol/speech/000077500000000000000000000000001523035502500250035ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/speech/__init__.py000066400000000000000000000000001523035502500271020ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/speech/request.py000066400000000000000000000072571523035502500270600ustar00rootroot00000000000000import io from typing import Any from mistral_common.imports import assert_soundfile_installed, is_soundfile_installed from mistral_common.protocol.base import BaseCompletionRequest from mistral_common.tokens.tokenizers.audio import Audio if is_soundfile_installed(): import soundfile as sf class SpeechRequest(BaseCompletionRequest): r"""Request for text-to-speech synthesis. Supports both preset voices and voice cloning via reference audio. Attributes: id: Optional unique identifier for the speech request. model: Optional model identifier for the speech synthesis. input: Text input to be converted to speech. voice: Optional preset voice identifier (e.g., 'Neutral Male', 'Neutral Female') to use for speech synthesis. ref_audio: Optional reference audio for voice cloning, provided as a base64-encoded string or raw bytes. Takes precedence over voice when both are provided. """ id: str | None = None model: str | None = None input: str voice: str | None = None ref_audio: str | bytes | None = None def to_openai(self, **kwargs: Any) -> dict[str, Any]: r"""Convert this SpeechRequest to an OpenAI-compatible request dictionary. Args: **kwargs: Additional key-value pairs to include in the request dictionary. Returns: An OpenAI-compatible request dictionary. """ openai_request: dict[str, Any] = self.model_dump(exclude={"ref_audio"}) assert_soundfile_installed() if self.ref_audio is not None: if isinstance(self.ref_audio, bytes): buffer = io.BytesIO(self.ref_audio) else: audio = Audio.from_base64(self.ref_audio) buffer = io.BytesIO() sf.write(buffer, audio.audio_array, audio.sampling_rate, format=audio.format) buffer.seek(0) openai_request["ref_audio"] = buffer openai_request["seed"] = openai_request.pop("random_seed") openai_request.update(kwargs) return openai_request @classmethod def from_openai(cls, openai_request: dict[str, Any], strict: bool = False) -> "SpeechRequest": r"""Create a SpeechRequest instance from an OpenAI-compatible request dictionary. Args: openai_request: The OpenAI request dictionary. strict: A flag indicating whether to perform strict validation of the audio data. Returns: An instance of SpeechRequest. """ seed = openai_request.get("seed") converted_dict: dict[str, Any] = {k: v for k, v in openai_request.items() if k in cls.model_fields} if (ref_audio := openai_request.get("ref_audio")) is not None: if isinstance(ref_audio, io.BytesIO): audio_bytes = ref_audio.getvalue() elif hasattr(ref_audio, "file"): audio_bytes = ref_audio.file.read() else: # Already a string (base64) or bytes audio_bytes = ref_audio if isinstance(audio_bytes, bytes): audio = Audio.from_bytes(audio_bytes, strict=strict) assert audio.format is not None, f"Audio format must be set, got {audio.format=}" converted_dict["ref_audio"] = audio.to_base64(audio.format) else: converted_dict["ref_audio"] = audio_bytes # OAI uses "voice" as a string or object with "id"; normalize to string voice = openai_request.get("voice") if isinstance(voice, dict): converted_dict["voice"] = voice["id"] converted_dict["random_seed"] = seed return cls(**converted_dict) python-mistral-common-1.11.7/src/mistral_common/protocol/transcription/000077500000000000000000000000001523035502500264335ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/transcription/__init__.py000066400000000000000000000000001523035502500305320ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/protocol/transcription/request.py000066400000000000000000000143311523035502500304770ustar00rootroot00000000000000import io from enum import Enum from typing import Any from pydantic import Field, model_validator from pydantic_extra_types.language_code import LanguageAlpha2 from mistral_common.base import MistralBase from mistral_common.imports import assert_soundfile_installed, is_soundfile_installed from mistral_common.protocol.base import BaseCompletionRequest from mistral_common.protocol.instruct.chunk import _detect_audio_format from mistral_common.tokens.tokenizers.audio import Audio if is_soundfile_installed(): import soundfile as sf class StreamingMode(str, Enum): DISABLED = "disabled" ONLINE = "online" OFFLINE = "offline" class TranscriptionRequest(BaseCompletionRequest): r"""A class representing a request for audio transcription. This class handles the conversion of audio data into a format suitable for transcription using the OpenAI API. It includes methods to convert the request to and from the OpenAI format. Attributes: id: An optional identifier for the transcription request. model: The model to be used for transcription. audio: The audio data to be transcribed. language: The language of the input audio in ISO-639-1 format (optional). strict_audio_validation: A flag indicating whether to perform strict validation of the audio data. """ id: str | None = None model: str | None = None audio: str | bytes language: LanguageAlpha2 | None = Field( None, description=( "The language of the input audio. Supplying the input language " "in ISO-639-1 format will improve language adherence." ), ) strict_audio_validation: bool = True streaming: StreamingMode = Field( default=StreamingMode.DISABLED, description=( "Whether to enable streaming for the transcription request. Online " "streaming means the audio is streamed to the server and the transcription is " "streamed back. Offline streaming means the audio is passed in one go to the server." ), ) target_streaming_delay_ms: int | None = Field( None, description=( "When streaming is enabled, the target streaming delay (in milli-seconds). " "This controls how much latency will the model be requested to target after " "it hears a word. Note: this is not supported by all models and model targets " "the target but may not strictly meet it." ), ) @model_validator(mode="before") @classmethod def _flatten_audio_dict(cls, values: dict[str, Any]) -> dict[str, Any]: r"""Extract audio data from a nested dict or legacy RawAudio payload.""" if not isinstance(values, dict): return values raw = values.get("audio") if isinstance(raw, MistralBase): raw = raw.model_dump() if isinstance(raw, dict) and "data" in raw: values["audio"] = raw["data"] return values def to_openai(self, exclude: tuple = (), **kwargs: Any) -> dict[str, list[dict[str, Any]]]: r"""Convert the transcription request into the OpenAI format. This method prepares the transcription request data for compatibility with the OpenAI API. It handles the conversion of audio data and additional parameters into the required format. Args: exclude: Fields to exclude from the conversion. kwargs: Additional parameters to be added to the request. Returns: The request in the OpenAI format. Raises: ImportError: If the required soundfile library is not installed. """ openai_request: dict[str, Any] = self.model_dump(exclude={"audio"}) assert_soundfile_installed() if isinstance(self.audio, bytes): buffer = io.BytesIO(self.audio) fmt = _detect_audio_format(self.audio) buffer.seek(0) else: assert isinstance(self.audio, str) audio = Audio.from_base64(self.audio) assert audio.format is not None fmt = audio.format.lower() buffer = io.BytesIO() sf.write(buffer, audio.audio_array, audio.sampling_rate, format=audio.format) # reset cursor to beginning buffer.seek(0) # OpenAI's client uses the filename extension from .name to set the Content-Type. buffer.name = f"audio.{fmt}" openai_request["file"] = buffer openai_request["seed"] = openai_request.pop("random_seed") openai_request.update(kwargs) # remove mistral-specific # TODO: revisit which fields to expose in the OpenAI format default_exclude = ("id", "max_tokens", "strict_audio_validation", "streaming") default_exclude += exclude for k in default_exclude: openai_request.pop(k, None) return openai_request @classmethod def from_openai(cls, openai_request: dict[str, Any], strict: bool = False) -> "TranscriptionRequest": r"""Create a TranscriptionRequest instance from an OpenAI request dictionary. This method converts an OpenAI request dictionary into a TranscriptionRequest instance, handling the conversion of audio data and other parameters. Args: openai_request: The OpenAI request dictionary. strict: A flag indicating whether to perform strict validation of the audio data. Returns: An instance of TranscriptionRequest. """ file = openai_request.get("file") seed = openai_request.get("seed") converted_dict = { k: v for k, v in openai_request.items() if (k in cls.model_fields and not (v is None and k in ["temperature", "top_p"])) } assert file is not None, file if isinstance(file, io.BytesIO): audio_bytes = file.getvalue() else: # for example if file is UploadFile, this should work audio_bytes = file.file.read() audio = Audio.from_bytes(audio_bytes, strict=strict) audio_str = audio.to_base64(audio.format) converted_dict["audio"] = audio_str converted_dict["random_seed"] = seed return cls(**converted_dict) python-mistral-common-1.11.7/src/mistral_common/protocol/utils.py000066400000000000000000000001531523035502500252450ustar00rootroot00000000000000import uuid def random_uuid() -> str: """Generate a random UUID.""" return str(uuid.uuid4().hex) python-mistral-common-1.11.7/src/mistral_common/py.typed000066400000000000000000000000001523035502500233600ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/tokens/000077500000000000000000000000001523035502500231765ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/tokens/__init__.py000066400000000000000000000000001523035502500252750ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/000077500000000000000000000000001523035502500253735ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/__init__.py000066400000000000000000000000001523035502500274720ustar00rootroot00000000000000python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/audio.py000066400000000000000000000731601523035502500270550ustar00rootroot00000000000000import base64 import io import logging import math import re import warnings from dataclasses import dataclass from enum import Enum from pathlib import Path from typing import TYPE_CHECKING, Any import numpy as np import requests as _requests_lib from mistral_common.deprecation import warn_once from mistral_common.imports import ( assert_soundfile_installed, assert_soxr_installed, is_soundfile_installed, is_soxr_installed, ) from mistral_common.protocol.instruct.chunk import AudioChunk, AudioURLChunk, AudioURLType if is_soxr_installed(): import soxr if is_soundfile_installed(): import soundfile as sf if TYPE_CHECKING: from mistral_common.protocol.instruct.chunk import RawAudio class AudioFormat(Enum): """Dynamic enum whose members depend on soundfile availability at runtime.""" else: if is_soundfile_installed(): AudioFormat = Enum("AudioFormat", {format_name: format_name for format_name in sf.available_formats()}) else: AudioFormat = Enum("AudioFormat", {"none": "none"}) logger = logging.getLogger(__name__) EXPECTED_FORMAT_VALUES = [v.value.lower() for v in AudioFormat.__members__.values()] class Audio: def __init__(self, audio_array: np.ndarray, sampling_rate: int, format: str) -> None: r"""Initialize an Audio instance with audio data, sampling rate, and format. Args: audio_array: The audio data as a numpy array. sampling_rate: The sampling rate of the audio in Hz. format: The format of the audio file. """ self.audio_array = audio_array self.sampling_rate = sampling_rate self.format = format self._check_valid() def __repr__(self) -> str: return ( f"Audio - sampling_rate={self.sampling_rate} Hz, " f"duration={len(self.audio_array) / self.sampling_rate:.2f}s, " f"shape={self.audio_array.shape}" ) def _check_valid(self) -> None: assert isinstance(self.audio_array, np.ndarray), type(self.audio_array) assert self.audio_array.ndim == 1, f"{self.audio_array.ndim=}" assert_soundfile_installed() assert self.format in EXPECTED_FORMAT_VALUES, f"{self.format=} not in {EXPECTED_FORMAT_VALUES=}" @property def duration(self) -> float: r"""Calculate the duration of the audio in seconds. Returns: The duration of the audio in seconds. """ duration: float = self.audio_array.shape[0] / self.sampling_rate return duration @staticmethod def from_url(url: str, strict: bool = True) -> "Audio": r"""Create an Audio instance from a URL. Args: url: The URL of the audio file. strict: Whether to strictly enforce mono audio. Returns: An instance of the Audio class. """ try: response = _requests_lib.get(url) response.raise_for_status() return Audio.from_bytes(response.content, strict=strict) except _requests_lib.RequestException as e: # Something went wrong with the request. raise ValueError(f"Failed to download audio from URL: {url}") from e except Exception as e: # Something went wrong with the audio file. raise ValueError(f"Failed to create Audio instance from URL: {url} .") from e @staticmethod def from_base64(audio_base64: str, strict: bool = True) -> "Audio": r"""Create an Audio instance from a base64 encoded string. Args: audio_base64: The base64 encoded audio data. strict: Whether to strictly enforce mono audio. Defaults to True. Returns: An instance of the Audio class. """ assert_soundfile_installed() if re.match(r"^data:audio/\w+;base64,", audio_base64): audio_base64 = audio_base64.split(",")[1] try: audio_bytes = base64.b64decode(audio_base64) except Exception as e: raise ValueError("base64 decoding failed. Please check the input string is a valid base64.") from e return Audio.from_bytes(audio_bytes, strict=strict) @staticmethod def from_file(file: str, strict: bool = True) -> "Audio": r"""Create an Audio instance from an audio file. Args: file: Path to the audio file. strict: Whether to strictly enforce mono audio. Defaults to True. Returns: An instance of the Audio class. """ assert_soundfile_installed() if isinstance(file, str) and file.startswith("file://"): file = file[7:] if not Path(file).exists(): raise FileNotFoundError(f"{file=} does not exist") with open(file, "rb") as f: audio_bytes = f.read() return Audio.from_bytes(audio_bytes, strict=strict) @staticmethod def from_bytes(audio_bytes: bytes, strict: bool = True) -> "Audio": r"""Create an Audio instance from bytes. Args: audio_bytes: The audio data as bytes. strict: Whether to strictly enforce mono audio. Defaults to True. Returns: An instance of the Audio class. """ assert_soundfile_installed() with io.BytesIO(audio_bytes) as audio_file: with sf.SoundFile(audio_file) as f: audio_array = f.read(dtype="float32") sampling_rate = f.samplerate audio_format = f.format format_enum = AudioFormat(audio_format) format = format_enum.value.lower() if audio_array.ndim != 1: if strict: raise ValueError(f"{audio_array.ndim=}") else: audio_array = audio_array.mean(axis=1) return Audio(audio_array=audio_array, sampling_rate=sampling_rate, format=format) def to_base64(self, format: str, prefix: bool = False) -> str: r"""Convert the audio data to a base64 encoded string. Args: format: The format to encode the audio in. prefix: Whether to add a data prefix to the base64 encoded string. Returns: The base64 encoded audio data. """ assert_soundfile_installed() assert format in EXPECTED_FORMAT_VALUES, f"{format=} not in {EXPECTED_FORMAT_VALUES=}" with io.BytesIO() as audio_file: sf.write(audio_file, self.audio_array, self.sampling_rate, format=format.upper()) audio_file.seek(0) base64_str = base64.b64encode(audio_file.read()).decode("utf-8") if prefix: base64_str = f"data:audio/{format.lower()};base64,{base64_str}" return base64_str @staticmethod def from_raw_audio(audio: "RawAudio") -> "Audio": r"""Create an Audio instance from a RawAudio object. Deprecated: Use `from_base64()` or `from_bytes()` instead. Will be removed in 1.13.0. Args: audio: The RawAudio object containing audio data. Returns: An instance of the Audio class. """ warn_once( "Audio.from_raw_audio", "Audio.from_raw_audio() is deprecated. " "Use Audio.from_base64() or Audio.from_bytes() instead. " "Will be removed in 1.13.0.", DeprecationWarning, stacklevel=2, ) if isinstance(audio.data, bytes): return Audio.from_bytes(audio.data) elif isinstance(audio.data, str): return Audio.from_base64(audio.data) else: raise ValueError(f"Unsupported audio data type: {type(audio.data)}") @staticmethod def from_audio_chunk(chunk: "AudioChunk") -> "Audio": r"""Create an Audio instance from an AudioChunk. Args: chunk: An AudioChunk with input_audio as str (base64) or bytes. Returns: An instance of the Audio class. """ input_audio = chunk.input_audio if isinstance(input_audio, bytes): return Audio.from_bytes(input_audio) elif isinstance(input_audio, str): return Audio.from_base64(input_audio) else: raise ValueError(f"Unsupported input_audio type: {type(input_audio)}") def resample(self, new_sampling_rate: int) -> None: r"""Resample audio data to a new sampling rate. Args: new_sampling_rate: The new sampling rate to resample the audio to. """ if self.sampling_rate == new_sampling_rate: return assert_soxr_installed() self.audio_array = soxr.resample(self.audio_array, self.sampling_rate, new_sampling_rate, quality="HQ") self.sampling_rate = new_sampling_rate # For offline streaming we're encoding the whole audio at once. # Because the model is delayed by + word_length # we must always add a buffer of max world length to the audio in the end. # For now we assume that there is no word that requires more than 10 tokens (0.8s) # which might not be the case in practice but it won't affect any eval results. OFFLINE_STREAMING_BUFFER_TOKENS = 10 def _check_mult_of(num_samples: int, mult_of: int) -> None: assert num_samples % mult_of == 0, f"{num_samples=} must be a multiple of {mult_of=}" class TranscriptionFormat(str, Enum): r"""Transcription format. Should be set by the tokenizer for correct encoding. Attributes: - INSTRUCT: The instruct format. - STREAMING: The streaming format. """ INSTRUCT = "instruct" STREAMING = "streaming" @dataclass class AudioSpectrogramConfig: r"""Configuration for generating an audio spectrogram. Attributes: num_mel_bins: Number of mel bins, typically 80 or 128. hop_length: Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients, typically 160. window_size: Window size of the Fourier transform, typically 400. """ # Number of mel bins, typically 80 or 128 num_mel_bins: int # Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients, typically 160 hop_length: int # Window size of the Fourier transform, typically 400 window_size: int def __post_init__(self) -> None: assert self.num_mel_bins > 0, self.num_mel_bins assert self.hop_length > 0, self.hop_length assert self.window_size > 0, self.window_size @dataclass class AudioConfig: r"""Configuration for audio processing. Attributes: sampling_rate: Sampling rate of the audio. frame_rate: Number of frames per second accepted by the tokenizer model. encoding_config: Configuration for audio spectrogram. chunk_length_s: Whether to pad an audio into multiples of chunk_length_s seconds (optional). voice_num_audio_tokens: Mapping from speaker voice name to number of audio tokens for that speaker's reference audio (optional, only for TTS). """ sampling_rate: int # number of frames per second accepted by the tokenizer model. frame_rate: float encoding_config: AudioSpectrogramConfig # Whether to pad an audio into multiples of chunk_length_s seconds chunk_length_s: float | None = None # If we're in streaming or non-streaming transcription_format: TranscriptionFormat = TranscriptionFormat.INSTRUCT # delay between the audio stream and text stream transcription_delay_ms: float | None = None # only relevant for streaming streaming_look_ahead_ms: float | None = None streaming_look_back_ms: float | None = None streaming_n_left_pad_tokens: int | None = None voice_num_audio_tokens: dict[str, int] | None = None def __post_init__(self) -> None: assert self.frame_rate > 0, self.frame_rate assert self.sampling_rate > 0, self.sampling_rate assert self.sampling_rate >= self.frame_rate, ( f"sampling_rate must be >= frame_rate so that each token spans at least one sample, " f"got sampling_rate={self.sampling_rate} and frame_rate={self.frame_rate}" ) if self.chunk_length_s is not None: assert self.chunk_length_s > 0, self.chunk_length_s assert self.chunk_frames > 0, ( f"chunk_length_s and sampling_rate must both be > 0, got {self.chunk_length_s} and {self.sampling_rate}" ) if not self.is_streaming: # make sure streaming params are only set for streaming use case assert self.transcription_delay_ms is None, f"{self.transcription_delay_ms=} must be None." assert self.streaming_look_ahead_ms is None, f"{self.streaming_look_ahead_ms=} must be None." assert self.streaming_look_back_ms is None, f"{self.streaming_look_back_ms=} must be None." assert self.streaming_n_left_pad_tokens is None, f"{self.streaming_n_left_pad_tokens=} must be None." if self.is_streaming: assert self.transcription_delay_ms is not None, f"{self.transcription_delay_ms=} must be set." assert self.streaming_look_ahead_ms is not None, f"{self.streaming_look_ahead_ms=} must be set." assert self.streaming_look_back_ms is not None, f"{self.streaming_look_back_ms=} must be set." assert self.streaming_n_left_pad_tokens is not None, f"{self.streaming_n_left_pad_tokens=} must be set." frame_duration_ms = 1000.0 / self.frame_rate assert self.transcription_delay_ms > 0, "{self.transcription_delay_ms=} must be > 0" assert self.transcription_delay_ms % frame_duration_ms == 0, ( f"{self.transcription_delay_ms=} must be a multiple of {frame_duration_ms=}" ) assert self.chunk_length_s is None, f"{self.chunk_length_s=} cannot be set in streaming." @property def is_streaming(self) -> bool: return self.transcription_format == TranscriptionFormat.STREAMING def num_audio_tokens(self, audio_len: int) -> int: if audio_len % self.encoding_config.hop_length != 0: audio_len = math.ceil(audio_len / self.encoding_config.hop_length - 1) else: audio_len = audio_len // self.encoding_config.hop_length return math.ceil(audio_len / self.audio_length_per_tok) @property def num_delay_tokens(self) -> int: # TODO(Patrick) - delete in 1.13.0 # only used in vLLM in voxtral_realtime.py warnings.warn("Use get_num_delay_tokens instead of num_delay_tokens", DeprecationWarning) return self.get_num_delay_tokens() def get_num_delay_tokens(self, transcription_delay_ms: float | None = None) -> int: assert self.is_streaming, f"Can't call get_num_delay_tokens if {self.is_streaming=}." if transcription_delay_ms is None: transcription_delay_ms = self.transcription_delay_ms # streaming pad tokens assert transcription_delay_ms is not None, f"Can't call get_num_delay_tokens if {transcription_delay_ms=}." return self.num_audio_tokens(self.delay_len(transcription_delay_ms)) def delay_len(self, transcription_delay_ms: float) -> int: return int(transcription_delay_ms / 1000.0 * self.sampling_rate) @property def frame_duration_ms(self) -> float: return 1000.0 / self.frame_rate @property def chunk_frames(self) -> int: r"""Calculate the number of frames per chunk.""" assert self.chunk_length_s is not None, f"Can't call chunk_frames if {self.chunk_length_s=}." return int(self.chunk_length_s * self.sampling_rate) @property def raw_audio_length_per_tok(self) -> int: return int(self.sampling_rate // self.frame_rate) @property def audio_length_per_tok(self) -> int: r"""Calculate the length of audio per token.""" downsample_factor = float(self.raw_audio_length_per_tok) downsample_factor /= self.encoding_config.hop_length return int(downsample_factor) def n_right_pad_tokens(self, transcription_delay_ms: float | None = None) -> int: assert self.is_streaming, f"Can't call n_right_pad_tokens if {self.is_streaming=}." # we need to pad on the right to ensure the models transcribes # - the induced delay on the prefill step (num_delay_tokens) # - the BOS token (1) # - a heuristic that defines a max token length for a single word # (OFFLINE_STREAMING_BUFFER_TOKENS) return (self.get_num_delay_tokens(transcription_delay_ms) + 1) + OFFLINE_STREAMING_BUFFER_TOKENS @property def n_left_pad_tokens(self) -> int: assert self.is_streaming, f"Can't call n_left_pad_tokens if {self.is_streaming=}." # We also pad on the left as this has shown to improve performance # simply by giving the model "more compute", we also add # - the same induced delay # - OFFLINE_STREAMING_BUFFER_TOKENS assert self.streaming_n_left_pad_tokens is not None, f"{self.streaming_n_left_pad_tokens=} must be set." return self.streaming_n_left_pad_tokens @dataclass class AudioEncoding: r"""Encapsulates the tokens and audio data for an audio chunk. Attributes: tokens: Text tokens corresponding to this audio chunk. audio: Original audio waveform data, or None when using a preset voice (no reference audio to forward to the model). """ tokens: list[int] audio: Audio | None @dataclass class SpecialAudioIDs: r"""Special text tokens corresponding to audio token sequence. Attributes: audio: Token representing audio. begin_audio: Token representing the beginning of audio. streaming_pad: Token representing streaming pad of audio. Only relevant for steaming models. text_to_audio: Token representing intent to convert text to audio. audio_to_text: Token representing intent to convert audio to text. """ audio: int | None begin_audio: int | None streaming_pad: int | None text_to_audio: int | None audio_to_text: int | None class AudioEncoder: r"""Encodes audio chunks into a format suitable for further processing. Attributes: audio_config: Configuration for audio processing. encoding_config: Configuration for audio spectrogram. special_ids: Special tokens for audio encoding. """ def __init__(self, audio_config: AudioConfig, special_ids: SpecialAudioIDs) -> None: self.audio_config = audio_config self.encoding_config = audio_config.encoding_config self.special_ids = special_ids def pad( self, audio_array: np.ndarray, sampling_rate: int, transcription_delay_ms: float | None = None, **kwargs: Any, ) -> np.ndarray: r"""Pad the audio array to the desired length. Args: audio_array: Audio data as a numpy array. sampling_rate: Sampling rate of the audio. transcription_delay_ms (optional): Delay in milliseconds for transcription. Returns: Padded audio array. """ # TODO(Patrick) - remove **kwargs as it's just there to swallow deprecated # keyword args from voxtral_realtime in vLLM. It was # relevant for the release. Remove in mistral_common version 1.13.0 if self.audio_config.chunk_length_s: next_multiple_of_chunk_frames = self.next_multiple_of_chunk_frames(audio_array.shape[-1], sampling_rate) audio_array = np.pad(audio_array, (0, next_multiple_of_chunk_frames - audio_array.shape[-1])) elif self.audio_config.is_streaming: left_pad, right_pad = self._get_streaming_pad(audio_array.shape[-1], transcription_delay_ms) # we pad both left & right as this leads to better performance audio_array = np.pad(audio_array, (left_pad, right_pad)) elif ( isinstance(self.encoding_config, AudioSpectrogramConfig) and audio_array.shape[-1] < self.encoding_config.window_size ): # minimum length for audios is at least one spectrogram frame audio_array = np.pad(audio_array, (0, self.encoding_config.window_size - audio_array.shape[-1])) return audio_array def get_padding_audio(self, transcription_delay_ms: float | None = None) -> tuple[Audio, Audio]: r"""Gets left and right padding for realtime audio models. Args: transcription_delay_ms (optional): Delay in milliseconds for transcription. Returns: Tuple of left and right padding for realtime audio models. """ left_pad, right_pad = self._get_streaming_pad(0, transcription_delay_ms) left_pad_audio = Audio( audio_array=np.zeros(left_pad, dtype=np.float32), sampling_rate=self.audio_config.sampling_rate, format="wav", ) right_pad_audio = Audio( audio_array=np.zeros(right_pad, dtype=np.float32), sampling_rate=self.audio_config.sampling_rate, format="wav", ) return left_pad_audio, right_pad_audio def _get_streaming_pad(self, num_samples: int, transcription_delay_ms: float | None = None) -> tuple[int, int]: # let's make sure the audio is a multiple of one "frame" token mult_of = self.audio_config.raw_audio_length_per_tok right_pad = int((mult_of - (num_samples % mult_of)) % mult_of) _extra_right_pad_tokens = self.audio_config.n_right_pad_tokens(transcription_delay_ms) _extra_right_pad_samples = int(mult_of * _extra_right_pad_tokens) _check_mult_of(_extra_right_pad_samples, mult_of) right_pad += _extra_right_pad_samples # We also pad on the left as this has shown to improve performance # simply by giving the model "more compute", we also add # - the same induced delay # - OFFLINE_STREAMING_BUFFER_TOKENS _extra_left_pad_tokens = self.audio_config.n_left_pad_tokens left_pad = int(mult_of * _extra_left_pad_tokens) _check_mult_of(left_pad, mult_of) return left_pad, right_pad def next_multiple_of_chunk_frames(self, audio_array_len: int, sampling_rate: int) -> int: r"""Calculate the next multiple of chunk frames. Args: audio_array_len: Length of the audio array. sampling_rate: Sampling rate of the audio. Returns: The next multiple of chunk frames. """ assert sampling_rate == self.audio_config.sampling_rate, ( f"Expected {sampling_rate=} to be {self.audio_config.sampling_rate=}" ) assert self.audio_config.chunk_length_s is not None, ( f"Can't call next_multiple_of_chunk_frames if {self.audio_config.chunk_length_s=}." ) return math.ceil(audio_array_len / self.audio_config.chunk_frames) * self.audio_config.chunk_frames def encode_streaming_tokens(self, transcription_delay_ms: float | None = None) -> list[int]: r"""Encode the streaming tokens given a transcription delay.""" assert isinstance(self.audio_config.encoding_config, AudioSpectrogramConfig), ( f"Audio encoder must be spectrogram encoder, got {self.audio_config.encoding_config=}" ) assert self.audio_config.transcription_delay_ms is not None # streaming pad tokens consist of silence we pad on left + delay tokens stream_pad_prefix_len = self.audio_config.n_left_pad_tokens + self.audio_config.get_num_delay_tokens( transcription_delay_ms ) tokens = [self.streaming_pad] * stream_pad_prefix_len return tokens def _encode_audio_tokens(self, signal_length: int) -> list[int]: # for spectrogram-based models, the waveform is downsampled by the hop_length when computing the log-mel if signal_length % self.encoding_config.hop_length != 0: signal_length = math.ceil(signal_length / self.encoding_config.hop_length - 1) else: signal_length = signal_length // self.encoding_config.hop_length num_audio_tokens = math.ceil(signal_length / self.audio_config.audio_length_per_tok) tokens = [self.begin_audio_token] + [self.audio_token] * num_audio_tokens return tokens def encode_audio(self, audio: Audio, transcription_delay_ms: float | None = None) -> AudioEncoding: r"""Encode an audio optionally with transcription delay.""" audio.resample(self.audio_config.sampling_rate) audio.audio_array = self.pad(audio.audio_array, self.audio_config.sampling_rate, transcription_delay_ms) if self.audio_config.transcription_format == TranscriptionFormat.STREAMING: tokens = self.encode_streaming_tokens(transcription_delay_ms) else: tokens = self._encode_audio_tokens(audio.audio_array.shape[0]) return AudioEncoding( tokens=tokens, audio=audio, ) def _encode_audio_tokens_for_speech_request(self, num_audio_tokens: int) -> list[int]: r"""Build the token sequence for a speech request's audio segment. Args: num_audio_tokens: Number of audio placeholder tokens to emit. Returns: List of token IDs: [BEGIN_AUDIO, AUDIO * num_audio_tokens]. """ tokens = [] tokens.append(self.begin_audio_token) tokens.extend([self.audio_token] * num_audio_tokens) return tokens def _get_num_audio_token_for_speech_request(self, audio_length: int) -> int: r"""Compute the number of audio tokens needed for a given audio length. Args: audio_length: Number of audio samples. Returns: Number of audio tokens (includes +1 for END_OUTPUT_AUDIO). """ return ( math.ceil((audio_length / self.audio_config.sampling_rate) * self.audio_config.frame_rate) + 1 ) # +1 for eoa (END_OUTPUT_AUDIO) def encode_audio_for_speech_request(self, audio: Audio | None, voice: str | None) -> AudioEncoding: r"""Encode audio or voice preset into an AudioEncoding for speech synthesis. Either `audio` (reference audio for voice cloning) or `voice` (preset name) must be provided. When `audio` is given it takes precedence. Args: audio: Reference audio waveform, or None to use a voice preset. voice: Preset voice name (e.g. 'Neutral Male', 'Neutral Female'), or None when using ref audio. Returns: AudioEncoding containing the token sequence and optional audio data. """ assert audio is not None or voice is not None, ( f"Either audio or voice must be defined to encode audio, got {audio=} and {voice=}" ) if audio is not None: audio.resample(self.audio_config.sampling_rate) num_audio_tokens = self._get_num_audio_token_for_speech_request(len(audio.audio_array)) else: assert self.audio_config.voice_num_audio_tokens is not None, ( "voice_num_audio_tokens must be set in audio config to use voice-based speech requests" ) assert voice is not None and voice in self.audio_config.voice_num_audio_tokens, ( f"Unknown voice {voice!r}, expected one of {list(self.audio_config.voice_num_audio_tokens)}" ) num_audio_tokens = self.audio_config.voice_num_audio_tokens[voice] tokens = self._encode_audio_tokens_for_speech_request(num_audio_tokens) return AudioEncoding( tokens=tokens, audio=audio, ) def _encode_audio_chunk(self, content: AudioChunk) -> AudioEncoding: audio = Audio.from_audio_chunk(content) return self.encode_audio(audio) def _encode_audio_url_chunk(self, content: AudioURLChunk) -> AudioEncoding: url_type = content.get_url_type() if url_type in {AudioURLType.file, AudioURLType.file_uri}: audio = Audio.from_file(content.url) elif url_type == AudioURLType.url: audio = Audio.from_url(content.url) else: audio = Audio.from_base64(content.url) return self.encode_audio(audio) def __call__(self, content: AudioChunk | AudioURLChunk) -> AudioEncoding: r"""Call the encoder on an audio chunk or URL chunk. Args: content: Audio or URL chunk to encode. Returns: Encoded audio data and tokens. """ if isinstance(content, AudioURLChunk): return self._encode_audio_url_chunk(content) elif isinstance(content, AudioChunk): return self._encode_audio_chunk(content) else: raise ValueError(f"Unsupported content type: {type(content)}") @property def audio_token(self) -> int: r"""Get the audio token.""" assert self.special_ids.audio is not None, f"{self.special_ids.audio=} must be set." return self.special_ids.audio @property def begin_audio_token(self) -> int: r"""Get the begin audio token.""" assert self.special_ids.begin_audio is not None, f"{self.special_ids.begin_audio=} must be set." return self.special_ids.begin_audio @property def streaming_pad(self) -> int: r"""Get the streaming pad token.""" assert self.special_ids.streaming_pad is not None, f"{self.special_ids.streaming_pad=} must be set." return self.special_ids.streaming_pad @property def text_to_audio_token(self) -> int: r"""Get the text_to_audio token.""" assert self.special_ids.text_to_audio is not None, f"{self.special_ids.text_to_audio=} must be set." return self.special_ids.text_to_audio @property def audio_to_text_token(self) -> int: r"""Get the audio_to_text token.""" assert self.special_ids.audio_to_text is not None, f"{self.special_ids.audio_to_text=} must be set." return self.special_ids.audio_to_text python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/base.py000066400000000000000000000345421523035502500266670ustar00rootroot00000000000000import warnings from abc import ABC, abstractmethod from enum import Enum from pathlib import Path from typing import Any, Generic, TypeVar import numpy as np from pydantic import ConfigDict, Field from mistral_common.base import MistralBase from mistral_common.protocol.fim.request import FIMRequest from mistral_common.protocol.instruct.chunk import ContentChunk from mistral_common.protocol.instruct.messages import ( AssistantMessageType, UserMessage, ) from mistral_common.protocol.instruct.request import InstructRequest from mistral_common.protocol.instruct.tool_calls import Tool from mistral_common.protocol.speech.request import SpeechRequest from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio, AudioEncoder from mistral_common.tokens.tokenizers.image import ImageEncoder from mistral_common.tokens.tokenizers.model_settings_builder import ModelSettingsBuilder class UserMessagePosition(str, Enum): """Where to encode available tools""" first = "first" last = "last" class SpecialTokens(str, Enum): r"""Enum of special tokens used in the tokenizer. Attributes: unk: The unknown token. bos: The beginning of string token. eos: The end of string token. begin_inst: The beginning of instruction token. end_inst: The end of instruction token. begin_tools: The beginning of tools token. end_tools: The end of tools token. begin_tool_results: The beginning of tool results token. end_tool_results: The end of tool results token. tool_calls: The tool calls token. img: The image token. pad: The pad token. img_break: The image break token. img_end: The image end token. prefix: The prefix token for FIM. middle: The middle token for FIM. suffix: The suffix token for FIM. begin_system: The beginning of system prompt token. end_system: The end of system prompt token. begin_tool_content: The beginning of tool content token. args: The args token. call_id: The call id token. audio: The audio token. begin_audio: The beginning of audio token. transcribe: The transcribe token. begin_think: The beginning of think token. end_think: The end of think token. streaming_pad: The streaming pad token. streaming_word: The streaming word token. text_to_audio: The text to audio token. audio_to_text: The audio to text token. Examples: >>> unk = SpecialTokens.unk """ unk = "" bos = "" eos = "" begin_inst = "[INST]" end_inst = "[/INST]" begin_tools = "[AVAILABLE_TOOLS]" end_tools = "[/AVAILABLE_TOOLS]" begin_tool_results = "[TOOL_RESULTS]" end_tool_results = "[/TOOL_RESULTS]" tool_calls = "[TOOL_CALLS]" img = "[IMG]" pad = "" img_break = "[IMG_BREAK]" img_end = "[IMG_END]" prefix = "[PREFIX]" middle = "[MIDDLE]" suffix = "[SUFFIX]" begin_system = "[SYSTEM_PROMPT]" end_system = "[/SYSTEM_PROMPT]" begin_tool_content = "[TOOL_CONTENT]" args = "[ARGS]" call_id = "[CALL_ID]" audio = "[AUDIO]" begin_audio = "[BEGIN_AUDIO]" transcribe = "[TRANSCRIBE]" begin_think = "[THINK]" end_think = "[/THINK]" streaming_pad = "[STREAMING_PAD]" streaming_word = "[STREAMING_WORD]" text_to_audio = "[NEXT_AUDIO_TEXT]" audio_to_text = "[REPEAT_AUDIO_TEXT]" begin_model_settings = "[MODEL_SETTINGS]" end_model_settings = "[/MODEL_SETTINGS]" class SpecialTokenPolicy(str, Enum): r"""What to do with special tokens when encoding/decoding. Attributes: IGNORE: Ignore special tokens. KEEP: Keep special tokens. RAISE: Raise an error if special tokens are found. """ IGNORE = "ignore" KEEP = "keep" RAISE = "raise" @classmethod def _missing_(cls, value: Any) -> Any: # Backward compatibility of int values. match value: case 0: return SpecialTokenPolicy.IGNORE case 1: return SpecialTokenPolicy.KEEP case 2: return SpecialTokenPolicy.RAISE return super()._missing_(value) class TokenizerVersion(str, Enum): r"""Enum of tokenizer versions. Allow to distinguish between different versions of the tokenizer and maintain backward compatibility. Attributes: v1: The first version of the tokenizer. v2: The second version of the tokenizer that includes special control tokens [INST], [\INST]. v3: The third version of the tokenizer that includes improved function calling. v7: The seventh version of the tokenizer that includes improved system prompt and function calling. v11: The eleventh version of the tokenizer that includes improved function calling. v13: The thirteenth version of the tokenizer that includes no call id tokenization and better prompt caching. Examples: >>> version = TokenizerVersion.v1 """ @property def _version_num(self) -> int: warnings.warn( "`TokenizerVersion._version_num` is deprecated in favor to `TokenizerVersion.version_num` and will " "be removed in 1.13.0." ) return self.version_num @property def version_num(self) -> int: return int(self.value[1:]) @property def supports_model_settings(self) -> bool: return self >= TokenizerVersion.v15 def __lt__(self, other: "str | TokenizerVersion") -> bool: if isinstance(other, str): other = TokenizerVersion(other) return self.version_num < other.version_num def __le__(self, other: "str | TokenizerVersion") -> bool: if isinstance(other, str): other = TokenizerVersion(other) return self.version_num <= other.version_num def __gt__(self, other: "str | TokenizerVersion") -> bool: if isinstance(other, str): other = TokenizerVersion(other) return self.version_num > other.version_num def __ge__(self, other: "str | TokenizerVersion") -> bool: if isinstance(other, str): other = TokenizerVersion(other) return self.version_num >= other.version_num v1 = "v1" # vocab_size = 32000 v2 = "v2" # vocab_size = 32768 with special control tokens [INST], [\INST] v3 = "v3" # vocab_size = 32768 (spm) OR 131072 (tekken) with improved function calling v7 = "v7" # vocab_size = 32768 (spm) or 131072 (tekken) with improved system prompt and function calling v11 = "v11" # 131072 (tekken) with improved function calling v13 = "v13" # 131072 (tekken) with no call id and better prompt caching v15 = "v15" # 131072 (tekken) with model settings class Tokenized(MistralBase): r"""A tokenized [`InstructRequest`][mistral_common.protocol.instruct.request.InstructRequest]. Attributes: tokens: The token ids. text: The text representation of the tokens. prefix_ids: The prefix ids for FIM. images: The loaded images associated with the tokens. Examples: >>> tokenized = Tokenized(tokens=[1, 2, 3], text="Hello world", prefix_ids=[1], images=[]) """ model_config = ConfigDict(arbitrary_types_allowed=True) tokens: list[int] text: str | None = None prefix_ids: list[int] | None = None images: list[np.ndarray] = Field(default_factory=list) audios: list[Audio] = Field(default_factory=list) class Tokenizer(ABC): @property @abstractmethod def n_words(self) -> int: r"""Vocabulary size of the tokenizer.""" @property @abstractmethod def special_ids(self) -> set[int]: r"""Ids of the special tokens.""" @property @abstractmethod def num_special_tokens(self) -> int: r"""The number of special tokens of the tokenizer.""" @property @abstractmethod def model_settings_builder(self) -> ModelSettingsBuilder | None: r"""The model settings builder, or None if unsupported by this version.""" @abstractmethod def vocab(self) -> list[str]: r"""All tokens in the vocabulary as strings.""" @abstractmethod def id_to_piece(self, token_id: int) -> str: r"""Convert a token id to the token str.""" @property @abstractmethod def bos_id(self) -> int: r"""id of the Beginning of String token.""" @property @abstractmethod def eos_id(self) -> int: r"""id of the End of String token.""" @property @abstractmethod def pad_id(self) -> int: r"""id of the Pad token.""" @property @abstractmethod def unk_id(self) -> int: r"""id of the Unk token.""" @abstractmethod def encode(self, s: str, bos: bool, eos: bool) -> list[int]: """Convert a string to a list of token ids.""" @abstractmethod def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE) -> str: r"""Decode the token ids to a string. Args: tokens: The token ids to decode. special_token_policy: The policy to use for special tokens. Returns: The decoded string. """ @abstractmethod def get_special_token(self, s: str) -> int: r"""Get the id of a control token.""" @abstractmethod def is_special(self, token: int | np.integer | str) -> bool: r"""Check if token id or token str is a special token.""" @property @abstractmethod def version(self) -> TokenizerVersion: r"""Get the version of the tokenizer.""" @abstractmethod def _to_string(self, tokens: list[int]) -> str: ... @property @abstractmethod def file_path(self) -> Path: r"""The file path of the tokenizer.""" ... InstructRequestType = TypeVar("InstructRequestType", bound=InstructRequest) FIMRequestType = TypeVar("FIMRequestType", bound=FIMRequest) TokenizedType = TypeVar("TokenizedType", bound=Tokenized) class InstructTokenizer(Generic[InstructRequestType, FIMRequestType, TokenizedType, AssistantMessageType]): r"""Base class for instruct tokenizers. Attributes: tokenizer: The tokenizer to use. image_encoder: The image encoder to use if any. audio_encoder: The audio encoder to use if any. """ tokenizer: Tokenizer image_encoder: ImageEncoder | None audio_encoder: AudioEncoder | None @property def version(self) -> TokenizerVersion: r"""The version of the tokenizer.""" return self.tokenizer.version def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None, audio_encoder: AudioEncoder | None ) -> None: r"""Initialize the instruct tokenizer. Args: tokenizer: The tokenizer to use. image_encoder: The image encoder to use if any. audio_encoder: The audio encoder to use if any. """ @abstractmethod def encode_instruct(self, request: InstructRequestType) -> TokenizedType: r"""Instruct request to Tokenized object Args: request: The instruct request to encode. Returns: The tokenized instruct request. """ @abstractmethod def encode_transcription(self, request: TranscriptionRequest) -> TokenizedType: r""" Encodes an audio transcription request into a tokenized format. This method processes a transcription request containing audio data, encodes the user message, and returns the tokenized output. Args: request: The transcription request object containing the audio data to be encoded. Returns: Tokenized: The tokenized representation of the audio data, including processed audio and tokens """ @abstractmethod def encode_speech_request(self, request: SpeechRequest) -> TokenizedType: r"""Encodes a speech synthesis request into a tokenized format. This method processes a speech request containing text input and optional reference audio or voice preset, and returns the tokenized output. Args: request: The speech request object containing the text and voice/audio data. Returns: Tokenized: The tokenized representation of the speech request. """ @abstractmethod def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy) -> str: r"""Convert token ids to string Args: tokens: The token ids to decode. special_token_policy: The policy to use for special tokens. Returns: The decoded string. """ @abstractmethod def encode_fim(self, request: FIMRequestType) -> TokenizedType: r"""FIM request to Tokenized object Args: request: The FIM request to encode. Returns: The tokenized FIM request. """ @abstractmethod def encode_user_message( self, message: UserMessage, available_tools: list[Tool] | None, is_last: bool, is_first: bool, system_prompt: str | None = None, force_img_first: bool = False, ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user message. Args: message: The user message to encode. available_tools: The available tools. is_last: Whether the message is the last one. is_first: Whether the message is the first one. system_prompt: The system prompt. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and images. """ ... @abstractmethod def encode_user_content( self, content: str | list[ContentChunk], is_last: bool, system_prompt: str | None = None, force_img_first: bool = False, ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user content. Args: content: The user content to encode. is_last: Whether the content is the last one. system_prompt: The system prompt. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and images. """ ... @abstractmethod def _to_string(self, tokens: list[int]) -> str: ... python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/image.py000066400000000000000000000143771523035502500270430ustar00rootroot00000000000000import base64 import logging from dataclasses import dataclass from enum import Enum from io import BytesIO import numpy as np from PIL import Image from mistral_common.image import SerializableImage, download_image from mistral_common.imports import assert_opencv_installed, is_opencv_installed from mistral_common.protocol.instruct.chunk import ImageChunk, ImageURLChunk logger = logging.getLogger(__name__) if is_opencv_installed(): import cv2 @dataclass class ImageEncoding: """A tokenized image. Attributes: tokens: The token ids. image: The image as a numpy array. Examples: >>> import numpy as np >>> image_encoding = ImageEncoding(tokens=[1, 2, 3], image=np.array([[0., 0.5, 1.]])) """ tokens: list[int] image: np.ndarray @dataclass class SpecialImageIDs: """Special image tokens ids. Attributes: img: The image token id. img_break: The image break token id. img_end: The image end token id. Examples: >>> special_image_ids = SpecialImageIDs(img=1, img_break=2, img_end=3) """ img: int img_break: int img_end: int def image_from_chunk(chunk: ImageURLChunk | ImageChunk) -> SerializableImage: r"""Get a serializable image from a chunk. Args: chunk: The chunk to get the image from. Returns: The image as a PIL Image object. """ if isinstance(chunk, ImageChunk): return chunk.image if chunk.get_url().startswith("data:image"): data = chunk.get_url().split(",")[1] image_data = base64.b64decode(data) return Image.open(BytesIO(image_data)) if chunk.get_url().startswith("file"): return Image.open(open(chunk.get_url().replace("file://", ""), "rb")) if chunk.get_url().startswith("http"): return download_image(chunk.get_url()) raise RuntimeError(f"Unsupported image url scheme {chunk.get_url()}") DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) # RGB DATASET_STD = (0.26862954, 0.26130258, 0.27577711) # RGB # only relevant for spm class MultiModalVersion(str, Enum): r"""Version of the image tokenizer.""" m1 = "m1" @property def config(self) -> "ImageConfig": if self.name == "m1": return ImageConfig(16, 1024) raise NotImplementedError(f"{self.name}") @dataclass class ImageConfig: r"""Configuration for the image tokenizers.""" image_patch_size: int max_image_size: int spatial_merge_size: int = 1 def _convert_to_rgb(image: Image.Image) -> Image.Image: r"""Convert a PIL image to RGB. We ensure transparent background becomes white. """ if image.mode == "RGB": return image if image.mode != "RGBA": image = image.convert("RGBA") white_bg: Image.Image = Image.new("RGBA", image.size, "WHITE") white_bg.paste(image, (0, 0), image) return white_bg.convert("RGB") def normalize( np_image: np.ndarray, mean: tuple[float, float, float], std: tuple[float, float, float], ) -> np.ndarray: r"""Normalize a tensor image with mean and standard deviation. Args: np_image: Image to be normalized. mean: Mean for each channel. std: Standard deviation for each channel. Returns: Normalized image with shape (C, H, W). """ np_image = np_image / 255.0 assert len(np_image.shape) == 3, f"{np_image.shape=}" assert np_image.shape[2] == len(mean) == len(std), f"{np_image.shape=}, {mean=}, {std=}" np_image = (np_image - mean) / std return np_image.transpose(2, 0, 1) def transform_image(image: Image.Image, new_size: tuple[int, int]) -> np.ndarray: r"""Transform an image to a numpy array with the given size. Args: image: Image to be transformed. new_size: New size of the image. Returns: Transformed image with shape (C, H, W). """ assert_opencv_installed() np_image = cv2.resize(np.array(_convert_to_rgb(image), dtype=np.float32), new_size, interpolation=cv2.INTER_CUBIC) return normalize(np_image, DATASET_MEAN, DATASET_STD) class ImageEncoder: r"""Image encoder for the image tokenizer.""" def __init__(self, image_config: ImageConfig, special_ids: SpecialImageIDs) -> None: r"""Initialize the image encoder. Args: image_config: Configuration for the image tokenizer. special_ids: Special image tokens ids. """ self.image_config = image_config self.special_ids = special_ids @property def mm_config(self) -> ImageConfig: # this property is deprecated, use image_config instead # TODO(Patrick) - throw deprecation warning once # changes implemented into vLLM and transformers return self.image_config def _image_to_num_tokens(self, img: Image.Image) -> tuple[int, int]: w: int | float h: int | float w, h = img.size ratio = max(h / self.image_config.max_image_size, w / self.image_config.max_image_size) if ratio > 1: w = round(w / ratio) h = round(h / ratio) width_tokens = (w - 1) // (self.image_config.image_patch_size * self.image_config.spatial_merge_size) + 1 height_tokens = (h - 1) // (self.image_config.image_patch_size * self.image_config.spatial_merge_size) + 1 return width_tokens, height_tokens def __call__(self, content: ImageChunk | ImageURLChunk) -> ImageEncoding: r"""Converts an image chunk to an image encoding. Args: content: image chunk to be converted. Returns: Image encoding. """ image = image_from_chunk(content) w, h = self._image_to_num_tokens(image) assert w > 0 assert h > 0 image_tokens = ([self.special_ids.img] * w + [self.special_ids.img_break]) * h image_tokens[-1] = self.special_ids.img_end new_image_size = ( w * self.image_config.image_patch_size * self.image_config.spatial_merge_size, h * self.image_config.image_patch_size * self.image_config.spatial_merge_size, ) processed_image = transform_image(image, new_image_size) return ImageEncoding(tokens=image_tokens, image=processed_image) @property def image_token(self) -> int: return self.special_ids.img python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/instruct.py000066400000000000000000001575211523035502500276330ustar00rootroot00000000000000import json import warnings from abc import abstractmethod from typing import Any, Generic, Sequence, overload import numpy as np from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidMessageStructureException, InvalidRequestException, TokenizerException, ) from mistral_common.protocol.fim.request import FIMRequest from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, ContentChunk, ImageChunk, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( UATS, AssistantMessage, AssistantMessageType, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.request import InstructRequest, ModelSettings from mistral_common.protocol.instruct.tool_calls import Tool, ToolCall from mistral_common.protocol.speech.request import SpeechRequest from mistral_common.protocol.transcription.request import StreamingMode, TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio, AudioEncoder, TranscriptionFormat from mistral_common.tokens.tokenizers.base import ( FIMRequestType, InstructRequestType, InstructTokenizer, SpecialTokenPolicy, SpecialTokens, Tokenized, TokenizedType, Tokenizer, UserMessagePosition, ) from mistral_common.tokens.tokenizers.image import ImageEncoder from mistral_common.tokens.tokenizers.tekken import Tekkenizer class InstructTokenizerBase( InstructTokenizer, Generic[InstructRequestType, FIMRequestType, TokenizedType, AssistantMessageType] ): r"""Base instruct tokenizer.""" def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ): r"""Initialize the instruct tokenizer. Args: tokenizer: The tokenizer to use. image_encoder: The image encoder to use if any. audio_encoder: The audio encoder to use. """ self.tokenizer = tokenizer self.image_encoder = image_encoder self.audio_encoder = audio_encoder super().__init__(tokenizer, image_encoder, audio_encoder) @property def mm_encoder(self) -> ImageEncoder | None: # this funtion is deprecated, use image_encoder instead # TODO(Patrick) - throw a deprecation warning once # changes applied to vllm and transformers return self.image_encoder def start(self) -> list[int]: r"""Return the start tokens.""" return [self.tokenizer.bos_id] @staticmethod def find_first_last_user(request: InstructRequest) -> tuple[int, int]: r"""Find the first and last user message in the request. Args: request: The request to search for user messages. Returns: The index of the first and last user message. """ last_user_idx = -1 first_user_idx = -1 for i, msg in list(enumerate(request.messages)): if isinstance(msg, UserMessage): if first_user_idx == -1: first_user_idx = i last_user_idx = i return first_user_idx, last_user_idx @abstractmethod def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Raises: NotImplementedError: The tool message is not implemented for the base tokenizer. """ raise NotImplementedError("Tool message not implemented") @abstractmethod def encode_assistant_message( self, message: AssistantMessageType, is_before_last_user_message: bool, continue_message: bool ) -> list[int]: r"""Encode an assistant message. Raises: NotImplementedError: The assistant message is not implemented for the base tokenizer. """ raise NotImplementedError("Assistant message not implemented") @abstractmethod def encode_think(self, chunk: ThinkChunk) -> list[int]: r"""Encode a think chunk. Raises: NotImplementedError: The think chunk is not implemented for the base tokenizer. """ raise NotImplementedError("Think chunk not implemented") def _truncate_for_max_tokens( self, tokenized: list[list[int] | None], messages: list[AssistantMessageType], max_tokens: int, last_user_message_index: int, ) -> None: # Tokenizer ⩽ V3 does not support truncation return @classmethod def validate_messages(cls, messages: list[UATS]) -> None: # We start validating messages for v7 return def encode_instruct( self, request: InstructRequest[AssistantMessageType, Tool], ) -> Tokenized: r"""Encode an instruct request. Args: request: The request to encode. Returns: The encoded tokens. """ # init at bos images: list[np.ndarray] = [] audios: list[Audio] = [] prefix_ids: list[int] | None = None tokens_list: list[list[int] | None] = [] # validate messages self.validate_messages(request.messages) # find last user message first_user_idx, last_user_idx = self.find_first_last_user(request) for msg_idx, msg in enumerate(request.messages): if ( request.continue_final_message and (msg_idx == len(request.messages) - 1) and not isinstance(msg, AssistantMessage) ): raise InvalidMessageStructureException( "Cannot continue final message if it is not an assistant message" ) if isinstance(msg, UserMessage): new_tokens, new_images, new_audios = self.encode_user_message( msg, request.available_tools, msg_idx == last_user_idx, msg_idx == first_user_idx, system_prompt=request.system_prompt, force_img_first=True, # img is always first when providing text/img chunk pair settings=request.settings, ) images.extend(new_images) audios.extend(new_audios) elif isinstance(msg, ToolMessage): new_tokens, new_images, new_audios = self.encode_tool_message(msg, msg_idx < last_user_idx) images.extend(new_images) audios.extend(new_audios) elif isinstance(msg, AssistantMessage): continue_message = request.continue_final_message and (msg_idx == len(request.messages) - 1) new_tokens = self.encode_assistant_message( msg, msg_idx < last_user_idx, continue_message=continue_message ) if msg_idx == len(request.messages) - 1: prefix_ids = new_tokens elif isinstance(msg, SystemMessage): new_tokens, new_audios = self.encode_system_message(msg) audios.extend(new_audios) else: raise TokenizerException(f"Unknown message type {type(msg)}") tokens_list.append(new_tokens) if request.truncate_at_max_tokens is not None: self._truncate_for_max_tokens( tokens_list, request.messages, request.truncate_at_max_tokens, last_user_idx, ) tokens = self.start() for tok in tokens_list: if tok is not None: tokens.extend(tok) return Tokenized( tokens=tokens, text=self.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP), prefix_ids=prefix_ids, images=images, audios=audios, ) def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE) -> str: r"""Decode tokens to a string. Args: tokens: The tokens to decode. special_token_policy: The policy to use for special tokens. Returns: The decoded string. """ return self.tokenizer.decode(tokens, special_token_policy=special_token_policy) def _to_string(self, tokens: list[int]) -> str: return self.tokenizer._to_string(tokens) class InstructTokenizerV1( InstructTokenizerBase, Generic[InstructRequestType, FIMRequestType, TokenizedType, AssistantMessageType] ): r"""Instruct tokenizer V1. This tokenizer has basic for messages. It does not support tools or image inputs. """ def encode_user_message( self, message: UserMessage, available_tools: list[Tool] | None, is_last: bool, is_first: bool, system_prompt: str | None = None, force_img_first: bool = False, settings: ModelSettings = ModelSettings.none(), ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user message. Args: message: The message to encode. available_tools: Not used. is_last: Not used. is_first: Whether the message is the first one. system_prompt: The system prompt. force_img_first: Not used. Returns: The encoded tokens and empty list. """ assert isinstance(message.content, str), "Message content must be normalized" assert self.image_encoder is None, "InstructTokenizerV1 cannot encode images" content = "" if is_first and system_prompt: content = system_prompt + "\n\n" + message.content else: content = message.content message_txt = f"[INST] {content} [/INST]" curr_tokens, image, audio = self.encode_user_content(content=message_txt, is_last=False, system_prompt=None) return curr_tokens, image, audio def encode_system_message(self, message: SystemMessage) -> tuple[list[int], list[Audio]]: raise NotImplementedError(f"System message encoding not implemented for {self.__class__.__name__}") def encode_user_content( self, content: str | list[ContentChunk], is_last: bool, system_prompt: str | None = None, force_img_first: bool = False, ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user content. Args: content: The content to encode. is_last: Whether the message is the last one. system_prompt: The system prompt. force_img_first: Not used. Returns: The encoded tokens and empty list. """ assert isinstance(content, str) if is_last and system_prompt: content = system_prompt + "\n\n" + content tokens = self.tokenizer.encode(content, bos=False, eos=False) return tokens, [], [] def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Raises: TokenizerException: The tool message is not implemented for this version. """ raise TokenizerException("Tools not implemented for tokenizer V1") def encode_assistant_message( self, message: AssistantMessageType, is_before_last_user_message: bool, continue_message: bool ) -> list[int]: r"""Encode an assistant message. Args: message: The message to encode. is_before_last_user_message: Not used. continue_message: Whether to continue the message generation. Only use this if the assistant message is the last message. Returns: The encoded tokens. """ assert isinstance(message, AssistantMessage), message if message.tool_calls is not None and len(message.tool_calls) > 0: raise TokenizerException("Tools not implemented for tokenizer V1") if continue_message and message.prefix: raise InvalidAssistantMessageException( "`continue_message` is only supported for assistant messages that have `prefix=False`." ) elif message.content: assert isinstance(message.content, str), "Message content must be a string for tokenizer < V13" curr_tokens = self.tokenizer.encode(message.content, bos=False, eos=False) else: raise TokenizerException(f"{message.content} // {message.tool_calls}") if not message.prefix and not continue_message: curr_tokens.append(self.tokenizer.eos_id) return curr_tokens def encode_think(self, chunk: ThinkChunk) -> list[int]: r"""Encode a think chunk. Raises: TokenizerException: The think chunk is not implemented for this version. """ raise TokenizerException("Think not implemented for tokenizer < V13.") def encode_fim(self, request: FIMRequest) -> Tokenized: r"""Encode a FIM request. Raises: TokenizerException: The FIM request is not implemented for this version. """ raise TokenizerException(f"FIM not available for {self.tokenizer.version}") def encode_transcription(self, request: TranscriptionRequest) -> Tokenized: raise TokenizerException(f"Transcription not available for {self.tokenizer.version}") def encode_speech_request(self, request: SpeechRequest) -> Tokenized: raise TokenizerException(f"Speech request not available for tokenizer {self.tokenizer.version.value}") class InstructTokenizerV2( InstructTokenizerV1, Generic[InstructRequestType, FIMRequestType, TokenizedType, AssistantMessageType] ): r"""Instruct tokenizer V2. This tokenizer adds supports to images, tools and FIM requests. """ _message_position_to_encode_tools_settings = UserMessagePosition.last def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ): r"""Initialize the tokenizer. Args: tokenizer: The tokenizer to use. image_encoder: The image encoder to use. audio_encoder: The audio encoder to use. """ super().__init__(tokenizer, image_encoder, audio_encoder) self.BEGIN_INST = self.tokenizer.get_special_token(SpecialTokens.begin_inst.value) self.END_INST = self.tokenizer.get_special_token(SpecialTokens.end_inst.value) self.BEGIN_AVAILABLE_TOOLS = self.tokenizer.get_special_token(SpecialTokens.begin_tools.value) self.END_AVAILABLE_TOOLS = self.tokenizer.get_special_token(SpecialTokens.end_tools.value) self.BEGIN_TOOL_RESULTS = self.tokenizer.get_special_token(SpecialTokens.begin_tool_results.value) self.END_TOOL_RESULTS = self.tokenizer.get_special_token(SpecialTokens.end_tool_results.value) self.TOOL_CALLS = self.tokenizer.get_special_token(SpecialTokens.tool_calls.value) self.BOS = self.tokenizer.get_special_token(SpecialTokens.bos.value) self.PREFIX = self.tokenizer.get_special_token(SpecialTokens.prefix.value) self.SUFFIX = self.tokenizer.get_special_token(SpecialTokens.suffix.value) def encode_user_message( self, message: UserMessage, available_tools: list[Tool] | None, is_last: bool, is_first: bool, system_prompt: str | None = None, force_img_first: bool = False, settings: ModelSettings = ModelSettings.none(), ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user message. Args: message: The message to encode. available_tools: The list of available tools if any. is_last: Whether the message is the last one. is_first: Not used. system_prompt: The system prompt. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and the list of images. """ do_encode_tools_settings = False do_encode_tools_settings |= is_first and ( self._message_position_to_encode_tools_settings == UserMessagePosition.first ) do_encode_tools_settings |= is_last and ( self._message_position_to_encode_tools_settings == UserMessagePosition.last ) tools_settings_tokens: list[int] = [] if do_encode_tools_settings and available_tools: tools = [tool.model_dump(exclude={"function": {"strict": True}}) for tool in available_tools] tools_json_tokens = self.tokenizer.encode(json.dumps(tools, ensure_ascii=False), bos=False, eos=False) tools_settings_tokens.extend( [ self.BEGIN_AVAILABLE_TOOLS, *tools_json_tokens, self.END_AVAILABLE_TOOLS, ] ) if do_encode_tools_settings: tools_settings_tokens.extend(self._encode_settings(settings=settings)) tokens, image, audio = self.encode_user_content( content=message.content, is_last=is_last, system_prompt=system_prompt, force_img_first=force_img_first, ) prefix_tokens = [*tools_settings_tokens, self.BEGIN_INST] suffix_tokens = [self.END_INST] curr_tokens = prefix_tokens + tokens + suffix_tokens return curr_tokens, image, audio def _parse_json_content(self, content: str) -> Any: try: return json.loads(content) except json.JSONDecodeError: return content def _parse_tool_content(self, content: str | list[ContentChunk]) -> Any: if isinstance(content, list): text_parts: list[str] = [] for chunk in content: assert isinstance(chunk, TextChunk), ( f"Tool content only supports text chunks, got {type(chunk).__name__}." ) text_parts.append(chunk.text) content = "".join(text_parts) return self._parse_json_content(content) def _prepare_tool_result(self, tool_message: ToolMessage) -> dict[str, Any]: r"""Bit of a hack due to the way tool results are tokenized.""" return { "name": tool_message.name, "content": self._parse_tool_content(tool_message.content), } def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Args: message: The message to encode. is_before_last_user_message: Whether the message is before the last user message. If true, the message is not encoded. Returns: The encoded tokens, images, and audios. """ if is_before_last_user_message: # don't tokenize last tool response before last user msg return [], [], [] # Currently only supports single tool results tool_result_str = json.dumps([self._prepare_tool_result(message)], ensure_ascii=False) curr_tokens = [ self.BEGIN_TOOL_RESULTS, *self.tokenizer.encode(tool_result_str, bos=False, eos=False), self.END_TOOL_RESULTS, ] return curr_tokens, [], [] def _prepare_function_call(self, tool_call: ToolCall) -> dict[str, Any]: r"""Bit of a hack due to the way function calls are tokenized.""" return { "name": tool_call.function.name, "arguments": self._parse_json_content(tool_call.function.arguments), } def _encode_normal_content_assistant_message(self, message: AssistantMessageType) -> list[int]: assert message.content, f"Assistant message must have content. Got {message}" assert isinstance(message.content, str), "Message content must be a string for tokenizer < V7" return self.tokenizer.encode(message.content.rstrip(" "), bos=False, eos=False) def _encode_tool_calls_in_assistant_message(self, message: AssistantMessageType) -> list[int]: assert message.tool_calls, f"Assistant message must have tool calls. Got {message}" prepared_tool_calls = [] for tool_call in message.tool_calls: prepared_tool_calls.append(self._prepare_function_call(tool_call)) tool_call_str = json.dumps(prepared_tool_calls, ensure_ascii=False) curr_tokens = [ self.TOOL_CALLS, *self.tokenizer.encode(tool_call_str, bos=False, eos=False), ] return curr_tokens def _encode_settings( self, settings: ModelSettings, ) -> list[int]: r"""Encode model settings as tokens. Returns empty list by default.""" assert self.tokenizer.model_settings_builder is None, "`model_settings_builder` not supported for this version." return [] def encode_assistant_message( self, message: AssistantMessageType, is_before_last_user_message: bool, continue_message: bool ) -> list[int]: r"""Encode an assistant message. Args: message: The message to encode. is_before_last_user_message: Whether the message is before the last user message. If has tools and true, the message is not encoded. continue_message: Whether to continue the message generation. Only use this if the assistant message is the last message. Returns: The encoded tokens. """ if message.tool_calls and message.content: raise ValueError(f"Cannot have tool calls and content defined in the same assistant message {message}") if continue_message and message.prefix: raise InvalidAssistantMessageException( "`continue_message` is only supported for assistant messages that have `prefix=False`." ) if message.tool_calls: if is_before_last_user_message: # don't tokenize tool call before last user message return [] curr_tokens = self._encode_tool_calls_in_assistant_message(message) elif message.content: assert isinstance(message.content, str), "Message content must be a string for tokenizer < V7" curr_tokens = self._encode_normal_content_assistant_message(message) else: raise TokenizerException(f"Invalid assistant message: {message.content}") if not message.prefix and not continue_message: curr_tokens.append(self.tokenizer.eos_id) return curr_tokens def _encode_infilling(self, text: str) -> list[int]: r"""Remove prefix space in the case of SentencePieceTokenizers.""" return self.tokenizer.encode("☺" + text, bos=False, eos=False)[2:] def encode_fim(self, request: FIMRequest) -> Tokenized: r"""Encode a FIM request. Args: request: The request to encode. Returns: The encoded tokens. """ prefix_tokens = self.tokenizer.encode(request.prompt, bos=False, eos=False) suffix_tokens = self._encode_infilling(request.suffix) if request.suffix else [] tokens = [ self.BOS, self.SUFFIX, *suffix_tokens, self.PREFIX, *prefix_tokens, ] return Tokenized(tokens=tokens, text=self.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP)) class InstructTokenizerV3( InstructTokenizerV2, Generic[InstructRequestType, FIMRequestType, TokenizedType, AssistantMessageType] ): r"""Instruct tokenizer V3. The only difference with V2 tokenizer is that it encodes the tool messages differently. """ def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ): r"""Initialize the tokenizer. Args: tokenizer: The tokenizer to use. image_encoder: The image encoder to use. audio_encoder: The audio encoder to use. """ super().__init__(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder) def _prepare_function_call(self, tool_call: ToolCall) -> dict[str, Any]: function_call = { "name": tool_call.function.name, "arguments": self._parse_json_content(tool_call.function.arguments), } if tool_call.id and tool_call.id != "null": function_call["id"] = tool_call.id return function_call def _prepare_tool_result(self, tool_message: ToolMessage) -> dict[str, Any]: assert tool_message.tool_call_id is not None, "Tool message has to have the tool call id defined in v3" return { "content": self._parse_tool_content(tool_message.content), "call_id": tool_message.tool_call_id, } def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Note: Same as [V2][mistral_common.tokens.tokenizers.instruct.InstructTokenizerV2.encode_tool_message] but tools are not wrapped in a list and the history is also tokenized. Args: message: The message to encode. is_before_last_user_message: Whether the message is before the last user message. If true, the message is not encoded. Returns: The encoded tokens, images, and audios. """ tool_result_str = json.dumps(self._prepare_tool_result(message), ensure_ascii=False) curr_tokens = [ self.BEGIN_TOOL_RESULTS, *self.tokenizer.encode(tool_result_str, bos=False, eos=False), self.END_TOOL_RESULTS, ] return curr_tokens, [], [] def encode_assistant_message( self, message: AssistantMessageType, is_before_last_user_message: bool, continue_message: bool ) -> list[int]: r"""Encode an assistant message. Note: Same as [V2][mistral_common.tokens.tokenizers.instruct.InstructTokenizerV2.encode_assistant_message] but always encode the tool history. continue_message: Whether to continue the message generation. Only use this if the assistant message is the last message. Args: message: The message to encode. is_before_last_user_message: Not used. Returns: The encoded tokens. """ return super().encode_assistant_message(message, False, continue_message) @overload def _encode_content_chunk(self, chunk: str | TextChunk | ThinkChunk) -> tuple[list[int], None, None]: ... @overload def _encode_content_chunk(self, chunk: ImageChunk | ImageURLChunk) -> tuple[list[int], np.ndarray, None]: ... @overload def _encode_content_chunk(self, chunk: AudioChunk | AudioURLChunk) -> tuple[list[int], None, Audio]: ... def _encode_content_chunk(self, chunk: str | ContentChunk) -> tuple[list[int], np.ndarray | None, Audio | None]: if isinstance(chunk, str): return self.tokenizer.encode(chunk, bos=False, eos=False), None, None elif isinstance(chunk, TextChunk): return self.tokenizer.encode(chunk.text, bos=False, eos=False), None, None elif isinstance(chunk, ThinkChunk): return self.encode_think(chunk), None, None elif isinstance(chunk, (ImageChunk, ImageURLChunk)): assert self.image_encoder is not None, "Make sure to define a image encoder at init" img_encoding = self.image_encoder(chunk) return img_encoding.tokens, img_encoding.image, None elif isinstance(chunk, (AudioChunk, AudioURLChunk)): # the following is only possible for >= v7 assert self.audio_encoder is not None, "Make sure to define a audio encoder at init" audio_encoding = self.audio_encoder(chunk) return audio_encoding.tokens, None, audio_encoding.audio else: raise ValueError(f"Unknown chunk type: {chunk}") @staticmethod def _maybe_put_image_first(content: list[ContentChunk]) -> list[ContentChunk]: # Compatibility hack: this belongs in normalization and will be removed in an upcoming version. image_types = (ImageChunk, ImageURLChunk) if ( len(content) >= 2 and isinstance(content[-1], image_types) and all(isinstance(chunk, TextChunk) for chunk in content[:-1]) ): return [content[-1], *content[:-1]] return content def _encode_content_chunks( self, content: Sequence[ContentChunk] ) -> tuple[list[int], list[np.ndarray], list[Audio]]: tokens: list[int] = [] images: list[np.ndarray] = [] audio: list[Audio] = [] for chunk in content: chunk_tokens, maybe_image, maybe_audio = self._encode_content_chunk(chunk) tokens.extend(chunk_tokens) if maybe_image is not None: images.append(maybe_image) if maybe_audio is not None: audio.append(maybe_audio) return tokens, images, audio def encode_user_content( self, content: str | list[ContentChunk], is_last: bool, system_prompt: str | None = None, force_img_first: bool = False, ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user content. Args: content: The content to encode. is_last: Whether the message is the last one. system_prompt: The system prompt. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and the images. """ if isinstance(content, str): return super().encode_user_content(content, is_last, system_prompt) tokens: list[int] = [] images: list[np.ndarray] = [] audio: list[Audio] = [] if force_img_first: content = self._maybe_put_image_first(content) first_chunk = True for chunk in content: content_str = "" if first_chunk and is_last and system_prompt: first_chunk = False content_str = system_prompt + "\n\n" tokens += self.tokenizer.encode(content_str, bos=False, eos=False) if isinstance(chunk, (AudioChunk, AudioURLChunk)): assert not content_str, ( f"It is not possible that `content` is non-empty when chunk is of type {type(chunk)}." ) chunk_tokens, maybe_image, chunk_audio = self._encode_content_chunk(chunk) assert maybe_image is None, f"Unexpected image for audio chunk {type(chunk).__name__}." audio.append(chunk_audio) elif isinstance(chunk, (ImageChunk, ImageURLChunk)): chunk_tokens, chunk_image, maybe_audio = self._encode_content_chunk(chunk) assert maybe_audio is None, f"Unexpected audio for image chunk {type(chunk).__name__}." images.append(chunk_image) else: chunk_tokens, maybe_image, maybe_audio = self._encode_content_chunk(chunk) assert maybe_image is None and maybe_audio is None, ( f"Unexpected image/audio for chunk {type(chunk).__name__}." ) tokens.extend(chunk_tokens) return tokens, images, audio class InstructTokenizerV7(InstructTokenizerV3): r"""Instruct tokenizer V7. The difference with V3 tokenizer is that it encodes the system prompts differently: - in V7 the system prompts are treated as separate SystemMessages - they are no longer prepended to the last user message - they are printed between special tokens """ def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ) -> None: r"""Initialize the tokenizer. Args: tokenizer: The tokenizer to use. image_encoder: The image encoder to use. audio_encoder: The audio encoder to use. """ super().__init__(tokenizer, image_encoder, audio_encoder) self.BEGIN_SYSTEM = self.tokenizer.get_special_token(SpecialTokens.begin_system.value) self.END_SYSTEM = self.tokenizer.get_special_token(SpecialTokens.end_system.value) self.BEGIN_TOOL_CONTENT = self.tokenizer.get_special_token(SpecialTokens.begin_tool_content.value) self.TRANSCRIBE = None if audio_encoder is not None and not audio_encoder.audio_config.is_streaming: self.TRANSCRIBE = self.tokenizer.get_special_token(SpecialTokens.transcribe.value) def _truncate_for_max_tokens( self, tokenized_messages: list[list[int] | None], messages: list[AssistantMessageType], max_tokens: int, last_user_message_index: int, ) -> None: # drop some messages to fit in max_tokens. Rules: # - don't drop any system messages # - when a user message is dropped, all following assistant|tool message should be dropped until the next # user message # - we never drop the last message to_drop = sum(len(t) for t in tokenized_messages if t is not None) - max_tokens def drop(idx: int) -> None: nonlocal to_drop if isinstance(messages[idx], SystemMessage): # never drop system messages return if idx == last_user_message_index: # never drop the last user message return tok = tokenized_messages[idx] assert tok is not None to_drop -= len(tok) tokenized_messages[idx] = None current_idx = 0 while to_drop > 0 and current_idx < len(messages): drop(current_idx) current_idx += 1 if isinstance(messages[current_idx - 1], UserMessage): # if we just dropped a UserMessage, # also drop everything until the next user message while current_idx < len(messages) and not isinstance(messages[current_idx], UserMessage): drop(current_idx) current_idx += 1 if to_drop > 0: raise TokenizerException("Input couldn't fit in truncate_at_max_token") def encode_system_message(self, message: SystemMessage) -> tuple[list[int], list[Audio]]: r"""Encode a system message. Args: message: The message to encode. Returns: The encoded tokens and audios. """ tokens = [self.BEGIN_SYSTEM] if isinstance(content := message.content, str): content = [TextChunk(text=content)] content_tokens, images, audios = self._encode_content_chunks(content) assert not images, f"System messages cannot contain images, got {len(images)}." tokens += content_tokens tokens.append(self.END_SYSTEM) return tokens, audios def encode_user_content( self, content: str | list[ContentChunk], is_last: bool, system_prompt: str | None = None, force_img_first: bool = False, ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user content. Args: content: The content to encode. is_last: Whether the message is the last one. system_prompt: The system prompt. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and the images. """ assert system_prompt is None, "in Tokenizer V7 we don't encode system prompts in user messages" if isinstance(content, str): return super().encode_user_content(content, is_last, system_prompt) if force_img_first: content = self._maybe_put_image_first(content) tokens, images, audio = self._encode_content_chunks(content) return tokens, images, audio def encode_user_message( self, message: UserMessage, available_tools: list[Tool] | None, is_last: bool, is_first: bool, system_prompt: str | None = None, force_img_first: bool = False, settings: ModelSettings = ModelSettings.none(), ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a user message. Args: message: The message to encode. available_tools: The list of available tools if any. is_last: Whether the message is the last one. is_first: Whether the message is the first one. system_prompt: Not used. force_img_first: Whether to force the image to be first. Returns: The encoded tokens and the list of images. """ assert system_prompt is None, "in Tokenizer V7 we don't encode system prompts in user messages" tokens, images, audio = super().encode_user_message( message, available_tools, is_last=is_last, is_first=is_first, system_prompt=None, force_img_first=force_img_first, settings=settings, ) return tokens, images, audio def encode_transcription(self, request: TranscriptionRequest) -> Tokenized: r""" Encodes an audio transcription request into a tokenized format. This method processes a transcription request containing audio data, encodes the user message, and returns the tokenized output. Args: request: The transcription request object containing the audio data to be encoded. Returns: Tokenized: The tokenized representation of the audio data, including processed audio and tokens """ assert self.audio_encoder is not None, f"Audio encoder must be defined, got {self.audio_encoder=}" if self.audio_encoder.audio_config.transcription_format == TranscriptionFormat.INSTRUCT: return self._encode_instruct_transcription(request) elif self.audio_encoder.audio_config.transcription_format == TranscriptionFormat.STREAMING: return self._encode_streaming_transcription(request) raise InvalidRequestException( "Transcription format should be one of 'instruct', 'streaming', got " f"{self.audio_encoder.audio_config.transcription_format=}." ) def _encode_instruct_transcription(self, request: TranscriptionRequest) -> Tokenized: assert request.streaming == StreamingMode.DISABLED, ( f"Request must not be in streaming mode, got {request.streaming=}" ) assert self.TRANSCRIBE is not None, f"{self.__class__.__name__} needs to have a TRANSCRIBE token" prefix = self.start() tokens, images, audio = self.encode_user_message( UserMessage(content=[AudioChunk(input_audio=request.audio)]), available_tools=[], is_last=True, is_first=True, system_prompt=None, settings=ModelSettings.none(), ) assert not images, f"Transcription input cannot contain images, got {len(images)}." tokens = [*prefix, *tokens] if request.language is not None: language_string = f"lang:{request.language}" # no space. tokens += self.tokenizer.encode(language_string, bos=False, eos=False) tokens.append(self.TRANSCRIBE) return Tokenized(tokens=tokens, text=self.tokenizer._to_string(tokens), audios=audio) def _encode_audio(self, audio: str | bytes, transcription_delay_ms: float | None = None) -> Tokenized: assert self.audio_encoder is not None, ( f"Audio encoder must be defined to encode audio, got {self.audio_encoder=}" ) _audio = Audio.from_base64(audio) if isinstance(audio, str) else Audio.from_bytes(audio) audio_enc = self.audio_encoder.encode_audio(_audio, transcription_delay_ms) audios = [audio_enc.audio] if audio_enc.audio is not None else [] return Tokenized( tokens=audio_enc.tokens, audios=audios, ) def _encode_streaming_transcription(self, request: TranscriptionRequest) -> Tokenized: if request.streaming == StreamingMode.OFFLINE: tokenized = self._encode_audio(request.audio, request.target_streaming_delay_ms) # we also add a BOS token in the beginning tokens = self.start() + tokenized.tokens audios = tokenized.audios elif request.streaming == StreamingMode.ONLINE: assert self.audio_encoder is not None left_pad, right_pad = self.audio_encoder.get_padding_audio(request.target_streaming_delay_ms) audios = [left_pad, right_pad] if len(request.audio) > 0: # TODO(Patrick) - remove this if statement in 1.13.0 # only left to keep vLLM backwards compatibility in # voxtral_realtime.py warnings.warn( f"Passing audio with {request.streaming=} is " "deprecated. Make sure to not pass any audio to `TranscriptionRequest` when doing" " online streaming.", FutureWarning, ) assert isinstance(request.audio, str) request_audio = Audio.from_base64(request.audio) audios = [ Audio( np.concatenate((left_pad.audio_array, request_audio.audio_array)), request_audio.sampling_rate, request_audio.format, ) ] else: assert not request.audio, ( "For online streaming, no audio bytes should be passed in the first request. " "Audio buffering is taken care of directly by vLLM." ) assert self.audio_encoder is not None, ( f"Audio encoder must be defined to encode audio, got {self.audio_encoder=}" ) # we also add a BOS token in the beginning tokens = self.start() + self.audio_encoder.encode_streaming_tokens(request.target_streaming_delay_ms) else: raise ValueError(f"Request must be in streaming mode, got {request.streaming=}") return Tokenized( tokens=tokens, text=self.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP), audios=audios, ) @classmethod def validate_messages(cls, messages: list[UATS]) -> None: r"""Validates that system prompts and audio chunks are not used together in v7.""" if cls._has_audio(messages): if any(isinstance(message, SystemMessage) for message in messages): raise ValueError("System messages are not yet allowed when audio is present") @staticmethod def _has_audio(messages: list[UATS]) -> bool: return any( isinstance(message, UserMessage) and isinstance(message.content, list) and any(isinstance(chunk, AudioChunk) for chunk in message.content) for message in messages ) def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Note: Same as [V3][mistral_common.tokens.tokenizers.instruct.InstructTokenizerV3.encode_tool_message] but tools are not wrapped in a list and history is also tokenized Args: message: The message to encode. is_before_last_user_message: Not used. Returns: The encoded tokens, images, and audios. """ assert message.tool_call_id is not None assert isinstance(message.content, str), "Message content must be normalized" tool_call_id_tokens = self.tokenizer.encode(message.tool_call_id, bos=False, eos=False) tokens = self.tokenizer.encode(message.content, bos=False, eos=False) prefix_tokens = [ self.BEGIN_TOOL_RESULTS, *tool_call_id_tokens, self.BEGIN_TOOL_CONTENT, ] curr_tokens = [ *prefix_tokens, *tokens, self.END_TOOL_RESULTS, ] return curr_tokens, [], [] def encode_assistant_message( self, message: AssistantMessageType, is_before_last_user_message: bool, continue_message: bool ) -> list[int]: r"""Encode an assistant message. Args: message: The message to encode. is_before_last_user_message: Not used. continue_message: Whether to continue the message generation. Only use this if the assistant message is the last message. Returns: The encoded tokens. """ if not message.content and not message.tool_calls: raise TokenizerException(f"Invalid assistant message: {message}") if continue_message and message.prefix: raise InvalidAssistantMessageException( "`continue_message` is only supported for assistant messages that have `prefix=False`." ) curr_tokens: list = [] if message.content: if isinstance(message.content, str): curr_tokens = self._encode_normal_content_assistant_message(message) elif isinstance(message.content, list): content_tokens, images, audios = self._encode_content_chunks(message.content) assert not images and not audios, ( f"Assistant messages cannot contain images or audios, got {len(images)} images " f"and {len(audios)} audios." ) curr_tokens += content_tokens if message.tool_calls: curr_tokens += self._encode_tool_calls_in_assistant_message(message) if not message.prefix and not continue_message: curr_tokens.append(self.tokenizer.eos_id) return curr_tokens def _encode_audio_for_speech_request(self, ref_audio: str | bytes | None, voice: str | None) -> Tokenized: r"""Encode reference audio or voice preset into a Tokenized object. Args: ref_audio: Base64-encoded string or raw bytes of reference audio, or None. voice: Preset voice name, or None. Returns: Tokenized object with audio tokens and optional audio data. """ assert ref_audio is not None or voice is not None, ( f"Either ref_audio or voice must be defined to encode audio, got {ref_audio=} and {voice=}" ) assert self.audio_encoder is not None, ( f"Audio encoder must be defined to encode audio, got {self.audio_encoder=}" ) _audio = None if ref_audio is not None: _audio = Audio.from_base64(ref_audio) if isinstance(ref_audio, str) else Audio.from_bytes(ref_audio) audio_enc = self.audio_encoder.encode_audio_for_speech_request(_audio, voice) return Tokenized( tokens=audio_enc.tokens, audios=[audio_enc.audio] if audio_enc.audio is not None else [], ) def encode_speech_request(self, request: SpeechRequest) -> Tokenized: r"""Encode a speech synthesis request into a tokenized sequence. Produces: [BOS] + audio_tokens + [TEXT_TO_AUDIO] + text_tokens + [AUDIO_TO_TEXT] + [BEGIN_AUDIO]. Args: request: The speech request containing input text and voice/audio data. Returns: Tokenized object with the full token sequence and optional audio data. """ assert self.audio_encoder is not None, ( f"Audio encoder must be defined to encode audio, got {self.audio_encoder=}" ) init_tokens = self.start() tokenized = Tokenized(tokens=init_tokens) tokenized_audio = self._encode_audio_for_speech_request(request.ref_audio, request.voice) tokenized.tokens.extend(tokenized_audio.tokens) tokenized.audios.extend(tokenized_audio.audios) tokens: list[int] = tokenized.tokens tokens.append(self.audio_encoder.text_to_audio_token) tokens.extend(self.tokenizer.encode(request.input, bos=False, eos=False)) tokens.append(self.audio_encoder.audio_to_text_token) tokens.append(self.audio_encoder.begin_audio_token) tokenized.tokens = tokens return tokenized class InstructTokenizerV11(InstructTokenizerV7): r"""Instruct tokenizer V11. The difference with V7 tokenizer is that it encodes tool calls differently: Tool call results are encoded as : - [begin tool call] call_name_tokens [call id] call_id_tokens [args] content tokens """ def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ) -> None: super().__init__(tokenizer, image_encoder, audio_encoder) self.ARGS = self.tokenizer.get_special_token(SpecialTokens.args.value) self.CALL_ID = self.tokenizer.get_special_token(SpecialTokens.call_id.value) def _encode_tool_calls_in_assistant_message(self, message: AssistantMessageType) -> list[int]: assert message.tool_calls, f"Assistant message must have tool calls. Got {message}" curr_tokens = [] for tool_call in message.tool_calls: prepared = self._prepare_function_call(tool_call) ids = [] if "id" in prepared: ids = [self.CALL_ID, *self.tokenizer.encode(prepared["id"], bos=False, eos=False)] curr_tokens += [ self.TOOL_CALLS, *self.tokenizer.encode(prepared["name"], bos=False, eos=False), *ids, self.ARGS, *self.tokenizer.encode(json.dumps(prepared["arguments"], ensure_ascii=False), bos=False, eos=False), ] return curr_tokens class InstructTokenizerV13(InstructTokenizerV11): r"""Instruct tokenizer V13. The difference with V11 tokenizer is that it encodes tool calls differently: - available tools are tokenized at the first user message. - call id is no longer tokenized for tool calls or results. """ _message_position_to_encode_tools_settings = UserMessagePosition.first def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ) -> None: super().__init__(tokenizer, image_encoder, audio_encoder) assert isinstance(tokenizer, Tekkenizer), f"Tokenizer must be a Tekkenizer. Got {type(tokenizer)}" if ( SpecialTokens.begin_think.value in tokenizer._special_tokens_reverse_vocab and SpecialTokens.end_think.value in tokenizer._special_tokens_reverse_vocab ): self.BEGIN_THINK: int | None = tokenizer.get_special_token(SpecialTokens.begin_think.value) self.END_THINK: int | None = tokenizer.get_special_token(SpecialTokens.end_think.value) else: self.BEGIN_THINK = None self.END_THINK = None def _encode_tool_calls_in_assistant_message(self, message: AssistantMessageType) -> list[int]: assert message.tool_calls, f"Assistant message must have tool calls. Got {message}" curr_tokens = [] for tool_call in message.tool_calls: assert tool_call.id and tool_call.id != "null" prepared = self._prepare_function_call(tool_call) curr_tokens += [ self.TOOL_CALLS, *self.tokenizer.encode(prepared["name"], bos=False, eos=False), self.ARGS, *self.tokenizer.encode(json.dumps(prepared["arguments"], ensure_ascii=False), bos=False, eos=False), ] return curr_tokens def encode_tool_message( self, message: ToolMessage, is_before_last_user_message: bool ) -> tuple[list[int], list[np.ndarray], list[Audio]]: r"""Encode a tool message. Args: message: The message to encode. is_before_last_user_message: Not used. Returns: The encoded tokens, images, and audios. """ assert message.tool_call_id is not None, "Tool call id must be provided for tokenizer >= v13" if isinstance(message.content, str): content_tokens = self.tokenizer.encode(message.content, bos=False, eos=False) images: list[np.ndarray] = [] audios: list[Audio] = [] else: content_tokens, images, audios = self._encode_content_chunks(message.content) curr_tokens = [ self.BEGIN_TOOL_RESULTS, *content_tokens, self.END_TOOL_RESULTS, ] return curr_tokens, images, audios def encode_think(self, chunk: ThinkChunk) -> list[int]: r"""Encode a thinking chunk. Args: chunk: The thinking chunk to encode. Returns: The encoded tokens. """ assert self.BEGIN_THINK is not None, "think tokens are not available for this tokenizer." assert self.END_THINK is not None, "think tokens are not available for this tokenizer." tokens = self.tokenizer.encode(chunk.thinking, bos=False, eos=False) think_tokens = [self.BEGIN_THINK, *tokens] if chunk.closed: think_tokens.append(self.END_THINK) return think_tokens @classmethod def validate_messages(cls, messages: list[UATS]) -> None: r"""Allows system prompts and audio chunks to coexist in v13.""" return class InstructTokenizerV15(InstructTokenizerV13): r"""Instruct tokenizer V15. Extends V13 with model settings encoding. Inherits V13's tool call behavior (no call ID in tool calls/results, first-position tools, think support). """ def __init__( self, tokenizer: Tokenizer, image_encoder: ImageEncoder | None = None, audio_encoder: AudioEncoder | None = None, ) -> None: super().__init__(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder) self.BEGIN_MODEL_SETTINGS = self.tokenizer.get_special_token(SpecialTokens.begin_model_settings.value) self.END_MODEL_SETTINGS = self.tokenizer.get_special_token(SpecialTokens.end_model_settings.value) def _validate_settings(self, settings: ModelSettings) -> None: r"""Validate model settings against the tokenizer's model settings builder.""" model_settings_builder = self.tokenizer.model_settings_builder if model_settings_builder is None: raise TokenizerException(f"Tokenizer {self.tokenizer.version} needs a `model_settings_builder`.") model_settings_builder.validate_settings(settings) def _encode_settings( self, settings: ModelSettings, ) -> list[int]: r"""Encode model settings as special-token-delimited JSON tokens.""" self._validate_settings(settings) if settings == ModelSettings.none(): return [] dumped_settings = json.dumps(settings.model_dump(exclude_none=True), ensure_ascii=False, sort_keys=True) setting_json_tokens = self.tokenizer.encode(dumped_settings, bos=False, eos=False) settings_tokens = [ self.BEGIN_MODEL_SETTINGS, *setting_json_tokens, self.END_MODEL_SETTINGS, ] return settings_tokens def encode_system_message(self, message: SystemMessage) -> tuple[list[int], list[Audio]]: r"""Encode a system message, rejecting ThinkChunk content.""" if isinstance(message.content, list): if any(isinstance(chunk, ThinkChunk) for chunk in message.content): raise TokenizerException("ThinkChunk in system message is not supported for this model") return super().encode_system_message(message) python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/mistral.py000066400000000000000000000436161523035502500274320ustar00rootroot00000000000000import warnings from pathlib import Path from typing import Any, Callable, Generic from mistral_common.exceptions import ( TokenizerException, ) from mistral_common.protocol.fim.request import FIMRequest from mistral_common.protocol.instruct.messages import ( UATS, AssistantMessageType, SystemMessageType, ToolMessageType, UserMessageType, ) from mistral_common.protocol.instruct.normalize import InstructRequestNormalizer, get_normalizer from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.validator import ( MistralRequestValidator, ValidationMode, get_validator, ) from mistral_common.protocol.speech.request import SpeechRequest from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import AudioConfig, AudioEncoder, SpecialAudioIDs from mistral_common.tokens.tokenizers.base import ( InstructRequest, InstructRequestType, InstructTokenizer, SpecialTokenPolicy, SpecialTokens, TokenizedType, TokenizerVersion, ) from mistral_common.tokens.tokenizers.image import ( ImageConfig, ImageEncoder, SpecialImageIDs, ) from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerV1, InstructTokenizerV2, InstructTokenizerV3, InstructTokenizerV7, InstructTokenizerV11, InstructTokenizerV13, InstructTokenizerV15, ) from mistral_common.tokens.tokenizers.sentencepiece import ( SentencePieceTokenizer, get_image_config, is_sentencepiece, ) from mistral_common.tokens.tokenizers.tekken import Tekkenizer, is_tekken from mistral_common.tokens.tokenizers.utils import download_tokenizer_from_hf_hub def load_image_encoder(image_config: ImageConfig, tokenizer: Tekkenizer | SentencePieceTokenizer) -> ImageEncoder: r"""Load a image encoder from a config and a tokenizer. Args: image_config: The image config. tokenizer: The tokenizer. Returns: The image encoder. """ special_ids = SpecialImageIDs( img=tokenizer.get_special_token(SpecialTokens.img.value), img_break=tokenizer.get_special_token(SpecialTokens.img_break.value), img_end=tokenizer.get_special_token(SpecialTokens.img_end.value), ) return ImageEncoder(image_config, special_ids) def load_audio_encoder(audio_config: AudioConfig, tokenizer: Tekkenizer) -> AudioEncoder: r"""Load a audio encoder from a config and a tokenizer. Args: audio_config: The audio config. tokenizer: The tokenizer. Returns: The audio encoder. """ def get_special_token_or_none(token: str) -> int | None: if not tokenizer.is_special(token): return None return tokenizer.get_special_token(token) special_ids = SpecialAudioIDs( audio=get_special_token_or_none(SpecialTokens.audio.value), begin_audio=get_special_token_or_none(SpecialTokens.begin_audio.value), streaming_pad=get_special_token_or_none(SpecialTokens.streaming_pad.value), text_to_audio=get_special_token_or_none(SpecialTokens.text_to_audio.value), audio_to_text=get_special_token_or_none(SpecialTokens.audio_to_text.value), ) return AudioEncoder(audio_config, special_ids) class MistralTokenizer( Generic[UserMessageType, AssistantMessageType, ToolMessageType, SystemMessageType, TokenizedType] ): r"""Mistral tokenizer. This class is a wrapper around a [InstructTokenizer][mistral_common.tokens.tokenizers.base.InstructTokenizer], a [MistralRequestValidator][mistral_common.protocol.instruct.validator.MistralRequestValidator] and a [InstructRequestNormalizer][mistral_common.protocol.instruct.normalize.InstructRequestNormalizer]. It provides a convenient interface to tokenize, validate ad normalize Mistral requests. Attributes: instruct_tokenizer: The instruct tokenizer to use. See [InstructTokenizer][mistral_common.tokens.tokenizers.instruct.InstructTokenizer]. """ def __init__( self, instruct_tokenizer: InstructTokenizer[InstructRequest, FIMRequest, TokenizedType, AssistantMessageType], validator: MistralRequestValidator[UserMessageType, AssistantMessageType, ToolMessageType, SystemMessageType], request_normalizer: InstructRequestNormalizer[ UserMessageType, AssistantMessageType, ToolMessageType, SystemMessageType, InstructRequestType ], ): r"""Initializes a `MistralTokenizer`. Args: instruct_tokenizer: The instruct tokenizer to use. validator: The request validator to use. request_normalizer: The request normalizer to use. """ self._chat_completion_request_validator = validator self._instruct_request_normalizer = request_normalizer self.instruct_tokenizer: InstructTokenizer[InstructRequest, FIMRequest, TokenizedType, AssistantMessageType] = ( instruct_tokenizer ) def __reduce__(self) -> tuple[Callable, tuple[Any, ...]]: """ Provides a recipe for pickling (serializing) this object, which is necessary for use with multiprocessing. Returns: A tuple of the factory function and the arguments to reconstruct the object from its source file. """ return MistralTokenizer.from_file, (self.instruct_tokenizer.tokenizer.file_path, self.mode) @property def mode(self) -> ValidationMode: r"""The validation mode of the tokenizer.""" return self._chat_completion_request_validator.mode @property def version(self) -> TokenizerVersion: r"""The version of the tokenizer.""" return self.instruct_tokenizer.tokenizer.version @classmethod def _data_path(cls) -> Path: return Path(__file__).parents[2] / "data" @classmethod def v1(cls) -> "MistralTokenizer": r"""Get the Mistral tokenizer v1.""" return cls.from_file(str(cls._data_path() / "tokenizer.model.v1"), mode=ValidationMode.test) @classmethod def v2(cls) -> "MistralTokenizer": r"""Get the Mistral tokenizer v2.""" return cls.from_file( str(cls._data_path() / "mistral_instruct_tokenizer_240216.model.v2"), mode=ValidationMode.test ) @classmethod def v3(cls, is_tekken: bool = False, is_mm: bool = False) -> "MistralTokenizer": r"""Get the Mistral tokenizer v3. Args: is_tekken: Whether the tokenizer is a tekken tokenizer. See [Tekkenizer][mistral_common.tokens.tokenizers.tekken.Tekkenizer]. is_mm: Whether to load image tokenizer. Returns: The Mistral tokenizer v3. """ if is_tekken and is_mm: tokenizer_name = "tekken_240911.json" elif is_tekken and not is_mm: tokenizer_name = "tekken_240718.json" elif not is_tekken and is_mm: raise ValueError("Multimodal tokenizer is currently only supported for tekken") else: tokenizer_name = "mistral_instruct_tokenizer_240323.model.v3" return cls.from_file(str(cls._data_path() / tokenizer_name), mode=ValidationMode.test) @classmethod def v7(cls, is_mm: bool = False) -> "MistralTokenizer": """Get the Mistral tokenizer v7. Args: is_mm: Whether to load the image tokenizer. Returns: The Mistral tokenizer v7. """ if is_mm: return cls.from_file( str(cls._data_path() / "mistral_instruct_tokenizer_241114.model.v7m1"), mode=ValidationMode.test ) else: return cls.from_file( str(cls._data_path() / "mistral_instruct_tokenizer_241114.model.v7"), mode=ValidationMode.test ) @classmethod def from_model(cls, model: str, strict: bool = True) -> "MistralTokenizer": r"""Deprecated in favor of `from_hf_hub` or `from_file`, will be removed in 1.13.0. Args: model: The model name. strict: Has to be True, not used. Returns: The Mistral tokenizer for the given model. """ warnings.warn( "`MistralTokenizer.from_model` is deprecated and will be removed in 1.13.0. " "Use `MistralTokenizer.from_hf_hub(...)` or `MistralTokenizer.from_file(...)` instead.", FutureWarning, ) if not strict: raise ValueError("strict has to be `True` since v1.10.0.") if model not in MODEL_NAME_TO_TOKENIZER_CLS: raise TokenizerException( f"Unrecognized model: {model}. Use `MistralTokenizer.from_hf_hub(...)` " f"or `MistralTokenizer.from_file(...)` to load newer or custom models." ) return MODEL_NAME_TO_TOKENIZER_CLS[model]() @staticmethod def from_hf_hub( repo_id: str, token: bool | str | None = None, revision: str | None = None, force_download: bool = False, local_files_only: bool = False, mode: ValidationMode = ValidationMode.test, ) -> "MistralTokenizer": r"""Download the Mistral tokenizer for a given Hugging Face repository ID. See [here](https://huggingface.co/mistralai/models) for a list of our OSS models. Args: repo_id: The Hugging Face repo ID. token: The Hugging Face token to use to download the tokenizer. revision: The revision of the model to use. If `None`, the latest revision will be used. mode: The validation mode to use. force_download: Whether to force the download of the tokenizer. If `True`, the tokenizer will be downloaded even if it is already cached. local_files_only: Whether to only use local files. If `True`, the tokenizer will be downloaded only if it is already cached. Returns: The Mistral tokenizer for the given model. """ tokenizer_path = download_tokenizer_from_hf_hub( repo_id=repo_id, token=token, revision=revision, force_download=force_download, local_files_only=local_files_only, ) return MistralTokenizer.from_file(tokenizer_path, mode=mode) @classmethod def from_file( cls, tokenizer_filename: str | Path, mode: ValidationMode = ValidationMode.test, ) -> "MistralTokenizer": r"""Loads a tokenizer from a file. Args: tokenizer_filename: The path to the tokenizer file. mode: The validation mode to use. Returns: The loaded tokenizer. """ tokenizer: SentencePieceTokenizer | Tekkenizer if is_tekken(tokenizer_filename): tokenizer = Tekkenizer.from_file(tokenizer_filename) image_config = tokenizer.image audio_config = tokenizer.audio elif is_sentencepiece(tokenizer_filename): tokenizer = SentencePieceTokenizer(tokenizer_filename) image_config = get_image_config(tokenizer_filename) # spm can't have audio audio_config = None else: raise TokenizerException(f"Unrecognized tokenizer file: {tokenizer_filename}") image_encoder = load_image_encoder(image_config, tokenizer) if image_config is not None else None audio_encoder = None if audio_config is not None: assert isinstance(tokenizer, Tekkenizer), "Audio is only supported for tekken tokenizers" audio_encoder = load_audio_encoder(audio_config, tokenizer) request_normalizer = get_normalizer(tokenizer.version, tokenizer.model_settings_builder) validator = get_validator(tokenizer.version, mode=mode) if tokenizer.version == TokenizerVersion.v1: assert image_encoder is None, "Tokenizer version needs to be >= v3" assert audio_encoder is None, "Tokenizer version needs to be >= v7" return MistralTokenizer( InstructTokenizerV1(tokenizer), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v2: assert image_encoder is None, "Tokenizer version needs to be >= v3" assert audio_encoder is None, "Tokenizer version needs to be >= v7" return MistralTokenizer( InstructTokenizerV2(tokenizer), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v3: assert audio_encoder is None, "Tokenizer version needs to be >= v7" return MistralTokenizer( InstructTokenizerV3(tokenizer, image_encoder=image_encoder), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v7: return MistralTokenizer( InstructTokenizerV7(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v11: return MistralTokenizer( InstructTokenizerV11(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v13: return MistralTokenizer( InstructTokenizerV13(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder), validator=validator, request_normalizer=request_normalizer, ) elif tokenizer.version == TokenizerVersion.v15: return MistralTokenizer( InstructTokenizerV15(tokenizer, image_encoder=image_encoder, audio_encoder=audio_encoder), validator=validator, request_normalizer=request_normalizer, ) raise TokenizerException(f"Unrecognized tokenizer filename: {tokenizer_filename}") def encode_chat_completion( self, request: ChatCompletionRequest[UATS], max_model_input_len: int | None = None ) -> TokenizedType: r"""Encodes a chat completion request. Args: request: The chat completion request to encode. max_model_input_len: The maximum length of the input to the model. If `None`, the input will not be truncated. Returns: The encoded chat completion request. """ validated_request = self._chat_completion_request_validator.validate_request(request) if max_model_input_len is None and request.truncate_for_context_length: # the max_model_input_len arg should not be optional ; # but this function is used in many small scripts that have no use # for truncation, and don't provide the max model len raise TokenizerException( "encoding a chat completion request with truncation, but no max model len was provided", ) instruct_request = self._instruct_request_normalizer.from_chat_completion_request(validated_request) if request.truncate_for_context_length: instruct_request.truncate_at_max_tokens = max_model_input_len return self.instruct_tokenizer.encode_instruct(instruct_request) def encode_transcription(self, request: TranscriptionRequest) -> TokenizedType: r"""Encodes a transcription request. Args: request: The transcription request to encode. Returns: The encoded transcription request. """ return self.instruct_tokenizer.encode_transcription(request) def encode_speech_request(self, request: SpeechRequest) -> TokenizedType: r"""Encodes a speech synthesis request. Args: request: The speech request to encode. Returns: The encoded speech request. """ return self.instruct_tokenizer.encode_speech_request(request) def encode_fim(self, request: FIMRequest) -> TokenizedType: r"""Encodes a fill in the middle request. Args: request: The fill in the middle request to encode. Returns: The encoded fill in the middle request. """ return self.instruct_tokenizer.encode_fim(request) def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE) -> str: r"""Decodes a list of tokens into a string. Args: tokens: The tokens to decode. special_token_policy: The policy to use for special tokens. Returns: The decoded string. """ return self.instruct_tokenizer.decode(tokens, special_token_policy=special_token_policy) def _to_string(self, tokens: list[int]) -> str: return self.instruct_tokenizer._to_string(tokens) MODEL_NAME_TO_TOKENIZER_CLS: dict[str, Callable[[], MistralTokenizer]] = { "ministral-8b-2410": lambda: MistralTokenizer.v3(is_tekken=True), "mistral-tiny-2312": MistralTokenizer.v2, "open-mistral-nemo-2407": lambda: MistralTokenizer.v3(is_tekken=True), "mistral-tiny-2407": MistralTokenizer.v3, "mistral-small-2312": MistralTokenizer.v2, "open-mixtral-8x22b-2404": MistralTokenizer.v3, "mistral-small-2402": MistralTokenizer.v2, "mistral-small-2409": lambda: MistralTokenizer.v3(is_tekken=True), "mistral-medium-2312": MistralTokenizer.v1, "mistral-large-2402": MistralTokenizer.v2, "mistral-large-2407": MistralTokenizer.v3, "mistral-large-2411": MistralTokenizer.v7, "pixtral-large-2411": lambda: MistralTokenizer.v7(is_mm=True), "codestral-2405": MistralTokenizer.v3, "codestral-mamba-2407": MistralTokenizer.v3, "pixtral-12b-2409": lambda: MistralTokenizer.v3(is_tekken=True, is_mm=True), } python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/model_settings_builder.py000066400000000000000000000177561523035502500325130ustar00rootroot00000000000000from enum import Enum from typing import Any, Generic, TypeVar, final from pydantic import model_validator from mistral_common.base import MistralBase from mistral_common.exceptions import InvalidRequestException from mistral_common.protocol.instruct.request import ChatCompletionRequest, ModelSettings, ReasoningEffort class ValidatorType(str, Enum): r"""Enumeration of validator types. Attributes: ENUM: Indicates that the validator is for enum values. """ ENUM = "enum" T = TypeVar("T") class FieldBuilder(MistralBase, Generic[T]): r"""Base class for field builders. This class serves as the base for all field builders in the validation framework. It ensures that all builders have a type attribute that specifies the kind of validation being performed. Attributes: type: The type of validator (e.g., ENUM). accepts_none: Whether the field accepts None as a valid value in the request. default: The default value to use when the field is None, if accepts_none is True. """ type: ValidatorType accepts_none: bool default: T | None @model_validator(mode="after") def validate_default_accept_none(self) -> "FieldBuilder": r"""Ensure a default value is only set when accepts_none is True.""" if not self.accepts_none and self.default is not None: raise ValueError( f"Default values can only be defined for accepts_none fields {self.accepts_none=} {self.default=}" ) return self def _validate_built_value(self, field_name: str, value: Any) -> None: r"""Validate a non-None built value. Must be implemented by subclasses.""" raise NotImplementedError(f"{field_name} is not supported") def _build_from_optional(self, field_name: str, value: T | None) -> T | None: r"""Resolve an optional value, substituting the default if value is None. Raises: InvalidRequestException: If value is None and the field does not accept None. """ if value is None: if not self.accepts_none: raise InvalidRequestException(f"{field_name} should be set for this model.") return self.default return value @final def validate_built_value(self, field_name: str, value: Any) -> None: r"""Validate a fully built value, including None checks. Raises: InvalidRequestException: If value is None when not permitted, or fails subclass validation. """ if value is None: if not (self.accepts_none and self.default is None): raise InvalidRequestException(f"{field_name} should be set for this model.") else: self._validate_built_value(field_name, value) @final def build_value(self, field_name: str, value: T | None) -> T | None: r"""Resolve and validate a field value, returning the final built result. Raises: InvalidRequestException: If the value is invalid or missing when required. """ value = self._build_from_optional(field_name, value) self.validate_built_value(field_name, value) return value E = TypeVar("E", bound=Enum) class EnumBuilder(FieldBuilder[E]): r"""Builder for enum fields. This class validates that enum fields contain only authorized values. It rejects duplicate values during initialization and ensures the allowed values list is non-empty when None is not accepted. Attributes: type: The type of validator (always ENUM for this class). values: List of allowed enum values. """ type: ValidatorType = ValidatorType.ENUM values: list[E] @model_validator(mode="after") def validate_unique_values(self) -> "EnumBuilder": r"""Ensure no duplicate values are present in the allowed values list.""" if len(set(self.values)) != len(self.values): raise ValueError(f"Duplicate values in {self.values=}") return self @model_validator(mode="after") def validate_empty_list(self) -> "EnumBuilder": r"""Ensure the allowed values list is non-empty when None is not accepted.""" if len(self.values) == 0 and not self.accepts_none: raise ValueError(f"Empty list of values for {self.values=} while not accepts_none.") return self @model_validator(mode="after") def validate_default(self) -> "EnumBuilder": r"""Ensure the default value, if set, is among the allowed values.""" if self.default is not None and self.default not in self.values: raise ValueError(f"Default value {self.default=} is not in {self.values=}.") return self def _validate_built_value(self, field_name: str, value: Any) -> None: r"""Check that value is one of the allowed enum values. Raises: InvalidRequestException: If no values are allowed, or value is not in the allowed list. """ if len(self.values) == 0: raise InvalidRequestException(f"{field_name} not supported for this model.") if value not in self.values: raise InvalidRequestException(f"{field_name} should be one of {self.values}, got {value}.") class ModelSettingsBuilder(MistralBase): r"""Builder for ModelSettings to ensure only authorized values are used. This class validates that model settings contain only authorized values for each field. It enforces a strict field matching approach where: - All fields in model settings must have corresponding builder fields. - All builder fields must have corresponding model settings fields. - Validation is performed only on matching fields. - Clear error messages are provided for field mismatches. Attributes: reasoning_effort: Builder for the allowed ReasoningEffort values, or None if unsupported. """ reasoning_effort: EnumBuilder[ReasoningEffort] | None = None @staticmethod def none() -> "ModelSettingsBuilder": r"""Return a ModelSettingsBuilder with no field builders configured.""" return ModelSettingsBuilder() def build_settings(self, request: ChatCompletionRequest) -> ModelSettings: r"""Build and validate a ModelSettings instance from a raw request. Iterates over all known fields, applies the corresponding builder, and constructs a validated ModelSettings object. Args: request: The incoming chat completion request. Returns: A validated ModelSettings instance. Raises: InvalidRequestException: If any field value is invalid or unsupported. """ dict_settings = {} for field_name in ModelSettingsBuilder.model_fields: # We have a CI test to ensure all fields match between ModelSettings and ModelSettingsEncoder. value = getattr(request, field_name) field_builder: FieldBuilder | None = getattr(self, field_name) if field_builder is not None: dict_settings[field_name] = field_builder.build_value(field_name, value) return ModelSettings.model_validate(dict_settings) def validate_settings(self, settings: ModelSettings) -> None: r"""Validate that all fields in a ModelSettings instance match the configured builders. Ensures that fields without a builder are unset, and fields with a builder hold a value that passes validation. Args: settings: The ModelSettings instance to validate. Raises: InvalidRequestException: If a field is set but has no builder, or fails its builder. """ for field_name in ModelSettingsBuilder.model_fields: value = getattr(settings, field_name) field_builder: FieldBuilder | None = getattr(self, field_name) if field_builder is None: if value is not None: raise InvalidRequestException(f"{field_name} not supported for this model") else: field_builder.validate_built_value(field_name, value) python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/multimodal.py000066400000000000000000000003331523035502500301130ustar00rootroot00000000000000# This file is deprecated. TODO(Patrick) - as soon as vllm & transformers have adapted # to new naming, throw deprecation warning and remove this file. from mistral_common.tokens.tokenizers.image import * # noqa: F403 python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/sentencepiece.py000066400000000000000000000240231523035502500305600ustar00rootroot00000000000000import logging import os import warnings from functools import cached_property from pathlib import Path from typing import TypeGuard import numpy as np from mistral_common.exceptions import TokenizerException from mistral_common.imports import assert_sentencepiece_installed, is_sentencepiece_installed from mistral_common.tokens.tokenizers.base import ( SpecialTokenPolicy, Tokenizer, TokenizerVersion, ) from mistral_common.tokens.tokenizers.image import ImageConfig, MultiModalVersion from mistral_common.tokens.tokenizers.model_settings_builder import ModelSettingsBuilder warnings.filterwarnings( action="once", category=FutureWarning, message=r".*`get_control_token` is deprecated.*", ) warnings.filterwarnings( action="once", category=FutureWarning, message=r".*`_control_tokens` is deprecated.*", ) if is_sentencepiece_installed(): from sentencepiece import SentencePieceProcessor def is_sentencepiece(path: str | Path) -> bool: r"""Check if the given path is a SentencePiece model.""" if isinstance(path, str): path = Path(path) instruct_versions = list(TokenizerVersion.__members__) mm_versions = list(MultiModalVersion.__members__) + [""] # allow no mm version suffixes = [f".model.{v}{m}" for v in instruct_versions for m in mm_versions] + [".model"] return path.is_file() and any(path.name.endswith(suffix) for suffix in suffixes) def get_spm_version(tokenizer_filename: str | Path, raise_deprecated: bool = False) -> TokenizerVersion: r"""Get the version of the tokenizer from the filename.""" tokenizer_filename = str(tokenizer_filename) _version_str = tokenizer_filename.split(".")[-1] if _version_str != "model": # filter tokenizer_filename == "/path/to/tokenizer.model" case _version_str = _version_str.split("m")[0] if _version_str == "model": if raise_deprecated: raise TokenizerException(f"Make sure to rename your tokenizer file to end with {tokenizer_filename}.v1.") # tokenizer.model => tokenizer.model.v1 return TokenizerVersion("v1") if _version_str not in TokenizerVersion.__members__: raise TokenizerException(f"Unrecognized tokenizer filename: {tokenizer_filename}") return TokenizerVersion(_version_str) def get_image_config(tokenizer_filename: str | Path) -> ImageConfig | None: r"""Get the image config from the tokenizer filename.""" tokenizer_filename = str(tokenizer_filename) _version_str = tokenizer_filename.split(".")[-1] if _version_str == "model" or "m" not in _version_str: return None _mm_version_str = "m" + _version_str.split("m")[-1] if _mm_version_str not in MultiModalVersion.__members__: raise TokenizerException(f"Unrecognized tokenizer filename: {tokenizer_filename}") return MultiModalVersion(_mm_version_str).config class SentencePieceTokenizer(Tokenizer): r"""[SentencePiece](https://github.com/google/sentencepiece) tokenizer.""" def __init__(self, model_path: str | Path, tokenizer_version: TokenizerVersion | None = None) -> None: r"""Initialize the `SentencePieceTokenizer`. Args: model_path: The path to the `SentencePiece` model. tokenizer_version: The version of the tokenizer. If not provided, it will be inferred from the model path. """ assert_sentencepiece_installed() self._logger = logging.getLogger(self.__class__.__name__) # reload tokenizer assert os.path.isfile(model_path), model_path self._model = SentencePieceProcessor( model_file=model_path if isinstance(model_path, str) else model_path.as_posix() ) assert self._model.vocab_size() == self._model.get_piece_size() self._vocab = [self._model.id_to_piece(i) for i in range(self.n_words)] self._version: TokenizerVersion = tokenizer_version or get_spm_version(model_path, raise_deprecated=False) self._file_path = Path(model_path) super().__init__() @property def file_path(self) -> Path: r"""The path to the tokenizer model.""" return self._file_path @property def version(self) -> TokenizerVersion: r"""The version of the tokenizer.""" return self._version @property def model_settings_builder(self) -> ModelSettingsBuilder | None: r"""Always returns None as SentencePiece does not support `model_settings_builder`.""" if self.version.supports_model_settings: raise ValueError(f"SentencePieceTokenizer does not support model settings for version {self.version}") return None def get_special_token(self, s: str) -> int: r"""Get the special token for the given string.""" return self._model.piece_to_id(s) # type: ignore def get_control_token(self, s: str) -> int: warnings.warn("`get_control_token` is deprecated. Use `get_special_token` instead.", FutureWarning) return self.get_special_token(s) @property def n_words(self) -> int: r"""Vocabulary size of the tokenizer.""" return self._model.vocab_size() # type: ignore @property def num_special_tokens(self) -> int: r"""The number of special tokens of the tokenizer.""" return len(self.special_ids) def vocab(self) -> list[str]: r"""Get all tokens in the vocabulary as strings.""" return self._vocab @cached_property def bos_id(self) -> int: r"""The beginning of sentence token id.""" return self._model.bos_id() # type: ignore @cached_property def eos_id(self) -> int: r"""The end of sentence token id.""" return self._model.eos_id() # type: ignore def is_special(self, token: int | np.integer | str) -> bool: """Return `True` if the passed `token` is a special token.""" if isinstance(token, (int, np.integer)): return self._model.IsControl(int(token)) # type: ignore elif isinstance(token, str): token_int = self._model.piece_to_id(token) return self._model.IsControl(token_int) # type: ignore else: raise TypeError(f"Expected int or str, got {type(token).__name__}") @cached_property def _control_tokens(self) -> set[int]: warnings.warn( "`_control_tokens` is deprecated. Make use of `is_special` or `special_ids` instead.", FutureWarning ) return self.special_ids @cached_property def special_ids(self) -> set[int]: r"""Ids of the special tokens.""" return {tok for tok in range(self.n_words) if self._model.IsControl(tok)} def encode(self, s: str, bos: bool, eos: bool) -> list[int]: r"""Encode the given string into a list of token ids. Args: s: The string to encode. bos: Whether to add the beginning of sentence token. eos: Whether to add the end of sentence token. Returns: The list of token ids. """ assert isinstance(s, str) t: list[int] = self._model.encode(s) if bos: t = [self.bos_id, *t] if eos: t = [*t, self.eos_id] return t def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE) -> str: r"""Decode the given list of token ids into a string. Note: Using `special_token_policy=SpecialTokenPolicy.KEEP` will keep the special tokens and the normal tokens as SentencePiece pieces. Args: tokens: The list of token ids. special_token_policy: The policy to use for special tokens. Returns: The decoded string. """ try: special_token_policy = SpecialTokenPolicy(special_token_policy) except ValueError as e: valid = ", ".join(repr(p.value) for p in SpecialTokenPolicy) raise ValueError( f"Invalid `special_token_policy` {special_token_policy!r}. Expected one of: {valid}." ) from e if special_token_policy == SpecialTokenPolicy.IGNORE: decoded = self._model.decode(tokens) assert isinstance(decoded, str), f"Sentencepiece model decoded a {type(decoded)}, not a string." return decoded return self._decode_with_special_tokens(tokens, special_token_policy) def id_to_piece(self, token_id: int) -> str: r"""Convert the given token id to a token piece.""" return self._model.id_to_piece(token_id) # type: ignore def _decode_with_special_tokens(self, tokens: list[int], special_token_policy: SpecialTokenPolicy) -> str: text_list = [] curr_tokens: list[int] = [] for tok in tokens: if self.is_special(tok): if special_token_policy == SpecialTokenPolicy.RAISE: raise ValueError("Decoding `tokens` that contain special tokens with special_token_policy=RAISE.") if curr_tokens: text_list.extend([self.id_to_piece(t) for t in curr_tokens]) curr_tokens = [] text_list.append(self.id_to_piece(tok)) else: curr_tokens.append(tok) if curr_tokens: if special_token_policy == SpecialTokenPolicy.RAISE: text_list.append(self._model.decode(curr_tokens)) else: text_list.extend([self.id_to_piece(t) for t in curr_tokens]) return "".join(text_list) def _to_string(self, tokens: list[int]) -> str: return self.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP) @property def pad_id(self) -> int: r"""The padding token id.""" return self._model.pad_id() # type: ignore @property def unk_id(self) -> int: r"""The unknown token id.""" return self._model.unk_id() # type: ignore def is_sentencepiece_tokenizer(tokenizer: Tokenizer) -> TypeGuard[SentencePieceTokenizer]: r"""Returns whether the tokenizer is a SentencePieceTokenizer.""" return isinstance(tokenizer, SentencePieceTokenizer) python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/tekken.py000066400000000000000000000524401523035502500272330ustar00rootroot00000000000000import base64 import json import logging import warnings from functools import cached_property from itertools import groupby from pathlib import Path from typing import TypedDict, TypeGuard import numpy as np import tiktoken from mistral_common.tokens.tokenizers.audio import AudioConfig, AudioSpectrogramConfig from mistral_common.tokens.tokenizers.base import ( SpecialTokenPolicy, SpecialTokens, Tokenizer, TokenizerVersion, ) from mistral_common.tokens.tokenizers.image import ImageConfig from mistral_common.tokens.tokenizers.model_settings_builder import ModelSettingsBuilder warnings.filterwarnings( action="once", category=FutureWarning, message=r".*`get_control_token` is deprecated.*", ) logger = logging.getLogger(__name__) def is_tekken(path: str | Path) -> bool: r"""Check if the given path is a tekken tokenizer file.""" if isinstance(path, str): path = Path(path) return path.is_file() and "tekken" in path.name and path.suffix == ".json" # Formatting specification of the JSON file class TokenInfo(TypedDict): r"""Token information in the JSON file. Attributes: rank: The rank of the token. token_bytes: The token in bytes, base64 encoded. token_str: The token in string format. """ rank: int token_bytes: str # base64 encoded token_str: str | None class SpecialTokenInfo(TypedDict): r"""Special token information in the JSON file. Attributes: rank: The rank of the token. token_str: The token in string format. is_control: Whether the token is a control token. """ rank: int token_str: str is_control: bool class TekkenConfig(TypedDict): r"""Tekken configuration in the JSON file. Attributes: pattern: The pattern of the tokenizer. num_vocab_tokens: The number of vocabulary tokens. default_vocab_size: The default vocabulary size. default_num_special_tokens: The default number of special tokens. version: The version of the tokenizer. """ pattern: str num_vocab_tokens: int default_vocab_size: int default_num_special_tokens: int version: str class ModelData(TypedDict): r"""The data of the tekken tokenizer model. Attributes: vocab: The vocabulary of the tokenizer. config: The configuration of the tokenizer. version: The version of the tokenizer. type: The type of the tokenizer. image: The image configuration of the tokenizer. """ vocab: list[TokenInfo] special_tokens: list[SpecialTokenInfo] | None config: TekkenConfig version: int type: str image: ImageConfig audio: AudioConfig class Tekkenizer(Tokenizer): r"""Tekken tokenizer. This tokenizer is based on the [tiktoken](https://github.com/openai/tiktoken) library. It fastens the tokenization for multiple languages. """ DEPRECATED_SPECIAL_TOKENS = ( SpecialTokenInfo(rank=0, token_str=SpecialTokens.unk, is_control=True), SpecialTokenInfo(rank=1, token_str=SpecialTokens.bos, is_control=True), SpecialTokenInfo(rank=2, token_str=SpecialTokens.eos, is_control=True), SpecialTokenInfo(rank=3, token_str=SpecialTokens.begin_inst, is_control=True), SpecialTokenInfo(rank=4, token_str=SpecialTokens.end_inst, is_control=True), SpecialTokenInfo(rank=5, token_str=SpecialTokens.begin_tools, is_control=True), SpecialTokenInfo(rank=6, token_str=SpecialTokens.end_tools, is_control=True), SpecialTokenInfo(rank=7, token_str=SpecialTokens.begin_tool_results, is_control=True), SpecialTokenInfo(rank=8, token_str=SpecialTokens.end_tool_results, is_control=True), SpecialTokenInfo(rank=9, token_str=SpecialTokens.tool_calls, is_control=True), SpecialTokenInfo(rank=10, token_str=SpecialTokens.img, is_control=True), SpecialTokenInfo(rank=11, token_str=SpecialTokens.pad, is_control=True), SpecialTokenInfo(rank=12, token_str=SpecialTokens.img_break, is_control=True), SpecialTokenInfo(rank=13, token_str=SpecialTokens.img_end, is_control=True), SpecialTokenInfo(rank=14, token_str=SpecialTokens.prefix, is_control=True), SpecialTokenInfo(rank=15, token_str=SpecialTokens.middle, is_control=True), SpecialTokenInfo(rank=16, token_str=SpecialTokens.suffix, is_control=True), SpecialTokenInfo(rank=17, token_str=SpecialTokens.begin_system, is_control=True), SpecialTokenInfo(rank=18, token_str=SpecialTokens.end_system, is_control=True), SpecialTokenInfo(rank=19, token_str=SpecialTokens.begin_tool_content, is_control=True), ) SPECIAL_TOKEN_TEMPLATE = "" # # note that params has a vocab_size field, but it's not used def __init__( self, vocab: list[TokenInfo], special_tokens: list[SpecialTokenInfo], pattern: str, vocab_size: int, num_special_tokens: int, version: TokenizerVersion, *, name: str = "tekkenizer", _path: str | Path | None = None, image_config: ImageConfig | None = None, audio_config: AudioConfig | None = None, model_settings_builder: ModelSettingsBuilder | None = None, ): r"""Initialize the tekken tokenizer. Args: vocab: The vocabulary of the tokenizer. special_tokens: The special tokens of the tokenizer. pattern: The pattern of the tokenizer. vocab_size: The vocabulary size of the tokenizer. num_special_tokens: The number of special tokens of the tokenizer. version: The version of the tokenizer. name: The name of the tokenizer. image_config: The image configuration of the tokenizer. audio_config: The audio configuration of the tokenizer. model_settings_builder: The builder for model settings, or None if unsupported. """ if not version.supports_model_settings and model_settings_builder is not None: raise ValueError( f"model_settings_builder is not supported for {version=} but got {model_settings_builder=}" ) assert vocab_size <= len(vocab) + num_special_tokens, ( vocab_size, len(vocab), num_special_tokens, ) self._vocab_size = vocab_size # The number of special tokens defined in the tokenizer json num_defined_special_tokens = len(set([t["token_str"] for t in special_tokens])) assert len(special_tokens) == num_defined_special_tokens, f"Special tokens must be unique: {special_tokens}" assert len(special_tokens) <= num_special_tokens special_filler = [ SpecialTokenInfo(rank=i, token_str=self.SPECIAL_TOKEN_TEMPLATE.format(id=i), is_control=True) for i in range(len(special_tokens), num_special_tokens) ] if special_filler: logger.info( f"Adding special tokens {special_filler[0]['token_str']}, ..., {special_filler[-1]['token_str']}" ) special_tokens = special_tokens + special_filler assert len(set([t["token_str"] for t in special_tokens])) == len(special_tokens) == num_special_tokens, ( special_tokens ) inner_vocab_size = vocab_size - num_special_tokens # reload vocab logger.info(f"Non special vocabulary size is {inner_vocab_size} with {num_special_tokens} special tokens.") self._tekken_token2id_nospecial = _reload_mergeable_ranks(vocab, max_vocab=inner_vocab_size) assert set(range(inner_vocab_size)) == set(self._tekken_token2id_nospecial.values()), ( inner_vocab_size, self._tekken_token2id_nospecial, ) self._model = tiktoken.Encoding( name=name, pat_str=pattern, mergeable_ranks=self._tekken_token2id_nospecial, special_tokens={}, # special tokens are handled manually ) self._version = version self._image_config = image_config self._audio_config = audio_config self._all_special_tokens = special_tokens self._special_token_ids = {t["rank"] for t in special_tokens} self._special_tokens_reverse_vocab = {t["token_str"]: t["rank"] for t in special_tokens} self._vocab = [self.id_to_piece(i) for i in range(vocab_size)] self._special_token_policy = SpecialTokenPolicy.IGNORE self._file_path = Path(_path) if _path is not None else None self._model_settings_builder = model_settings_builder @property def file_path(self) -> Path: r"""The path to the tokenizer file.""" if self._file_path is None: raise ValueError("The tokenizer was not loaded from a file.") return self._file_path @property def model_settings_builder(self) -> ModelSettingsBuilder | None: r"""The model settings builder, or None if unsupported by this version.""" return self._model_settings_builder @classmethod def from_file(cls: type["Tekkenizer"], path: str | Path) -> "Tekkenizer": r"""Load the tekken tokenizer from a file. Args: path: The path to the tokenizer file. Returns: The tekken tokenizer. """ if isinstance(path, str): path = Path(path) assert path.exists(), path with open(path, "r", encoding="utf-8") as f: untyped = json.load(f) _version_str = untyped["config"].get("version") if _version_str not in TokenizerVersion.__members__: raise ValueError( f"Unknown version: {_version_str} in {path}. " f"Make sure to use a valid version string: {list(TokenizerVersion.__members__)}" ) assert _version_str is not None version = TokenizerVersion(_version_str) special_tokens_dicts: list[SpecialTokenInfo] | None = untyped.get("special_tokens", None) if special_tokens_dicts is None: # Tokenizer > v7 should find special tokens in the tokenizer file if version > TokenizerVersion.v7: raise ValueError( f"Special tokens not found in {path}. " "Please update your tokenizer file and include all special tokens you need." ) else: special_tokens = list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS) else: special_tokens = [token for token in special_tokens_dicts] untyped["special_tokens"] = special_tokens if mm := untyped.get("multimodal"): # deprecated - only allowed for tokenizers <= v11 if version > TokenizerVersion.v11: raise ValueError( f"The image config has to be called 'image' in {path} for tokenizers of version {version.value}." ) untyped["image"] = ImageConfig(**mm) elif image := untyped.get("image"): untyped["image"] = ImageConfig(**image) if audio := untyped.get("audio"): encoding_config = audio.pop("audio_encoding_config") encoding_config = AudioSpectrogramConfig(**encoding_config) untyped["audio"] = AudioConfig(encoding_config=encoding_config, **audio) if ( model_settings_builder := untyped.get("model_settings_builder") ) is not None and not version.supports_model_settings: raise ValueError( f"model_settings_builder is not supported for {version=} but got {model_settings_builder=}" ) elif model_settings_builder is not None: model_settings_builder = ModelSettingsBuilder.model_validate(model_settings_builder) model_data: ModelData = untyped return cls( vocab=model_data["vocab"], special_tokens=special_tokens, pattern=model_data["config"]["pattern"], vocab_size=model_data["config"]["default_vocab_size"], num_special_tokens=model_data["config"]["default_num_special_tokens"], version=version, name=path.name.replace(".json", ""), image_config=model_data.get("image"), audio_config=model_data.get("audio"), model_settings_builder=model_settings_builder, _path=path, ) @property def image(self) -> ImageConfig | None: r"""The image configuration of the tokenizer.""" return self._image_config @image.setter def image(self, value: ImageConfig) -> None: raise ValueError("Can only set Image config at init") @property def audio(self) -> AudioConfig | None: r"""The audio configuration of the tokenizer. Returns: The audio configuration object if it exists, otherwise None. """ return self._audio_config @audio.setter def audio(self, value: AudioConfig) -> None: raise ValueError("Can only set Audio config at init") @property def num_special_tokens(self) -> int: r"""The number of special tokens of the tokenizer.""" return len(self._all_special_tokens) @property def n_words(self) -> int: r"""Vocabulary size of the tokenizer.""" return self._vocab_size @cached_property def special_ids(self) -> set[int]: r"""Ids of the special tokens.""" return {token["rank"] for token in self._all_special_tokens} @property def version(self) -> TokenizerVersion: r"""The version of the tokenizer.""" return self._version @cached_property def bos_id(self) -> int: r"""The beginning of sentence token id.""" return self.get_special_token("") @cached_property def eos_id(self) -> int: r"""The end of sentence token id.""" return self.get_special_token("") @cached_property def pad_id(self) -> int: r"""The padding token id.""" return self.get_special_token("") @cached_property def unk_id(self) -> int: r"""The unknown token id.""" return self.get_special_token("") def vocab(self) -> list[str]: r"""Get all tokens in the vocabulary as strings. Note: This will collapse all tokens for which we have a decoding error into the string. This is bad and results in things like len(set(vocab)) != len(vocab)). Returns: The vocabulary of the tokenizer. """ # when returning self._vocab this will collapse # all tokens for which we have a decoding error into # the string. This is bad and results in things # like len(set(vocab)) != len(vocab)) # be careful when using self._vocab return self._vocab def encode(self, s: str, bos: bool, eos: bool) -> list[int]: r"""Encode a string into a list of token ids. Args: s: The string to encode. bos: Whether to add the beginning of sentence token. eos: Whether to add the end of sentence token. Returns: The list of token ids. """ tokens: list[int] = self._model.encode(s) tokens = [t + self.num_special_tokens for t in tokens] if bos: tokens = [self.bos_id, *tokens] if eos: tokens = [*tokens, self.eos_id] return tokens def _decode_all(self, tokens: list[int], special_token_policy: SpecialTokenPolicy) -> list[str]: # Lump special and non-special tokens together to minimize calls to decode decoded: list[str] = [] for is_special, group in groupby(tokens, lambda t: t < self.num_special_tokens): if is_special: if special_token_policy == SpecialTokenPolicy.RAISE: raise ValueError( f"Decoding `tokens` that contain special tokens ({list(group)}) is not allowed. \n" "Either make sure `tokens` do not include any special tokens or, " "if you want to decode `tokens` that includes special tokens, " "change the tokenizer's special token policy to IGNORE or KEEP: \n" "```\nfrom mistral_common.tokens.tokenizers.mistral import MistralTokenizer" "\nfrom mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy" "\n\ntokenizer = MistralTokenizer.v3(is_tekken=True)" "\ntekken = tokenizer.instruct_tokenizer.tokenizer" "\ntekken.special_token_policy = SpecialTokenPolicy.IGNORE # or SpecialTokenPolicy.KEEP" "\n```" ) elif special_token_policy == SpecialTokenPolicy.KEEP: decoded.extend(self._all_special_tokens[t]["token_str"] for t in group) elif special_token_policy == SpecialTokenPolicy.IGNORE: continue # TODO: Could use "tokens_str" from vocab.json # but need to handle null cases. else: decoded.append(self._model.decode([t - self.num_special_tokens for t in group])) return decoded def is_byte(self, token_id: int) -> bool: r"""Check if a token id is a byte token.""" return 0 <= token_id - self.num_special_tokens < 256 def get_special_token(self, s: str) -> int: r"""Get the token id of a special token.""" if s in self._special_tokens_reverse_vocab: return self._special_tokens_reverse_vocab[s] else: raise ValueError(f"Unknown control token {s}") def is_special(self, token: int | np.integer | str) -> bool: """Return `True` if the passed `token` is a special token.""" if isinstance(token, (int, np.integer)): return token in self._special_token_ids elif isinstance(token, str): return token in self._special_tokens_reverse_vocab else: raise TypeError(f"Expected int or str, got {type(token).__name__}") def get_control_token(self, s: str) -> int: warnings.warn("`get_control_token` is deprecated. Use `get_special_token` instead.", FutureWarning) return self.get_special_token(s) def decode(self, tokens: list[int], special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE) -> str: r"""Decode a list of token ids into a string. Args: tokens: The list of token ids to decode. special_token_policy: The policy for handling special tokens. Returns: The decoded string. """ try: special_token_policy = SpecialTokenPolicy(special_token_policy) except ValueError as e: valid = ", ".join(repr(p.value) for p in SpecialTokenPolicy) raise ValueError( f"Invalid `special_token_policy` {special_token_policy!r}. Expected one of: {valid}." ) from e return "".join(self._decode_all(tokens, special_token_policy=special_token_policy)) def _to_string(self, tokens: list[int]) -> str: return self.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP) def id_to_piece(self, token_id: int) -> str: r"""Convert a token id to its string representation.""" return self.decode([token_id], special_token_policy=SpecialTokenPolicy.KEEP) def id_to_byte_piece( self, token_id: int, special_token_policy: SpecialTokenPolicy = SpecialTokenPolicy.IGNORE ) -> bytes: r"""Convert a token id to its byte representation. Args: token_id: The token id to convert. special_token_policy: The policy for handling special tokens. Returns: The byte representation of the token. """ if token_id < self.num_special_tokens: if special_token_policy == SpecialTokenPolicy.KEEP: return self._all_special_tokens[token_id]["token_str"].encode("utf-8") elif special_token_policy == SpecialTokenPolicy.RAISE: raise ValueError(f"{token_id} is a special token") elif special_token_policy == SpecialTokenPolicy.IGNORE: return b"" else: raise ValueError(f"Unknown special token policy {special_token_policy}") return self._model.decode_single_token_bytes(token_id - self.num_special_tokens) def _reload_mergeable_ranks( vocab: list[TokenInfo], max_vocab: int | None = None, ) -> dict[bytes, int]: r"""Reload our tokenizer JSON file and convert it to Tiktoken format.""" if max_vocab is not None: assert len(vocab) >= max_vocab, (len(vocab), max_vocab) if len(vocab) > max_vocab: vocab = vocab[:max_vocab] logger.info(f"Cutting non special vocabulary to first {len(vocab)} tokens.") # build ranks ranks: dict[bytes, int] = {} for i, x in enumerate(vocab): assert x.keys() == {"rank", "token_bytes", "token_str"} assert x["rank"] == i merge = base64.b64decode(x["token_bytes"]) assert i >= 256 or merge == bytes([i]), (i, merge) ranks[merge] = x["rank"] # sanity check assert len(ranks) == len(vocab) assert set(ranks.values()) == set(range(len(ranks))) return ranks def is_tekkenizer(tokenizer: Tokenizer) -> TypeGuard[Tekkenizer]: r"""Returns whether the tokenizer is a Tekkenizer.""" return isinstance(tokenizer, Tekkenizer) python-mistral-common-1.11.7/src/mistral_common/tokens/tokenizers/utils.py000066400000000000000000000165101523035502500271100ustar00rootroot00000000000000import logging import os from pathlib import Path from typing import Iterator import requests from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.image import MultiModalVersion _hub_installed: bool try: import huggingface_hub import huggingface_hub.constants import huggingface_hub.errors _hub_installed = True except ImportError: _hub_installed = False logger = logging.getLogger(__name__) def _assert_hub_installed() -> None: if not _hub_installed: raise ImportError( "Please install the `huggingface_hub` package to use this method.\n" "Run `pip install mistral-common[hf-hub]` to install it." ) def chunks(lst: list[str], chunk_size: int) -> Iterator[list[str]]: r"""Chunk a list into smaller lists of a given size. Args: lst: The list to chunk. chunk_size: The size of each chunk. Returns: An iterator over the chunks. Examples: >>> all_chunks = list(chunks([1, 2, 3, 4, 5], 2)) """ for i in range(0, len(lst), chunk_size): yield lst[i : i + chunk_size] def list_local_hf_repo_files(repo_id: str, revision: str | None) -> list[str]: r"""list the files of a local Hugging Face repo. Args: repo_id: The Hugging Face repo ID. revision: The revision of the model to use. If `None`, the latest revision will be used. """ _assert_hub_installed() repo_cache = Path(huggingface_hub.constants.HF_HUB_CACHE) / huggingface_hub.constants.REPO_ID_SEPARATOR.join( ["models", *repo_id.split("/")] ) if revision is None: revision = huggingface_hub.constants.DEFAULT_REVISION if not revision: return [] # Snapshots are keyed by commit hash, so resolve a branch/tag revision (e.g. "main") via `refs`. revision_file = repo_cache / "refs" / revision if revision_file.is_file(): revision = revision_file.read_text().strip() revision_dir = repo_cache / "snapshots" / revision if revision_dir.is_dir(): return os.listdir(revision_dir) return [] def _filter_valid_tokenizer_files(files: list[str]) -> list[tuple[str, str]]: r"""Filter the valid tokenizer files from a list of files. Args: files: The list of files to filter. Returns: The list of tuples of file names and paths to the valid tokenizer files. """ valid_tokenizer_files = [] instruct_versions = list(TokenizerVersion.__members__) mm_versions = list(MultiModalVersion.__members__) + [""] # allow no mm version sentencepiece_suffixes = [f".model.{v}{m}" for v in instruct_versions for m in mm_versions] + [".model"] for file in files: pathlib_file = Path(file) file_name = pathlib_file.name suffix = "".join(pathlib_file.suffixes) if file_name == "tekken.json": valid_tokenizer_files.append((file_name, file)) elif suffix in sentencepiece_suffixes: valid_tokenizer_files.append((file_name, file)) return valid_tokenizer_files def get_one_valid_tokenizer_file(files: list[str]) -> str: r"""Get one valid tokenizer file from a list of files. Args: files: The list of files to filter. Returns: The path to the tokenizer file. """ valid_tokenizer_file_names_and_files = _filter_valid_tokenizer_files(files) if len(valid_tokenizer_file_names_and_files) == 0: raise ValueError("No tokenizer file found.") # If there are multiple tokenizer files, we use tekken.json if it exists, otherwise the versioned one. if len(valid_tokenizer_file_names_and_files) > 1: for file_name, tokenizer_file in valid_tokenizer_file_names_and_files: if "tekken.json" == file_name: return tokenizer_file tokenizer_file = sorted(valid_tokenizer_file_names_and_files, key=lambda x: x[0])[-1][1] logger.warning(f"Multiple valid tokenizer files found. Using {tokenizer_file}.") else: tokenizer_file = valid_tokenizer_file_names_and_files[0][1] return tokenizer_file def download_tokenizer_from_hf_hub( repo_id: str, cache_dir: str | Path | None = None, token: bool | str | None = None, revision: str | None = None, force_download: bool = False, local_files_only: bool = False, ) -> str: r"""Download the tokenizer file of a Mistral model from the Hugging Face Hub. See [here](https://huggingface.co/mistralai/models) for a list of our OSS models. Note: You need to install the `huggingface_hub` package to use this method. Please run `pip install mistral-common[hf-hub]` to install it. Args: repo_id: The Hugging Face repo ID. cache_dir: The directory where the tokenizer will be cached. token: The Hugging Face token to use to download the tokenizer. revision: The revision of the model to use. If `None`, the latest revision will be used. force_download: Whether to force the download of the tokenizer. If `True`, the tokenizer will be downloaded even if it is already cached. local_files_only: Whether to only use local files. If `True`, the tokenizer will be downloaded only if it is already cached. Returns: The downloaded tokenizer local path for the given model ID. """ _assert_hub_installed() if force_download and local_files_only: raise ValueError("You cannot force the download of the tokenizer if you only want to use local files.") if not local_files_only: try: hf_api = huggingface_hub.HfApi() repo_files = hf_api.list_repo_files(repo_id, revision=revision, token=token) local_files_only = False except ( requests.ConnectionError, requests.HTTPError, requests.Timeout, huggingface_hub.errors.OfflineModeIsEnabled, ) as e: if force_download: raise e repo_files = list_local_hf_repo_files(repo_id=repo_id, revision=revision) local_files_only = True logger.info("Could not connect to the Hugging Face Hub. Using local files only.") if len(repo_files) == 0: raise FileNotFoundError( f"Could not connect to the Hugging Face Hub and no local files were found for the repo ID {repo_id}" f" and revision {revision}. Please check your internet connection and try again." ) from e else: repo_files = list_local_hf_repo_files(repo_id=repo_id, revision=revision) if len(repo_files) == 0: raise FileNotFoundError( f"No local files found for the repo ID {repo_id} and revision {revision}. Please check the repo ID and" " the revision or try to download the tokenizer without setting `local_files_only` to `True`." ) try: tokenizer_file = get_one_valid_tokenizer_file(files=repo_files) except ValueError: raise ValueError(f"No valid tokenizer file found in the repo {repo_id}.") tokenizer_path = huggingface_hub.hf_hub_download( repo_id=repo_id, cache_dir=cache_dir, filename=tokenizer_file, token=token, revision=revision, local_files_only=local_files_only, force_download=force_download, ) return tokenizer_path python-mistral-common-1.11.7/tests/000077500000000000000000000000001523035502500172235ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/__init__.py000066400000000000000000000000001523035502500213220ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/000077500000000000000000000000001523035502500201345ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/chat_templates/000077500000000000000000000000001523035502500231315ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/chat_templates/v1.jinja000066400000000000000000000164741523035502500245100ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {{- '[INST] ' }} {%- if loop.index0 == 0 and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- message['content']}} {%- elif message['content'] | length > 0 %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {{- block['text'] }} {%- else %} {{- raise_exception('Only text chunks are supported in user message content.') }} {%- endif %} {%- endfor %} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- ' [/INST]' }} {#- Assistant messages supports text content or text chunks. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is string and message['content'] != '' %} {{- message['content'] }} {%- elif message['content'] | length > 0 %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {{- block['text'] }} {%- else %} {{- raise_exception('Only text chunks are supported in assistant message contents.') }} {%- endif %} {%- endfor %} {#- End of sequence token for each assistant messages. #} {%- else %} {{- raise_exception('Assistant message content must be non-empty.') }} {%- endif %} {{- eos_token}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user and assistant roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v11.jinja000066400000000000000000000224411523035502500245600ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content') -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[CALL_ID]' + id + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents') -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v11_audio.jinja000066400000000000000000000240101523035502500257330ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {%- if messages[0]['role'] == 'system' or default_system_message != '' %} {%- set has_sp = true %} {%- else %} {%- set has_sp = false %} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_audio) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_audio and block['type'] in ['input_audio', 'audio_url'] -%} {%- if has_sp -%} {{- raise_exception('Audio chunks are not supported in user message content when system prompt is provided.') -}} {%- endif -%} {{- '[AUDIO]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, input_audio and audio_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, input_audio and audio_url', support_audio=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_audio=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[CALL_ID]' + id + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_audio=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v11_image.jinja000066400000000000000000000244351523035502500257270ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[CALL_ID]' + id + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v11_image_think.jinja000066400000000000000000000304151523035502500271170ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '' -}}{%- endif -%} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(user_content, 'user message content', supported_types_desc='text, image and image_url', support_thinking=false, support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(message['content'], 'assistant message contents', supported_types_desc='text and thinking', support_thinking=true, support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[CALL_ID]' + id + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(message['content'], 'system message contents', supported_types_desc='text and thinking', support_thinking=true, support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v11_think.jinja000066400000000000000000000266261523035502500257660ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '' -}}{%- endif -%} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' -}} {{- render_content(message['content'], 'user message content', supported_types_desc='text', support_thinking=false) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(message['content'], 'assistant message contents', supported_types_desc='text and thinking', support_thinking=true) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[CALL_ID]' + id + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(message['content'], 'system message contents', supported_types_desc='text and thinking', support_thinking=true) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v13.jinja000066400000000000000000000212061523035502500245600ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_emitted %} {{- available_tools }} {%- set ns.available_tools_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {%- set user_content = message['content'] | sort(attribute='type') %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content') -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents') -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v13_audio.jinja000066400000000000000000000221651523035502500257460ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {%- if messages[0]['role'] == 'system' or default_system_message != '' %} {%- set has_sp = true %} {%- else %} {%- set has_sp = false %} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_audio) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_audio and block['type'] in ['input_audio', 'audio_url'] -%} {%- if has_sp -%} {{- raise_exception('Audio chunks are not supported in user message content when system prompt is provided.') -}} {%- endif -%} {{- '[AUDIO]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, input_audio and audio_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_emitted %} {{- available_tools }} {%- set ns.available_tools_emitted = true %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, input_audio and audio_url', support_audio=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_audio=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_audio=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v13_image.jinja000066400000000000000000000226121523035502500257240ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_emitted %} {{- available_tools }} {%- set ns.available_tools_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v13_image_think.jinja000066400000000000000000000266711523035502500271320ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '[THINK]' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '[/THINK]' -}}{%- endif -%} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_emitted %} {{- available_tools }} {%- set ns.available_tools_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_thinking=false, support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text and thinking', support_thinking=true, support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text and thinking', support_thinking=true, support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v13_think.jinja000066400000000000000000000254721523035502500257660ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '[THINK]' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '[/THINK]' -}}{%- endif -%} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_emitted %} {{- available_tools }} {%- set ns.available_tools_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {%- set user_content = message['content'] | sort(attribute='type') %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text', support_thinking=false) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text and thinking', support_thinking=true) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text and thinking', support_thinking=true) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v15.jinja000066400000000000000000000233511523035502500245650ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools and model settings definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {%- if reasoning_effort is not defined or reasoning_effort is none %} {%- set reasoning_effort = 'none' %} {%- endif %} {%- if reasoning_effort not in ['none', 'high'] %} {{- raise_exception('reasoning_effort must be either "none" or "high"') }} {%- endif %} {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- set ns_msg = namespace(msg_text_parts=[]) %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %} {%- else %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- set ns_msg.msg_text_parts = [] %} {%- endif %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- endif %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_and_settings_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_and_settings_emitted %} {{- available_tools }} {{- model_settings }} {%- set ns.available_tools_and_settings_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {%- set user_content = message['content'] | sort(attribute='type') %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content') -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages (multimodal). #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' -}} {{- render_content(content=message['content'], context_name='tool message contents') -}} {{- '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents') -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v15_audio.jinja000066400000000000000000000236701523035502500257520ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools and model settings definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {%- if reasoning_effort is not defined or reasoning_effort is none %} {%- set reasoning_effort = 'none' %} {%- endif %} {%- if reasoning_effort not in ['none', 'high'] %} {{- raise_exception('reasoning_effort must be either "none" or "high"') }} {%- endif %} {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_audio) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_audio and block['type'] in ['input_audio', 'audio_url'] -%} {{- '[AUDIO]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- set ns_msg = namespace(msg_text_parts=[]) %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %} {%- else %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- set ns_msg.msg_text_parts = [] %} {%- endif %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- endif %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_and_settings_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, input_audio and audio_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_and_settings_emitted %} {{- available_tools }} {{- model_settings }} {%- set ns.available_tools_and_settings_emitted = true %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, input_audio and audio_url', support_audio=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_audio=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages (multimodal). #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' -}} {{- render_content(content=message['content'], context_name='tool message contents', supported_types_desc='text and audio', support_audio=true) -}} {{- '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text and audio', support_audio=true) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v15_image.jinja000066400000000000000000000250511523035502500257260ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools and model settings definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {%- if reasoning_effort is not defined or reasoning_effort is none %} {%- set reasoning_effort = 'none' %} {%- endif %} {%- if reasoning_effort not in ['none', 'high'] %} {{- raise_exception('reasoning_effort must be either "none" or "high"') }} {%- endif %} {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- set ns_msg = namespace(msg_text_parts=[]) %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %} {%- else %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- set ns_msg.msg_text_parts = [] %} {%- endif %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- endif %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_and_settings_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_and_settings_emitted %} {{- available_tools }} {{- model_settings }} {%- set ns.available_tools_and_settings_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages (multimodal). #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' -}} {{- render_content(content=message['content'], context_name='tool message contents', supported_types_desc='text and image', support_images=true) -}} {{- '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v15_image_think.jinja000066400000000000000000000311441523035502500271230ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools and model settings definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {%- if reasoning_effort is not defined or reasoning_effort is none %} {%- set reasoning_effort = 'none' %} {%- endif %} {%- if reasoning_effort not in ['none', 'high'] %} {{- raise_exception('reasoning_effort must be either "none" or "high"') }} {%- endif %} {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '[THINK]' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '[/THINK]' -}}{%- endif -%} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- set ns_msg = namespace(msg_text_parts=[]) %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %} {%- else %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- set ns_msg.msg_text_parts = [] %} {%- endif %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- endif %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_and_settings_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_and_settings_emitted %} {{- available_tools }} {{- model_settings }} {%- set ns.available_tools_and_settings_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_thinking=false, support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text and thinking', support_thinking=true, support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages (multimodal). #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' -}} {{- render_content(content=message['content'], context_name='tool message contents', supported_types_desc='text and image', support_thinking=false, support_images=true) -}} {{- '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_thinking=false, support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v15_think.jinja000066400000000000000000000277061523035502500257720ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools and model settings definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {%- if reasoning_effort is not defined or reasoning_effort is none %} {%- set reasoning_effort = 'none' %} {%- endif %} {%- if reasoning_effort not in ['none', 'high'] %} {{- raise_exception('reasoning_effort must be either "none" or "high"') }} {%- endif %} {%- set model_settings = '[MODEL_SETTINGS]{"reasoning_effort": "' + reasoning_effort + '"}[/MODEL_SETTINGS]' %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_thinking) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_thinking and block['type'] == 'thinking' -%} {{- '[THINK]' + block['thinking'] -}} {%- if block.get('closed', true) -%}{{- '[/THINK]' -}}{%- endif -%} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {#- Convert reasoning / reasoning_content to a leading thinking chunk. #} {%- set reasoning = msg.get('reasoning_content', msg.get('reasoning', none)) %} {%- if reasoning is not none and reasoning != '' %} {%- if msg['content'] is not none and msg['content'] is not string %} {%- for block in msg['content'] %} {%- if block['type'] == 'thinking' %} {{- raise_exception('Message cannot have both thinking chunks in content and a top-level `reasoning` or `reasoning_content` field.') }} {%- endif %} {%- endfor %} {%- endif %} {%- set think_chunk = {'type': 'thinking', 'thinking': reasoning} %} {%- if msg['content'] is string and msg['content'] != '' %} {%- set new_content = [think_chunk, {'type': 'text', 'text': msg['content']}] %} {%- elif msg['content'] is not none and msg['content'] is not string and msg['content'] | length > 0 %} {%- set new_content = [think_chunk] + msg['content'] | list %} {%- else %} {%- set new_content = [think_chunk] %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set msg = {'role': msg['role'], 'content': new_content, 'tool_calls': msg['tool_calls']} %} {%- else %} {%- set msg = {'role': msg['role'], 'content': new_content} %} {%- endif %} {%- endif %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- set ns_msg = namespace(msg_text_parts=[]) %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_msg.msg_text_parts = ns_msg.msg_text_parts + [block['text']] %} {%- else %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- set ns_msg.msg_text_parts = [] %} {%- endif %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- if ns_msg.msg_text_parts | length > 0 %} {%- set ns_c.text_parts = ns_c.text_parts + [ns_msg.msg_text_parts | join('')] %} {%- endif %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(available_tools_and_settings_emitted=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if not ns.available_tools_and_settings_emitted %} {{- available_tools }} {{- model_settings }} {%- set ns.available_tools_and_settings_emitted = true %} {%- endif %} {%- if message['content'] is not string and message['content'] %} {%- set user_content = message['content'] | sort(attribute='type') %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text', support_thinking=false) -}} {{- '[/INST]' }} {#- Assistant messages supports text and thinking content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text and thinking', support_thinking=true) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- for tool in message['tool_calls'] %} {{- '[TOOL_CALLS]' }} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- name + '[ARGS]' + arguments }} {%- endfor %} {%- endif %} {{- eos_token }} {#- Tool messages (multimodal). #} {%- elif message['role'] == 'tool' %} {{- '[TOOL_RESULTS]' -}} {{- render_content(content=message['content'], context_name='tool message contents', supported_types_desc='text', support_thinking=false) -}} {{- '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_thinking=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v1_spm.jinja000066400000000000000000000166241523035502500253640ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {{- ' [INST] ' }} {%- if loop.index0 == 0 and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- message['content']}} {%- elif message['content'] | length > 0 %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {{- block['text'] }} {%- else %} {{- raise_exception('Only text chunks are supported in user message content.') }} {%- endif %} {%- endfor %} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- ' [/INST]' }} {%- if loop.index < loop.length %} {{- " " }} {%- endif %} {#- Assistant messages supports text content or text chunks. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is string and message['content'] != '' %} {{- message['content'] }} {%- elif message['content'] | length > 0 %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {{- block['text'] }} {%- else %} {{- raise_exception('Only text chunks are supported in assistant message contents.') }} {%- endif %} {%- endfor %} {#- End of sequence token for each assistant messages. #} {%- else %} {{- raise_exception('Assistant message content must be non-empty.') }} {%- endif %} {{- eos_token}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user and assistant roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v2.jinja000066400000000000000000000254371523035502500245100ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' }} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- elif message['content'] | length > 0 %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {{- eos_token }} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index > ns.max_idx_user %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + '}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {{- eos_token }} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index <= ns.max_idx_user %} {%- else %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {#- Tool messages supports int, float or text content. #} {%- elif message['role'] == 'tool' and ns.index > ns.max_idx_user %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['name'] is undefined or message['name'] is none %} {{- raise_exception('Tool message must have a name.') }} {%- endif %} {%- set tool_message = {'name': message['name'], 'content': tool_content} %} {{- '[TOOL_RESULTS][' + (tool_message|tojson) + '][/TOOL_RESULTS]' }} {#- Raise exception for unsupported roles. #} {%- elif message['role'] != 'tool' or ns.index > ns.max_idx_user %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v2_spm.jinja000066400000000000000000000267531523035502500253710ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS] ' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1, add_space=false, prev_tool=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST] ' }} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- elif message['content'] | length > 0 %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {%- if loop.index < loop.length %} {%- set ns.add_space=true %} {%- endif %} {%- set ns.prev_tool=false %} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if message['content'] %} {%- if ns.add_space %} {{- ' ' }} {%- set ns.add_space=false %} {%- endif %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {{- eos_token }} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index > ns.max_idx_user %} {%- if ns.add_space %} {%- set ns.add_space=false %} {%- endif %} {{- '[TOOL_CALLS] [' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + '}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {{- eos_token }} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 and ns.index <= ns.max_idx_user %} {%- else %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- set ns.prev_tool=false %} {#- Tool messages supports int, float or text content. #} {%- elif message['role'] == 'tool' and ns.index > ns.max_idx_user %} {%- if ns.add_space %} {%- if not ns.prev_tool %} {{- ' '}} {%- endif %} {%- set ns.add_space=false %} {%- endif %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['name'] is undefined or message['name'] is none %} {{- raise_exception('Tool message must have a name.') }} {%- endif %} {%- set tool_message = {'name': message['name'], 'content': tool_content} %} {{- '[TOOL_RESULTS] [' + (tool_message|tojson) + '][/TOOL_RESULTS]' }} {%- set ns.prev_tool=true %} {%- set ns.add_space=true %} {#- Raise exception for unsupported roles. #} {%- elif message['role'] != 'tool' or ns.index > ns.max_idx_user %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v3.jinja000066400000000000000000000263561523035502500245120ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' }} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- elif message['content'] | length > 0 %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %} {{- '[TOOL_RESULTS]' + (tool_message|tojson) + '[/TOOL_RESULTS]' }} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v3_image.jinja000066400000000000000000000300371523035502500256430ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' }} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {%- elif message['content'] | length > 0 %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {{- render_content(content=blocks, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %} {{- '[TOOL_RESULTS]' + (tool_message|tojson) + '[/TOOL_RESULTS]' }} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v3_image_spm.jinja000066400000000000000000000320671523035502500265270ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS] ' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images, initial_prev_img) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- set _ns = namespace(prev_img=initial_prev_img) -%} {%- for block in content -%} {%- if _ns.prev_img and block['type'] == 'text' -%} {{- ' ' -}} {%- endif -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- set _ns.prev_img = false -%} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- set _ns.prev_img = true -%} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1, add_space=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' }} {%- set added_sp = false %} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {%- set added_sp = true %} {{- ' ' + system_message + '\n\n' }} {%- elif message['content'] is string %} {{- ' ' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, image and image_url', support_images=true, initial_prev_img=not added_sp) -}} {%- elif message['content'] | length > 0 %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {{- render_content(content=blocks, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true, initial_prev_img=not added_sp) -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {%- if loop.index < loop.length %} {%- set ns.add_space=true %} {%- endif %} {%- set ns.prev_tool=false %} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if message['content'] %} {%- if ns.add_space %} {{- ' ' }} {%- set ns.add_space=false %} {%- endif %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false, initial_prev_img=false) -}} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- if ns.add_space %} {%- set ns.add_space=false %} {%- endif %} {{- '[TOOL_CALLS] [' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- else %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {{- eos_token }} {%- set ns.prev_tool=false %} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if ns.add_space %} {%- if not ns.prev_tool %} {{- ' '}} {%- endif %} {%- set ns.add_space=false %} {%- endif %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %} {{- '[TOOL_RESULTS] ' + (tool_message|tojson) + '[/TOOL_RESULTS]' }} {%- set ns.prev_tool=true %} {%- set ns.add_space=true %} {#- Raise exception for unsupported roles. #} {%- elif message['role'] != 'tool' or ns.index > ns.max_idx_user %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v3_spm.jinja000066400000000000000000000273551523035502500253710ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {#- Extract all system messages and merge into system prompt. #} {%- set ns_sys = namespace(system_parts=[], filtered=[]) %} {%- for message in messages %} {%- if message['role'] == 'system' %} {%- if message['content'] is string %} {%- set ns_sys.system_parts = ns_sys.system_parts + [message['content']] %} {%- else %} {%- for block in message['content'] %} {%- if block['type'] == 'text' %} {%- set ns_sys.system_parts = ns_sys.system_parts + [block['text']] %} {%- else %} {{- raise_exception('Only text chunks are supported in system message content.') }} {%- endif %} {%- endfor %} {%- endif %} {%- else %} {%- set ns_sys.filtered = ns_sys.filtered + [message] %} {%- endif %} {%- endfor %} {%- if ns_sys.system_parts | length > 0 %} {%- set system_message = ns_sys.system_parts | join('\n\n') %} {%- else %} {%- set system_message = default_system_message %} {%- endif %} {%- set loop_messages = ns_sys.filtered %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS] ' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1, add_space=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] != 'user' %} {{- raise_exception('Conversation must start with a user message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST] ' }} {%- if (ns.index == ns.max_idx_user) and system_message != '' %} {{- system_message + '\n\n' }} {%- endif %} {%- if message['content'] is string %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- elif message['content'] | length > 0 %} {{- render_content(content=message['content'], context_name='user message content') -}} {%- else %} {{- raise_exception('User message must have a string or a list of chunks in content') }} {%- endif %} {{- '[/INST]' }} {%- if loop.index < loop.length %} {%- set ns.add_space=true %} {%- endif %} {%- set ns.prev_tool=false %} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if message['content'] is not none and message['content'] | length > 0 and message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- raise_exception('Assistant message cannot have both content and tool calls.') }} {%- endif %} {%- if message['content'] %} {%- if ns.add_space %} {{- ' ' }} {%- set ns.add_space=false %} {%- endif %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- elif message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- if ns.add_space %} {%- set ns.add_space=false %} {%- endif %} {{- '[TOOL_CALLS] [' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- else %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {{- eos_token }} {%- set ns.prev_tool=false %} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if ns.add_space %} {%- if not ns.prev_tool %} {{- ' '}} {%- endif %} {%- set ns.add_space=false %} {%- endif %} {# Try to parse 'content' as int or float if possible #} {%- set tool_content = message['content']|string %} {# Try to parse as int #} {%- set parsed_int = message['content']|int %} {% if parsed_int|string == message['content'] %} {%- set tool_content = parsed_int %} {# If int fails, try to parse as float #} {%- else %} {%- set parsed_float = message['content']|float %} {%- if parsed_float|string == message['content'] %} {%- set tool_content = parsed_float %} {%- endif %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {%- set tool_message = {'content': tool_content, 'call_id': tool_id} %} {{- '[TOOL_RESULTS] ' + (tool_message|tojson) + '[/TOOL_RESULTS]' }} {%- set ns.prev_tool=true %} {%- set ns.add_space=true %} {#- Raise exception for unsupported roles. #} {%- elif message['role'] != 'tool' or ns.index > ns.max_idx_user %} {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v7.jinja000066400000000000000000000227311523035502500245070ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content') -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents') -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v7_audio.jinja000066400000000000000000000243001523035502500256620ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {%- if messages[0]['role'] == 'system' or default_system_message != '' %} {%- set has_sp = true %} {%- else %} {%- set has_sp = false %} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_audio) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_audio and block['type'] in ['input_audio', 'audio_url'] -%} {%- if has_sp -%} {{- raise_exception('Audio chunks are not supported in user message content when system prompt is provided.') -}} {%- endif -%} {{- '[AUDIO]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, input_audio and audio_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST]' -}} {{- render_content(content=message['content'], context_name='user message content', supported_types_desc='text, input_audio and audio_url', support_audio=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_audio=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_audio=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v7_image.jinja000066400000000000000000000247251523035502500256560ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- else %} {%- set user_content = message['content'] %} {%- endif %} {{- '[INST]' -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true) -}} {{- '[/INST]' }} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {{- '[TOOL_CALLS][' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS]' + tool_id + '[TOOL_CONTENT]' + message['content']|string + '[/TOOL_RESULTS]' }} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT]' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_images=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v7_image_spm.jinja000066400000000000000000000271371523035502500265350ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS] ' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name, supported_types_desc, support_images, initial_prev_img) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- set _ns = namespace(prev_img=initial_prev_img) -%} {%- for block in content -%} {%- if _ns.prev_img and block['type'] == 'text' -%} {{- ' ' -}} {%- endif -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- set _ns.prev_img = false -%} {%- elif support_images and block['type'] in ['image', 'image_url'] -%} {{- '[IMG]' -}} {%- set _ns.prev_img = true -%} {%- else -%} {{- raise_exception('Only ' + supported_types_desc + ' chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1, add_space=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text, image and image_url content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {%- if message['content'] is not string and message['content'] %} {#- When content has exactly one image and one text block, put image first. #} {%- if message['content'] | length == 2 and message['content'][0]['type'] == 'text' and message['content'][1]['type'] in ['image', 'image_url'] %} {%- set blocks = [message['content'][1], message['content'][0]] %} {%- else %} {%- set blocks = message['content'] %} {%- endif %} {%- set user_content = blocks %} {%- set inst_tag = '[INST]' %} {%- else %} {%- set user_content = message['content'] %} {%- set inst_tag = '[INST] ' %} {%- endif %} {{- inst_tag -}} {{- render_content(content=user_content, context_name='user message content', supported_types_desc='text, image and image_url', support_images=true, initial_prev_img=not added_sp) -}} {{- '[/INST]' }} {%- if loop.index < loop.length %} {%- set ns.add_space=true %} {%- endif %} {%- set ns.prev_tool=false %} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {%- if ns.add_space %} {{- ' ' }} {%- set ns.add_space=false %} {%- endif %} {{- render_content(content=message['content'], context_name='assistant message contents', supported_types_desc='text', support_images=false, initial_prev_img=false) -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- if ns.add_space %} {%- set ns.add_space=false %} {%- endif %} {{- '[TOOL_CALLS] [' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {%- set ns.prev_tool=false %} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if ns.add_space %} {%- if not ns.prev_tool %} {{- ' '}} {%- endif %} {%- set ns.add_space=false %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS] ' + tool_id + '[TOOL_CONTENT] ' + message['content']|string + '[/TOOL_RESULTS]' }} {%- set ns.prev_tool=true %} {%- set ns.add_space=true %} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT] ' -}} {{- render_content(content=message['content'], context_name='system message contents', supported_types_desc='text', support_images=false, initial_prev_img=false) -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/chat_templates/v7_spm.jinja000066400000000000000000000242261523035502500253670ustar00rootroot00000000000000{#- Default system message if no system prompt is passed. #} {%- set default_system_message = '' %} {#- Begin of sequence token. #} {{- bos_token }} {#- Handle system prompt if it exists. #} {%- set loop_messages = messages %} {%- if messages[0]['role'] != 'system' and default_system_message != '' %} {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} {%- endif %} {#- Tools definition #} {%- set available_tools = '' %} {%- set has_tools = false %} {%- if tools is defined and tools is not none and tools|length > 0 %} {%- set has_tools = true %} {%- set available_tools = '[AVAILABLE_TOOLS] ' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} {%- endif %} {#- Macros #} {%- macro render_content(content, context_name) -%} {%- if content is string -%} {{- content -}} {%- elif content -%} {%- for block in content -%} {%- if block['type'] == 'text' -%} {{- block['text'] -}} {%- else -%} {{- raise_exception('Only text chunks are supported in ' + context_name + '.') -}} {%- endif -%} {%- endfor -%} {%- else -%} {{- raise_exception(context_name + ' must have non-empty content.') -}} {%- endif -%} {%- endmacro -%} {#- Aggregate consecutive messages with the same role except system and tool. #} {#- A sentinel message is appended so the last group gets flushed inside the loop. #} {%- set ns_agg = namespace(messages=[], current_group=[], current_role=none) %} {%- for message in loop_messages + [{'role': '__sentinel__'}] %} {%- if message['role'] != ns_agg.current_role or message['role'] == 'system' or message['role'] == 'tool' %} {%- if ns_agg.current_role == 'tool' %} {%- set ns_agg.messages = ns_agg.messages + ns_agg.current_group %} {%- elif ns_agg.current_role is not none %} {%- set ns_c = namespace(text_parts=[], chunks=[], has_non_text=false, tool_calls=[]) %} {%- for msg in ns_agg.current_group %} {%- if msg['content'] is string %} {%- set ns_c.text_parts = ns_c.text_parts + [msg['content']] %} {%- elif msg['content'] is not none %} {%- for block in msg['content'] %} {%- if block['type'] == 'text' %} {%- set ns_c.text_parts = ns_c.text_parts + [block['text']] %} {%- else %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- set ns_c.text_parts = [] %} {%- endif %} {%- set ns_c.chunks = ns_c.chunks + [block] %} {%- set ns_c.has_non_text = true %} {%- endif %} {%- endfor %} {%- endif %} {%- if msg['tool_calls'] is defined and msg['tool_calls'] is not none %} {%- set ns_c.tool_calls = ns_c.tool_calls + msg['tool_calls'] | list %} {%- endif %} {%- endfor %} {%- if ns_c.has_non_text %} {%- if ns_c.text_parts | length > 0 %} {%- set ns_c.chunks = ns_c.chunks + [{'type': 'text', 'text': ns_c.text_parts | join('\n\n')}] %} {%- endif %} {%- set merged_content = ns_c.chunks %} {%- else %} {%- set merged_content = ns_c.text_parts | join('\n\n') %} {%- endif %} {%- if ns_c.tool_calls | length > 0 %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content, 'tool_calls': ns_c.tool_calls}] %} {%- else %} {%- set ns_agg.messages = ns_agg.messages + [{'role': ns_agg.current_role, 'content': merged_content}] %} {%- endif %} {%- endif %} {%- if message['role'] != '__sentinel__' %} {%- set ns_agg.current_group = [message] %} {%- set ns_agg.current_role = message['role'] %} {%- endif %} {%- else %} {%- set ns_agg.current_group = ns_agg.current_group + [message] %} {%- endif %} {%- endfor %} {%- set loop_messages = ns_agg.messages %} {#- Validates message ordering. #} {%- set ns = namespace(max_idx_user=-1, add_space=false) %} {%- if loop_messages | length > 0 and loop_messages[0]['role'] not in ['user', 'system'] %} {{- raise_exception('Conversation must start with a user or system message, got ' + loop_messages[0]['role'] + '.') }} {%- endif %} {%- set ns_order = namespace(previous_role=none) %} {%- for message in loop_messages %} {%- set current_role = message['role'] %} {%- if ns_order.previous_role is not none %} {%- if ns_order.previous_role == 'system' %} {%- if current_role not in ['user', 'assistant', 'system'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'user' %} {%- if current_role not in ['assistant', 'system', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'assistant' %} {%- if current_role not in ['assistant', 'user', 'tool'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- elif ns_order.previous_role == 'tool' %} {%- if current_role not in ['assistant', 'tool', 'user'] %} {{- raise_exception('Unexpected role \'' + current_role + '\' after role \'' + ns_order.previous_role + '\'') }} {%- endif %} {%- endif %} {%- endif %} {%- if message.role == 'user' %} {%- set ns.max_idx_user = ns.max_idx_user + 1 %} {%- endif %} {%- set ns_order.previous_role = current_role %} {%- endfor %} {%- set ns.index = 0 %} {#- Handle conversation messages. #} {%- for message in loop_messages %} {#- User messages supports text content. #} {%- if message['role'] == 'user' %} {%- if (ns.index == ns.max_idx_user) and has_tools %} {{- available_tools }} {%- endif %} {{- '[INST] ' -}} {{- render_content(content=message['content'], context_name='user message content') -}} {{- '[/INST]' }} {%- if loop.index < loop.length %} {%- set ns.add_space=true %} {%- endif %} {%- set ns.prev_tool=false %} {#- Assistant messages supports text content. #} {%- elif message['role'] == 'assistant' %} {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} {%- endif %} {%- if message['content'] %} {%- if ns.add_space %} {{- ' ' }} {%- set ns.add_space=false %} {%- endif %} {{- render_content(content=message['content'], context_name='assistant message contents') -}} {%- endif %} {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} {%- if ns.add_space %} {%- set ns.add_space=false %} {%- endif %} {{- '[TOOL_CALLS] [' }} {%- for tool in message['tool_calls'] %} {%- set name = tool['function']['name'] %} {%- set arguments = tool['function']['arguments'] %} {%- set id = tool['id']%} {%- if id is not defined or id|length != 9 %} {{- raise_exception('Tool call must have an id of 9 characters or numbers.') }} {%- endif %} {%- if arguments is not string %} {%- set arguments = arguments|tojson|safe %} {%- elif arguments == '' %} {%- set arguments = '{}' %} {%- endif %} {{- '{"name": "' + name + '", "arguments": ' + arguments + ', "id": "' + id + '"}' }} {%- if loop.length > 1 and loop.index < loop.length %} {{- ', ' }} {%- endif %} {%- endfor %} {{- ']' }} {%- endif %} {{- eos_token }} {%- set ns.prev_tool=false %} {#- Tool messages only supports text content. #} {%- elif message['role'] == 'tool' %} {%- if ns.add_space %} {%- if not ns.prev_tool %} {{- ' '}} {%- endif %} {%- set ns.add_space=false %} {%- endif %} {%- if message['call_id'] is not undefined and message['call_id']|length == 9 %} {%- set tool_id = message['call_id'] %} {%- elif message['tool_call_id'] is not undefined and message['tool_call_id']|length == 9 %} {%- set tool_id = message['tool_call_id'] %} {%- else %} {{- raise_exception('Tool message must have a call_id or tool_call_id of 9 characters or numbers.') }} {%- endif %} {{- '[TOOL_RESULTS] ' + tool_id + '[TOOL_CONTENT] ' + message['content']|string + '[/TOOL_RESULTS]' }} {%- set ns.prev_tool=true %} {%- set ns.add_space=true %} {#- System messages. #} {%- elif message['role'] == 'system' %} {{- '[SYSTEM_PROMPT] ' -}} {{- render_content(content=message['content'], context_name='system message contents') -}} {{- '[/SYSTEM_PROMPT]' -}} {#- Raise exception for unsupported roles. #} {%- else %} {{- raise_exception('Only user, assistant, system and tool roles are supported, got ' + message['role'] + '.') }} {%- endif %} {%- if message['role'] == 'user' %} {%- set ns.index = ns.index + 1 %} {%- endif %} {%- endfor %} python-mistral-common-1.11.7/tests/data/emoji.lark000066400000000000000000000001001523035502500221010ustar00rootroot00000000000000start: emoji+ emoji: /(\p{Emoji_Presentation}|\p{Emoji}\uFE0F)/ python-mistral-common-1.11.7/tests/data/samples/000077500000000000000000000000001523035502500216005ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/get_weather_full/000077500000000000000000000000001523035502500251205ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/get_weather_full/sample.json000066400000000000000000000071341523035502500273010ustar00rootroot00000000000000{ "messages": [ { "role": "system", "content": "Don't make assumptions about what values to plug into functions." }, { "role": "system", "content": "Ask for clarification if a user request is ambiguous." }, { "role": "user", "content": "What's the weather like today" }, { "role": "assistant", "content": "Sure, can you please provide me with your location?" }, { "role": "user", "content": "I'm in Paris, France." }, { "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"location\":\"Paris, France\",\"format\":\"celsius\"}", "name": "get_current_weather" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "get_current_weather", "content": "22" }, { "content": "The current temperature in Paris, France is 22 degrees Celsius.", "role": "assistant" }, { "role": "user", "content": "what is the weather going to be like in Paris, France over the next x days" }, { "content": "Please tell me the number of days you want the weather forecast for.", "role": "assistant" }, { "role": "user", "content": "5 days" }, { "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"location\":\"Paris, France\",\"format\":\"celsius\",\"num_days\":5}", "name": "get_n_day_weather_forecast" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "get_n_day_weather_forecast", "content": "{\"2024-05-22\":\"22\",\"2024-05-23\":\"23\",\"2024-05-24\":\"24\",\"2024-05-25\":\"25\",\"2024-05-26\":\"26\"}" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." } }, "required": [ "location", "format" ] } } }, { "type": "function", "function": { "name": "get_n_day_weather_forecast", "description": "Get an N-day weather forecast", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." }, "num_days": { "type": "integer", "description": "The number of days to forecast" } }, "required": [ "location", "format", "num_days" ] } } } ] }python-mistral-common-1.11.7/tests/data/samples/get_weather_full/text_v2.txt000066400000000000000000000043401523035502500272550ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁Don't▁make▁assumptions▁about▁what▁values▁to▁plug▁into▁functions.<0x0A><0x0A>Ask▁for▁clarification▁if▁a▁user▁request▁is▁ambiguous.<0x0A><0x0A>5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5}}][TOOL_RESULTS]▁[{"name":▁"get_n_day_weather_forecast",▁"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"}}][/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_full/text_v3.txt000066400000000000000000000047161523035502500272650ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST][TOOL_CALLS]▁[{"name":▁"get_current_weather",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius"},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁22,▁"call_id":▁"123456789"}[/TOOL_RESULTS]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁Don't▁make▁assumptions▁about▁what▁values▁to▁plug▁into▁functions.<0x0A><0x0A>Ask▁for▁clarification▁if▁a▁user▁request▁is▁ambiguous.<0x0A><0x0A>5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"},▁"call_id":▁"123456789"}[/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_full/tokens_v2.json000066400000000000000000000172041523035502500277310ustar00rootroot00000000000000{ 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"system", "content": "Ask for clarification if a user request is ambiguous." }, { "role": "user", "content": "What's the weather like today" }, { "role": "assistant", "content": "Sure, can you please provide me with your location?", "tool_calls": null }, { "role": "user", "content": "I'm in Paris, France." }, { "content": "The current temperature in Paris, France is 22 degrees Celsius.", "role": "assistant", "tool_calls": null }, { "role": "user", "content": "what is the weather going to be like in Paris, France over the next x days" }, { "content": "Please tell me the number of days you want the weather forecast for.", "role": "assistant", "tool_calls": null }, { "role": "user", "content": "5 days" }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\n \"location\": \"Paris, France\",\n \"format\": \"celsius\",\n \"num_days\": 5\n}", "name": "get_n_day_weather_forecast" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "get_n_day_weather_forecast", "content": "{\"2024-05-22\": \"22\", \"2024-05-23\": \"23\", \"2024-05-24\": \"24\", \"2024-05-25\": \"25\", \"2024-05-26\": \"26\"}" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." } }, "required": [ "location", "format" ] } } }, { "type": "function", "function": { "name": "get_n_day_weather_forecast", "description": "Get an N-day weather forecast", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." }, "num_days": { "type": "integer", "description": "The number of days to forecast" } }, "required": [ "location", "format", "num_days" ] } } } ] }python-mistral-common-1.11.7/tests/data/samples/get_weather_no_history/text_v2.txt000066400000000000000000000043401523035502500305100ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁Don't▁make▁assumptions▁about▁what▁values▁to▁plug▁into▁functions.<0x0A><0x0A>Ask▁for▁clarification▁if▁a▁user▁request▁is▁ambiguous.<0x0A><0x0A>5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5}}][TOOL_RESULTS]▁[{"name":▁"get_n_day_weather_forecast",▁"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"}}][/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_no_history/text_v3.txt000066400000000000000000000043471523035502500305200ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁Don't▁make▁assumptions▁about▁what▁values▁to▁plug▁into▁functions.<0x0A><0x0A>Ask▁for▁clarification▁if▁a▁user▁request▁is▁ambiguous.<0x0A><0x0A>5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"},▁"call_id":▁"123456789"}[/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_no_history/tokens_v2.json000066400000000000000000000172041523035502500311640ustar00rootroot00000000000000{ 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1081, 2032, 1113, 29508, 29518, 29538, 29549, 29550, 29552, 29555, 29551, 29542, 18163, 9 ] }python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/000077500000000000000000000000001523035502500275775ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/sample.json000066400000000000000000000070201523035502500317520ustar00rootroot00000000000000{ "messages": [ { "role": "user", "content": "What's the weather like today" }, { "role": "assistant", "content": "Sure, can you please provide me with your location?", "tool_calls": null }, { "role": "user", "content": "I'm in Paris, France." }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\n \"location\": \"Paris, France\",\n \"format\": \"celsius\"\n}", "name": "get_current_weather" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "get_current_weather", "content": "22" }, { "content": "The current temperature in Paris, France is 22 degrees Celsius.", "role": "assistant", "tool_calls": null }, { "role": "user", "content": "what is the weather going to be like in Paris, France over the next x days" }, { "content": "Please tell me the number of days you want the weather forecast for.", "role": "assistant", "tool_calls": null }, { "role": "user", "content": "5 days" }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\n \"location\": \"Paris, France\",\n \"format\": \"celsius\",\n \"num_days\": 5\n}", "name": "get_n_day_weather_forecast" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "get_n_day_weather_forecast", "content": "{\"2024-05-22\": \"22\", \"2024-05-23\": \"23\", \"2024-05-24\": \"24\", \"2024-05-25\": \"25\", \"2024-05-26\": \"26\"}" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." } }, "required": [ "location", "format" ] } } }, { "type": "function", "function": { "name": "get_n_day_weather_forecast", "description": "Get an N-day weather forecast", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "format": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The temperature unit to use. Infer this from the users location." }, "num_days": { "type": "integer", "description": "The number of days to forecast" } }, "required": [ "location", "format", "num_days" ] } } } ] }python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/text_v2.txt000066400000000000000000000040611523035502500317340ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5}}][TOOL_RESULTS]▁[{"name":▁"get_n_day_weather_forecast",▁"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"}}][/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/text_v3.txt000066400000000000000000000044371523035502500317440ustar00rootroot00000000000000[INST]▁What's▁the▁weather▁like▁today[/INST]▁Sure,▁can▁you▁please▁provide▁me▁with▁your▁location?[INST]▁I'm▁in▁Paris,▁France.[/INST][TOOL_CALLS]▁[{"name":▁"get_current_weather",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius"},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁22,▁"call_id":▁"123456789"}[/TOOL_RESULTS]▁The▁current▁temperature▁in▁Paris,▁France▁is▁22▁degrees▁Celsius.[INST]▁what▁is▁the▁weather▁going▁to▁be▁like▁in▁Paris,▁France▁over▁the▁next▁x▁days[/INST]▁Please▁tell▁me▁the▁number▁of▁days▁you▁want▁the▁weather▁forecast▁for.[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"get_current_weather",▁"description":▁"Get▁the▁current▁weather",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."}},▁"required":▁["location",▁"format"]}}},▁{"type":▁"function",▁"function":▁{"name":▁"get_n_day_weather_forecast",▁"description":▁"Get▁an▁N-day▁weather▁forecast",▁"parameters":▁{"type":▁"object",▁"properties":▁{"location":▁{"type":▁"string",▁"description":▁"The▁city▁and▁state,▁e.g.▁San▁Francisco,▁CA"},▁"format":▁{"type":▁"string",▁"enum":▁["celsius",▁"fahrenheit"],▁"description":▁"The▁temperature▁unit▁to▁use.▁Infer▁this▁from▁the▁users▁location."},▁"num_days":▁{"type":▁"integer",▁"description":▁"The▁number▁of▁days▁to▁forecast"}},▁"required":▁["location",▁"format",▁"num_days"]}}}][/AVAILABLE_TOOLS][INST]▁5▁days[/INST][TOOL_CALLS]▁[{"name":▁"get_n_day_weather_forecast",▁"arguments":▁{"location":▁"Paris,▁France",▁"format":▁"celsius",▁"num_days":▁5},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁{"2024-05-22":▁"22",▁"2024-05-23":▁"23",▁"2024-05-24":▁"24",▁"2024-05-25":▁"25",▁"2024-05-26":▁"26"},▁"call_id":▁"123456789"}[/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/tokens_v2.json000066400000000000000000000163511523035502500324120ustar00rootroot00000000000000{ 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29549, 1316, 1113, 29518, 29502, 29518, 29549, 29501, 29502, 29550, 29501, 29518, 29550, 2032, 1113, 29518, 29550, 1316, 1113, 29518, 29502, 29518, 29549, 29501, 29502, 29550, 29501, 29518, 29552, 2032, 1113, 29518, 29552, 29507, 1743, 29561, 9 ] }python-mistral-common-1.11.7/tests/data/samples/get_weather_no_system_prompt/tokens_v3.json000066400000000000000000000206741523035502500324160ustar00rootroot00000000000000{ "tokens": [ 1, 3, 2592, 29510, 29481, 1040, 8854, 1505, 3922, 4, 13643, 29493, 1309, 1136, 5433, 3852, 1296, 1163, 1342, 5491, 29572, 2, 3, 1083, 29510, 29487, 1065, 6233, 29493, 5611, 29491, 4, 5, 1501, 7567, 1629, 2032, 1113, 1295, 29498, 3790, 29498, 1537, 1991, 1316, 1113, 17452, 2032, 10598, 3501, 2032, 1113, 4684, 1046, 29493, 5611, 1316, 1113, 4530, 2032, 1113, 29485, 1958, 3938, 8474, 1113, 1081, 2032, 1113, 29508, 29518, 29538, 29549, 29550, 29552, 29555, 29551, 29542, 29507, 10925, 2, 8, 10598, 4557, 2032, 29473, 29518, 29518, 29493, 1113, 3613, 29498, 1081, 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1113, 29518, 29502, 29518, 29549, 29501, 29502, 29550, 29501, 29518, 29552, 2032, 1113, 29518, 29552, 8474, 1113, 3613, 29498, 1081, 2032, 1113, 29508, 29518, 29538, 29549, 29550, 29552, 29555, 29551, 29542, 18163, 9 ] }python-mistral-common-1.11.7/tests/data/samples/no_tools/000077500000000000000000000000001523035502500234345ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/no_tools/sample.json000066400000000000000000000013541523035502500256130ustar00rootroot00000000000000{ "messages": [ { "role": "user", "content": "What's the result of 5 + 5?" }, { "role": "assistant", "content": "The result of 5 + 5 is 10.", "tool_calls": null }, { "role": "user", "content": "What is the square root of 64?" }, { "role": "assistant", "content": "The square root of 64 is 8, because 8 x 8 equals 64.", "tool_calls": null }, { "role": "user", "content": "Can you multiply the results of the previous two questions?" }, { "role": "assistant", "content": "Sure! The result of 10 x 8 is 80.", "tool_calls": null }, { "role": "user", "content": "Thanks" } ], "tools": [] }python-mistral-common-1.11.7/tests/data/samples/no_tools/text_v1.txt000066400000000000000000000006711523035502500255730ustar00rootroot00000000000000▁[INST]▁What's▁the▁result▁of▁5▁+▁5?▁[/INST]▁The▁result▁of▁5▁+▁5▁is▁10.▁[INST]▁What▁is▁the▁square▁root▁of▁64?▁[/INST]▁The▁square▁root▁of▁64▁is▁8,▁because▁8▁x▁8▁equals▁64.▁[INST]▁Can▁you▁multiply▁the▁results▁of▁the▁previous▁two▁questions?▁[/INST]▁Sure!▁The▁result▁of▁10▁x▁8▁is▁80.▁[INST]▁Thanks▁[/INST]python-mistral-common-1.11.7/tests/data/samples/no_tools/text_v2.txt000066400000000000000000000006411523035502500255710ustar00rootroot00000000000000[INST]▁What's▁the▁result▁of▁5▁+▁5?[/INST]▁The▁result▁of▁5▁+▁5▁is▁10.[INST]▁What▁is▁the▁square▁root▁of▁64?[/INST]▁The▁square▁root▁of▁64▁is▁8,▁because▁8▁x▁8▁equals▁64.[INST]▁Can▁you▁multiply▁the▁results▁of▁the▁previous▁two▁questions?[/INST]▁Sure!▁The▁result▁of▁10▁x▁8▁is▁80.[INST]▁Thanks[/INST]python-mistral-common-1.11.7/tests/data/samples/no_tools/text_v3.txt000066400000000000000000000006411523035502500255720ustar00rootroot00000000000000[INST]▁What's▁the▁result▁of▁5▁+▁5?[/INST]▁The▁result▁of▁5▁+▁5▁is▁10.[INST]▁What▁is▁the▁square▁root▁of▁64?[/INST]▁The▁square▁root▁of▁64▁is▁8,▁because▁8▁x▁8▁equals▁64.[INST]▁Can▁you▁multiply▁the▁results▁of▁the▁previous▁two▁questions?[/INST]▁Sure!▁The▁result▁of▁10▁x▁8▁is▁80.[INST]▁Thanks[/INST]python-mistral-common-1.11.7/tests/data/samples/no_tools/tokens_v1.json000066400000000000000000000032451523035502500262440ustar00rootroot00000000000000{ "tokens": [ 1, 733, 16289, 28793, 1824, 28742, 28713, 272, 1204, 302, 28705, 28782, 648, 28705, 28782, 28804, 733, 28748, 16289, 28793, 415, 1204, 302, 28705, 28782, 648, 28705, 28782, 349, 28705, 28740, 28734, 28723, 2, 733, 16289, 28793, 1824, 349, 272, 7930, 5557, 302, 28705, 28784, 28781, 28804, 733, 28748, 16289, 28793, 415, 7930, 5557, 302, 28705, 28784, 28781, 349, 28705, 28783, 28725, 1096, 28705, 28783, 1318, 28705, 28783, 21588, 28705, 28784, 28781, 28723, 2, 733, 16289, 28793, 2418, 368, 17669, 346, 272, 2903, 302, 272, 3454, 989, 4224, 28804, 733, 28748, 16289, 28793, 12875, 28808, 415, 1204, 302, 28705, 28740, 28734, 1318, 28705, 28783, 349, 28705, 28783, 28734, 28723, 2, 733, 16289, 28793, 8868, 733, 28748, 16289, 28793 ] }python-mistral-common-1.11.7/tests/data/samples/no_tools/tokens_v2.json000066400000000000000000000025771523035502500262540ustar00rootroot00000000000000{ "tokens": [ 1, 3, 2592, 29510, 29481, 1040, 1972, 1070, 29473, 29550, 1416, 29473, 29550, 29572, 4, 1183, 1972, 1070, 29473, 29550, 1416, 29473, 29550, 1117, 29473, 29508, 29502, 29491, 2, 3, 2592, 1117, 1040, 8698, 6325, 1070, 29473, 29552, 29549, 29572, 4, 1183, 8698, 6325, 1070, 29473, 29552, 29549, 1117, 29473, 29551, 29493, 1864, 29473, 29551, 2086, 29473, 29551, 22356, 29473, 29552, 29549, 29491, 2, 3, 3186, 1136, 18437, 1114, 1040, 3671, 1070, 1040, 4222, 1757, 4992, 29572, 4, 13643, 29576, 1183, 1972, 1070, 29473, 29508, 29502, 2086, 29473, 29551, 1117, 29473, 29551, 29502, 29491, 2, 3, 9636, 4 ] }python-mistral-common-1.11.7/tests/data/samples/no_tools/tokens_v3.json000066400000000000000000000025771523035502500262550ustar00rootroot00000000000000{ "tokens": [ 1, 3, 2592, 29510, 29481, 1040, 1972, 1070, 29473, 29550, 1416, 29473, 29550, 29572, 4, 1183, 1972, 1070, 29473, 29550, 1416, 29473, 29550, 1117, 29473, 29508, 29502, 29491, 2, 3, 2592, 1117, 1040, 8698, 6325, 1070, 29473, 29552, 29549, 29572, 4, 1183, 8698, 6325, 1070, 29473, 29552, 29549, 1117, 29473, 29551, 29493, 1864, 29473, 29551, 2086, 29473, 29551, 22356, 29473, 29552, 29549, 29491, 2, 3, 3186, 1136, 18437, 1114, 1040, 3671, 1070, 1040, 4222, 1757, 4992, 29572, 4, 13643, 29576, 1183, 1972, 1070, 29473, 29508, 29502, 2086, 29473, 29551, 1117, 29473, 29551, 29502, 29491, 2, 3, 9636, 4 ] }python-mistral-common-1.11.7/tests/data/samples/parallel_calls/000077500000000000000000000000001523035502500245525ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/parallel_calls/sample.json000066400000000000000000000075261523035502500267400ustar00rootroot00000000000000{ "tools": [ { "type": "function", "function": { "name": "send_email", "description": "Send an email to a recipient", "parameters": { "type": "object", "properties": { "attachment": { "type": "string", "description": "The path or URL of an attachment (optional)" }, "body": { "type": "string", "description": "The body/content of the email" }, "subject": { "type": "string", "description": "The subject of the email" }, "to": { "type": "string", "description": "The email address of the recipient" } }, "required": [ "to", "subject", "body" ] } } } ], "messages": [ { "role": "user", "content": "I need to send a report to my manager. The report is saved as a PDF at 'report.pdf'. The email address is manager@company.com. The subject should be \"Monthly Report\" and the body should say \"Attached is the monthly report for your review.\"" }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"manager@company.com\"}", "name": "send_email" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to manager@company.com" }, { "role": "assistant", "content": "I've sent the report to your manager. Anything else?" }, { "role": "user", "content": "Great! Can you also send the same report to the finance team? Their email addresses are finance1@company.com and finance2@company.com." }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"finance1@company.com\"}", "name": "send_email" } }, { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"finance2@company.com.\"}", "name": "send_email" } } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to finance1@company.com" }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to finance2@company.com" } ] }python-mistral-common-1.11.7/tests/data/samples/parallel_calls/text_v2.txt000066400000000000000000000030541523035502500267100ustar00rootroot00000000000000[INST] I need to send a report to my manager. The report is saved as a PDF at 'report.pdf'. The email address is manager@company.com. The subject should be "Monthly Report" and the body should say "Attached is the monthly report for your review."[/INST] I've sent the report to your manager. Anything else?[AVAILABLE_TOOLS] [{"type": "function", "function": {"name": "send_email", "description": "Send an email to a recipient", "parameters": {"type": "object", "properties": {"attachment": {"type": "string", "description": "The path or URL of an attachment (optional)"}, "body": {"type": "string", "description": "The body/content of the email"}, "subject": {"type": "string", "description": "The subject of the email"}, "to": {"type": "string", "description": "The email address of the recipient"}}, "required": ["to", "subject", "body"]}}}][/AVAILABLE_TOOLS][INST] Great! Can you also send the same report to the finance team? Their email addresses are finance1@company.com and finance2@company.com.[/INST][TOOL_CALLS] [{"name": "send_email", "arguments": {"attachment": "report.pdf", "body": "Attached is the monthly report for your review.", "subject": "Monthly Report", "to": "finance1@company.com"}}, {"name": "send_email", "arguments": {"attachment": "report.pdf", "body": "Attached is the monthly report for your review.", "subject": "Monthly Report", "to": "finance2@company.com."}}][TOOL_RESULTS] [{"name": "send_email", "content": "Email sent to finance1@company.com"}, {"name": "send_email", "content": "Email sent to finance2@company.com"}][/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/parallel_calls/text_v3.txt000066400000000000000000000045361523035502500267170ustar00rootroot00000000000000[INST]▁I▁need▁to▁send▁a▁report▁to▁my▁manager.▁The▁report▁is▁saved▁as▁a▁PDF▁at▁'report.pdf'.▁The▁email▁address▁is▁manager@company.com.▁The▁subject▁should▁be▁"Monthly▁Report"▁and▁the▁body▁should▁say▁"Attached▁is▁the▁monthly▁report▁for▁your▁review."[/INST][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"manager@company.com"},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁manager@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS]▁I've▁sent▁the▁report▁to▁your▁manager.▁Anything▁else?[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"send_email",▁"description":▁"Send▁an▁email▁to▁a▁recipient",▁"parameters":▁{"type":▁"object",▁"properties":▁{"attachment":▁{"type":▁"string",▁"description":▁"The▁path▁or▁URL▁of▁an▁attachment▁(optional)"},▁"body":▁{"type":▁"string",▁"description":▁"The▁body/content▁of▁the▁email"},▁"subject":▁{"type":▁"string",▁"description":▁"The▁subject▁of▁the▁email"},▁"to":▁{"type":▁"string",▁"description":▁"The▁email▁address▁of▁the▁recipient"}},▁"required":▁["to",▁"subject",▁"body"]}}}][/AVAILABLE_TOOLS][INST]▁Great!▁Can▁you▁also▁send▁the▁same▁report▁to▁the▁finance▁team?▁Their▁email▁addresses▁are▁finance1@company.com▁and▁finance2@company.com.[/INST][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance1@company.com"},▁"id":▁"123456789"},▁{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance2@company.com."},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁finance1@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁finance2@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/parallel_calls/tokens_v2.json000066400000000000000000000133221523035502500273600ustar00rootroot00000000000000{ "tokens": [ 1, 3, 1083, 1695, 1066, 4848, 1032, 3032, 1066, 1354, 7824, 29491, 1183, 3032, 1117, 9454, 1158, 1032, 19288, 1206, 1232, 10914, 29491, 9752, 4903, 1183, 5695, 3730, 1117, 7824, 29586, 18766, 29491, 1443, 29491, 1183, 4585, 1791, 1115, 1113, 16373, 1114, 8199, 29507, 1072, 1040, 2955, 1791, 2083, 1113, 3285, 3462, 1117, 1040, 15658, 3032, 1122, 1342, 4826, 1379, 4, 1083, 29510, 1101, 3430, 1040, 3032, 1066, 1342, 7824, 29491, 28753, 1880, 29572, 2, 6, 1501, 7567, 1891, 2032, 1113, 3396, 1316, 1113, 3396, 2032, 10598, 1629, 2032, 1113, 5989, 29498, 7372, 1316, 1113, 7286, 2032, 1113, 9131, 1164, 5695, 1066, 1032, 24609, 1316, 1113, 12206, 2032, 10598, 1891, 2032, 1113, 3582, 1316, 1113, 11491, 2032, 10598, 28918, 2032, 10598, 1891, 2032, 1113, 2195, 1316, 1113, 7286, 2032, 1113, 1782, 3207, 1210, 9834, 1070, 1164, 23185, 1093, 15264, 29499, 8474, 1113, 3448, 2032, 10598, 1891, 2032, 1113, 2195, 1316, 1113, 7286, 2032, 1113, 1782, 2955, 29516, 4557, 1070, 1040, 5695, 8474, 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29491, 9752, 1316, 1113, 3448, 2032, 1113, 3285, 3462, 1117, 1040, 15658, 3032, 1122, 1342, 4826, 9959, 1113, 17334, 2032, 1113, 16373, 1114, 8199, 1316, 1113, 1300, 2032, 1113, 3781, 1385, 29518, 29586, 18766, 29491, 1443, 1379, 1743, 29561, 2, 8, 1501, 7567, 1629, 2032, 1113, 5989, 29498, 7372, 1316, 1113, 4557, 2032, 1113, 11763, 3430, 1066, 16746, 29508, 29586, 18766, 29491, 1443, 8474, 10598, 1629, 2032, 1113, 5989, 29498, 7372, 1316, 1113, 4557, 2032, 1113, 11763, 3430, 1066, 16746, 29518, 29586, 18766, 29491, 1443, 29507, 10925, 9 ] }python-mistral-common-1.11.7/tests/data/samples/parallel_calls/tokens_v3.json000066400000000000000000000173361523035502500273720ustar00rootroot00000000000000{ "tokens": [ 1, 3, 1083, 1695, 1066, 4848, 1032, 3032, 1066, 1354, 7824, 29491, 1183, 3032, 1117, 9454, 1158, 1032, 19288, 1206, 1232, 10914, 29491, 9752, 4903, 1183, 5695, 3730, 1117, 7824, 29586, 18766, 29491, 1443, 29491, 1183, 4585, 1791, 1115, 1113, 16373, 1114, 8199, 29507, 1072, 1040, 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3430, 1066, 16746, 29518, 29586, 18766, 29491, 1443, 1316, 1113, 3613, 29498, 1081, 2032, 1113, 29508, 29518, 29538, 29549, 29550, 29552, 29555, 29551, 29542, 18163, 9 ] }python-mistral-common-1.11.7/tests/data/samples/several_calls/000077500000000000000000000000001523035502500244175ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/data/samples/several_calls/sample.json000066400000000000000000000077221523035502500266030ustar00rootroot00000000000000{ "tools": [ { "type": "function", "function": { "name": "send_email", "description": "Send an email to a recipient", "parameters": { "type": "object", "properties": { "attachment": { "type": "string", "description": "The path or URL of an attachment (optional)" }, "body": { "type": "string", "description": "The body/content of the email" }, "subject": { "type": "string", "description": "The subject of the email" }, "to": { "type": "string", "description": "The email address of the recipient" } }, "required": [ "to", "subject", "body" ] } } } ], "messages": [ { "role": "user", "content": "I need to send a report to my manager. The report is saved as a PDF at 'report.pdf'. The email address is manager@company.com. The subject should be \"Monthly Report\" and the body should say \"Attached is the monthly report for your review.\"" }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"manager@company.com\"}", "name": "send_email" }, "type": "function" } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to manager@company.com" }, { "role": "assistant", "content": "I've sent the report to your manager. Anything else?" }, { "role": "user", "content": "Great! Can you also send the same report to the finance team? Their email addresses are finance1@company.com and finance2@company.com." }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"finance1@company.com\"}", "name": "send_email" } } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to finance1@company.com" }, { "content": null, "role": "assistant", "tool_calls": [ { "id": "123456789", "function": { "arguments": "{\"attachment\": \"report.pdf\", \"body\": \"Attached is the monthly report for your review.\", \"subject\": \"Monthly Report\", \"to\": \"finance2@company.com.\"}", "name": "send_email" } } ] }, { "tool_call_id": "123456789", "role": "tool", "name": "send_email", "content": "Email sent to finance2@company.com" } ] }python-mistral-common-1.11.7/tests/data/samples/several_calls/text_v2.txt000066400000000000000000000037071523035502500265620ustar00rootroot00000000000000[INST]▁I▁need▁to▁send▁a▁report▁to▁my▁manager.▁The▁report▁is▁saved▁as▁a▁PDF▁at▁'report.pdf'.▁The▁email▁address▁is▁manager@company.com.▁The▁subject▁should▁be▁"Monthly▁Report"▁and▁the▁body▁should▁say▁"Attached▁is▁the▁monthly▁report▁for▁your▁review."[/INST]▁I've▁sent▁the▁report▁to▁your▁manager.▁Anything▁else?[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"send_email",▁"description":▁"Send▁an▁email▁to▁a▁recipient",▁"parameters":▁{"type":▁"object",▁"properties":▁{"attachment":▁{"type":▁"string",▁"description":▁"The▁path▁or▁URL▁of▁an▁attachment▁(optional)"},▁"body":▁{"type":▁"string",▁"description":▁"The▁body/content▁of▁the▁email"},▁"subject":▁{"type":▁"string",▁"description":▁"The▁subject▁of▁the▁email"},▁"to":▁{"type":▁"string",▁"description":▁"The▁email▁address▁of▁the▁recipient"}},▁"required":▁["to",▁"subject",▁"body"]}}}][/AVAILABLE_TOOLS][INST]▁Great!▁Can▁you▁also▁send▁the▁same▁report▁to▁the▁finance▁team?▁Their▁email▁addresses▁are▁finance1@company.com▁and▁finance2@company.com.[/INST][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance1@company.com"}}][TOOL_RESULTS]▁[{"name":▁"send_email",▁"content":▁"Email▁sent▁to▁finance1@company.com"}][/TOOL_RESULTS][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance2@company.com."}}][TOOL_RESULTS]▁[{"name":▁"send_email",▁"content":▁"Email▁sent▁to▁finance2@company.com"}][/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/several_calls/text_v3.txt000066400000000000000000000045571523035502500265670ustar00rootroot00000000000000[INST]▁I▁need▁to▁send▁a▁report▁to▁my▁manager.▁The▁report▁is▁saved▁as▁a▁PDF▁at▁'report.pdf'.▁The▁email▁address▁is▁manager@company.com.▁The▁subject▁should▁be▁"Monthly▁Report"▁and▁the▁body▁should▁say▁"Attached▁is▁the▁monthly▁report▁for▁your▁review."[/INST][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"manager@company.com"},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁manager@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS]▁I've▁sent▁the▁report▁to▁your▁manager.▁Anything▁else?[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"send_email",▁"description":▁"Send▁an▁email▁to▁a▁recipient",▁"parameters":▁{"type":▁"object",▁"properties":▁{"attachment":▁{"type":▁"string",▁"description":▁"The▁path▁or▁URL▁of▁an▁attachment▁(optional)"},▁"body":▁{"type":▁"string",▁"description":▁"The▁body/content▁of▁the▁email"},▁"subject":▁{"type":▁"string",▁"description":▁"The▁subject▁of▁the▁email"},▁"to":▁{"type":▁"string",▁"description":▁"The▁email▁address▁of▁the▁recipient"}},▁"required":▁["to",▁"subject",▁"body"]}}}][/AVAILABLE_TOOLS][INST]▁Great!▁Can▁you▁also▁send▁the▁same▁report▁to▁the▁finance▁team?▁Their▁email▁addresses▁are▁finance1@company.com▁and▁finance2@company.com.[/INST][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance1@company.com"},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁finance1@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS][TOOL_CALLS]▁[{"name":▁"send_email",▁"arguments":▁{"attachment":▁"report.pdf",▁"body":▁"Attached▁is▁the▁monthly▁report▁for▁your▁review.",▁"subject":▁"Monthly▁Report",▁"to":▁"finance2@company.com."},▁"id":▁"123456789"}][TOOL_RESULTS]▁{"content":▁"Email▁sent▁to▁finance2@company.com",▁"call_id":▁"123456789"}[/TOOL_RESULTS]python-mistral-common-1.11.7/tests/data/samples/several_calls/tokens_v2.json000066400000000000000000000134661523035502500272360ustar00rootroot00000000000000{ 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29507, 10925, 2, 8, 10598, 4557, 2032, 1113, 11763, 3430, 1066, 16746, 29518, 29586, 18766, 29491, 1443, 1316, 1113, 3613, 29498, 1081, 2032, 1113, 29508, 29518, 29538, 29549, 29550, 29552, 29555, 29551, 29542, 18163, 9 ] }python-mistral-common-1.11.7/tests/experimental/000077500000000000000000000000001523035502500217205ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/experimental/__init__.py000066400000000000000000000000001523035502500240170ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/experimental/test_app.py000066400000000000000000000461001523035502500241120ustar00rootroot00000000000000from unittest.mock import patch import pytest import requests from fastapi import FastAPI from fastapi.encoders import jsonable_encoder from fastapi.testclient import TestClient from mistral_common.experimental.app.main import create_app from mistral_common.experimental.app.models import OpenAIChatCompletionRequest from mistral_common.protocol.instruct.chunk import ( TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import InstructRequestNormalizerV13 from mistral_common.protocol.instruct.request import ChatCompletionRequest, InstructRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidatorV13, ValidationMode, ) from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV13 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab @pytest.fixture(scope="module") def tekken_app() -> FastAPI: return create_app(MistralTokenizer._data_path() / "tekken_240911.json", ValidationMode.test) @pytest.fixture(scope="module") def tekken_tokenizer() -> MistralTokenizer: return MistralTokenizer.from_file(MistralTokenizer._data_path() / "tekken_240911.json", ValidationMode.test) @pytest.fixture(scope="module") def spm_tokenizer() -> MistralTokenizer: return MistralTokenizer.from_file( MistralTokenizer._data_path() / "mistral_instruct_tokenizer_241114.model.v7m1", ValidationMode.finetuning ) @pytest.fixture(scope="module") def mistral_tokenizer_v13() -> MistralTokenizer: return MistralTokenizer( instruct_tokenizer=InstructTokenizerV13( Tekkenizer( quick_vocab( [ b"Hello", b",", b" ", b"world", b"!", b"How", b"can", b"I", b"assist", b"you", b"today", b"?", b'"', b"a", b"b", b"c", b"d", b"{", b"}", b"1", b"2", b"call", b"_", b":", ] ), special_tokens=get_special_tokens(TokenizerVersion.v13, add_think=True), pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v13, ) ), validator=MistralRequestValidatorV13(), request_normalizer=InstructRequestNormalizerV13( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ), ) @pytest.fixture(scope="module") def spm_app() -> FastAPI: return create_app( MistralTokenizer._data_path() / "mistral_instruct_tokenizer_241114.model.v7m1", ValidationMode.finetuning ) @pytest.fixture(scope="module") def tekken_client(tekken_app: FastAPI) -> TestClient: return TestClient(tekken_app) @pytest.fixture(scope="module") def tekken_v13_app(mistral_tokenizer_v13: MistralTokenizer) -> FastAPI: return create_app(mistral_tokenizer_v13) @pytest.fixture(scope="module") def tekken_v13_client(tekken_v13_app: FastAPI) -> TestClient: return TestClient(tekken_v13_app) @pytest.fixture(scope="module") def spm_client(spm_app: FastAPI) -> TestClient: return TestClient(spm_app) @pytest.fixture(scope="module") def tekken_messages() -> list[ChatMessage]: return [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Hello, world!"), ] @pytest.fixture(scope="module") def tekken_message_tokens(tekken_tokenizer: MistralTokenizer, tekken_messages: list[ChatMessage]) -> list[int]: tokens: list[int] = tekken_tokenizer.encode_chat_completion(ChatCompletionRequest(messages=tekken_messages)).tokens return tokens @pytest.fixture(scope="module") def spm_messages() -> list[ChatMessage]: return [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Hello, how are you ?!"), AssistantMessage(content="I'm fine, thank you ! How can I help you ?"), ] @pytest.fixture(scope="module") def spm_message_tokens(spm_tokenizer: MistralTokenizer, spm_messages: list[ChatMessage]) -> list[int]: tokens: list[int] = spm_tokenizer.encode_chat_completion(ChatCompletionRequest(messages=spm_messages)).tokens return tokens @pytest.fixture(scope="module") def tekken_request(tekken_messages: list[ChatMessage]) -> ChatCompletionRequest: return ChatCompletionRequest( messages=tekken_messages, tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather in a given location", parameters={ "type": "object", "properties": { "location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, }, ) ) ], ) @pytest.fixture(scope="module") def tekken_oai_request(tekken_request: ChatCompletionRequest) -> OpenAIChatCompletionRequest: return OpenAIChatCompletionRequest.model_validate(tekken_request.to_openai()) @pytest.fixture(scope="module") def tekken_request_tokens(tekken_tokenizer: MistralTokenizer, tekken_request: ChatCompletionRequest) -> list[int]: tokens: list[int] = tekken_tokenizer.encode_chat_completion(tekken_request).tokens return tokens @pytest.fixture(scope="module") def spm_request(spm_messages: list[ChatMessage]) -> ChatCompletionRequest: return ChatCompletionRequest( messages=spm_messages, ) @pytest.fixture(scope="module") def spm_oai_request(spm_request: ChatCompletionRequest) -> OpenAIChatCompletionRequest: return OpenAIChatCompletionRequest.model_validate(spm_request.to_openai()) @pytest.fixture(scope="module") def spm_request_tokens(spm_tokenizer: MistralTokenizer, spm_request: ChatCompletionRequest) -> list[int]: tokens: list[int] = spm_tokenizer.encode_chat_completion(spm_request).tokens return tokens @pytest.mark.parametrize(["client_fixture"], [("tekken_client",), ("spm_client",)]) def test_redirect_to_docs(client_fixture: str, request: pytest.FixtureRequest) -> None: client: TestClient = request.getfixturevalue(client_fixture) response = client.get("/") assert response.status_code == 200 @pytest.mark.parametrize( ["client_fixture", "request_fixture", "tokens_fixture"], [ ( "tekken_client", "tekken_request", "tekken_request_tokens", ), ("spm_client", "spm_request", "spm_request_tokens"), ( "tekken_client", "tekken_oai_request", "tekken_request_tokens", ), ("spm_client", "spm_oai_request", "spm_request_tokens"), ], ) def test_tokenize_request( client_fixture: str, request_fixture: str, tokens_fixture: str, request: pytest.FixtureRequest ) -> None: chat_request: ChatCompletionRequest | OpenAIChatCompletionRequest = request.getfixturevalue(request_fixture) tokens: list[int] = request.getfixturevalue(tokens_fixture) client: TestClient = request.getfixturevalue(client_fixture) response = client.post("/v1/tokenize", json=jsonable_encoder(chat_request)) assert response.status_code == 200 assert response.json() == tokens @pytest.mark.parametrize( ["tokenizer_fixture", "client_fixture"], [("tekken_tokenizer", "tekken_client"), ("spm_tokenizer", "spm_client")] ) def test_detokenize_string(tokenizer_fixture: str, client_fixture: str, request: pytest.FixtureRequest) -> None: prompt = "Hello, world!" tokenizer: MistralTokenizer = request.getfixturevalue(tokenizer_fixture) client: TestClient = request.getfixturevalue(client_fixture) encoded_prompt = tokenizer.instruct_tokenizer.tokenizer.encode(prompt, bos=True, eos=True) response_with_special = client.post( "/v1/detokenize/string", json={"tokens": encoded_prompt, "special_token_policy": SpecialTokenPolicy.KEEP} ) assert response_with_special.status_code == 200 assert response_with_special.json() == tokenizer.instruct_tokenizer.tokenizer.decode( encoded_prompt, special_token_policy=SpecialTokenPolicy.KEEP ) response_without_special = client.post( "/v1/detokenize/string", json={"tokens": encoded_prompt, "special_token_policy": SpecialTokenPolicy.IGNORE} ) assert response_without_special.status_code == 200 assert response_without_special.json() == tokenizer.instruct_tokenizer.tokenizer.decode( encoded_prompt, special_token_policy=SpecialTokenPolicy.IGNORE ) response_empty_tokens = client.post( "/v1/detokenize/string", json={"tokens": [], "special_token_policy": SpecialTokenPolicy.IGNORE} ) assert response_empty_tokens.status_code == 400 assert response_empty_tokens.json()["detail"] == "Tokens list cannot be empty." response_special_error = client.post( "/v1/detokenize/string", json={"tokens": encoded_prompt, "special_token_policy": SpecialTokenPolicy.RAISE} ) assert response_special_error.status_code == 400 @pytest.mark.parametrize("prefix", [False, True]) @pytest.mark.parametrize( ["tokenizer_fixture", "client_fixture"], [("tekken_tokenizer", "tekken_client"), ("spm_tokenizer", "spm_client")] ) def test_detokenize_assistant_message( prefix: bool, tokenizer_fixture: str, client_fixture: str, request: pytest.FixtureRequest ) -> None: # Test 1: # Detokenize only content tokenizer: MistralTokenizer = request.getfixturevalue(tokenizer_fixture) client: TestClient = request.getfixturevalue(client_fixture) content = "Hello, world!" encoded_content = tokenizer.instruct_tokenizer.tokenizer.encode(content, bos=True, eos=not prefix) response = client.post("/v1/detokenize/", json=encoded_content) assert response.status_code == 200 assert AssistantMessage.model_validate(response.json()) == AssistantMessage(content=content, prefix=prefix) # Test 2: # Detokenize content and tool calls content = "Hello, world!" tool_calls = [ ToolCall( id="call_1", function=FunctionCall(name="get_current_weather", arguments='{"location": "Paris"}'), ), ToolCall( id="call_2", function=FunctionCall( name="git_clone", arguments='{"repository": "https://github.com/mistralai/mistral-common"}' ), ), ] encoded_content = tokenizer.instruct_tokenizer.tokenizer.encode(content, bos=True, eos=False) encoded_tool_calls: list[int] = tokenizer.instruct_tokenizer._encode_tool_calls_in_assistant_message( # type: ignore[attr-defined] AssistantMessage(tool_calls=tool_calls) ) if not prefix: encoded_tool_calls.append(tokenizer.instruct_tokenizer.tokenizer.eos_id) encoded_tokens = encoded_content + encoded_tool_calls response = client.post("/v1/detokenize/", json=encoded_tokens) assert response.status_code == 200 assert AssistantMessage.model_validate(response.json()) == AssistantMessage( content=content, tool_calls=tool_calls, prefix=prefix ) # Test 3: # Detokenize only tool calls encoded_tokens = tokenizer.instruct_tokenizer._encode_tool_calls_in_assistant_message( # type: ignore[attr-defined] AssistantMessage(tool_calls=tool_calls) ) if not prefix: encoded_tokens.append(tokenizer.instruct_tokenizer.tokenizer.eos_id) response = client.post("/v1/detokenize/", json=encoded_tokens) assert response.status_code == 200 assert AssistantMessage.model_validate(response.json()) == AssistantMessage(tool_calls=tool_calls, prefix=prefix) # Test 4: # Detokenize empty tokens response = client.post("/v1/detokenize/", json=[]) assert response.status_code == 400 assert response.json()["detail"] == "Tokens list cannot be empty." # Test 6: # Wrong tool call format response = client.post("/v1/detokenize/", json=encoded_tool_calls[: (-2 if not prefix else -1)]) assert response.status_code == 400 assert response.json()["detail"] == "Invalid tool call tokenization. Expected a JSON list of tool calls." @pytest.mark.parametrize( "assistant_message", [ AssistantMessage( content=[ TextChunk(text="Hello, world!"), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Let me think about this..."), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Let me think about this..."), ThinkChunk(thinking="I need to consider all options."), TextChunk(text="Here is my final answer."), ], ), AssistantMessage( content=[ ThinkChunk(thinking="Let me think about this..."), ThinkChunk(thinking="I need to consider all options.", closed=False), ], prefix=True, ), AssistantMessage( content=[ ThinkChunk(thinking="Let me think about this..."), ThinkChunk(thinking="I need to consider all options.", closed=False), ], prefix=True, tool_calls=[ ToolCall( id="call_1", function=FunctionCall(name="get_current_weather", arguments='{"location": "Paris"}'), ), ], ), ], ) def test_detokenize_assistant_message_think_chunks( assistant_message: AssistantMessage, mistral_tokenizer_v13: MistralTokenizer, tekken_v13_client: TestClient ) -> None: encoded_tokens = mistral_tokenizer_v13.instruct_tokenizer.encode_assistant_message(assistant_message, False, False) # type: ignore[attr-defined] response = tekken_v13_client.post("/v1/detokenize/", json=encoded_tokens) assert response.status_code == 200 assistant_message.tool_calls = ( [ToolCall.model_validate(tool_call.model_dump(exclude={"id"})) for tool_call in assistant_message.tool_calls] if assistant_message.tool_calls else None ) if ( isinstance(assistant_message.content, list) and len(assistant_message.content) == 1 and isinstance(assistant_message.content[0], TextChunk) ): assistant_message.content = assistant_message.content[0].text assert AssistantMessage.model_validate(response.json()) == assistant_message class MockResponse: def __init__(self, status_code: int, json_data: list | dict | None = None, text: str | None = None) -> None: self.json_data = json_data self.status_code = status_code self.text = text def json(self) -> list | dict: if self.json_data is None: raise ValueError("No JSON data available") return self.json_data @pytest.mark.parametrize( "engine_request", [ { "messages": [{"role": "user", "content": "Hello, world!"}], "temperature": 0.1, }, ChatCompletionRequest(messages=[UserMessage(content="Hello, world!")], temperature=0.1), OpenAIChatCompletionRequest( messages=[{"role": "user", "content": "Hello, world!"}], tools=[{"type": "function", "function": {"name": "get_current_weather", "parameters": {}}}], ), ], ) @pytest.mark.parametrize( "output_assistant_message", [ AssistantMessage(content="Hello, world!"), AssistantMessage( content="Hello, world!", tool_calls=[ToolCall(id="1a2bc3d4e", function=FunctionCall(name="get_current_weather", arguments="{}"))], ), AssistantMessage( content=[ ThinkChunk(thinking="Let me think about this..."), ThinkChunk(thinking="I need to consider all options."), TextChunk(text="Here is my final answer."), ], ), ], ) def test_generate( mistral_tokenizer_v13: MistralTokenizer, tekken_v13_client: TestClient, engine_request: dict | ChatCompletionRequest | OpenAIChatCompletionRequest, output_assistant_message: AssistantMessage, ) -> None: output_tokens = mistral_tokenizer_v13.instruct_tokenizer.encode_assistant_message( # type: ignore[attr-defined] output_assistant_message, False, False ) if output_assistant_message.tool_calls: output_assistant_message = AssistantMessage( content=output_assistant_message.content, tool_calls=[ ToolCall(**tool_call.model_dump(exclude={"id"})) for tool_call in output_assistant_message.tool_calls ], ) with patch("mistral_common.experimental.app.routers.requests.post") as mock_generate: mock_generate.return_value = MockResponse(200, {"tokens": output_tokens}) response = tekken_v13_client.post("/v1/chat/completions", json=jsonable_encoder(engine_request)) assert response.status_code == 200 assert AssistantMessage(**response.json()) == output_assistant_message def test_generate_error(tekken_v13_client: TestClient) -> None: with patch("mistral_common.experimental.app.routers.requests.post") as mock_generate: mock_generate.return_value = MockResponse(400, text="Error") response = tekken_v13_client.post( "/v1/chat/completions", json={"messages": [{"role": "user", "content": "Hello, world!"}]} ) assert response.status_code == 400 assert response.json() == {"detail": "Error"} with patch("mistral_common.experimental.app.routers.requests.post") as mock_generate: mock_generate.side_effect = requests.exceptions.RequestException("Error") response = tekken_v13_client.post( "/v1/chat/completions", json={"messages": [{"role": "user", "content": "Hello, world!"}]} ) assert response.status_code == 500 assert response.json() == {"detail": "Error"} with patch("mistral_common.experimental.app.routers.requests.post") as mock_generate: mock_generate.side_effect = requests.exceptions.Timeout() response = tekken_v13_client.post( "/v1/chat/completions", json={"messages": [{"role": "user", "content": "Hello, world!"}]} ) assert response.status_code == 504 assert response.json() == {"detail": "Timeout"} python-mistral-common-1.11.7/tests/experimental/test_think.py000066400000000000000000000026431523035502500244530ustar00rootroot00000000000000import pytest from mistral_common.experimental.think import _split_content_and_think_chunks @pytest.mark.parametrize( "tokens, expected", [ ([], []), ([1, 2, 3, 4, 5], [([1, 2, 3, 4, 5], False)]), ([1, 2, 3, 100, 4, 5, 200, 6, 7, 8], [([1, 2, 3], False), ([100, 4, 5, 200], True), ([6, 7, 8], False)]), ( [100, 1, 2, 3, 200, 4, 5, 6, 100, 7, 8, 9, 200], [([100, 1, 2, 3, 200], True), ([4, 5, 6], False), ([100, 7, 8, 9, 200], True)], ), ([100, 1, 2, 3, 200, 100, 4, 5, 6, 200], [([100, 1, 2, 3, 200], True), ([100, 4, 5, 6, 200], True)]), ([100, 1, 2, 3, 200, 100, 5, 6, 7], [([100, 1, 2, 3, 200], True), ([100, 5, 6, 7], True)]), ], ) def test_split_content_and_think_chunks(tokens: list[int], expected: list[tuple[list[int], bool]]) -> None: content_or_think_chunks = _split_content_and_think_chunks(tokens, 100, 200) assert content_or_think_chunks == expected @pytest.mark.parametrize( "tokens, error_message", [ ([100, 1, 2, 3, 100, 200, 100, 5, 6, 7], r"Nested think chunks are not allowed."), ([100, 1, 2, 3, 200, 200, 5, 6, 7, 100], r"End think token found without a begin think token."), ], ) def test_split_content_and_think_chunks_error(tokens: list[int], error_message: str) -> None: with pytest.raises(ValueError, match=error_message): _split_content_and_think_chunks(tokens, 100, 200) python-mistral-common-1.11.7/tests/experimental/test_tools.py000066400000000000000000000230331523035502500244720ustar00rootroot00000000000000import pytest from mistral_common.experimental.tools import ( InvalidArgsToolCallError, InvalidToolCallError, _decode_tool_calls, _split_content_and_tool_calls, _split_integer_list_by_value, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import InstructRequestNormalizerV7, InstructRequestNormalizerV13 from mistral_common.protocol.instruct.request import InstructRequest from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidatorV5, MistralRequestValidatorV13, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV11, InstructTokenizerV13 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab def fixture_mistral_tokenizer_v11() -> MistralTokenizer: return MistralTokenizer( instruct_tokenizer=InstructTokenizerV11( Tekkenizer( quick_vocab( [ b"Hello", b",", b" ", b"world", b"!", b"How", b"can", b"I", b"assist", b"you", b"today", b"?", b'"', b"a", b"b", b"c", b"d", b"{", b"}", b":", ] ), special_tokens=get_special_tokens(TokenizerVersion.v11), pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v11, ), ), validator=MistralRequestValidatorV5(), request_normalizer=InstructRequestNormalizerV7( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ), ) def fixture_mistral_tokenizer_v13() -> MistralTokenizer: return MistralTokenizer( instruct_tokenizer=InstructTokenizerV13( Tekkenizer( quick_vocab( [ b"Hello", b",", b" ", b"world", b"!", b"How", b"can", b"I", b"assist", b"you", b"today", b"?", b'"', b"a", b"b", b"c", b"d", b"{", b"}", b"1", b"2", b"call", b"_", b":", ] ), special_tokens=get_special_tokens(TokenizerVersion.v13), pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v13, ) ), validator=MistralRequestValidatorV13(), request_normalizer=InstructRequestNormalizerV13( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ), ) def test_find_content_tool_calls() -> None: # Test 1: No tool calls tokens = [1, 2, 3, 4, 5] assert _split_content_and_tool_calls(tokens, 6) == ([1, 2, 3, 4, 5], ()) # Test 2: One tool call tokens = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] assert _split_content_and_tool_calls(tokens, 6) == ([1, 2, 3, 4, 5], ([6, 7, 8, 9, 10],)) # Test 3: Multiple tool calls tokens = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 6, 11, 12, 13, 14] assert _split_content_and_tool_calls(tokens, 6) == ([1, 2, 3, 4, 5], ([6, 7, 8, 9, 10], [6, 11, 12, 13, 14])) # Test 4: No content tokens = [6, 7, 8, 9, 10] assert _split_content_and_tool_calls(tokens, 6) == ([], ([6, 7, 8, 9, 10],)) @pytest.mark.parametrize( "tokenizer", ( MistralTokenizer.v2(), MistralTokenizer.v3(), MistralTokenizer.v7(), MistralTokenizer( instruct_tokenizer=InstructTokenizerV11( Tekkenizer( quick_vocab( [ b"Hello", b",", b" ", b"world", b"!", b"How", b"can", b"I", b"assist", b"you", b"today", b"?", b'"', b"a", b"b", b"c", b"d", b"{", b"}", b":", ] ), special_tokens=get_special_tokens(TokenizerVersion.v11), pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v11, ), ), validator=MistralRequestValidatorV5(), request_normalizer=InstructRequestNormalizerV7( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ), ), fixture_mistral_tokenizer_v11(), fixture_mistral_tokenizer_v13(), ), ) @pytest.mark.parametrize( "tool_calls", ( [ToolCall(id="call_1", function=FunctionCall(name="ab", arguments="{}"))], [ ToolCall(id="call_1", function=FunctionCall(name="ab", arguments="{}")), ToolCall(id="call_2", function=FunctionCall(name="cd", arguments='{"a": "b", "c": "d"}')), ], ), ) def test_decode_tool_calls(tokenizer: MistralTokenizer, tool_calls: list[ToolCall]) -> None: # Test 1: # valid tool calls encoded_tool_calls = tokenizer.instruct_tokenizer._encode_tool_calls_in_assistant_message( # type: ignore[attr-defined] AssistantMessage(tool_calls=tool_calls) ) splitted_tool_calls = _split_integer_list_by_value( encoded_tool_calls, tokenizer.instruct_tokenizer.tokenizer.get_special_token("[TOOL_CALLS]") ) decoded_tool_calls = _decode_tool_calls(splitted_tool_calls, tokenizer.instruct_tokenizer.tokenizer) if tokenizer.instruct_tokenizer.tokenizer.version != TokenizerVersion.v11: assert len(decoded_tool_calls) == len(tool_calls) for decoded_tool_call, tool_call in zip(decoded_tool_calls, tool_calls): assert decoded_tool_call.model_dump(exclude={"id"}) == tool_call.model_dump(exclude={"id"}) else: # v11 is the only version that encode the id assert decoded_tool_calls == tool_calls versions_inf_v11 = [ TokenizerVersion.v2, TokenizerVersion.v3, TokenizerVersion.v7, ] # Test 2: # invalid tool calls (remove a JSON needed token from the args) if tokenizer.instruct_tokenizer.tokenizer.version in versions_inf_v11: arg_index = tokenizer.instruct_tokenizer.tokenizer.get_special_token("[TOOL_CALLS]") else: arg_index = tokenizer.instruct_tokenizer.tokenizer.get_special_token("[ARGS]") first_args_index = splitted_tool_calls[0].index(arg_index) splitted_tool_calls = ( splitted_tool_calls[0][: first_args_index + 1] + splitted_tool_calls[0][first_args_index + 2 :], # make the args invalid *splitted_tool_calls[1:], ) with pytest.raises( InvalidToolCallError if tokenizer.instruct_tokenizer.tokenizer.version in versions_inf_v11 else InvalidArgsToolCallError ): _decode_tool_calls(splitted_tool_calls, tokenizer.instruct_tokenizer.tokenizer) def test_decode_tool_calls_v11_without_id() -> None: tokenizer = fixture_mistral_tokenizer_v11() tool_call = ToolCall(id="call_1", function=FunctionCall(name="ab", arguments="{}")) encoded_tool_call: list[int] = tokenizer.instruct_tokenizer._encode_tool_calls_in_assistant_message( # type: ignore[attr-defined] AssistantMessage(tool_calls=[tool_call]) ) call_id_token = tokenizer.instruct_tokenizer.tokenizer.get_special_token("[CALL_ID]") args_token = tokenizer.instruct_tokenizer.tokenizer.get_special_token("[ARGS]") call_id_index = encoded_tool_call.index(call_id_token) args_token_index = encoded_tool_call.index(args_token) encoded_tool_call = encoded_tool_call[:call_id_index] + encoded_tool_call[args_token_index:] # remove the id decoded_tool_call = _decode_tool_calls([encoded_tool_call], tokenizer.instruct_tokenizer.tokenizer) assert len(decoded_tool_call) == 1 assert decoded_tool_call[0].model_dump(exclude={"id"}) == tool_call.model_dump(exclude={"id"}) python-mistral-common-1.11.7/tests/experimental/test_utils.py000066400000000000000000000011101523035502500244620ustar00rootroot00000000000000from mistral_common.experimental.utils import _split_integer_list_by_value def test_split_integer_list_by_value() -> None: # Test 1: One split assert _split_integer_list_by_value([1, 2, 3, 4, 5], 3) == ([1, 2], [3, 4, 5]) # Test 2: No value assert _split_integer_list_by_value([1, 2, 3, 4, 5], 6) == ([1, 2, 3, 4, 5],) # Test 3: No split assert _split_integer_list_by_value([1, 2, 3, 4, 5], 1) == ([1, 2, 3, 4, 5],) # Test 4: Multiple splits assert _split_integer_list_by_value([1, 2, 3, 4, 5, 3, 5, 6, 7], 3) == ([1, 2], [3, 4, 5], [3, 5, 6, 7]) python-mistral-common-1.11.7/tests/fixtures/000077500000000000000000000000001523035502500210745ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/fixtures/__init__.py000066400000000000000000000000001523035502500231730ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/fixtures/audio.py000066400000000000000000000011521523035502500225460ustar00rootroot00000000000000import numpy as np from mistral_common.protocol.instruct.chunk import AudioChunk, AudioURL, AudioURLChunk from mistral_common.tokens.tokenizers.audio import Audio def get_dummy_audio_chunk() -> AudioChunk: sampling_rate = 16000 audio_array = np.zeros(1600) audio = Audio( audio_array=audio_array, sampling_rate=sampling_rate, format="wav", ) return AudioChunk(input_audio=audio.to_base64("wav")) def get_dummy_audio_url_chunk() -> AudioURLChunk: audio_chunk = get_dummy_audio_chunk() return AudioURLChunk(audio_url=AudioURL(url=str(audio_chunk.input_audio))) python-mistral-common-1.11.7/tests/fixtures/chunks.py000066400000000000000000000025521523035502500227450ustar00rootroot00000000000000from typing import Any from PIL import Image from mistral_common.protocol.instruct.chunk import ( ContentChunk, ImageChunk, ImageURLChunk, TextChunk, ThinkChunk, ) from tests.fixtures.audio import get_dummy_audio_chunk, get_dummy_audio_url_chunk def get_content_chunk(name: str) -> ContentChunk: r"""Return a single instance of the requested content chunk type. Args: name: One of "text", "image", "image_url", "audio", "audio_url" or "think". Returns: A content chunk of the requested type. """ chunks: dict[str, ContentChunk] = { "text": TextChunk(text="hello"), "image": ImageChunk(image=Image.new("RGB", (4, 4), "red")), "image_url": ImageURLChunk(image_url="data:image/png;base64,iVBORw0"), "audio": get_dummy_audio_chunk(), "audio_url": get_dummy_audio_url_chunk(), "think": ThinkChunk(thinking="reasoning"), } return chunks[name] def get_content_chunks(names: tuple[str, ...]) -> list[Any]: r"""Return a list of content chunks for the requested type names. The element type is intentionally `Any` so the chunks can be fed to any message role constructor (including deliberately invalid combinations) in tests, leaving the runtime Pydantic validation to enforce the actual rules. """ return [get_content_chunk(name) for name in names] python-mistral-common-1.11.7/tests/guidance/000077500000000000000000000000001523035502500210025ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/guidance/__init__.py000066400000000000000000000000001523035502500231010ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/guidance/test_guidance.py000066400000000000000000001721301523035502500241760ustar00rootroot00000000000000import warnings from pathlib import Path from typing import Any, Literal import llguidance as llg import pytest from pydantic import BaseModel, ConfigDict, Field, model_validator import mistral_common.deprecation from mistral_common.guidance.grammar_factory import JINJA_PATHS, GrammarFactory, _convert_tool_calls, _GrammarVariant from mistral_common.protocol.instruct.chunk import TextChunk, ThinkChunk from mistral_common.protocol.instruct.messages import AssistantMessage from mistral_common.protocol.instruct.normalize import get_normalizer from mistral_common.protocol.instruct.request import ReasoningEffort from mistral_common.protocol.instruct.tool_calls import ( Function, FunctionCall, FunctionName, NamedToolChoice, Tool, ToolCall, ToolChoice, ToolChoiceEnum, ToolTypes, ) from mistral_common.protocol.instruct.validator import ValidationMode, get_validator from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens, Tokenizer, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerBase, InstructTokenizerV11, InstructTokenizerV13, InstructTokenizerV15, ) from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.model_settings_builder import EnumBuilder, ModelSettingsBuilder from mistral_common.tokens.tokenizers.tekken import Tekkenizer, is_tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab EMOJI_LARK_PATH = Path(__file__).parent.parent / "data" / "emoji.lark" Mode = Literal["auto", "any", "none", "required"] _AUTO_ANY_REQUIRED: tuple[Mode, Mode, Mode] = ("auto", "any", "required") _NUM_SPECIAL_TOKENS = 100 _EXTRA_TOKENS = [ b"de", b"he", b"llo", "😃".encode("utf-8"), "😂".encode("utf-8"), "😊".encode("utf-8"), "😍".encode("utf-8"), "😘".encode("utf-8"), "😗".encode("utf-8"), "😙".encode("utf-8"), "😚".encode("utf-8"), "😋".encode("utf-8"), "😛".encode("utf-8"), "😜".encode("utf-8"), "😝".encode("utf-8"), "🤑".encode("utf-8"), "🤗".encode("utf-8"), "🤔".encode("utf-8"), "🤐".encode("utf-8"), "😐".encode("utf-8"), "😑".encode("utf-8"), "😶".encode("utf-8"), "😬".encode("utf-8"), "こ".encode("utf-8"), "ん".encode("utf-8"), "に".encode("utf-8"), "ち".encode("utf-8"), "は".encode("utf-8"), "مرحبا".encode("utf-8"), "بكم".encode("utf-8"), "في".encode("utf-8"), "عالم".encode("utf-8"), "الذكاء".encode("utf-8"), "الاصطناعي".encode("utf-8"), ] def _build_tekken_mistral_tokenizer( version: TokenizerVersion, add_think: bool = False, model_settings_builder: ModelSettingsBuilder | None = None, ) -> MistralTokenizer: r"""Builds a MistralTokenizer wrapping a programmatic Tekkenizer.""" special_tokens = get_special_tokens(version, add_think=add_think) vocab = quick_vocab(_EXTRA_TOKENS) tekkenizer = Tekkenizer( vocab, special_tokens=special_tokens, pattern=r"(?s:.+)", vocab_size=len(vocab) + _NUM_SPECIAL_TOKENS, num_special_tokens=_NUM_SPECIAL_TOKENS, version=version, model_settings_builder=model_settings_builder, ) match version: case TokenizerVersion.v11: instruct_tokenizer = InstructTokenizerV11(tekkenizer) case TokenizerVersion.v13: instruct_tokenizer = InstructTokenizerV13(tekkenizer) case TokenizerVersion.v15: instruct_tokenizer = InstructTokenizerV15(tekkenizer) case _: raise ValueError(f"Unsupported version for programmatic Tekken build: {version}") normalizer = get_normalizer(version, tekkenizer.model_settings_builder) validator = get_validator(version, mode=ValidationMode.test) return MistralTokenizer(instruct_tokenizer, validator=validator, request_normalizer=normalizer) @pytest.fixture(scope="module") def v11_tekken() -> MistralTokenizer: return _build_tekken_mistral_tokenizer(TokenizerVersion.v11) @pytest.fixture(scope="module") def v13_tekken() -> MistralTokenizer: return _build_tekken_mistral_tokenizer(TokenizerVersion.v13, add_think=True) @pytest.fixture(scope="module") def v13_no_think_tekken() -> MistralTokenizer: return _build_tekken_mistral_tokenizer(TokenizerVersion.v13, add_think=False) _V15_MODEL_SETTINGS_BUILDER = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=list(ReasoningEffort), accepts_none=True, default=None, ), ) @pytest.fixture(scope="module") def v15_tekken() -> MistralTokenizer: return _build_tekken_mistral_tokenizer( TokenizerVersion.v15, add_think=True, model_settings_builder=_V15_MODEL_SETTINGS_BUILDER ) _PAYMENT_PARAMS: dict[str, Any] = { "type": "object", "additionalProperties": False, "properties": {"transaction_id": {"type": "string", "description": "The transaction id."}}, "required": ["transaction_id"], } class ToolProvider: @staticmethod def retrieve_payment_status(strict: bool) -> Tool: return Tool( function=Function( name="retrieve_payment_status", description="Get payment status of a transaction", strict=strict, parameters=_PAYMENT_PARAMS, ) ) @staticmethod def retrieve_payment_date(strict: bool) -> Tool: return Tool( function=Function( name="retrieve_payment_date", description="Get payment date of a transaction", strict=strict, parameters=_PAYMENT_PARAMS, ) ) class SchemaProvider: @staticmethod def basic_person() -> dict[str, Any]: class Person(BaseModel): model_config = ConfigDict(extra="forbid") name: str age: int return Person.model_json_schema() @staticmethod def basic_dict_of_list() -> dict[str, Any]: class DoMerge(BaseModel): model_config = ConfigDict(extra="forbid") new_clusters: dict[str, list[str]] = Field(default_factory=dict) return DoMerge.model_json_schema() class TestCase(BaseModel): __test__ = False model_config = ConfigDict(arbitrary_types_allowed=True) tokenizer: Tokenizer mode: Literal["auto", "any", "none", "required"] | NamedToolChoice tokens: list[int] should_fail_on: int | None case_name: str parallel_tool_calls: bool = True tools: list[Tool] | None = None json_schema: dict[str, Any] | None = None # When set, uses this raw lark grammar instead of GrammarFactory.get_lark raw_lark: str | None = None @model_validator(mode="after") def validate_should_fail_on(self) -> "TestCase": if self.should_fail_on is not None and self.should_fail_on < 0: self.should_fail_on += len(self.tokens) if self.should_fail_on < 0: raise ValueError( f"should_fail_on={self.should_fail_on + len(self.tokens)} " f"is out of bounds for tokens of length {len(self.tokens)}" ) return self @property def name(self) -> str: return f"tekken_{self.tokenizer.version.value}_{self.mode}_{self.case_name}" def _encode_content( instruct_tokenizer: InstructTokenizerBase, content: str | list[Any], ) -> list[int]: tokenizer = instruct_tokenizer.tokenizer if isinstance(content, str): return instruct_tokenizer.encode_assistant_message( AssistantMessage(content=content), is_before_last_user_message=False, continue_message=False ) tool_calls = [x for x in content if isinstance(x, ToolCall)] content_chunks = [x for x in content if not isinstance(x, ToolCall)] tokens: list[int] = [] if content_chunks: tokens = instruct_tokenizer.encode_assistant_message( AssistantMessage(content=content_chunks), is_before_last_user_message=False, continue_message=False, ) # The instruct tokenizer appends EOS after content, but when tool calls follow, # the EOS should come after the last tool call, not after the content. Strip it # here so the tool call tokens are appended directly after content tokens. while tokens and tokens[-1] == tokenizer.eos_id: tokens.pop() for tc in tool_calls: tokens += [ tokenizer.get_special_token("[TOOL_CALLS]"), *tokenizer.encode(tc.function.name, bos=False, eos=False), tokenizer.get_special_token("[ARGS]"), *tokenizer.encode(tc.function.arguments, bos=False, eos=False), ] tokens.append(tokenizer.eos_id) return tokens def _find_first_rejection( factory: GrammarFactory, tokens: list[int], mode: ToolChoice, tools: list[Tool] | None, ) -> int: r"""Finds the index of the first token rejected by the grammar. Args: factory: The grammar factory. tokens: The token sequence to test. mode: The tool choice mode (literal or NamedToolChoice). tools: The tools to pass to grammar generation. Returns: The index of the first rejected token. Raises: ValueError: If all tokens are accepted. """ template = factory.select_jinja_template() grammar = factory.get_lark_from_jinja( template=template, mode=mode, tools=tools, json_schema=None, parallel_tool_calls=True ) matcher = llg.LLMatcher(factory.llg_tokenizer, grammar) for i, token in enumerate(tokens): if not matcher.consume_token(token): return i raise ValueError("All tokens were accepted — expected a rejection") def _token_debug_repr(tokenizer: Tekkenizer, token_id: int) -> str: return repr(tokenizer.id_to_byte_piece(token_id, SpecialTokenPolicy.KEEP)) def _generate_general_cases(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) assert isinstance(tokenizer, Tekkenizer) cases: list[TestCase] = [] items = { "newline": "\n", "blank": "_", "text": "Hello!", "text_with_newlines": "Hello!\n\nHow are you?\nI'm fine, thanks!", "emojis": "😃😂😊😍😘😗😙😚😋😛😜😝🤑🤗🤔🤐😐😑😶😬", "japanese": "こんにちは", "arabic": "مرحبا بكم في عالم الذكاء الاصطناعي", } for case_name, content in items.items(): tokens = _encode_content(instruct_tokenizer, content) cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, case_name=case_name, should_fail_on=None, mode="auto", ) ) return cases def _count_prefix_tokens(tokenizer: Tokenizer, full_text: str, prefix: str) -> int: r"""Counts the number of tokens that cover the prefix bytes in the full-text tokenization. BPE tokenization is context-dependent, so encoding a prefix in isolation may produce different tokens than encoding the full string. This helper encodes the full string and counts how many tokens are needed to cover the byte-length of the prefix. Args: tokenizer: The tokenizer to use. full_text: The complete text to tokenize. prefix: The prefix whose byte-length determines the token count. Returns: The number of tokens covering the prefix bytes. """ assert is_tekkenizer(tokenizer) prefix_byte_len = len(prefix.encode("utf-8")) tokens = tokenizer.encode(full_text, bos=False, eos=False) byte_count = 0 for i, t in enumerate(tokens): byte_count += len(tokenizer.id_to_byte_piece(t, SpecialTokenPolicy.IGNORE)) if byte_count >= prefix_byte_len: return i + 1 return len(tokens) def _generate_emoji_cases(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) emoji_lark = EMOJI_LARK_PATH.read_text(encoding="utf-8") cases: list[TestCase] = [] items: dict[str, tuple[str, int | None]] = { "emojis_valid_a": ("😃😂😊😍😘😗😙😚😋😛😜😝🤑🤗🤔🤐😐😑😶😬", None), "emojis_valid_b": ("😃😃😃", None), "emojis_invalid_text": ("😃smile", _count_prefix_tokens(tokenizer, "😃smile", "😃")), "emojis_invalid_space": ("😃 ", _count_prefix_tokens(tokenizer, "😃 ", "😃")), } for case_name, (text, should_fail_on) in items.items(): tokens = tokenizer.encode(text, bos=False, eos=False) cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, case_name=case_name, should_fail_on=should_fail_on, mode="auto", raw_lark=emoji_lark, ) ) return cases def _generate_cases_tool_calls(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] items: list[tuple[str, list[ToolCall], dict[Mode, int | None]]] = [ ( "single_fcall", [ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}'))], {"auto": None, "none": 0}, ), ( "multi_fcall", [ ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ToolCall(function=FunctionCall(name="hello_1", arguments='{"arg1": "val1", "arg2": "val2"}')), ToolCall(function=FunctionCall(name="hello_2_3", arguments='{"arg1": "val1", "arg2": "val2"}')), ], {"auto": None, "none": 0}, ), ( "emoji_fcall", [ ToolCall( function=FunctionCall(name="he🧦🧦o", arguments='{"arg1": "🐱", "arg2": "🐶", "arg🧦": "🧦"}'), ) ], {"auto": None, "none": 0}, ), ( "pretty_printed_args", [ ToolCall( function=FunctionCall( name="hello", arguments='{\n "arg1": "val1",\n "arg2": "val2"\n }\n', ), ) ], {"auto": None, "none": 0}, ), ( "japanese_fcall", [ToolCall(function=FunctionCall(name="こんにちは", arguments='{"こん": "にちは"}'))], {"auto": None, "none": 0}, ), ] for case_name, content, valid_for in items: tokens = _encode_content(instruct_tokenizer, content) for mode, should_fail_on in valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode=mode, ) ) reasoning_valid_for: dict[Mode, int | None] = {"auto": None, "none": 0} for mode, should_fail_on in reasoning_valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=f"{case_name}_reasoning", mode=mode, ) ) # Broken / missing args edge cases token_items: list[tuple[str, list[int], dict[Mode, int | None]]] = [ ( "fcall_broken_args", [ tokenizer.get_special_token("[TOOL_CALLS]"), *tokenizer.encode("hello", bos=False, eos=False), tokenizer.get_special_token("[ARGS]"), *tokenizer.encode('{"a', bos=False, eos=False), tokenizer.eos_id, ], {"auto": -1, "none": 0}, ), ( "fcall_missing_args", [ tokenizer.get_special_token("[TOOL_CALLS]"), *tokenizer.encode("hello", bos=False, eos=False), tokenizer.get_special_token("[ARGS]"), tokenizer.get_special_token("[TOOL_CALLS]"), ], {"auto": -1, "none": 0}, ), ] for case_name, tokens, valid_for in token_items: for mode, should_fail_on in valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode=mode, ) ) return cases def _generate_cases_text_and_tool_calls(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] content = [ TextChunk(text="Hello!"), ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ] tokens = _encode_content(instruct_tokenizer, content) text_len = len(tokenizer.encode("Hello!", bos=False, eos=False)) valid_for: dict[Mode, int | None] = {"auto": None, "none": text_len} for mode, should_fail_on in valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name="text_fcall", mode=mode, ) ) reasoning_valid_for: dict[Mode, int | None] = {"auto": None, "none": text_len} for mode, should_fail_on in reasoning_valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name="text_fcall_reasoning", mode=mode, ) ) return cases def _generate_cases_thinking_v11(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: r"""Generate thinking test cases for v11 (base grammar — accepts literal as free text).""" instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] # Note: "any"/"required" modes require tools, so only "auto"/"none" are used here (tools=None). # v11 uses base grammar (auto-selected): literal ... is free-form text content. thinks: list[tuple[str, list[Any], dict[Mode, int | None]]] = [ ( "force_think", [TextChunk(text="Hello world!")], {"auto": None, "none": None}, ), ( "think_without_response", [TextChunk(text="Hello!")], {"auto": None, "none": None}, ), ( "unclosed_think", [TextChunk(text="Hello!")], {"auto": None, "none": None}, ), ( "plain_think_with_response", [TextChunk(text="Hello!World!")], { "auto": None, "none": None, }, ), ( "think_with_tool_call", [ TextChunk(text="Hello!"), ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ], { # auto: content? fcalls path — text is content, [TOOL_CALLS] starts fcalls "auto": None, "none": len(tokenizer.encode("Hello!", bos=False, eos=False)), }, ), ( "think_with_text_and_tool_call", [ TextChunk(text="Hello!Ho!"), ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ], { # auto: content? fcalls path — all text is content?, then [TOOL_CALLS] starts fcalls "auto": None, "none": len(tokenizer.encode("Hello!Ho!", bos=False, eos=False)), }, ), ] for case_name, content, valid_for in thinks: tokens = _encode_content(instruct_tokenizer, content) for mode, should_fail_on in valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode=mode, ) ) return cases def _generate_cases_thinking(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: r"""Generate thinking test cases for v13+ (structured think grammar with [THINK]/[/THINK] tokens).""" instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] def _think_tokens(text: str) -> list[int]: r"""Helper to encode a ThinkChunk.""" assert isinstance(instruct_tokenizer, InstructTokenizerV13) return instruct_tokenizer.encode_think(ThinkChunk(thinking=text)) # Note: "any"/"required" modes require tools, so only "auto"/"none" are used here (tools=None). thinks: list[tuple[str, list[Any], dict[Mode, int | None]]] = [ ( "plain_text", [TextChunk(text="Hello world!")], {"auto": None, "none": None}, ), ( "plain_think", [ThinkChunk(thinking="Hello!")], {"auto": -1, "none": -1}, ), ( "plain_think_with_response", [ThinkChunk(thinking="Hello!"), TextChunk(text="World!")], {"auto": None, "none": None}, ), ( "think_with_tool_call", [ ThinkChunk(thinking="Hello!"), ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ], { "auto": None, "none": len(_think_tokens("Hello!")), }, ), ( "think_text_tool_call", [ ThinkChunk(thinking="Hello!"), TextChunk(text="World!"), ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}')), ], { "auto": None, "none": len(_think_tokens("Hello!")) + len(tokenizer.encode("World!", bos=False, eos=False)), }, ), ] for case_name, content, valid_for in thinks: tokens = _encode_content(instruct_tokenizer, content) for mode, should_fail_on in valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode=mode, ) ) return cases def _generate_single_tool_call(mistral_tokenizer: MistralTokenizer) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] single_call = [ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1", "arg2": "val2"}'))] single_tokens = _encode_content(instruct_tokenizer, single_call) # Only "auto" can be used without tools; "any"/"required" require tools. cases.append( TestCase( tokenizer=tokenizer, tokens=single_tokens, should_fail_on=None, case_name="single_tool_call", mode="auto", parallel_tool_calls=False, ) ) cases.append( TestCase( tokenizer=tokenizer, tokens=single_tokens, should_fail_on=0, case_name="single_tool_call", mode="none", parallel_tool_calls=False, ) ) reasoning_valid_for: dict[Mode, int | None] = {"auto": None, "none": 0} for mode, should_fail_on in reasoning_valid_for.items(): cases.append( TestCase( tokenizer=tokenizer, tokens=single_tokens, should_fail_on=should_fail_on, case_name="single_tool_call_reasoning", mode=mode, parallel_tool_calls=False, ) ) multi_calls = [ ToolCall(function=FunctionCall(name="fn1", arguments='{"arg1": "val1", "arg2": "val2"}')), ToolCall(function=FunctionCall(name="fn2", arguments='{"arg1": "val1", "arg2": "val2"}')), ] multi_tokens = _encode_content(instruct_tokenizer, multi_calls) single_tokens_with_eos = _encode_content(instruct_tokenizer, [multi_calls[0]]) fail_idx = len(single_tokens_with_eos) - 1 # Only "auto" can be used without tools; "any"/"required" require tools. cases.append( TestCase( tokenizer=tokenizer, tokens=multi_tokens, should_fail_on=fail_idx, case_name="multi_tool_call_disallowed", mode="auto", parallel_tool_calls=False, ) ) return cases def _generate_strict_tool_calls(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] tools_strict = [ToolProvider.retrieve_payment_date(strict=True)] # 1. Non-strict tools — any function name/args accepted non_strict_call = [ToolCall(function=FunctionCall(name="fn1", arguments='{"arg1": "val1", "arg2": "val2"}'))] non_strict_tokens = _encode_content(instruct_tokenizer, non_strict_call) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=non_strict_tokens, should_fail_on=None, case_name="single_non_strict_tool_call", mode=mode, tools=[ToolProvider.retrieve_payment_date(strict=False)], ) ) # 2. Correct strict tool call strict_call = [ ToolCall(function=FunctionCall(name="retrieve_payment_date", arguments='{"transaction_id": "12345"}')) ] strict_tokens = _encode_content(instruct_tokenizer, strict_call) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=strict_tokens, should_fail_on=None, case_name="single_strict_tool_call", mode=mode, tools=[ToolProvider.retrieve_payment_date(strict=True)], ) ) # 3. Wrong args for strict tool — it must fail somewhere before the end. wrong_args_call = [ToolCall(function=FunctionCall(name="retrieve_payment_date", arguments='{"bogus": "12345"}'))] wrong_args_tokens = _encode_content(instruct_tokenizer, wrong_args_call) bogus_start = _find_first_rejection(factory, wrong_args_tokens, mode=ToolChoiceEnum.auto, tools=tools_strict) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=wrong_args_tokens, should_fail_on=bogus_start, case_name="strict_tool_call_wrong_args", mode=mode, tools=tools_strict, ) ) # 4. Wrong name for strict tool wrong_name_call = [ToolCall(function=FunctionCall(name="fn1", arguments='{"transaction_id": "12345"}'))] wrong_name_tokens = _encode_content(instruct_tokenizer, wrong_name_call) fail_on_name = _find_first_rejection(factory, wrong_name_tokens, mode=ToolChoiceEnum.auto, tools=tools_strict) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=wrong_name_tokens, should_fail_on=fail_on_name, case_name="strict_tool_call_wrong_name", mode=mode, tools=tools_strict, ) ) # 5. Multiple strict tool calls (both correct) multi_strict = [ ToolCall(function=FunctionCall(name="retrieve_payment_date", arguments='{"transaction_id": "12345"}')), ToolCall(function=FunctionCall(name="retrieve_payment_status", arguments='{"transaction_id": "12345"}')), ] multi_strict_tokens = _encode_content(instruct_tokenizer, multi_strict) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=multi_strict_tokens, should_fail_on=None, case_name="multiple_strict_tool_calls", mode=mode, tools=[ ToolProvider.retrieve_payment_date(strict=True), ToolProvider.retrieve_payment_status(strict=True), ], ) ) # 6. Reasoning variants — base (v11) and think (v13+) grammars both accept strict tool calls for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=strict_tokens, should_fail_on=None, case_name="strict_tool_call_reasoning", mode=mode, tools=tools_strict, ) ) for mode in _AUTO_ANY_REQUIRED: cases.append( TestCase( tokenizer=tokenizer, tokens=multi_strict_tokens, should_fail_on=None, case_name="multiple_strict_tool_calls_reasoning", mode=mode, tools=[ ToolProvider.retrieve_payment_date(strict=True), ToolProvider.retrieve_payment_status(strict=True), ], ) ) return cases def _generate_named_tool_choice(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: r"""Generate test cases for NamedToolChoice (forcing a specific tool).""" instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] tools = [ ToolProvider.retrieve_payment_date(strict=False), ToolProvider.retrieve_payment_status(strict=False), ] # 1. NamedToolChoice for retrieve_payment_date with correct function call named_tool_date = NamedToolChoice( type=ToolTypes.function, function=FunctionName(name="retrieve_payment_date"), ) correct_date_call = [ ToolCall( function=FunctionCall( name="retrieve_payment_date", arguments='{"transaction_id": "12345"}', ) ) ] correct_date_tokens = _encode_content(instruct_tokenizer, correct_date_call) cases.append( TestCase( tokenizer=tokenizer, tokens=correct_date_tokens, should_fail_on=None, case_name="named_tool_choice_correct", mode=named_tool_date, tools=tools, parallel_tool_calls=True, ) ) cases.append( TestCase( tokenizer=tokenizer, tokens=correct_date_tokens, should_fail_on=None, case_name="named_tool_choice_correct", mode=named_tool_date, tools=tools, parallel_tool_calls=False, ) ) # 2. Non-strict NamedToolChoice should NOT enforce JSON arguments schema — arbitrary_args_call = [ ToolCall( function=FunctionCall( name="retrieve_payment_date", arguments='{"completely": "arbitrary", "keys": [1, 2, 3]}', ) ) ] arbitrary_args_tokens = _encode_content(instruct_tokenizer, arbitrary_args_call) cases.append( TestCase( tokenizer=tokenizer, tokens=arbitrary_args_tokens, should_fail_on=None, case_name="named_tool_choice_non_strict_arbitrary_args", mode=named_tool_date, tools=tools, parallel_tool_calls=True, ) ) # 3. NamedToolChoice should reject a different tool name wrong_tool_call = [ ToolCall( function=FunctionCall( name="retrieve_payment_status", arguments='{"transaction_id": "12345"}', ) ) ] wrong_tool_tokens = _encode_content(instruct_tokenizer, wrong_tool_call) fail_idx = _find_first_rejection( factory, wrong_tool_tokens, mode=named_tool_date, tools=tools, ) cases.append( TestCase( tokenizer=tokenizer, tokens=wrong_tool_tokens, should_fail_on=fail_idx, case_name="named_tool_choice_wrong_tool", mode=named_tool_date, tools=tools, parallel_tool_calls=True, ) ) # 4. NamedToolChoice — base (v11) and think (v13+) grammars both accept named tool calls cases.append( TestCase( tokenizer=tokenizer, tokens=correct_date_tokens, should_fail_on=None, case_name="named_tool_choice_reasoning", mode=named_tool_date, tools=tools, ) ) # 5. NamedToolChoice with strict tool should validate arguments strict_tools = [ToolProvider.retrieve_payment_date(strict=True)] named_tool_strict = NamedToolChoice( type=ToolTypes.function, function=FunctionName(name="retrieve_payment_date"), ) cases.append( TestCase( tokenizer=tokenizer, tokens=correct_date_tokens, should_fail_on=None, case_name="named_tool_choice_strict_correct", mode=named_tool_strict, tools=strict_tools, ) ) wrong_args_call = [ ToolCall( function=FunctionCall( name="retrieve_payment_date", arguments='{"wrong_arg": "12345"}', ) ) ] wrong_args_tokens = _encode_content(instruct_tokenizer, wrong_args_call) fail_on_args = _find_first_rejection( factory, wrong_args_tokens, mode=named_tool_strict, tools=strict_tools, ) cases.append( TestCase( tokenizer=tokenizer, tokens=wrong_args_tokens, should_fail_on=fail_on_args, case_name="named_tool_choice_strict_wrong_args", mode=named_tool_strict, tools=strict_tools, ) ) return cases def _find_first_json_schema_rejection( factory: GrammarFactory, tokens: list[int], json_schema: dict[str, Any], ) -> int: r"""Finds the index of the first token rejected by the JSON schema grammar. Args: factory: The grammar factory. tokens: The token sequence to test. json_schema: The JSON schema to validate against. Returns: The index of the first rejected token. Raises: ValueError: If all tokens are accepted. """ template = factory.select_jinja_template() grammar = factory.get_lark_for_json_schema(template=template, json_schema=json_schema) matcher = llg.LLMatcher(factory.llg_tokenizer, grammar) for i, token in enumerate(tokens): if not matcher.consume_token(token): return i raise ValueError("All tokens were accepted — expected a rejection") def _generate_json_schema(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] items: list[tuple[str, str, int | None, dict[str, Any]]] = [ ( "basic_person_valid", '{"name": "John", "age": 30}', None, SchemaProvider.basic_person(), ), ( "invalid_json_missing_curly_bracket", '"name": "John", "age": 30}', 0, {"type": "object"}, ), ( "valid_json_white_spaces", '\n {"name": "John", "age": 30}', None, {"type": "object"}, ), ( "invalid_json_backslash_f", '\f{"name": "John", "age": 30}', 0, {"type": "object"}, ), ( "basic_person_non_strict_valid", '{"age": "John", "name": 30}', None, {"type": "object"}, ), ( "domerge_valid", '{"new_clusters": {"b": ["a", "b", "c"], "d": ["e"]} }', None, SchemaProvider.basic_dict_of_list(), ), ] for case_name, text, should_fail_on, json_schema in items: tokens = _encode_content(instruct_tokenizer, text) cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode="auto", json_schema=json_schema, ) ) # Cases where should_fail_on must be computed dynamically because the exact # rejection index depends on the grammar engine's internal byte-level parsing. person_invalid_tokens = _encode_content(instruct_tokenizer, '{"age": "John", "name": 30}') person_schema = SchemaProvider.basic_person() cases.append( TestCase( tokenizer=tokenizer, tokens=person_invalid_tokens, should_fail_on=_find_first_json_schema_rejection(factory, person_invalid_tokens, person_schema), case_name="basic_person_invalid", mode="auto", json_schema=person_schema, ) ) return cases def _generate_json_schema_reasoning(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] # Valid JSON should be accepted even under reasoning templates with json_only items: list[tuple[str, str, int | None, dict[str, Any]]] = [ ( "json_schema_reasoning_valid", '{"name": "John", "age": 30}', None, SchemaProvider.basic_person(), ), ( "json_schema_reasoning_whitespace_valid", '\n {"name": "John", "age": 30}', None, {"type": "object"}, ), ( "json_schema_reasoning_invalid_bracket", '"name": "John", "age": 30}', 0, {"type": "object"}, ), ] for case_name, text, should_fail_on, json_schema in items: tokens = _encode_content(instruct_tokenizer, text) cases.append( TestCase( tokenizer=tokenizer, tokens=tokens, should_fail_on=should_fail_on, case_name=case_name, mode="auto", json_schema=json_schema, ) ) return cases def _generate_json_only_negative(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) cases: list[TestCase] = [] # Plain text should be rejected by json_only grammar text_tokens = _encode_content(instruct_tokenizer, "Hello world!") cases.append( TestCase( tokenizer=tokenizer, tokens=text_tokens, should_fail_on=0, case_name="json_only_rejects_text", mode="auto", json_schema={"type": "object"}, ) ) # Tool calls should be rejected by json_only grammar tool_call_tokens = _encode_content( instruct_tokenizer, [ToolCall(function=FunctionCall(name="hello", arguments='{"arg1": "val1"}'))], ) cases.append( TestCase( tokenizer=tokenizer, tokens=tool_call_tokens, should_fail_on=0, case_name="json_only_rejects_tool_call", mode="auto", json_schema={"type": "object"}, ) ) # Plain text is also rejected by json_only grammar regardless of grammar variant cases.append( TestCase( tokenizer=tokenizer, tokens=text_tokens, should_fail_on=0, case_name="json_only_reasoning_rejects_text", mode="auto", json_schema={"type": "object"}, ) ) return cases def _generate_json_schema_think_with_json( mistral_tokenizer: MistralTokenizer, factory: GrammarFactory ) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer tokenizer = instruct_tokenizer.tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) assert isinstance(instruct_tokenizer, InstructTokenizerV13) cases: list[TestCase] = [] json_schema = SchemaProvider.basic_person() valid_json = '{"name": "John", "age": 30}' def _think_tokens(text: str) -> list[int]: return instruct_tokenizer.encode_think(ThinkChunk(thinking=text)) # Think + valid JSON should be accepted with json_only + reasoning think_json_tokens = [ *_think_tokens("Let me think about this..."), *tokenizer.encode(valid_json, bos=False, eos=False), tokenizer.eos_id, ] cases.append( TestCase( tokenizer=tokenizer, tokens=think_json_tokens, should_fail_on=None, case_name="think_with_json_valid", mode="auto", json_schema=json_schema, ) ) json_only_tokens = _encode_content(instruct_tokenizer, valid_json) cases.append( TestCase( tokenizer=tokenizer, tokens=json_only_tokens, should_fail_on=None, case_name="think_with_json_no_think_valid", mode="auto", json_schema=json_schema, ) ) think_text_tokens = [ *_think_tokens("Let me think..."), *tokenizer.encode("Hello world!", bos=False, eos=False), tokenizer.eos_id, ] fail_idx = len(_think_tokens("Let me think...")) cases.append( TestCase( tokenizer=tokenizer, tokens=think_text_tokens, should_fail_on=fail_idx, case_name="think_with_json_rejects_text_after_think", mode="auto", json_schema=json_schema, ) ) return cases def _generate_cases(mistral_tokenizer: MistralTokenizer, factory: GrammarFactory) -> list[TestCase]: instruct_tokenizer = mistral_tokenizer.instruct_tokenizer assert isinstance(instruct_tokenizer, InstructTokenizerBase) tokenizer_version = instruct_tokenizer.tokenizer.version cases = _generate_general_cases(mistral_tokenizer) cases += _generate_emoji_cases(mistral_tokenizer) cases += _generate_json_schema(mistral_tokenizer, factory) cases += _generate_json_schema_reasoning(mistral_tokenizer, factory) cases += _generate_json_only_negative(mistral_tokenizer, factory) cases += _generate_cases_tool_calls(mistral_tokenizer) cases += _generate_single_tool_call(mistral_tokenizer) cases += _generate_strict_tool_calls(mistral_tokenizer, factory) cases += _generate_named_tool_choice(mistral_tokenizer, factory) cases += _generate_cases_text_and_tool_calls(mistral_tokenizer) if tokenizer_version >= TokenizerVersion.v13: cases += _generate_cases_thinking(mistral_tokenizer) else: cases += _generate_cases_thinking_v11(mistral_tokenizer) # v15+ supports think_with_json (thinking before JSON in json_only mode) if tokenizer_version.supports_model_settings: cases += _generate_json_schema_think_with_json(mistral_tokenizer, factory) return cases _grammar_factories: dict[int, GrammarFactory] = {} def _get_grammar_factory(mistral_tokenizer: MistralTokenizer) -> GrammarFactory: tok_id = id(mistral_tokenizer) if tok_id not in _grammar_factories: _grammar_factories[tok_id] = GrammarFactory(mistral_tokenizer) return _grammar_factories[tok_id] _ALL_TOKENIZERS: list[MistralTokenizer] = [ _build_tekken_mistral_tokenizer(TokenizerVersion.v11), _build_tekken_mistral_tokenizer(TokenizerVersion.v13, add_think=True), _build_tekken_mistral_tokenizer( TokenizerVersion.v15, add_think=True, model_settings_builder=_V15_MODEL_SETTINGS_BUILDER ), ] _ALL_CASES: list[TestCase] = [] _ALL_MISTRAL_TOKENIZERS: dict[int, MistralTokenizer] = {} for mistral_tokenizer in _ALL_TOKENIZERS: tokenizer = mistral_tokenizer.instruct_tokenizer.tokenizer _ALL_MISTRAL_TOKENIZERS[id(tokenizer)] = mistral_tokenizer factory = _get_grammar_factory(mistral_tokenizer) _ALL_CASES.extend(_generate_cases(mistral_tokenizer, factory)) class TestGrammarFactory: @pytest.mark.parametrize("test_case", _ALL_CASES, ids=lambda tc: tc.name) def test_grammar(self, test_case: TestCase) -> None: mistral_tokenizer = _ALL_MISTRAL_TOKENIZERS[id(test_case.tokenizer)] factory = _get_grammar_factory(mistral_tokenizer) if test_case.raw_lark is not None: grammar = test_case.raw_lark elif test_case.json_schema is not None and test_case.tools is None: template = factory.select_jinja_template() grammar = factory.get_lark_for_json_schema(template=template, json_schema=test_case.json_schema) else: template = factory.select_jinja_template() resolved_mode: ToolChoice if isinstance(test_case.mode, NamedToolChoice): resolved_mode = test_case.mode else: resolved_mode = ToolChoiceEnum(test_case.mode) grammar = factory.get_lark_from_jinja( template=template, mode=resolved_mode, tools=test_case.tools, json_schema=test_case.json_schema, parallel_tool_calls=test_case.parallel_tool_calls, ) matcher = llg.LLMatcher(factory.llg_tokenizer, grammar) assert is_tekkenizer(test_case.tokenizer) debug_tokens = [_token_debug_repr(test_case.tokenizer, t) for t in test_case.tokens] for i, token in enumerate(test_case.tokens): debug_bytes = _token_debug_repr(test_case.tokenizer, token) if token != test_case.tokenizer.eos_id: assert not matcher.is_stopped(), ( f"Matcher stopped before token {i} id={token}\n\n" f"Grammar:\n{grammar}\n\nTokens: {debug_tokens}\n\nIds: {test_case.tokens}" ) accepted = matcher.consume_token(token) if i == test_case.should_fail_on: if accepted: raise AssertionError( f"Token {token}={debug_bytes} at pos {i} was accepted but should have been rejected." f"\n\nGrammar:\n{grammar}\n\nTokens: {debug_tokens}\n\nIds: {test_case.tokens}" ) break elif not accepted: raise AssertionError( f"Token {token}={debug_bytes} at pos {i} was rejected but should have been accepted." f"\n\nGrammar:\n{grammar}\n\nTokens: {debug_tokens}\n\nIds: {test_case.tokens}" ) # For fully accepted sequences, verify the matcher reached a valid terminal state. # Raw lark grammars (e.g., emoji matcher) don't consume EOS, so they may not reach # a stopped state — skip the check for those. if test_case.should_fail_on is None and test_case.raw_lark is None: assert matcher.is_stopped(), ( f"Matcher did not reach terminal state after consuming all tokens.\n\n" f"Grammar:\n{grammar}\n\nTokens: {debug_tokens}\n\nIds: {test_case.tokens}" ) @pytest.mark.parametrize( ("tokenizer", "expected"), [ (MistralTokenizer.v1(), False), (MistralTokenizer.v3(is_tekken=True), False), (_build_tekken_mistral_tokenizer(TokenizerVersion.v11), True), (_build_tekken_mistral_tokenizer(TokenizerVersion.v13, add_think=True), True), ( _build_tekken_mistral_tokenizer( TokenizerVersion.v15, add_think=True, model_settings_builder=_V15_MODEL_SETTINGS_BUILDER ), True, ), ], ) def test_grammar_factory_is_supported(self, tokenizer: MistralTokenizer, expected: bool) -> None: assert GrammarFactory.is_supported(tokenizer) is expected @pytest.mark.parametrize( "tokenizer", [MistralTokenizer.v1(), MistralTokenizer.v3(is_tekken=True)], ) def test_grammar_factory_init_rejects_unsupported(self, tokenizer: MistralTokenizer) -> None: with pytest.raises(ValueError, match="Guidance requires a Tekken tokenizer with version >= v11"): GrammarFactory(tokenizer) @pytest.mark.parametrize("mode", [ToolChoiceEnum.any, ToolChoiceEnum.required]) def test_get_lark_rejects_any_required_without_tools( self, v11_tekken: MistralTokenizer, mode: ToolChoiceEnum ) -> None: factory = GrammarFactory(v11_tekken) template = factory.select_jinja_template() with pytest.raises(ValueError, match="please ensure to pass tools"): factory.get_lark_from_jinja( template=template, mode=mode, tools=None, json_schema=None, parallel_tool_calls=True ) def test_get_lark_rejects_named_tool_not_in_tools(self, v11_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v11_tekken) template = factory.select_jinja_template() named = NamedToolChoice(function=FunctionName(name="non_existent")) tools = [ToolProvider.retrieve_payment_date(strict=True)] with pytest.raises(ValueError, match="no tools with this name"): factory.get_lark_from_jinja( template=template, mode=named, tools=tools, json_schema=None, parallel_tool_calls=True ) @pytest.mark.parametrize("mode", [ToolChoiceEnum.any, ToolChoiceEnum.required]) @pytest.mark.parametrize("tools", [None, []]) def test_any_required_without_tools_raises( self, v11_tekken: MistralTokenizer, mode: ToolChoiceEnum, tools: list[Tool] | None ) -> None: factory = GrammarFactory(v11_tekken) template = factory.select_jinja_template() with pytest.raises(ValueError, match="please ensure to pass tools"): factory.get_lark_from_jinja( template=template, tools=tools, mode=mode, json_schema=None, parallel_tool_calls=True ) @pytest.mark.parametrize("tools", [None, [], [Tool(function=Function(name="existing_tool", parameters={}))]]) def test_named_wrong_tools_raises(self, v11_tekken: MistralTokenizer, tools: list[Tool] | None) -> None: factory = GrammarFactory(v11_tekken) template = factory.select_jinja_template() with pytest.raises(ValueError, match="no tools with this name"): factory.get_lark_from_jinja( template=template, tools=tools, mode=NamedToolChoice(function=FunctionName(name="non_existent_tool")), json_schema=None, parallel_tool_calls=True, ) class TestSelectJinjaTemplate: @pytest.mark.parametrize( ("fixture_name", "expected_variant"), [ ("v13_tekken", _GrammarVariant.think), ("v13_no_think_tekken", _GrammarVariant.base), ("v11_tekken", _GrammarVariant.base), ("v15_tekken", _GrammarVariant.think), ], ids=["v13_think", "v13_no_think", "v11", "v15_think"], ) def test_select_jinja_template( self, request: pytest.FixtureRequest, fixture_name: str, expected_variant: _GrammarVariant ) -> None: tokenizer: MistralTokenizer = request.getfixturevalue(fixture_name) factory = GrammarFactory(tokenizer) result = factory.select_jinja_template() expected = JINJA_PATHS[expected_variant].read_text(encoding="utf-8") assert result == expected def test_select_jinja_template_raises_on_only_begin_think(self, v13_no_think_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v13_no_think_tekken) factory._special_token_map[SpecialTokens.begin_think.value] = "<[99]>" with pytest.raises(AssertionError, match=r"both \[THINK\] and \[/THINK\] should be defined or none of them\."): factory.select_jinja_template() def test_select_jinja_template_raises_on_only_end_think(self, v13_no_think_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v13_no_think_tekken) factory._special_token_map[SpecialTokens.end_think.value] = "<[99]>" with pytest.raises(AssertionError, match=r"both \[THINK\] and \[/THINK\] should be defined or none of them\."): factory.select_jinja_template() def test_select_jinja_template_reasoning_accepted_and_ignored(self, v11_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v11_tekken) expected = factory.select_jinja_template() mistral_common.deprecation._warned_keys.discard("select_jinja_template.reasoning") assert factory.select_jinja_template(reasoning=True) == expected mistral_common.deprecation._warned_keys.discard("select_jinja_template.reasoning") assert factory.select_jinja_template(reasoning=False) == expected def test_select_jinja_template_reasoning_true_warns_deprecation(self, v11_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v11_tekken) mistral_common.deprecation._warned_keys.discard("select_jinja_template.reasoning") with pytest.warns(DeprecationWarning): factory.select_jinja_template(reasoning=True) def test_select_jinja_template_reasoning_none_no_warn(self, v11_tekken: MistralTokenizer) -> None: factory = GrammarFactory(v11_tekken) with warnings.catch_warnings(): warnings.simplefilter("error", DeprecationWarning) factory.select_jinja_template() @pytest.mark.parametrize( ("fixture_name", "content"), [ ("v13_no_think_tekken", "hello world"), ("v11_tekken", "reasoninganswer"), ], ids=["v13_no_think_plain_content", "v11_literal_think"], ) def test_base_grammar_accepts_content( self, request: pytest.FixtureRequest, fixture_name: str, content: str ) -> None: """Checks base-selected grammars accept the content.""" tokenizer: MistralTokenizer = request.getfixturevalue(fixture_name) factory = GrammarFactory(tokenizer) template = factory.select_jinja_template() grammar = factory.get_lark_from_jinja( template=template, mode=ToolChoiceEnum.auto, tools=None, json_schema=None, parallel_tool_calls=True, ) tekkenizer = tokenizer.instruct_tokenizer.tokenizer tokens = [*tekkenizer.encode(content, bos=False, eos=False), tekkenizer.eos_id] matcher = llg.LLMatcher(factory.llg_tokenizer, grammar) for token in tokens: assert matcher.consume_token(token) assert matcher.is_stopped() def _stub_get_special_token_id(token_name: str) -> str: r"""Returns a stub lark grammar token for testing.""" return f"<[{token_name}]>" class TestConvertToolCalls: def test_none_mode(self) -> None: result = _convert_tool_calls( tools=None, mode=ToolChoiceEnum.none, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert result == "" def test_none_mode_with_tools(self) -> None: tools = [ToolProvider.retrieve_payment_date(strict=True)] result = _convert_tool_calls( tools=tools, mode=ToolChoiceEnum.none, parallel_tool_calls=True, get_special_token_id=_stub_get_special_token_id, ) assert result == "" def test_auto_mode_no_tools(self) -> None: result = _convert_tool_calls( tools=None, mode=ToolChoiceEnum.auto, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert "<[[TOOL_CALLS]]>" in result assert "<[[ARGS]]>" in result assert "/.+/" in result assert not result.startswith("(") assert not result.endswith(")+") def test_auto_mode_non_strict(self) -> None: tools = [ToolProvider.retrieve_payment_date(strict=False)] result = _convert_tool_calls( tools=tools, mode=ToolChoiceEnum.auto, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert "<[[TOOL_CALLS]]>" in result assert "/.+/" in result assert not result.startswith("(") assert not result.endswith(")+") def test_auto_mode_strict(self) -> None: tools = [ ToolProvider.retrieve_payment_date(strict=True), ToolProvider.retrieve_payment_status(strict=True), ] result = _convert_tool_calls( tools=tools, mode=ToolChoiceEnum.auto, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert '"retrieve_payment_date"' in result assert '"retrieve_payment_status"' in result assert " | " in result assert result.count("(") >= 2 assert not result.endswith(")+") def test_auto_mode_single_strict(self) -> None: tools = [ToolProvider.retrieve_payment_date(strict=True)] result = _convert_tool_calls( tools=tools, mode=ToolChoiceEnum.auto, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert '"retrieve_payment_date"' in result assert " | " not in result assert not result.startswith("(") assert not result.endswith(")+") def test_named_tool_choice_non_strict(self) -> None: named = NamedToolChoice(function=FunctionName(name="retrieve_payment_date")) tools = [ToolProvider.retrieve_payment_date(strict=False)] result = _convert_tool_calls( tools=tools, mode=named, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert '"retrieve_payment_date"' in result assert "/.+/" not in result assert not result.endswith(")+") def test_named_tool_choice_strict(self) -> None: named = NamedToolChoice(function=FunctionName(name="retrieve_payment_date")) tools = [ ToolProvider.retrieve_payment_date(strict=True), ToolProvider.retrieve_payment_status(strict=True), ] result = _convert_tool_calls( tools=tools, mode=named, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert '"retrieve_payment_date"' in result assert '"retrieve_payment_status"' not in result assert " | " not in result assert not result.startswith("(") assert not result.endswith(")+") def test_parallel_tool_calls(self) -> None: result = _convert_tool_calls( tools=None, mode=ToolChoiceEnum.auto, parallel_tool_calls=True, get_special_token_id=_stub_get_special_token_id, ) assert result.startswith("(") and result.endswith(")+") def test_empty_params_strict_tool(self) -> None: tool = Tool(function=Function(name="empty_fn", parameters={}, strict=True)) result = _convert_tool_calls( tools=[tool], mode=ToolChoiceEnum.auto, parallel_tool_calls=False, get_special_token_id=_stub_get_special_token_id, ) assert '"additionalProperties": false' in result assert '"properties": {}' in result assert not result.startswith("(") assert not result.endswith(")+") python-mistral-common-1.11.7/tests/guidance/test_tokenizer.py000066400000000000000000000223541523035502500244330ustar00rootroot00000000000000import re from unittest.mock import MagicMock import llguidance as llg import pytest from mistral_common.guidance.tokenizer import MistralLLGTokenizer, from_mistral_tokenizer from mistral_common.protocol.instruct.normalize import get_normalizer from mistral_common.protocol.instruct.validator import ValidationMode, get_validator from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens, Tokenizer, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV7 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenInfo, Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab _NUM_SPECIAL_TOKENS = 100 _EXTRA_TOKENS = [b"a", b"b", b"c", b"f", b"de", b"he", b"llo"] @pytest.fixture(scope="module") def tekkenizer() -> Tekkenizer: special_tokens = get_special_tokens(TokenizerVersion.v7) return Tekkenizer( quick_vocab(_EXTRA_TOKENS), special_tokens=special_tokens, pattern=r".+", vocab_size=256 + _NUM_SPECIAL_TOKENS, num_special_tokens=_NUM_SPECIAL_TOKENS, version=TokenizerVersion.v7, ) @pytest.fixture(scope="module") def llg_tokenizer(tekkenizer: Tekkenizer) -> MistralLLGTokenizer: return MistralLLGTokenizer(tekkenizer) @pytest.fixture(scope="module") def mistral_tokenizer(tekkenizer: Tekkenizer) -> MistralTokenizer: instruct_tokenizer = InstructTokenizerV7(tekkenizer) normalizer = get_normalizer(tekkenizer.version, tekkenizer.model_settings_builder) validator = get_validator(tekkenizer.version, mode=ValidationMode.test) return MistralTokenizer(instruct_tokenizer, validator=validator, request_normalizer=normalizer) @pytest.fixture(scope="module") def ll_tokenizer(mistral_tokenizer: MistralTokenizer) -> llg.LLTokenizer: return from_mistral_tokenizer(mistral_tokenizer) class TestMistralLLGTokenizer: def test_init_rejects_non_tekkenizer(self) -> None: mock_tokenizer = MagicMock(spec=Tokenizer) with pytest.raises(TypeError, match="Guidance only supports Tekken tokenizers"): MistralLLGTokenizer(mock_tokenizer) def test_eos_and_bos_ids(self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer) -> None: assert llg_tokenizer.eos_token_id == tekkenizer.eos_id assert llg_tokenizer.bos_token_id == tekkenizer.bos_id def test_tokens_length(self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer) -> None: assert len(llg_tokenizer.tokens) == tekkenizer.n_words def test_special_token_ids_count(self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer) -> None: assert len(llg_tokenizer.special_token_ids) == tekkenizer.num_special_tokens def test_all_special_tokens_are_angle_bracketed(self, llg_tokenizer: MistralLLGTokenizer) -> None: for i in llg_tokenizer.special_token_ids: token_bytes = llg_tokenizer.tokens[i] token_str = token_bytes.decode("utf-8") assert re.fullmatch(r"<.*>", token_str), f"Special token at id={i} is not angle-bracketed: {token_str!r}" def test_special_token_conversion(self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer) -> None: checked = 0 for token in SpecialTokens: try: rank = tekkenizer.get_special_token(token.value) except ValueError: continue llg_bytes = llg_tokenizer.tokens[rank] if re.fullmatch(r"\[.*\]", token.value): expected = token.value.replace("[", "<").replace("]", ">") else: expected = token.value assert llg_bytes == expected.encode("utf-8"), ( f"Token {token.name} at rank={rank}: expected {expected!r}, got {llg_bytes!r}" ) checked += 1 # Filler tokens () should be preserved as-is for i in range(tekkenizer.num_special_tokens): piece = tekkenizer.id_to_piece(i) if re.fullmatch(r"", piece): assert llg_tokenizer.tokens[i] == piece.encode("utf-8"), ( f"Filler token at id={i}: expected {piece!r}, got {llg_tokenizer.tokens[i]!r}" ) checked += 1 assert checked == tekkenizer.num_special_tokens def test_non_special_tokens_match_byte_pieces( self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer ) -> None: for i in range(tekkenizer.num_special_tokens, tekkenizer.n_words): expected = tekkenizer.id_to_byte_piece(i, SpecialTokenPolicy.RAISE) assert llg_tokenizer.tokens[i] == expected, ( f"Token at id={i}: expected {expected!r}, got {llg_tokenizer.tokens[i]!r}" ) def test_call_encodes_string(self, tekkenizer: Tekkenizer, llg_tokenizer: MistralLLGTokenizer) -> None: test_string = "abc" assert llg_tokenizer(test_string) == tekkenizer.encode(test_string, bos=False, eos=False) def test_init_rejects_invalid_special_token_format(self) -> None: base = list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS) next_rank = len(base) special_tokens: list[SpecialTokenInfo] = [ *base, SpecialTokenInfo(rank=next_rank, token_str="INVALID_NO_BRACKETS", is_control=True), ] vocab = quick_vocab() num_special = len(special_tokens) tekkenizer = Tekkenizer( vocab, special_tokens=special_tokens, pattern=r".+", vocab_size=len(vocab) + num_special, num_special_tokens=num_special, version=TokenizerVersion.v7, ) with pytest.raises(ValueError, match="Invalid special token"): MistralLLGTokenizer(tekkenizer) def test_init_rejects_duplicate_special_tokens(self) -> None: base = list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS) next_rank = len(base) # Both [CUSTOM] and map to after bracket conversion special_tokens: list[SpecialTokenInfo] = [ *base, SpecialTokenInfo(rank=next_rank, token_str="", is_control=True), SpecialTokenInfo(rank=next_rank + 1, token_str="[CUSTOM]", is_control=True), ] vocab = quick_vocab() num_special = len(special_tokens) tekkenizer = Tekkenizer( vocab, special_tokens=special_tokens, pattern=r".+", vocab_size=len(vocab) + num_special, num_special_tokens=num_special, version=TokenizerVersion.v7, ) with pytest.raises(ValueError, match="Duplicate special token"): MistralLLGTokenizer(tekkenizer) class TestFromMistralTokenizer: def test_properties(self, tekkenizer: Tekkenizer, ll_tokenizer: llg.LLTokenizer) -> None: assert isinstance(ll_tokenizer, llg.LLTokenizer) assert ll_tokenizer.vocab_size == tekkenizer.n_words assert ll_tokenizer.eos_token == tekkenizer.eos_id for i in range(tekkenizer.num_special_tokens): assert ll_tokenizer.is_special_token(i), f"Token id={i} should be special" def test_tokenize_str_matches_tekkenizer(self, tekkenizer: Tekkenizer, ll_tokenizer: llg.LLTokenizer) -> None: test_strings = ["abc", "hello", "de", "abcdefhello"] for s in test_strings: expected = tekkenizer.encode(s, bos=False, eos=False) result = ll_tokenizer.tokenize_str(s) assert result == expected, f"Mismatch for {s!r}: expected {expected}, got {result}" def test_decode_str_roundtrip(self, tekkenizer: Tekkenizer, ll_tokenizer: llg.LLTokenizer) -> None: test_strings = ["abc", "hello", "de"] for s in test_strings: tokens = tekkenizer.encode(s, bos=False, eos=False) decoded = ll_tokenizer.decode_str(tokens) assert decoded == s, f"Roundtrip failed for {s!r}: got {decoded!r}" def test_decode_bytes_roundtrip(self, tekkenizer: Tekkenizer, ll_tokenizer: llg.LLTokenizer) -> None: test_strings = ["abc", "hello"] for s in test_strings: tokens = tekkenizer.encode(s, bos=False, eos=False) decoded = ll_tokenizer.decode_bytes(tokens) assert decoded == s.encode("utf-8"), f"Roundtrip failed for {s!r}: got {decoded!r}" def test_decode_special_tokens(self, tekkenizer: Tekkenizer, ll_tokenizer: llg.LLTokenizer) -> None: for token in SpecialTokens: try: rank = tekkenizer.get_special_token(token.value) except ValueError: continue if re.fullmatch(r"\[.*\]", token.value): expected = token.value.replace("[", "<").replace("]", ">") else: expected = token.value decoded_str = ll_tokenizer.decode_str([rank]) assert decoded_str == expected, ( f"decode_str for {token.name} (id={rank}): expected {expected!r}, got {decoded_str!r}" ) decoded_bytes = ll_tokenizer.decode_bytes([rank]) assert decoded_bytes == expected.encode("utf-8"), ( f"decode_bytes for {token.name} (id={rank}): " f"expected {expected.encode('utf-8')!r}, got {decoded_bytes!r}" ) python-mistral-common-1.11.7/tests/integrations/000077500000000000000000000000001523035502500217315ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/__init__.py000066400000000000000000000000001523035502500240300ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/000077500000000000000000000000001523035502500247265ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/__init__.py000066400000000000000000000000001523035502500270250ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/conftest.py000066400000000000000000000146331523035502500271340ustar00rootroot00000000000000r"""Chat template test configuration. Provides package-level fixtures, parametrize config lists, and invalid-message fixtures used across all chat template test layers. Reusable helper functions live in `helpers.py`. Test data constants (`REQUEST_*`) live in `fixtures_data.py`. """ from collections.abc import Generator from typing import Any from unittest.mock import patch import pytest from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import _IMAGE, TestConfig # All configurations for output comparison tests (including SPM). ALL_CONFIGS: list[TestConfig] = [ # Non-SPM TestConfig(version=TokenizerVersion.v1), TestConfig(version=TokenizerVersion.v2), TestConfig(version=TokenizerVersion.v3), TestConfig(version=TokenizerVersion.v3, image=True), TestConfig(version=TokenizerVersion.v7), TestConfig(version=TokenizerVersion.v7, image=True), TestConfig(version=TokenizerVersion.v7, audio=True), TestConfig(version=TokenizerVersion.v11), TestConfig(version=TokenizerVersion.v11, image=True), TestConfig(version=TokenizerVersion.v11, audio=True), TestConfig(version=TokenizerVersion.v13), TestConfig(version=TokenizerVersion.v13, image=True), TestConfig(version=TokenizerVersion.v13, audio=True), TestConfig(version=TokenizerVersion.v13, think=True), TestConfig(version=TokenizerVersion.v13, image=True, think=True), TestConfig(version=TokenizerVersion.v15), TestConfig(version=TokenizerVersion.v15, image=True), TestConfig(version=TokenizerVersion.v15, audio=True), TestConfig(version=TokenizerVersion.v15, think=True), TestConfig(version=TokenizerVersion.v15, image=True, think=True), # SPM TestConfig(version=TokenizerVersion.v1, spm=True), TestConfig(version=TokenizerVersion.v2, spm=True), TestConfig(version=TokenizerVersion.v3, spm=True), TestConfig(version=TokenizerVersion.v3, spm=True, image=True), TestConfig(version=TokenizerVersion.v7, spm=True), TestConfig(version=TokenizerVersion.v7, spm=True, image=True), # Plain thinking (v11 only) TestConfig(version=TokenizerVersion.v11, plain_think=True), TestConfig(version=TokenizerVersion.v11, image=True, plain_think=True), ] # Parametrization configs for transformers tests (excludes plain_think). ALL_TRANSFORMERS_CONFIGS: list[TestConfig] = [c for c in ALL_CONFIGS if not c.plain_think] @pytest.fixture(scope="module") def invalid_sp_think() -> dict[str, Any]: r"""Invalid system message containing a think chunk.""" return { "messages": [ { "role": "system", "content": [ {"type": "text", "text": "Hello"}, {"type": "think", "thinking": "Hello"}, ], } ] } @pytest.fixture(scope="module") def invalid_sp_random() -> dict[str, Any]: r"""Invalid system message containing an unknown chunk type.""" return { "messages": [ { "role": "system", "content": [ {"type": "text", "text": "Hello"}, {"type": "random", "random": "Hello"}, ], } ] } @pytest.fixture(scope="module") def invalid_assistant_think() -> dict[str, Any]: r"""Invalid assistant message containing a think chunk.""" return { "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Hello"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": "Hello"}, {"type": "think", "thinking": "Hello"}, ], }, ] } @pytest.fixture(scope="module") def invalid_assistant_random() -> dict[str, Any]: r"""Invalid assistant message containing an unknown chunk type.""" return { "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Hello"}, ], }, { "role": "assistant", "content": [ {"type": "text", "text": "Hello"}, {"type": "random", "random": "Hello"}, ], }, ] } @pytest.fixture(scope="module") def invalid_user_image() -> dict[str, Any]: r"""Invalid user message containing an image chunk.""" return { "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Hello"}, {"type": "image", "image_url": "Hello"}, ], } ] } @pytest.fixture(scope="module") def invalid_user_audio() -> dict[str, Any]: r"""Invalid user message containing an audio chunk.""" return { "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Hello"}, {"type": "audio", "audio_url": "Hello"}, ], } ] } @pytest.fixture(scope="module") def invalid_user_random() -> dict[str, Any]: r"""Invalid user message containing an unknown chunk type.""" return { "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Hello"}, {"type": "random", "random": "Hello"}, ], } ] } def _config_id(c: TestConfig) -> str: r"""Generate a human-readable test ID for a config.""" parts = [c.version.value] if c.spm: parts.append("spm") if c.image: parts.append("img") if c.audio: parts.append("aud") if c.think: parts.append("think") if c.plain_think: parts.append("plain_think") return "_".join(parts) @pytest.fixture(autouse=True, scope="package") def mock_download_image() -> Generator[None, None, None]: r"""Mock `download_image` to return a dummy image for all tests.""" with patch("mistral_common.image.download_image") as mock_download: mock_download.return_value = _IMAGE with patch("mistral_common.tokens.tokenizers.image.download_image") as mock_download2: mock_download2.return_value = _IMAGE yield python-mistral-common-1.11.7/tests/integrations/chat_templates/fixtures_data.py000066400000000000000000001235511523035502500301510ustar00rootroot00000000000000r"""Test data constants for chat template tests.""" from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, ImageChunk, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import _AUDIO, _AUDIO_URL, _IMAGE, _IMAGE_URL _TOOLS = [ Tool( function=Function( name="tool1", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", "required": ["location"], } }, }, ) ), Tool(function=Function(name="tool2", parameters={})), ] # -- Request fixtures -- REQUEST_ONE_TURN_TEST = ChatCompletionRequest( messages=[ UserMessage(content="User says hello"), ] ) REQUEST_ONE_TURN_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), ] ) REQUEST_ONE_TURN_WITH_SYSTEM_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), ] ) REQUEST_ONE_TURN_WITH_SYSTEM_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), ] ) REQUEST_MULTI_TURN_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), ] ) REQUEST_MULTI_TURN_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), AssistantMessage(content="Assistant says hi"), ] ) REQUEST_MULTI_TURN_WITH_SYSTEM_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), ] ) REQUEST_MULTI_TURN_WITH_SYSTEM_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), AssistantMessage(content="Assistant says hi"), ] ) REQUEST_MULTI_TURN_WITH_TOOLS_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_TOOLS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Assistant says hi"), UserMessage(content="User says how are you ?"), AssistantMessage(content="Assistant says hi"), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TEST_2 = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), UserMessage(content="bye"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TRAIN_2 = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), UserMessage(content="bye"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="aya", tool_call_id="023456789"), AssistantMessage(content="wow 32", tool_calls=[]), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_CONTENT_AND_TOOLS_CALLS_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), UserMessage(content="bye"), AssistantMessage( content="Assistant says hi, let me fetch the weather for you.", tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_WITH_CONTENT_AND_TOOLS_CALLS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage(content="Whether is 32 degrees in San Francisco, CA"), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), UserMessage(content="bye"), AssistantMessage( content="Assistant says hi, let me fetch the weather for you.", tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="aya", tool_call_id="023456789"), AssistantMessage(content="wow 32", tool_calls=[]), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), UserMessage(content="What does that mean?"), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), UserMessage(content="What does that mean?"), AssistantMessage(content="The temperature is 32 degrees in San Francisco."), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_FULL_LOOP_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="sunny", tool_call_id="023456789"), UserMessage(content="Now what about Tokyo?"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="234567890", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "Tokyo, JP", }, ), ), ], ), ToolMessage(content="28", tool_call_id="234567890"), AssistantMessage(content="San Francisco is 32 and sunny, Tokyo is 28."), UserMessage(content="Thanks!"), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_FULL_LOOP_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="sunny", tool_call_id="023456789"), UserMessage(content="Now what about Tokyo?"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="234567890", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "Tokyo, JP", }, ), ), ], ), ToolMessage(content="28", tool_call_id="234567890"), AssistantMessage(content="San Francisco is 32 and sunny, Tokyo is 28."), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_WITH_CONTENT_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content="Let me check the weather for you.", tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="sunny", tool_call_id="023456789"), UserMessage(content="What does that mean?"), ], tools=_TOOLS, ) REQUEST_TOOL_THEN_USER_WITH_CONTENT_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="User says hello"), AssistantMessage( content="Let me check the weather for you.", tool_calls=[ ToolCall( id="123456789", function=FunctionCall( name="tool1", arguments={ # type: ignore[arg-type] "location": "San Francisco, CA", }, ), ), ToolCall( id="023456789", function=FunctionCall( name="tool2", arguments={}, # type: ignore[arg-type] ), ), ], ), ToolMessage(content="32", tool_call_id="123456789"), ToolMessage(content="sunny", tool_call_id="023456789"), UserMessage(content="What does that mean?"), AssistantMessage(content="It is 32 degrees and sunny in San Francisco."), ], tools=_TOOLS, ) REQUEST_MULTI_TURN_IMAGE_URL_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageURLChunk(image_url=_IMAGE_URL), ImageURLChunk(image_url=_IMAGE_URL), ] ), AssistantMessage(content="Assistant answers It is a red square."), UserMessage(content=[TextChunk(text="User says thanks.")]), ] ) REQUEST_MULTI_TURN_IMAGE_URL_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageURLChunk(image_url=_IMAGE_URL), ImageURLChunk(image_url=_IMAGE_URL), ] ), AssistantMessage(content="Assistant answers It is a red square."), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ] ) REQUEST_MULTI_TURN_IMAGE_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageChunk(image=_IMAGE), ] ), AssistantMessage(content="Assistant answers It is a red square."), UserMessage(content=[TextChunk(text="User says thanks.")]), ] ) REQUEST_MULTI_TURN_IMAGE_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageChunk(image=_IMAGE), ] ), AssistantMessage(content="Assistant answers It is a red square."), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ] ) REQUEST_MULTI_TURN_AUDIO_URL_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage( content=[ TextChunk(text="Users asks what is this audio ?"), AudioURLChunk(audio_url=_AUDIO_URL), AudioURLChunk(audio_url=_AUDIO_URL), ] ), AssistantMessage(content="Assistant answers it is a music."), UserMessage(content=[TextChunk(text="User says thanks.")]), ] ) REQUEST_MULTI_TURN_AUDIO_URL_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage( content=[ TextChunk(text="Users asks what is this audio ?"), AudioURLChunk(audio_url=_AUDIO_URL), ] ), AssistantMessage(content="Assistant answers it is a music."), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ] ) REQUEST_MULTI_TURN_AUDIO_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage( content=[ TextChunk(text="Users asks what is this audio ?"), AudioChunk(input_audio=_AUDIO), ] ), AssistantMessage(content="Assistant answers it is a music."), UserMessage(content=[TextChunk(text="User says thanks.")]), ] ) REQUEST_MULTI_TURN_AUDIO_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage( content=[ TextChunk(text="Users asks what is this audio ?"), AudioChunk(input_audio=_AUDIO), AudioChunk(input_audio=_AUDIO), ] ), AssistantMessage(content="Assistant answers it is a music."), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ] ) REQUEST_MULTI_TURN_IMAGE_AND_THINKING_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are a helpful assistant that can think."), ThinkChunk(thinking="You need to think here."), TextChunk(text="Here you need to answer."), ], ), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageURLChunk(image_url=_IMAGE_URL), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Assistant thinks it's a red square."), TextChunk(text="Assistant says wow I need to think."), TextChunk(text="Assistant says it is a red square."), ], tool_calls=[], ), UserMessage(content=[TextChunk(text="User says thanks.")]), ], ) REQUEST_MULTI_TURN_THINKING_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are a helpful assistant that can think."), ThinkChunk(thinking="You need to think here."), TextChunk(text="Here you need to answer."), ], ), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Assistant thinks it's a red square."), TextChunk(text="Assistant says wow I need to think."), TextChunk(text="Assistant says it is a red square."), ], tool_calls=[], ), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ], ) REQUEST_MULTI_TURN_THINKING_TEST = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are a helpful assistant that can think."), ThinkChunk(thinking="You need to think here."), TextChunk(text="Here you need to answer."), ], ), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Assistant thinks it's a red square."), TextChunk(text="Assistant says wow I need to think."), TextChunk(text="Assistant says it is a red square."), ], tool_calls=[], ), UserMessage(content=[TextChunk(text="User says thanks.")]), ], ) REQUEST_MULTI_TURN_IMAGE_AND_THINKING_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are a helpful assistant that can think."), ThinkChunk(thinking="You need to think here."), TextChunk(text="Here you need to answer."), ], ), UserMessage( content=[ TextChunk(text="User asks what is this image ?"), ImageURLChunk(image_url=_IMAGE_URL), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Assistant thinks it's a red square."), TextChunk(text="Assistant says wow I need to think."), TextChunk(text="Assistant says it is a red square."), ], tool_calls=[], ), UserMessage(content=[TextChunk(text="User says thanks.")]), AssistantMessage(content=[TextChunk(text="Assistant says you're welcome.")]), ], ) # -- Message aggregation test fixtures -- REQUEST_CONSECUTIVE_USERS_TEST = ChatCompletionRequest( messages=[ UserMessage(content="Hello"), UserMessage(content="World"), ] ) REQUEST_CONSECUTIVE_USERS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="Hello"), UserMessage(content="World"), AssistantMessage(content="Hi there"), ] ) REQUEST_CONSECUTIVE_USERS_WITH_SYSTEM_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are helpful."), UserMessage(content="Hello"), UserMessage(content="World"), AssistantMessage(content="Hi there"), ] ) REQUEST_CONSECUTIVE_ASSISTANTS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="Hello"), AssistantMessage(content="Hi"), AssistantMessage(content="How can I help?"), UserMessage(content="Thanks"), AssistantMessage(content="You're welcome"), ] ) REQUEST_MULTIPLE_SYSTEMS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="System prompt 1."), SystemMessage(content="System prompt 2."), UserMessage(content="Hello"), AssistantMessage(content="Hi"), ] ) REQUEST_CONSECUTIVE_USERS_IMAGE_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="What is this?"), UserMessage( content=[ ImageChunk(image=_IMAGE), TextChunk(text="Describe it"), ] ), AssistantMessage(content="It's an image."), ] ) # -- Multi-chunk aggregation test fixtures -- REQUEST_CONSECUTIVE_USERS_TEXT_CHUNKS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="First as string"), UserMessage(content=[TextChunk(text="Second as chunk")]), UserMessage(content=[TextChunk(text="Third part A"), TextChunk(text="Third part B")]), AssistantMessage(content="Response"), ] ) REQUEST_CONSECUTIVE_ASSISTANTS_TEXT_CHUNKS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="Hello"), AssistantMessage(content="First as string"), AssistantMessage(content=[TextChunk(text="Second as chunk")]), AssistantMessage(content=[TextChunk(text="Third part A"), TextChunk(text="Third part B")]), UserMessage(content="Thanks"), AssistantMessage(content="Response"), ] ) REQUEST_CONSECUTIVE_USERS_MULTI_IMAGE_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content=[TextChunk(text="Describe this"), ImageChunk(image=_IMAGE), TextChunk(text="What color?")]), UserMessage(content=[TextChunk(text="Also this"), ImageChunk(image=_IMAGE), TextChunk(text="What shape?")]), AssistantMessage(content="Both are red squares."), ] ) REQUEST_CONSECUTIVE_USERS_AUDIO_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage( content=[ TextChunk(text="Listen to this"), AudioURLChunk(audio_url=_AUDIO_URL), TextChunk(text="What language?"), ] ), UserMessage( content=[ TextChunk(text="And this"), AudioURLChunk(audio_url=_AUDIO_URL), TextChunk(text="Transcribe it"), ] ), AssistantMessage(content="Both are in English."), ] ) REQUEST_CONSECUTIVE_ASSISTANTS_THINK_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="Solve this problem"), AssistantMessage( content=[ ThinkChunk(thinking="Let me think..."), TextChunk(text="Hmm."), TextChunk(text="I need more context."), ] ), AssistantMessage( content=[ ThinkChunk(thinking="Now I understand."), TextChunk(text="OK."), TextChunk(text="The answer is 42."), ] ), UserMessage(content="Thanks"), AssistantMessage(content="You're welcome"), ] ) REQUEST_CONSECUTIVE_ASSISTANTS_TOOL_CALLS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What's the weather?"), AssistantMessage(content="Let me check."), AssistantMessage( content="Fetching data.", tool_calls=[ ToolCall( id="123456789", function=FunctionCall(name="tool1", arguments={"location": "Paris"}), # type: ignore[arg-type] ), ToolCall( id="023456789", function=FunctionCall(name="tool1", arguments={"location": "London"}), # type: ignore[arg-type] ), ], ), ToolMessage(content="22", tool_call_id="123456789"), ToolMessage(content="15", tool_call_id="023456789"), AssistantMessage(content="Paris: 22, London: 15"), UserMessage(content="Thanks"), AssistantMessage(content="Welcome"), ], tools=[ Tool( function=Function( name="tool1", parameters={"type": "object", "properties": {"location": {"type": "string"}}}, ) ), ], ) REQUEST_SYSTEM_TEXT_CHUNKS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content=[TextChunk(text="You are helpful."), TextChunk(text="Be concise.")]), UserMessage(content="Hello"), AssistantMessage(content="Hi"), ] ) REQUEST_CONSECUTIVE_SYSTEMS_THINK_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content=[TextChunk(text="Rule A"), TextChunk(text="Rule B"), ThinkChunk(thinking="Think 1")]), SystemMessage(content=[ThinkChunk(thinking="Think 2"), TextChunk(text="Rule C"), TextChunk(text="Rule D")]), UserMessage(content="Hello"), AssistantMessage(content="Hi"), ] ) REQUEST_MID_CONV_SYSTEM_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="Hello"), SystemMessage(content="New instruction."), AssistantMessage(content="Got it"), ] ) REQUEST_MID_CONV_SYSTEM_WITH_CONSECUTIVE_USERS_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="Be helpful."), UserMessage(content="Hello"), UserMessage(content="World"), SystemMessage(content="Now be concise."), AssistantMessage(content="Got it"), ] ) # -- Multimodal content in non-user messages (v15+) -- REQUEST_TOOL_IMAGE_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="What is in this image?"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="tl1mg2345", function=FunctionCall( name="tool1", arguments={"location": "San Francisco, CA"}, # type: ignore[arg-type] ), ), ], ), ToolMessage( content=[ TextChunk(text="Here is the result."), ImageURLChunk(image_url=_IMAGE_URL), ], tool_call_id="tl1mg2345", ), AssistantMessage(content="The tool returned an image of a red square."), ], tools=_TOOLS, ) REQUEST_TOOL_AUDIO_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="What does this sound like?"), AssistantMessage( content=None, tool_calls=[ ToolCall( id="tl1mg2345", function=FunctionCall( name="tool1", arguments={"location": "San Francisco, CA"}, # type: ignore[arg-type] ), ), ], ), ToolMessage( content=[ TextChunk(text="Here is the audio result."), AudioChunk(input_audio=_AUDIO), ], tool_call_id="tl1mg2345", ), AssistantMessage(content="The tool returned audio of a sine wave."), ], tools=_TOOLS, ) REQUEST_SYSTEM_AUDIO_TRAIN = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are an audio assistant."), AudioChunk(input_audio=_AUDIO), ], ), UserMessage(content="What was that sound?"), AssistantMessage(content="That was a sine wave tone."), ], ) def _get_conversations( tokenizer_version: TokenizerVersion, validation_mode: ValidationMode, image: bool, audio: bool, think: bool, ) -> list[ChatCompletionRequest]: r"""Build a list of test conversations for the given configuration. Conversations are selected based on the tokenizer version (controls tool call and aggregation scenarios), validation mode (test vs finetuning), and modality flags (image, audio, think). Args: tokenizer_version: Determines which tool/aggregation scenarios are included. validation_mode: Test mode selects inference-style requests, finetuning mode selects training-style requests with additional aggregation fixtures. image: Whether to include image-related conversations. audio: Whether to include audio-related conversations. think: Whether to include thinking-related conversations. Returns: Deep-copied list of `ChatCompletionRequest` instances for the given configuration. """ conversations: list[ChatCompletionRequest] = ( [ REQUEST_ONE_TURN_TEST, REQUEST_ONE_TURN_WITH_SYSTEM_TEST, REQUEST_MULTI_TURN_TEST, REQUEST_MULTI_TURN_WITH_SYSTEM_TEST, ] if validation_mode == ValidationMode.test else [ REQUEST_ONE_TURN_TRAIN, REQUEST_ONE_TURN_WITH_SYSTEM_TRAIN, REQUEST_MULTI_TURN_TRAIN, REQUEST_MULTI_TURN_WITH_SYSTEM_TRAIN, ] ) if tokenizer_version > TokenizerVersion.v1: if validation_mode == ValidationMode.test: conversations.extend( [ REQUEST_MULTI_TURN_WITH_TOOLS_TEST, REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TEST, REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TEST_2, REQUEST_TOOL_THEN_USER_TEST, REQUEST_TOOL_THEN_USER_FULL_LOOP_TEST, ] ) else: conversations.extend( [ REQUEST_MULTI_TURN_WITH_TOOLS_TRAIN, REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TRAIN, REQUEST_MULTI_TURN_WITH_TOOLS_CALLS_TRAIN_2, REQUEST_TOOL_THEN_USER_TRAIN, REQUEST_TOOL_THEN_USER_FULL_LOOP_TRAIN, ] ) if tokenizer_version > TokenizerVersion.v7: if validation_mode == ValidationMode.test: conversations.extend( [ REQUEST_MULTI_TURN_WITH_CONTENT_AND_TOOLS_CALLS_TEST, REQUEST_TOOL_THEN_USER_WITH_CONTENT_TEST, ] ) else: conversations.extend( [ REQUEST_MULTI_TURN_WITH_CONTENT_AND_TOOLS_CALLS_TRAIN, REQUEST_TOOL_THEN_USER_WITH_CONTENT_TRAIN, ] ) if image: if validation_mode == ValidationMode.test: conversations.extend([REQUEST_MULTI_TURN_IMAGE_URL_TEST, REQUEST_MULTI_TURN_IMAGE_TEST]) else: conversations.extend([REQUEST_MULTI_TURN_IMAGE_URL_TRAIN, REQUEST_MULTI_TURN_IMAGE_TRAIN]) if audio: if validation_mode == ValidationMode.test: conversations.extend([REQUEST_MULTI_TURN_AUDIO_URL_TEST, REQUEST_MULTI_TURN_AUDIO_TEST]) else: conversations.extend([REQUEST_MULTI_TURN_AUDIO_URL_TRAIN, REQUEST_MULTI_TURN_AUDIO_TRAIN]) if think: if validation_mode == ValidationMode.test: conversations.extend([REQUEST_MULTI_TURN_THINKING_TEST]) else: conversations.extend([REQUEST_MULTI_TURN_THINKING_TRAIN]) if image and think: if validation_mode == ValidationMode.test: conversations.extend([REQUEST_MULTI_TURN_IMAGE_AND_THINKING_TEST]) else: conversations.extend([REQUEST_MULTI_TURN_IMAGE_AND_THINKING_TRAIN]) # Message aggregation test fixtures (finetuning only since last msg must be assistant) if validation_mode == ValidationMode.finetuning: conversations.extend( [ REQUEST_CONSECUTIVE_USERS_TRAIN, REQUEST_CONSECUTIVE_USERS_WITH_SYSTEM_TRAIN, REQUEST_CONSECUTIVE_ASSISTANTS_TRAIN, REQUEST_MULTIPLE_SYSTEMS_TRAIN, ] ) if tokenizer_version >= TokenizerVersion.v3: conversations.extend( [ REQUEST_CONSECUTIVE_USERS_TEXT_CHUNKS_TRAIN, REQUEST_CONSECUTIVE_ASSISTANTS_TEXT_CHUNKS_TRAIN, REQUEST_SYSTEM_TEXT_CHUNKS_TRAIN, ] ) if image: conversations.extend( [ REQUEST_CONSECUTIVE_USERS_IMAGE_TRAIN, REQUEST_CONSECUTIVE_USERS_MULTI_IMAGE_TRAIN, ] ) if audio: conversations.append(REQUEST_CONSECUTIVE_USERS_AUDIO_TRAIN) if think: conversations.extend( [ REQUEST_CONSECUTIVE_ASSISTANTS_THINK_TRAIN, REQUEST_CONSECUTIVE_SYSTEMS_THINK_TRAIN, ] ) else: conversations.append(REQUEST_CONSECUTIVE_USERS_TEST) # v7+ only: mid-conversation system messages and combined aggregation scenarios if tokenizer_version >= TokenizerVersion.v7 and validation_mode == ValidationMode.finetuning: conversations.extend( [ REQUEST_MID_CONV_SYSTEM_TRAIN, REQUEST_MID_CONV_SYSTEM_WITH_CONSECUTIVE_USERS_TRAIN, REQUEST_CONSECUTIVE_ASSISTANTS_TOOL_CALLS_TRAIN, ] ) # v15+ only: multimodal content in non-user messages (finetuning only) if tokenizer_version >= TokenizerVersion.v15 and validation_mode == ValidationMode.finetuning: if image: conversations.extend( [ REQUEST_TOOL_IMAGE_TRAIN, ] ) if audio: conversations.append(REQUEST_SYSTEM_AUDIO_TRAIN) conversations.append(REQUEST_TOOL_AUDIO_TRAIN) conversations = [c.model_copy(deep=True) for c in conversations] if think and tokenizer_version >= TokenizerVersion.v15: for conv in conversations: for message in conv.messages: if isinstance(message, SystemMessage) and isinstance(message.content, list): message.content = [ TextChunk(text="\n".join([c.text for c in message.content if isinstance(c, TextChunk)])) ] return conversations python-mistral-common-1.11.7/tests/integrations/chat_templates/helpers.py000066400000000000000000000267031523035502500267520ustar00rootroot00000000000000r"""Reusable helper functions for chat template tests. This module contains utility functions used by test files across the `unit/` and `transformers/` subdirectories. It intentionally has no fixtures or test data -- those live in `conftest.py` and `fixtures_data.py`. HuggingFace-specific helpers live in `hf_utils.py`. """ import json import shutil from dataclasses import dataclass from pathlib import Path from typing import Any import numpy as np from jinja2 import BaseLoader from jinja2.sandbox import ImmutableSandboxedEnvironment from PIL import Image from mistral_common.integrations.chat_templates.template_generator import TemplateConfig from mistral_common.protocol.instruct.request import ChatCompletionRequest, ReasoningEffort from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.audio import Audio from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.model_settings_builder import EnumBuilder, ModelSettingsBuilder from tests.test_tekken import get_special_tokens # Golden template files live in the data/ tree (outside src/). _GOLDEN_DIR = Path(__file__).parent.parent.parent / "data" / "chat_templates" SPM_WHITESPACE = "▁" @dataclass(frozen=True) class TestConfig: r"""Test configuration for chat template parametrization.""" __test__ = False version: TokenizerVersion spm: bool = False image: bool = False audio: bool = False think: bool = False plain_think: bool = False def _make_config(c: TestConfig) -> TemplateConfig: r"""Create a `TemplateConfig` from a test config.""" return TemplateConfig( version=c.version, spm=c.spm, image_support=c.image, audio_support=c.audio, thinking_support=c.think, plain_thinking_support=c.plain_think, use_special_token_variables=True, ) def _load_golden_template(config: TemplateConfig) -> str: r"""Load the static golden template for a config.""" parts = [config.version.value] if config.image_support and config.any_thinking_support: parts.append("image_think") elif config.image_support: parts.append("image") elif config.audio_support: parts.append("audio") elif config.any_thinking_support: parts.append("think") if config.spm: parts.append("spm") filename = "_".join(parts) + ".jinja" path = _GOLDEN_DIR / filename if not path.exists(): raise FileNotFoundError(f"Golden template not found: {path}") return path.read_text() def render_template( template: str, messages: list[Any], tools: list[Any] | None = None, reasoning_effort: str | None = None ) -> str: r"""Render a Jinja2 template with the given messages using a pure Jinja2 sandbox. This function tests template rendering independently of HuggingFace transformers. It uses ImmutableSandboxedEnvironment directly, while `encode_transformers` tests integration with the HuggingFace `render_jinja_template` function. Both should produce identical output for the same input. """ def raise_exception(msg: str) -> None: raise ValueError(msg) env = ImmutableSandboxedEnvironment(loader=BaseLoader()) env.globals["raise_exception"] = raise_exception jinja_template = env.from_string(template) render_kwargs: dict[str, Any] = { "messages": messages, "tools": tools, "bos_token": "", "eos_token": "", } # Only add reasoning_effort for v15+ templates that support it if reasoning_effort is not None: render_kwargs["reasoning_effort"] = reasoning_effort return jinja_template.render(**render_kwargs) def encode_mistral_common(mistral_tokenizer: MistralTokenizer, chat_request: ChatCompletionRequest, spm: bool) -> str: r"""Encode a chat request using mistral-common tokenizer. Returns the text representation (not token IDs) for parity comparison with the Jinja template rendering. Token ID comparison is handled separately via `encode_hf_tokens`. """ mistral_encoded = str(mistral_tokenizer.encode_chat_completion(chat_request).text) # Collapse each image token sequence ([IMG]...[IMG_BREAK]...[IMG_END]) into a single [IMG]. # Each sequence has exactly one [IMG_END], so replacing inner tokens and converting [IMG_END] # preserves one [IMG] per image (important for multi-image inputs). mistral_encoded = mistral_encoded.replace("[IMG]", "").replace("[IMG_BREAK]", "").replace("[IMG_END]", "[IMG]") # Remove audio tokens except one per audio mistral_encoded = mistral_encoded.replace("[AUDIO]", "").replace("[BEGIN_AUDIO]", "[AUDIO]") if spm: mistral_encoded = mistral_encoded.replace(SPM_WHITESPACE, " ").replace("<0x0A>", "\n") return mistral_encoded def _create_dummy_image() -> Image.Image: r"""Create a simple dummy 2x2 red square image for testing.""" return Image.new("RGB", (2, 2), color="red") def _sin_wave(sampling_rate: int, duration: float) -> np.ndarray: r"""Generate a sine wave numpy array.""" return np.sin(np.ones([int(duration * sampling_rate)])) def _sample_audio() -> Audio: r"""Create a sample `Audio` instance for testing.""" sampling_rate = 44100 original_array = _sin_wave(sampling_rate, 1) return Audio( audio_array=original_array, sampling_rate=sampling_rate, format="wav", ) _IMAGE_URL = "https://upload.wikimedia.org/wikipedia/commons/7/78/Red_Square_%282x2_Pixel%29.png" _IMAGE = _create_dummy_image() _AUDIO_URL = _sample_audio().to_base64("wav") _AUDIO = _AUDIO_URL def _build_tekken_json(config: TestConfig, output_dir: Path) -> Path: r"""Build a tekken.json file for the given test config. Constructs a complete Tekken tokenizer JSON file by combining the base vocabulary from the shipped tokenizer with version-specific special tokens and optional image/audio/model_settings configuration. The file is written to `output_dir / "tekken.json"` and can be loaded by `MistralTokenizer.from_file`. Args: config: Test configuration specifying version and feature flags. output_dir: Directory to write the JSON file into. Returns: Path to the written tekken.json file. """ with open(MistralTokenizer._data_path() / "tekken_240911.json", "r", encoding="utf-8") as f: base_data = json.load(f) special_tokens = get_special_tokens( tokenizer_version=config.version, add_audio=config.audio, add_think=config.think ) tekken_data: dict[str, Any] = { "config": { "pattern": base_data["config"]["pattern"], "default_vocab_size": base_data["config"]["default_vocab_size"], "default_num_special_tokens": 100, "version": config.version.value, }, "vocab": base_data["vocab"], "special_tokens": special_tokens, } if config.image: tekken_data["image"] = { "image_patch_size": 2, "max_image_size": 10, "spatial_merge_size": 1, } if config.audio: tekken_data["audio"] = { "sampling_rate": 24_000, "frame_rate": 12.5, "audio_encoding_config": { "num_mel_bins": 128, "window_size": 400, "hop_length": 160, }, } if config.version.supports_model_settings: model_settings_builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder( accepts_none=True, default=ReasoningEffort.none, values=[ReasoningEffort.none, ReasoningEffort.high], ) ) tekken_data["model_settings_builder"] = model_settings_builder.model_dump(mode="json") tekken_path = output_dir / "tekken.json" with open(tekken_path, "w", encoding="utf-8") as f: json.dump(tekken_data, f, ensure_ascii=False) return tekken_path def _build_spm_path(config: TestConfig, output_dir: Path) -> Path: r"""Copy the SPM model file with the correct version suffix. The SPM tokenizer version is determined by the filename suffix (e.g., `.model.v3` for v3). Image support is indicated by an `m1` suffix (e.g., `.model.v3m1`). The shipped v7m1 model file is copied with the appropriate suffix for the requested config. Args: config: Test configuration specifying version and image flag. output_dir: Directory to copy the file into. Returns: Path to the copied model file. """ source = MistralTokenizer._data_path() / "mistral_instruct_tokenizer_241114.model.v7m1" suffix = f".model.{config.version.value}" if config.image: suffix += "m1" dest = output_dir / f"tokenizer{suffix}" shutil.copy2(source, dest) return dest def _get_mistral_tekkenizer(config: TestConfig, output_dir: Path, validation_mode: ValidationMode) -> MistralTokenizer: r"""Build a `MistralTokenizer` with Tekken backend via `from_file`. Writes a tekken.json with the desired version and features via `_build_tekken_json`, then loads it through the production `MistralTokenizer.from_file` path. Args: config: Test configuration specifying version, image, audio, think. output_dir: Directory to write the temporary tekken.json file. validation_mode: Validation mode (test or finetuning). Returns: A configured `MistralTokenizer` instance. """ tekken_path = _build_tekken_json(config, output_dir) return MistralTokenizer.from_file(str(tekken_path), mode=validation_mode) def _get_mistral_sentencepiece( config: TestConfig, output_dir: Path, validation_mode: ValidationMode ) -> MistralTokenizer: r"""Build a `MistralTokenizer` with SentencePiece backend via `from_file`. Copies the shipped SPM model with the correct version suffix via `_build_spm_path`, then loads it through the production `MistralTokenizer.from_file` path. Args: config: Test configuration specifying version and image flag. output_dir: Directory to copy the SPM model file into. validation_mode: Validation mode (test or finetuning). Returns: A configured `MistralTokenizer` instance. """ spm_path = _build_spm_path(config, output_dir) return MistralTokenizer.from_file(str(spm_path), mode=validation_mode) def _get_mistral_tokenizer( spm: bool, tokenizer_version: TokenizerVersion, validation_mode: ValidationMode, image: bool, audio: bool, think: bool, output_dir: Path, ) -> MistralTokenizer: r"""Get a `MistralTokenizer` instance for testing. Args: spm: Whether to use SentencePiece backend instead of Tekken. tokenizer_version: Version of the tokenizer to build. validation_mode: Validation mode (test or finetuning). image: Whether to attach an image encoder. audio: Whether to attach an audio encoder (Tekken only). think: Whether to enable thinking special tokens (Tekken only). output_dir: Temporary directory for tokenizer files. Returns: A configured `MistralTokenizer` instance. """ config = TestConfig( version=tokenizer_version, spm=spm, image=image, audio=audio, think=think, plain_think=False, ) if spm: return _get_mistral_sentencepiece(config, output_dir, validation_mode) return _get_mistral_tekkenizer(config, output_dir, validation_mode) python-mistral-common-1.11.7/tests/integrations/chat_templates/hf_utils.py000066400000000000000000000225401523035502500271200ustar00rootroot00000000000000r"""HuggingFace transformers/tokenizers utilities for chat template tests. This module contains helpers that depend on `transformers` and `tokenizers`. General-purpose helpers live in `helpers.py`. """ import json from pathlib import Path from typing import Any, cast try: from transformers import PreTrainedTokenizerFast from transformers.utils.chat_template_utils import render_jinja_template _HAS_TRANSFORMERS = True except ImportError: _HAS_TRANSFORMERS = False from mistral_common.protocol.instruct.request import ChatCompletionRequest def _to_openai_request(chat_request: ChatCompletionRequest, keep_name_for_tools: bool = False) -> dict[str, Any]: r"""Convert a ChatCompletionRequest to OpenAI format. Args: chat_request: The chat completion request to convert. keep_name_for_tools: If True, preserve tool message `name` fields that `to_openai` strips by default. Returns: OpenAI-format request dictionary. """ openai_request = chat_request.to_openai() if keep_name_for_tools: for openai_message, chat_message in zip(openai_request["messages"], chat_request.messages): if chat_message.role == "tool": openai_message["name"] = chat_message.name return openai_request def encode_transformers( chat_template: str, chat_request: ChatCompletionRequest, keep_name_for_tools: bool = False ) -> str: r"""Encode a chat request using the transformers render_jinja_template. Converts a ChatCompletionRequest to OpenAI format and renders through the HuggingFace transformers rendering pipeline. """ assert _HAS_TRANSFORMERS, "transformers is required" openai_request = _to_openai_request(chat_request, keep_name_for_tools) return _render_via_transformers(chat_template, openai_request) def encode_transformers_from_openai(chat_template: str, openai_request: dict[str, Any]) -> str: r"""Encode a raw OpenAI-format dict using the transformers render_jinja_template. Use this for tests with hand-crafted dicts (e.g., invalid input tests) that cannot be represented as a ChatCompletionRequest. """ assert _HAS_TRANSFORMERS, "transformers is required" return _render_via_transformers(chat_template, openai_request) def _build_hf_tokenizer(tekken_path: Path, chat_template: str) -> "PreTrainedTokenizerFast": r"""Build a `PreTrainedTokenizerFast` from a tekken.json file. Converts the Tekken BPE vocabulary to a HuggingFace tokenizers format, preserving the Tekkenizer's ID scheme: special tokens at IDs 0 to `num_special - 1`, regular BPE tokens at IDs `num_special` and above. All special tokens in the file are unconditionally registered as `AddedToken` objects, matching Tekkenizer behavior where every token in the special range is treated as special. .. note:: TODO: The ID-offset conversion done here should be fixed upstream in transformers' `MistralConverter` so that it produces IDs matching the Tekkenizer natively. Until then, we apply the offset manually to ensure a fair token-level comparison. Args: tekken_path: Path to the tekken.json file. chat_template: Jinja chat template string to set on the tokenizer. Returns: A configured `PreTrainedTokenizerFast` with matching token IDs. """ import base64 from functools import lru_cache from tokenizers import Regex, decoders, pre_tokenizers, processors from tokenizers import Tokenizer as HFTokenizer from tokenizers.models import BPE from transformers import AddedToken from transformers.convert_slow_tokenizer import bytes_to_unicode with open(tekken_path, "r", encoding="utf-8") as f: data = json.load(f) pattern: str = data["config"]["pattern"] special_tokens_list: list[dict[str, Any]] = data["special_tokens"] num_special: int = data["config"]["default_num_special_tokens"] vocab_size: int = data["config"]["default_vocab_size"] inner_vocab_size = vocab_size - num_special vocab_entries: list[dict[str, Any]] = data["vocab"][:inner_vocab_size] byte_encoder = bytes_to_unicode() @lru_cache def token_bytes_to_string(b: bytes) -> str: return "".join([byte_encoder[ord(char)] for char in b.decode("latin-1")]) # Decode raw bytes for each vocab entry raw_tokens = [base64.b64decode(entry["token_bytes"]) for entry in vocab_entries] rank_set = set(raw_tokens) token_to_rank: dict[bytes, int] = {token: rank for rank, token in enumerate(raw_tokens)} # Build BPE vocab with regular tokens at IDs num_special+ and special tokens at 0..num_special-1. # All special token slots (active and filler) must appear in the BPE vocab so the # HuggingFace tokenizer recognizes IDs 0 through num_special-1. bpe_vocab: dict[str, int] = {} defined_ids: set[int] = set() for st in special_tokens_list: bpe_vocab[st["token_str"]] = st["rank"] defined_ids.add(st["rank"]) for i in range(num_special): if i not in defined_ids: bpe_vocab[f""] = i for rank, token in enumerate(raw_tokens): bpe_vocab[token_bytes_to_string(token)] = rank + num_special # Extract BPE merges (same algorithm as MistralConverter.extract_vocab_merges_from_model) merges: list[tuple[bytes, bytes, int]] = [] for rank, token in enumerate(raw_tokens): if len(token) == 1: continue local: list[tuple[bytes, bytes, int]] = [] for index in range(1, len(token)): piece_l, piece_r = token[:index], token[index:] if piece_l in rank_set and piece_r in rank_set and (piece_l + piece_r) in rank_set: local.append((piece_l, piece_r, rank)) local = sorted(local, key=lambda x: (token_to_rank[x[0]], token_to_rank[x[1]])) merges.extend(local) merges = sorted(merges, key=lambda val: val[2]) bpe_merges = [(token_bytes_to_string(m[0]), token_bytes_to_string(m[1])) for m in merges] # Create tokenizer with BPE model tokenizer = HFTokenizer(BPE(bpe_vocab, bpe_merges, fuse_unk=False)) if hasattr(tokenizer.model, "ignore_merges"): tokenizer.model.ignore_merges = True # Set pre-tokenizer, decoder, and post-processor tokenizer.pre_tokenizer = pre_tokenizers.Sequence( [ pre_tokenizers.Split(Regex(pattern), behavior="isolated", invert=False), pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False), ] ) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) # Register all special tokens in added_tokens_decoder, matching Tekkenizer # behavior where every token in the special range is treated as special. added_tokens_decoder: dict[int, AddedToken] = {} for st in special_tokens_list: added_tokens_decoder[st["rank"]] = AddedToken(st["token_str"], special=True, normalized=False) for i in range(num_special): if i not in defined_ids: added_tokens_decoder[i] = AddedToken(f"", special=True, normalized=False) return PreTrainedTokenizerFast( tokenizer_object=tokenizer, added_tokens_decoder=added_tokens_decoder, bos_token="", eos_token="", chat_template=chat_template, ) def encode_hf_tokens( hf_tokenizer: "PreTrainedTokenizerFast", chat_request: ChatCompletionRequest, keep_name_for_tools: bool = False ) -> list[int]: r"""Get token IDs from a standard HuggingFace tokenizer. Converts the `ChatCompletionRequest` to OpenAI format and encodes via `apply_chat_template(tokenize=True)`. Args: hf_tokenizer: A `PreTrainedTokenizerFast` instance. chat_request: The chat completion request to encode. keep_name_for_tools: If True, preserve tool message `name` fields. Returns: Token IDs produced by the HuggingFace tokenizer. """ openai_request = _to_openai_request(chat_request, keep_name_for_tools) messages = openai_request["messages"] tools = openai_request.get("tools", None) if tools is not None: for tool in tools: tool["function"].pop("strict", None) template_kwargs: dict[str, Any] = {} reasoning_effort = openai_request.get("reasoning_effort") if reasoning_effort is not None: template_kwargs["reasoning_effort"] = reasoning_effort result = hf_tokenizer.apply_chat_template( conversation=messages, tools=tools, tokenize=True, return_dict=False, padding=False, truncation=False, **template_kwargs, ) return cast(list[int], result) def _render_via_transformers(chat_template: str, openai_request: dict[str, Any]) -> str: r"""Shared rendering logic for transformers-based encoding.""" for tool in openai_request.get("tools", []): tool["function"].pop("strict", False) reasoning_effort = openai_request.get("reasoning_effort") template_kwargs: dict[str, Any] = {} if reasoning_effort is not None: template_kwargs["reasoning_effort"] = reasoning_effort encoded = render_jinja_template( [openai_request["messages"]], tools=openai_request.get("tools", None), chat_template=chat_template, bos_token="", eos_token="", **template_kwargs, )[0][0] assert isinstance(encoded, str), type(encoded) return encoded python-mistral-common-1.11.7/tests/integrations/chat_templates/test_parity.py000066400000000000000000000110541523035502500276500ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.template_generator import build_chat_template from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.conftest import ALL_CONFIGS, _config_id from tests.integrations.chat_templates.fixtures_data import _get_conversations from tests.integrations.chat_templates.helpers import ( TestConfig, _load_golden_template, _make_config, render_template, ) def _request_to_render_args(request: ChatCompletionRequest) -> dict[str, Any]: r"""Convert a ChatCompletionRequest to render_template kwargs. Enriches tool messages with the `name` field resolved from the preceding assistant's `tool_calls`, which the v2 Jinja template requires but `ToolMessage.to_openai()` omits. """ openai = request.to_openai() messages = openai["messages"] # Build a mapping from tool_call_id -> function name so tool messages # can carry the `name` field expected by v2 templates. tool_call_names: dict[str, str] = {} for msg in messages: for tc in msg.get("tool_calls", []): tc_id = tc.get("id") fn_name = tc.get("function", {}).get("name") if tc_id and fn_name: tool_call_names[tc_id] = fn_name for msg in messages: if msg.get("role") == "tool" and "name" not in msg: tc_id = msg.get("tool_call_id") if tc_id and tc_id in tool_call_names: msg["name"] = tool_call_names[tc_id] kwargs: dict[str, Any] = { "messages": messages, } if "tools" in openai and openai["tools"]: kwargs["tools"] = openai["tools"] if (reasoning := openai.get("reasoning_effort")) is not None: kwargs["reasoning_effort"] = reasoning return kwargs @pytest.mark.parametrize( "config", [c for c in ALL_CONFIGS if not c.plain_think], ids=[_config_id(c) for c in ALL_CONFIGS if not c.plain_think], ) def test_dynamic_matches_static(config: TestConfig) -> None: template_config = _make_config(config) dynamic = build_chat_template(template_config) static = _load_golden_template(template_config) assert dynamic == static @pytest.mark.parametrize( "config", [c for c in ALL_CONFIGS if not c.plain_think], ids=[_config_id(c) for c in ALL_CONFIGS if not c.plain_think], ) def test_dynamic_template_produces_same_output(config: TestConfig) -> None: template_config = _make_config(config) static_template = _load_golden_template(template_config) dynamic_template = build_chat_template(template_config) # Test with a simple conversation messages: list[Any] = [ {"role": "user", "content": "Hello"}, ] # Only add assistant message for training validation if config.version != TokenizerVersion.v1: messages.append({"role": "assistant", "content": "Hi there!"}) static_output = render_template(static_template, messages) dynamic_output = render_template(dynamic_template, messages) assert static_output == dynamic_output, ( f"Output mismatch for {config}\n\nStatic output: {static_output}\nDynamic output: {dynamic_output}" ) # Comprehensive functional tests — covers all configs except plain_think # (plain think template parity is tested separately in test_unit.py::test_plain_think_static_dynamic_parity) @pytest.mark.parametrize( "config", [c for c in ALL_CONFIGS if not c.plain_think], ids=[_config_id(c) for c in ALL_CONFIGS if not c.plain_think], ) def test_dynamic_template_comprehensive(config: TestConfig) -> None: template_config = _make_config(config) static_template = _load_golden_template(template_config) dynamic_template = build_chat_template(template_config) for mode in (ValidationMode.finetuning, ValidationMode.test): conversations = _get_conversations(config.version, mode, config.image, config.audio, config.think) for idx, request in enumerate(conversations): render_args = _request_to_render_args(request) static_output = render_template(static_template, **render_args) dynamic_output = render_template(dynamic_template, **render_args) assert static_output == dynamic_output, ( f"Output mismatch for {config}, mode={mode}, conversation={idx}\n\n" f"Static output: {static_output}\n" f"Dynamic output: {dynamic_output}" ) python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/000077500000000000000000000000001523035502500274535ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/__init__.py000066400000000000000000000000001523035502500315520ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/conftest.py000066400000000000000000000000631523035502500316510ustar00rootroot00000000000000import pytest pytest.importorskip("transformers") python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/test_core_parity.py000066400000000000000000000251251523035502500334110ustar00rootroot00000000000000from pathlib import Path from typing import Any import pytest from jinja2.exceptions import TemplateError from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import AssistantMessage, UserMessage from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from tests.integrations.chat_templates.conftest import ( ALL_TRANSFORMERS_CONFIGS, _config_id, ) from tests.integrations.chat_templates.fixtures_data import _get_conversations from tests.integrations.chat_templates.helpers import ( TestConfig, _build_spm_path, _build_tekken_json, encode_mistral_common, ) from tests.integrations.chat_templates.hf_utils import ( _build_hf_tokenizer, encode_hf_tokens, encode_transformers, encode_transformers_from_openai, ) class TestTransformersMistralCommonParity: @pytest.mark.parametrize( "config", ALL_TRANSFORMERS_CONFIGS, ids=[_config_id(c) for c in ALL_TRANSFORMERS_CONFIGS], ) @pytest.mark.parametrize("mode", [ValidationMode.test, ValidationMode.finetuning]) def test_chat_template(self, config: TestConfig, mode: ValidationMode, tmp_path: Path) -> None: conversations = _get_conversations(config.version, mode, config.image, config.audio, config.think) if config.spm: tokenizer_path = _build_spm_path(config, tmp_path) else: tokenizer_path = _build_tekken_json(config, tmp_path) mistral_tokenizer = MistralTokenizer.from_file(str(tokenizer_path), mode=mode) chat_template = generate_chat_template( spm=config.spm, tokenizer_version=config.version, image_support=config.image, audio_support=config.audio, thinking_support=config.think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) # Build HF tokenizer for Tekken-based token ID comparison hf_tokenizer = None if not config.spm: hf_tokenizer = _build_hf_tokenizer(tokenizer_path, chat_template) if config.version <= TokenizerVersion.v2: for conv in conversations: for message in conv.messages: # v1/v2 expect string content, not structured chunks. # Mutate here rather than duplicating fixtures per version. if isinstance(message, (UserMessage, AssistantMessage)) and isinstance(message.content, list): assert len(message.content) == 1 and isinstance(message.content[0], TextChunk), ( "Only text content is supported for v1 and v2" ) message.content = str(message.content[0].text) for conversation in conversations: for message in conversation.messages: if message.role == "tool" and message.name is None: message.name = "tool" # Run transformers first since encode_mistral_common may mutate the conversation in-place transformers_encoded = encode_transformers(chat_template, conversation, keep_name_for_tools=True) # Token ID comparison: apply_chat_template(tokenize=True) vs mistral-common # Skipped for image/audio configs because multimodal tokens are generated # by encoders (not from text), so token IDs won't match. # Skipped for SPM because building an HF tokenizer from SPM is a separate concern. # Skipped for V1 because V1 emits control markers (e.g. [INST]) as literal # text, but the HF tokenizer treats them as special tokens and encodes # them as single IDs, making token-level parity impossible. if not config.image and not config.audio and not config.spm and config.version > TokenizerVersion.v1: assert hf_tokenizer is not None hf_tokens = encode_hf_tokens(hf_tokenizer, conversation.model_copy(deep=True), keep_name_for_tools=True) mc_tokens = mistral_tokenizer.encode_chat_completion(conversation.model_copy(deep=True)).tokens assert mc_tokens == hf_tokens mistral_common_encoded = encode_mistral_common(mistral_tokenizer, conversation, config.spm) assert mistral_common_encoded == transformers_encoded @pytest.mark.parametrize( "config", ALL_TRANSFORMERS_CONFIGS, ids=[_config_id(c) for c in ALL_TRANSFORMERS_CONFIGS], ) def test_role_error(self, config: TestConfig) -> None: chat_template = generate_chat_template( spm=config.spm, tokenizer_version=config.version, image_support=config.image, audio_support=config.audio, thinking_support=config.think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) # Starting with assistant is rejected by the first-message constraint invalid_starts_with_assistant = { "messages": [ {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "Hello"}, ] } if config.version >= TokenizerVersion.v7: first_msg_match = r"Conversation must start with a user or system message, got assistant\." else: first_msg_match = r"Conversation must start with a user message, got assistant\." with pytest.raises(TemplateError, match=first_msg_match): encode_transformers_from_openai(chat_template, invalid_starts_with_assistant) # Invalid role after user is caught by the transition table invalid_role = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "invalid", "content": "Hello"}, ] } with pytest.raises(TemplateError, match=r"Unexpected role 'invalid' after role 'user'"): encode_transformers_from_openai(chat_template, invalid_role) # Tool after user is rejected by the transition table (tool can only follow assistant or tool) if config.version >= TokenizerVersion.v2: invalid_tool_after_user = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "tool", "content": "result", "tool_call_id": "123456789"}, ] } with pytest.raises(TemplateError, match=r"Unexpected role 'tool' after role 'user'"): encode_transformers_from_openai(chat_template, invalid_tool_after_user) # System after assistant is rejected for v7+ (system stays in loop_messages) if config.version >= TokenizerVersion.v7: invalid_system_after_assistant = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "system", "content": "New system prompt"}, {"role": "user", "content": "World"}, ] } with pytest.raises(TemplateError, match=r"Unexpected role 'system' after role 'assistant'"): encode_transformers_from_openai(chat_template, invalid_system_after_assistant) @pytest.mark.parametrize( "config", ALL_TRANSFORMERS_CONFIGS, ids=[_config_id(c) for c in ALL_TRANSFORMERS_CONFIGS], ) def test_invalid_chunks( self, config: TestConfig, invalid_sp_think: dict[str, Any], invalid_sp_random: dict[str, Any], invalid_assistant_think: dict[str, Any], invalid_assistant_random: dict[str, Any], invalid_user_image: dict[str, Any], invalid_user_audio: dict[str, Any], invalid_user_random: dict[str, Any], ) -> None: sp_invalids = [invalid_sp_random, invalid_sp_think] assistant_invalids = [invalid_assistant_random, invalid_assistant_think] user_invalids = [invalid_user_image, invalid_user_audio, invalid_user_random] invalid_convs = [invalid_sp_random, invalid_user_random, invalid_assistant_random] if not config.think: invalid_convs += [invalid_sp_think, invalid_assistant_think] if not config.image: invalid_convs += [invalid_user_image] if not config.audio: invalid_convs += [invalid_user_audio] chat_template = generate_chat_template( spm=config.spm, tokenizer_version=config.version, image_support=config.image, audio_support=config.audio, thinking_support=config.think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) # Not using parametrize here because invalid_convs depends on the version/image/audio/think # parameters from the outer parametrize. Each sub-case is identifiable via the TemplateError # match string which includes the role and allowed chunks. is_v15_plus = config.version >= TokenizerVersion.v15 for conv in invalid_convs: msg_template = "Only {chunks} chunks are supported in {role} message content." if conv in sp_invalids: if config.think and not is_v15_plus: chunks = "text and thinking" elif config.audio and is_v15_plus: chunks = "text and audio" else: chunks = "text" role = "system" elif conv in user_invalids: chunks = "text" if config.image: chunks += ", image and image_url" if config.audio: chunks += ", input_audio and audio_url" role = "user" elif conv in assistant_invalids: desc_parts = ["text"] if config.think: desc_parts.append("thinking") if len(desc_parts) == 1: chunks = "text" else: chunks = ", ".join(desc_parts[:-1]) + " and " + desc_parts[-1] role = "assistant" err_msg = msg_template.format(chunks=chunks, role=role) with pytest.raises(TemplateError, match=err_msg): encode_transformers_from_openai(chat_template, conv) test_default_system_prompt.py000066400000000000000000000135701523035502500354440ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/transformersimport pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.hf_utils import encode_transformers_from_openai CONV_NO_SYSTEM: dict[str, list[dict[str, str]]] = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] } class TestDefaultSystemPromptInjection: DEFAULT_SYSTEM_PROMPT = "You are a helpful AI assistant." CONV_WITH_SYSTEM: dict[str, list[dict[str, str]]] = { "messages": [ {"role": "system", "content": "You are a custom assistant."}, {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] } @pytest.mark.parametrize( ("version", "spm", "expected_fragment"), [ (TokenizerVersion.v3, False, "[INST]You are a helpful AI assistant.\n\nHello[/INST]"), (TokenizerVersion.v7, False, "[SYSTEM_PROMPT]You are a helpful AI assistant.[/SYSTEM_PROMPT]"), (TokenizerVersion.v3, True, "[INST] You are a helpful AI assistant.\n\nHello[/INST]"), (TokenizerVersion.v7, True, "[SYSTEM_PROMPT]You are a helpful AI assistant.[/SYSTEM_PROMPT]"), (TokenizerVersion.v11, False, "[SYSTEM_PROMPT]You are a helpful AI assistant.[/SYSTEM_PROMPT]"), (TokenizerVersion.v13, False, "[SYSTEM_PROMPT]You are a helpful AI assistant.[/SYSTEM_PROMPT]"), ], ) def test_default_prompt_used( self, version: TokenizerVersion, spm: bool, expected_fragment: str, ) -> None: chat_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=self.DEFAULT_SYSTEM_PROMPT, plain_thinking_support=False, use_special_token_variables=True, ) output = encode_transformers_from_openai(chat_template, CONV_NO_SYSTEM) assert self.DEFAULT_SYSTEM_PROMPT in output assert expected_fragment in output @pytest.mark.parametrize( "version", [TokenizerVersion.v3, TokenizerVersion.v7, TokenizerVersion.v11, TokenizerVersion.v13], ) def test_default_prompt_ignored_when_user_provides(self, version: TokenizerVersion) -> None: chat_template = generate_chat_template( spm=False, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=self.DEFAULT_SYSTEM_PROMPT, plain_thinking_support=False, use_special_token_variables=True, ) output = encode_transformers_from_openai(chat_template, self.CONV_WITH_SYSTEM) assert self.DEFAULT_SYSTEM_PROMPT not in output assert "You are a custom assistant." in output def test_legacy_no_default_prompt(self) -> None: chat_template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v3, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) output = encode_transformers_from_openai(chat_template, CONV_NO_SYSTEM) assert "[INST]Hello[/INST]" in output def test_modern_no_default_prompt(self) -> None: chat_template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) output = encode_transformers_from_openai(chat_template, CONV_NO_SYSTEM) assert "[SYSTEM_PROMPT]" not in output assert "[/SYSTEM_PROMPT]" not in output def test_v15_default_system_prompt(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=self.DEFAULT_SYSTEM_PROMPT, plain_thinking_support=False, use_special_token_variables=True, ) result = encode_transformers_from_openai(template, CONV_NO_SYSTEM) assert self.DEFAULT_SYSTEM_PROMPT in result assert "[SYSTEM_PROMPT]" in result assert "[MODEL_SETTINGS]" in result result_with_system = encode_transformers_from_openai(template, self.CONV_WITH_SYSTEM) assert self.DEFAULT_SYSTEM_PROMPT not in result_with_system assert "You are a custom assistant." in result_with_system class TestSystemPromptEscaping: @pytest.mark.parametrize( "prompt", [ "You're a helpful assistant! Use \"quotes\" and 'apostrophes'.", "use \\ for escaping", "Line 1\nLine 2\nLine 3", r"Step \1: initialize, Step \2: run", "Use {{ variable }} and {% if true %}block{% endif %}", ], ) def test_special_chars_preserved(self, prompt: str) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=prompt, plain_thinking_support=False, use_special_token_variables=True, ) result = encode_transformers_from_openai(template, CONV_NO_SYSTEM) assert prompt in result python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/test_thinking.py000066400000000000000000000162651523035502500327110ustar00rootroot00000000000000from pathlib import Path import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.protocol.instruct.chunk import TextChunk, ThinkChunk from mistral_common.protocol.instruct.messages import AssistantMessage, SystemMessage, UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.fixtures_data import _get_conversations from tests.integrations.chat_templates.helpers import _get_mistral_tokenizer, encode_mistral_common from tests.integrations.chat_templates.hf_utils import encode_transformers class TestPlainThinkingValidation: def test_plain_thinking_v11_works(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) assert "" in template assert "" in template assert "[THINK]" not in template class TestPlainThinkParity: @pytest.mark.parametrize( ("image", "mode"), [ (False, ValidationMode.test), (False, ValidationMode.finetuning), (True, ValidationMode.test), (True, ValidationMode.finetuning), ], ) def test_plain_think_vs_mistral_common_baseline(self, image: bool, mode: ValidationMode, tmp_path: Path) -> None: version = TokenizerVersion.v11 conversations = _get_conversations(version, mode, image, audio=False, think=False) mistral_tok = _get_mistral_tokenizer( spm=False, tokenizer_version=version, validation_mode=mode, image=image, audio=False, think=False, output_dir=tmp_path, ) plain_think_template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) for conversation in conversations: plain_think_encoded = encode_transformers(plain_think_template, conversation) mistral_encoded = encode_mistral_common(mistral_tok, conversation, spm=False) assert plain_think_encoded == mistral_encoded @pytest.mark.parametrize("image", [False, True]) def test_plain_think_vs_mistral_common_with_thinking(self, image: bool, tmp_path: Path) -> None: version = TokenizerVersion.v11 mistral_tok = _get_mistral_tokenizer( spm=False, tokenizer_version=version, validation_mode=ValidationMode.finetuning, image=image, audio=False, think=False, output_dir=tmp_path, ) plain_think_template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) # Build a conversation with thinking chunks think_conversation = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage( content=[ TextChunk(text="You are a helpful assistant."), ThinkChunk(thinking="System reasoning."), TextChunk(text="Be concise."), ], ), UserMessage(content=[TextChunk(text="What is 2+2?")]), AssistantMessage( content=[ ThinkChunk(thinking="Simple arithmetic."), TextChunk(text="4."), ], tool_calls=[], ), UserMessage(content=[TextChunk(text="Thanks.")]), AssistantMessage(content=[TextChunk(text="You're welcome.")]), ], ) # Same conversation without thinking chunks no_think_conversation = ChatCompletionRequest( # type: ignore[type-var] messages=[ SystemMessage(content="You are a helpful assistant.Be concise."), UserMessage(content=[TextChunk(text="What is 2+2?")]), AssistantMessage(content="4.", tool_calls=[]), UserMessage(content=[TextChunk(text="Thanks.")]), AssistantMessage(content=[TextChunk(text="You're welcome.")]), ], ) # Render with plain think template (has tags) plain_think_encoded = encode_transformers(plain_think_template, think_conversation) # Render baseline without think chunks via mistral-common baseline_encoded = encode_mistral_common(mistral_tok, no_think_conversation, spm=False) # Verify think tags are present assert "System reasoning." in plain_think_encoded assert "Simple arithmetic." in plain_think_encoded assert "[THINK]" not in plain_think_encoded # Verify stripping think tags recovers the baseline stripped = plain_think_encoded.replace("System reasoning.", "").replace( "Simple arithmetic.", "" ) assert stripped == baseline_encoded @pytest.mark.parametrize("image", [False, True]) @pytest.mark.parametrize("mode", [ValidationMode.test, ValidationMode.finetuning]) def test_plain_think_comprehensive(self, image: bool, mode: ValidationMode, tmp_path: Path) -> None: version = TokenizerVersion.v11 # Use non-thinking conversations (plain think uses same base conversations) conversations = _get_conversations(version, mode, image, audio=False, think=False) mistral_tok = _get_mistral_tokenizer( spm=False, tokenizer_version=version, validation_mode=mode, image=image, audio=False, think=False, output_dir=tmp_path, ) plain_think_template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) for conversation in conversations: # Plain think template with non-think conversations should match v11 base plain_encoded = encode_transformers(plain_think_template, conversation) mistral_encoded = encode_mistral_common(mistral_tok, conversation, spm=False) assert plain_encoded == mistral_encoded python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/test_tools.py000066400000000000000000000073671523035502500322410ustar00rootroot00000000000000import pytest from jinja2.exceptions import TemplateError from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.hf_utils import encode_transformers_from_openai class TestToolCallValidation: @pytest.mark.parametrize( ("spm", "version", "image", "audio", "think"), [ (False, TokenizerVersion.v3, False, False, False), (True, TokenizerVersion.v3, False, False, False), (False, TokenizerVersion.v3, True, False, False), (True, TokenizerVersion.v3, True, False, False), (False, TokenizerVersion.v7, False, False, False), (True, TokenizerVersion.v7, False, False, False), (False, TokenizerVersion.v7, True, False, False), (True, TokenizerVersion.v7, True, False, False), (False, TokenizerVersion.v7, False, True, False), (False, TokenizerVersion.v11, False, False, False), (False, TokenizerVersion.v11, True, False, False), (False, TokenizerVersion.v11, False, True, False), ], ) def test_tool_call_id_length( self, spm: bool, version: TokenizerVersion, image: bool, audio: bool, think: bool, ) -> None: invalid_id_conv = { "messages": [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "1", "function": {"name": "func", "arguments": "{}"}}], }, ] } chat_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) with pytest.raises(TemplateError, match="Tool call must have an id of 9 characters or numbers."): encode_transformers_from_openai(chat_template, invalid_id_conv) @pytest.mark.parametrize( ("spm", "version", "image", "audio", "think"), [ (False, TokenizerVersion.v2, False, False, False), (True, TokenizerVersion.v2, False, False, False), (False, TokenizerVersion.v3, False, False, False), (True, TokenizerVersion.v3, False, False, False), (False, TokenizerVersion.v3, True, False, False), (True, TokenizerVersion.v3, True, False, False), ], ) def test_content_with_tool_calls_rejected( self, spm: bool, version: TokenizerVersion, image: bool, audio: bool, think: bool, ) -> None: invalid_message_conv = { "messages": [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": "hey", "tool_calls": [{"id": "123456789", "function": {"name": "func", "arguments": "{}"}}], }, ] } chat_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) with pytest.raises(TemplateError, match="Assistant message cannot have both content and tool calls."): encode_transformers_from_openai(chat_template, invalid_message_conv) python-mistral-common-1.11.7/tests/integrations/chat_templates/transformers/test_v15.py000066400000000000000000000063101523035502500314770ustar00rootroot00000000000000from typing import Optional import pytest from jinja2.exceptions import TemplateError from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.hf_utils import encode_transformers_from_openai class TestV15ReasoningEffort: @pytest.mark.parametrize( ("spm", "version", "image", "audio", "think"), [ (False, TokenizerVersion.v15, False, False, False), (False, TokenizerVersion.v15, True, False, False), (False, TokenizerVersion.v15, False, False, True), (False, TokenizerVersion.v15, True, False, True), ], ) @pytest.mark.parametrize( "reasoning_effort", [None, "high", "none"], ids=["no_effort", "high", "none"], ) def test_valid_reasoning_effort( self, spm: bool, version: TokenizerVersion, image: bool, audio: bool, think: bool, reasoning_effort: Optional[str], ) -> None: chat_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) conv: dict = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, ], } if reasoning_effort is not None: conv["reasoning_effort"] = reasoning_effort result = encode_transformers_from_openai(chat_template, conv) assert "[MODEL_SETTINGS]" in result @pytest.mark.parametrize( ("spm", "version", "image", "audio", "think"), [ (False, TokenizerVersion.v15, False, False, False), (False, TokenizerVersion.v15, True, False, False), (False, TokenizerVersion.v15, False, False, True), (False, TokenizerVersion.v15, True, False, True), ], ) @pytest.mark.parametrize( "reasoning_effort", ["low", "medium", "invalid_value"], ) def test_invalid_reasoning_effort( self, spm: bool, version: TokenizerVersion, image: bool, audio: bool, think: bool, reasoning_effort: str, ) -> None: chat_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=think, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) conv = { "messages": [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, ], "reasoning_effort": reasoning_effort, } with pytest.raises(TemplateError, match='reasoning_effort must be either "none" or "high"'): encode_transformers_from_openai(chat_template, conv) python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/000077500000000000000000000000001523035502500257055ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/__init__.py000066400000000000000000000000001523035502500300040ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_audio.py000066400000000000000000000047571523035502500304340ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import render_template class TestAudioSystemPrompt: @pytest.mark.parametrize( "version", [TokenizerVersion.v7, TokenizerVersion.v11, TokenizerVersion.v13], ) def test_audio_with_system_prompt_raises(self, version: TokenizerVersion) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "system", "content": "You are helpful."}, { "role": "user", "content": [ {"type": "text", "text": "What is this audio?"}, {"type": "input_audio", "input_audio": {"data": "base64data", "format": "wav"}}, ], }, {"role": "assistant", "content": "An audio clip."}, ] with pytest.raises( ValueError, match="Audio chunks are not supported in user message content when system prompt is provided" ): render_template(template, messages) def test_v15_audio_with_system_prompt_succeeds(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "system", "content": "You are helpful."}, { "role": "user", "content": [ {"type": "text", "text": "What is this audio?"}, {"type": "input_audio", "input_audio": {"data": "base64data", "format": "wav"}}, ], }, {"role": "assistant", "content": "An audio clip."}, ] output = render_template(template, messages) assert "[AUDIO]" in output assert "[SYSTEM_PROMPT]" in output python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_config_validation.py000066400000000000000000000051201523035502500327730ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.template_generator import TemplateConfig from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.conftest import ALL_CONFIGS from tests.integrations.chat_templates.helpers import TestConfig class TestInvalidConfigs: @pytest.mark.parametrize( ("kwargs", "error_match"), [ ({"version": TokenizerVersion.v11, "spm": True}, "SPM tokenizer is not supported"), ( {"version": TokenizerVersion.v7, "image_support": True, "audio_support": True}, "Image and audio support are mutually exclusive", ), ({"version": TokenizerVersion.v1, "image_support": True}, "Image support is only available"), ({"version": TokenizerVersion.v3, "audio_support": True}, "Audio support is only available"), ({"version": TokenizerVersion.v7, "thinking_support": True}, "Thinking support is only available"), ( {"version": TokenizerVersion.v13, "audio_support": True, "thinking_support": True}, "Audio and thinking support are mutually exclusive", ), ( {"version": TokenizerVersion.v11, "thinking_support": True, "plain_thinking_support": True}, "Plain thinking support and thinking support are mutually exclusive", ), ( {"version": TokenizerVersion.v15, "plain_thinking_support": True}, "Plain thinking support is only available for tokenizer version v11", ), ( {"version": TokenizerVersion.v11, "audio_support": True, "plain_thinking_support": True}, "Audio and plain thinking support are mutually exclusive", ), ], ) def test_invalid_config(self, kwargs: dict[str, Any], error_match: str) -> None: with pytest.raises(ValueError, match=error_match): TemplateConfig(**kwargs) class TestValidConfigs: @pytest.mark.parametrize("config", ALL_CONFIGS) def test_all_configs_are_valid(self, config: TestConfig) -> None: template_config = TemplateConfig( version=config.version, spm=config.spm, image_support=config.image, audio_support=config.audio, thinking_support=config.think, plain_thinking_support=config.plain_think, ) assert isinstance(template_config.has_tools, bool) assert isinstance(template_config.any_thinking_support, bool) python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_convert_tokenizer.py000066400000000000000000000140741523035502500330760ustar00rootroot00000000000000from pathlib import Path import pytest from mistral_common.exceptions import TokenizerException from mistral_common.integrations.chat_templates.chat_templates import ( convert_tokenizer_to_chat_template, generate_chat_template, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _build_spm_path, _build_tekken_json class TestConvertTokenizerToChatTemplate: def test_tekken_v13_thinking(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v13, think=True) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) assert result == expected assert "[THINK]" in result def test_tekken_v3_image(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v3, image=True) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v3, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) assert result == expected def test_tekken_v7_audio(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v7, audio=True) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) assert result == expected def test_tekken_v11_plain_thinking(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v11) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) assert result == expected assert "" in result assert "[THINK]" not in result def test_spm_v7(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v7, spm=True) path = _build_spm_path(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=True, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) assert result == expected def test_system_prompt_embedded(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v7) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template( tokenizer_file=path, system_prompt="You are helpful.", ) assert "You are helpful." in result def test_use_special_token_variables_true(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v7) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template( tokenizer_file=path, use_special_token_variables=True, ) assert "bos_token" in result assert "''" not in result def test_use_special_token_variables_false(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v7) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template( tokenizer_file=path, use_special_token_variables=False, ) assert "''" in result assert "bos_token" not in result def test_tekken_v11_audio_no_plain_thinking(self, tmp_path: Path) -> None: config = TestConfig(version=TokenizerVersion.v11, audio=True) path = _build_tekken_json(config=config, output_dir=tmp_path) result = convert_tokenizer_to_chat_template(tokenizer_file=path) expected = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) assert result == expected assert "[AUDIO]" in result assert "" not in result def test_unrecognized_file_raises_tokenizer_exception(self, tmp_path: Path) -> None: invalid_path = tmp_path / "tokenizer.txt" invalid_path.write_text("not a tokenizer") with pytest.raises(TokenizerException, match="Unrecognized tokenizer file:"): convert_tokenizer_to_chat_template(tokenizer_file=invalid_path) python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_images.py000066400000000000000000000055061523035502500305710ustar00rootroot00000000000000from typing import Any from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import render_template class TestImageBlockOrdering: def test_image_two_blocks_sorted_by_type(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) # Text first, then image — should be sorted so image appears before text messages: list[dict[str, Any]] = [ { "role": "user", "content": [ {"type": "text", "text": "What is this?"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg=="}}, ], }, {"role": "assistant", "content": "An image."}, ] output = render_template(template, messages) # [IMG] should appear before "What is this?" img_pos = output.find("[IMG]") text_pos = output.find("What is this?") assert img_pos != -1 assert text_pos != -1 assert img_pos < text_pos, "Image token should appear before text in 2-block content" def test_v13_image_more_than_two_blocks_preserves_order(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) # Three blocks: text, image, text — NOT sorted (only 2-block content is sorted) messages: list[dict[str, Any]] = [ { "role": "user", "content": [ {"type": "text", "text": "First"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg=="}}, {"type": "text", "text": "Second"}, ], }, {"role": "assistant", "content": "Described."}, ] output = render_template(template, messages) first_pos = output.find("First") img_pos = output.find("[IMG]") second_pos = output.find("Second") assert first_pos != -1 assert img_pos != -1 assert second_pos != -1 assert first_pos < img_pos < second_pos, "With 3+ blocks and image_support, original order is preserved" python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_template_generation.py000066400000000000000000000422541523035502500333530ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.integrations.chat_templates.template_generator import ( TemplateConfig, _render_content_call, build_chat_template, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _load_golden_template, _make_config, render_template class TestGenerateChatTemplateAPI: @pytest.fixture() def v13_template(self) -> str: return generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) def test_basic_generation(self, v13_template: str) -> None: assert "{%- set default_system_message = '' %}" in v13_template assert "bos_token" in v13_template def test_default_system_prompt(self) -> None: template_with_prompt = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=False, thinking_support=False, default_system_prompt="You are a helpful assistant.", plain_thinking_support=False, use_special_token_variables=True, ) assert "You are a helpful assistant." in template_with_prompt def test_basic_static_dynamic_parity(self, v13_template: str) -> None: config = _make_config(TestConfig(version=TokenizerVersion.v13)) static_template = _load_golden_template(config) messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] static_output = render_template(static_template, messages) dynamic_output = render_template(v13_template, messages) assert static_output == dynamic_output def test_empty_messages_raises(self) -> None: config = TemplateConfig(version=TokenizerVersion.v15) template = build_chat_template(config) with pytest.raises(Exception, match="list object has no element 0"): render_template(template, messages=[]) def test_double_quotes_in_default_system_prompt(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=False, thinking_support=False, default_system_prompt='You are "the best" assistant.', plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [{"role": "user", "content": "Hello"}] result = render_template(template, messages=messages) assert 'You are "the best" assistant.' in result class TestTokenVariables: @pytest.mark.parametrize( ("use_vars", "present", "absent"), [ (True, ["bos_token", "eos_token"], ["{{- '' }}", "{{- '' }}"]), (False, ["''", "''"], ["bos_token", "eos_token"]), ], ) def test_token_variable_content(self, use_vars: bool, present: list[str], absent: list[str]) -> None: config = TemplateConfig(version=TokenizerVersion.v7, use_special_token_variables=use_vars) template = build_chat_template(config) for expected in present: assert expected in template for unexpected in absent: assert unexpected not in template def test_use_special_token_variables_false_renders_correctly(self) -> None: config = TemplateConfig(version=TokenizerVersion.v15, use_special_token_variables=False) template = build_chat_template(config) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there"}, ] result = render_template(template, messages=messages) assert "" in result assert "" in result def test_use_special_token_variables_default_is_false(self) -> None: config = TemplateConfig(version=TokenizerVersion.v7) assert config.use_special_token_variables is False class TestMessageContentValidation: @pytest.mark.parametrize("content", [[], None]) @pytest.mark.parametrize("version", [TokenizerVersion.v3, TokenizerVersion.v7, TokenizerVersion.v15]) def test_user_empty_content_renders_empty_turn(self, version: TokenizerVersion, content: list[Any] | None) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": content}, ] output = render_template(template, messages) assert "[INST]" in output assert "[/INST]" in output @pytest.mark.parametrize("version", [TokenizerVersion.v7, TokenizerVersion.v13, TokenizerVersion.v15]) def test_assistant_empty_content_no_tool_calls_raises(self, version: TokenizerVersion) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": ""}, ] with pytest.raises( ValueError, match="Assistant message must have a string or a list of chunks in content or a list of tool calls", ): render_template(template, messages) def test_v1_empty_assistant_content_raises(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v1, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": []}, ] with pytest.raises(ValueError, match="Assistant message content must be non-empty"): render_template(template, messages) def test_v1_invalid_assistant_chunk_type_raises(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v1, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": [{"type": "image", "url": "http://example.com/img.png"}]}, ] with pytest.raises(ValueError, match="Only text chunks are supported in assistant message contents"): render_template(template, messages) class TestRenderContentCall: r"""Unit tests for `_render_content_call` helper.""" def test_minimal_text_only_config(self) -> None: """Text-only config emits only content and context_name.""" config = TemplateConfig(version=TokenizerVersion.v7) result = _render_content_call( config=config, content_expr="message['content']", context_name="user message content", supported_types_desc="text", support_thinking=False, support_images=False, support_audio=False, ) assert result == "render_content(content=message['content'], context_name='user message content')" @pytest.mark.parametrize( ("config", "call_kwargs", "present", "absent"), [ pytest.param( TemplateConfig(version=TokenizerVersion.v13, thinking_support=True), { "content_expr": "message['content']", "context_name": "assistant message contents", "supported_types_desc": "text and thinking", "support_thinking": True, "support_images": False, "support_audio": False, }, [ "content=message['content']", "context_name='assistant message contents'", "supported_types_desc='text and thinking'", "support_thinking=true", ], ["support_images", "support_audio"], id="thinking_assistant", ), pytest.param( TemplateConfig(version=TokenizerVersion.v7, image_support=True), { "content_expr": "user_content", "context_name": "user message content", "supported_types_desc": "text, image and image_url", "support_thinking": False, "support_images": True, "support_audio": False, }, ["support_images=true", "supported_types_desc='text, image and image_url'"], ["support_thinking", "support_audio"], id="image_user", ), pytest.param( TemplateConfig(version=TokenizerVersion.v7, audio_support=True), { "content_expr": "message['content']", "context_name": "user message content", "supported_types_desc": "text, input_audio and audio_url", "support_thinking": False, "support_images": False, "support_audio": True, }, ["support_audio=true", "supported_types_desc='text, input_audio and audio_url'"], ["support_thinking", "support_images"], id="audio_user", ), ], ) def test_feature_config_emits_correct_args( self, config: TemplateConfig, call_kwargs: dict, present: list[str], absent: list[str], ) -> None: """Feature-specific config emits the expected args and omits absent ones.""" result = _render_content_call(config=config, **call_kwargs) for substring in present: assert substring in result for substring in absent: assert substring not in result def test_exact_thinking_image_assistant(self) -> None: """Full exact-string check for thinking+image config at assistant site.""" config = TemplateConfig(version=TokenizerVersion.v13, thinking_support=True, image_support=True) result = _render_content_call( config=config, content_expr="message['content']", context_name="assistant message contents", supported_types_desc="text and thinking", support_thinking=True, support_images=False, support_audio=False, ) assert result == ( "render_content(" "content=message['content'], " "context_name='assistant message contents', " "supported_types_desc='text and thinking', " "support_thinking=true, " "support_images=false)" ) def test_exact_spm_image_user(self) -> None: """Full exact-string check for SPM+image config at user site.""" config = TemplateConfig(version=TokenizerVersion.v3, spm=True, image_support=True) result = _render_content_call( config=config, content_expr="user_content", context_name="user message content", supported_types_desc="text, image and image_url", support_thinking=False, support_images=True, support_audio=False, initial_prev_img="not added_sp", ) assert result == ( "render_content(" "content=user_content, " "context_name='user message content', " "supported_types_desc='text, image and image_url', " "support_images=true, " "initial_prev_img=not added_sp)" ) def test_tool_ban_thinking_not_in_desc(self) -> None: """Tool call site with thinking config must have support_thinking=false and no 'thinking' in desc.""" config = TemplateConfig(version=TokenizerVersion.v15, thinking_support=True) result = _render_content_call( config=config, content_expr="message['content']", context_name="tool message contents", supported_types_desc="text", support_thinking=False, support_images=False, support_audio=False, ) assert "support_thinking=false" in result # The supported_types_desc must not mention thinking desc_value = result.split("supported_types_desc='")[1].split("'")[0] assert "thinking" not in desc_value def test_arg_order_matches_macro_declaration(self) -> None: """Args follow macro-declared order: content, context_name, supported_types_desc, support_thinking, support_images.""" # noqa: E501 config = TemplateConfig(version=TokenizerVersion.v13, thinking_support=True, image_support=True) result = _render_content_call( config=config, content_expr="message['content']", context_name="assistant message contents", supported_types_desc="text and thinking", support_thinking=True, support_images=False, support_audio=False, ) idx_content = result.index("content=") idx_context = result.index("context_name=") idx_desc = result.index("supported_types_desc=") idx_thinking = result.index("support_thinking=") idx_images = result.index("support_images=") assert idx_content < idx_context < idx_desc < idx_thinking < idx_images def test_image_config_emits_support_images(self) -> None: """Image config emits support_images, no support_thinking or support_audio.""" config = TemplateConfig(version=TokenizerVersion.v7, image_support=True) result = _render_content_call( config=config, content_expr="user_content", context_name="user message content", supported_types_desc="text, image and image_url", support_thinking=False, support_images=True, support_audio=False, ) assert "support_images=true" in result assert "support_thinking" not in result assert "support_audio" not in result def test_spm_image_emits_initial_prev_img(self) -> None: """SPM+image config emits initial_prev_img.""" config = TemplateConfig(version=TokenizerVersion.v3, spm=True, image_support=True) result = _render_content_call( config=config, content_expr="user_content", context_name="user message content", supported_types_desc="text, image and image_url", support_thinking=False, support_images=True, support_audio=False, initial_prev_img="not added_sp", ) assert "initial_prev_img=not added_sp" in result def test_non_spm_does_not_emit_initial_prev_img(self) -> None: """Non-SPM image config does not emit initial_prev_img.""" config = TemplateConfig(version=TokenizerVersion.v7, image_support=True) result = _render_content_call( config=config, content_expr="user_content", context_name="user message content", supported_types_desc="text, image and image_url", support_thinking=False, support_images=True, support_audio=False, ) assert "initial_prev_img" not in result def test_audio_config_emits_support_audio(self) -> None: """Audio config emits support_audio and supported_types_desc, no image or thinking.""" config = TemplateConfig(version=TokenizerVersion.v7, audio_support=True) result = _render_content_call( config=config, content_expr="message['content']", context_name="user message content", supported_types_desc="text, input_audio and audio_url", support_thinking=False, support_images=False, support_audio=True, ) assert "support_audio=true" in result assert "supported_types_desc='text, input_audio and audio_url'" in result assert "support_thinking" not in result assert "support_images" not in result python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_thinking.py000066400000000000000000000502111523035502500311300ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.integrations.chat_templates.template_generator import TemplateConfig, build_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _load_golden_template, _make_config, render_template THINK_CONFIGS = [ (TokenizerVersion.v13, False, False), (TokenizerVersion.v13, True, False), (TokenizerVersion.v15, False, False), (TokenizerVersion.v15, True, False), ] class TestReasoningContentConversion: @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_content_to_thinking_chunk(self, version: TokenizerVersion, image: bool, audio: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "reasoning_content": "Let me add 2 and 2.", "content": "The answer is 4."}, ] output = render_template(template, messages) assert "[THINK]Let me add 2 and 2.[/THINK]The answer is 4." in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_field_to_thinking_chunk(self, version: TokenizerVersion, image: bool, audio: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "What is 3+3?"}, {"role": "assistant", "reasoning": "Adding three and three.", "content": "The answer is 6."}, ] output = render_template(template, messages) assert "[THINK]Adding three and three.[/THINK]The answer is 6." in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_content_takes_precedence_over_reasoning( self, version: TokenizerVersion, image: bool, audio: bool ) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", "reasoning_content": "RC wins", "reasoning": "R loses", "content": "Hello!", }, ] output = render_template(template, messages) assert "[THINK]RC wins[/THINK]Hello!" in output assert "R loses" not in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_content_only_no_text_content(self, version: TokenizerVersion, image: bool, audio: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, {"role": "assistant", "reasoning_content": "Just thinking...", "content": ""}, ] output = render_template(template, messages) assert "[THINK]Just thinking...[/THINK]" in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_content_with_tool_calls(self, version: TokenizerVersion, image: bool, audio: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "What's the weather?"}, { "role": "assistant", "reasoning_content": "I should call a tool.", "content": "", "tool_calls": [ { "id": "abc123", "type": "function", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}, }, ], }, {"role": "tool", "name": "get_weather", "content": "22C", "tool_call_id": "abc123"}, {"role": "assistant", "content": "It's 22C in Paris."}, ] tools: list[dict[str, Any]] = [ { "type": "function", "function": { "name": "get_weather", "description": "", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, }, } ] output = render_template(template, messages, tools=tools) assert "[THINK]I should call a tool.[/THINK]" in output assert "get_weather" in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_aggregation_consecutive_assistants( self, version: TokenizerVersion, image: bool, audio: bool ) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Solve this."}, {"role": "assistant", "reasoning_content": "Step 1 reasoning", "content": "Partial answer."}, {"role": "assistant", "reasoning_content": "Step 2 reasoning", "content": "Final answer."}, ] output = render_template(template, messages) # Both reasoning traces should appear as thinking chunks assert "[THINK]Step 1 reasoning[/THINK]" in output assert "[THINK]Step 2 reasoning[/THINK]" in output # Text from both messages should be aggregated assert "Partial answer." in output assert "Final answer." in output @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_static_dynamic_parity(self, version: TokenizerVersion, image: bool, audio: bool) -> None: config = _make_config(TestConfig(version=version, image=image, audio=audio, think=True)) static_template = _load_golden_template(config) dynamic_template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "reasoning_content": "Thinking...", "content": "Hi!"}, {"role": "user", "content": "How are you?"}, {"role": "assistant", "reasoning": "Considering the question.", "content": "I'm well!"}, ] static_output = render_template(static_template, messages) dynamic_output = render_template(dynamic_template, messages) assert static_output == dynamic_output def test_reasoning_content_with_none_content(self) -> None: config = TemplateConfig(version=TokenizerVersion.v15, thinking_support=True) template = build_chat_template(config) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": None, "reasoning_content": "Let me think about this.", }, ] result = render_template(template, messages=messages) assert "[THINK]" in result assert "Let me think about this." in result @pytest.mark.parametrize("field", ["reasoning_content", "reasoning"]) @pytest.mark.parametrize(("version", "image", "audio"), THINK_CONFIGS) def test_reasoning_with_existing_think_chunks_raises( self, version: TokenizerVersion, image: bool, audio: bool, field: str ) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=audio, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) reasoning_text = ( "Top-level reasoning" if field == "reasoning_content" else "Top-level reasoning via reasoning field" ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", field: reasoning_text, "content": [ {"type": "thinking", "thinking": "Inline thinking"}, {"type": "text", "text": "Hello!"}, ], }, ] with pytest.raises( ValueError, match="Message cannot have both thinking chunks in content and a top-level" " `reasoning` or `reasoning_content` field", ): render_template(template, messages) class TestReasoningEdgeCases: def test_non_think_template_ignores_reasoning_field(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, {"role": "assistant", "reasoning_content": "This is ignored", "content": "Hello!"}, ] output = render_template(template, messages) assert "[THINK]" not in output assert "Hello!" in output @pytest.mark.parametrize("value", ["", None]) def test_empty_or_none_reasoning_ignored(self, value: str | None) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, {"role": "assistant", "reasoning_content": value, "content": "Hello!"}, ] output = render_template(template, messages) assert "[THINK]" not in output assert "Hello!" in output class TestClosedAttribute: @pytest.mark.parametrize( ("version", "image"), [ (TokenizerVersion.v13, False), (TokenizerVersion.v13, True), (TokenizerVersion.v15, False), (TokenizerVersion.v15, True), ], ) def test_closed_true_emits_close_tag(self, version: TokenizerVersion, image: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Done thinking"}, {"type": "text", "text": "Hello!"}, ], }, ] output = render_template(template, messages) assert "[THINK]Done thinking[/THINK]Hello!" in output @pytest.mark.parametrize( ("version", "image"), [ (TokenizerVersion.v13, False), (TokenizerVersion.v13, True), (TokenizerVersion.v15, False), (TokenizerVersion.v15, True), ], ) def test_closed_false_omits_close_tag(self, version: TokenizerVersion, image: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=image, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Ongoing thought", "closed": False}, ], }, ] output = render_template(template, messages) assert "[THINK]Ongoing thought" in output assert "[/THINK]" not in output class TestPlainThink: def test_plain_think_template_produces_correct_output(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Solve this"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Let me think..."}, {"type": "text", "text": "The answer is 42."}, ], }, ] output = render_template(template, messages) assert "Let me think...The answer is 42." in output assert "[THINK]" not in output assert "[/THINK]" not in output @pytest.mark.parametrize("image", [False, True]) def test_plain_think_static_dynamic_parity(self, image: bool) -> None: config = _make_config(TestConfig(version=TokenizerVersion.v11, image=image, plain_think=True)) static_template = _load_golden_template(config) dynamic_template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=image, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Thinking..."}, {"type": "text", "text": "Hi!"}, ], }, ] static_output = render_template(static_template, messages) dynamic_output = render_template(dynamic_template, messages) assert static_output == dynamic_output def test_plain_think_reasoning_content_conversion(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "reasoning_content": "Let me add.", "content": "4."}, ] output = render_template(template, messages) assert "Let me add.4." in output assert "[THINK]" not in output def test_plain_think_closed_false(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Still thinking...", "closed": False}, ], }, ] output = render_template(template, messages) assert "Still thinking..." in output assert "" not in output def test_plain_think_image_template(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ { "role": "user", "content": [ {"type": "text", "text": "What is this?"}, {"type": "image_url", "image_url": "http://example.com/img.png"}, ], }, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "It looks like..."}, {"type": "text", "text": "A red square."}, ], }, ] output = render_template(template, messages) assert "[IMG]" in output assert "It looks like...A red square." in output assert "[THINK]" not in output def test_plain_think_reasoning_with_existing_think_chunks_raises(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=True, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hi"}, { "role": "assistant", "reasoning_content": "Top-level reasoning", "content": [ {"type": "thinking", "thinking": "Inline thinking"}, {"type": "text", "text": "Hello!"}, ], }, ] with pytest.raises( ValueError, match="Message cannot have both thinking chunks in content and a top-level" " `reasoning` or `reasoning_content` field", ): render_template(template, messages) python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_tools.py000066400000000000000000000564541523035502500304740ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _load_golden_template, _make_config, render_template class TestToolRoleSupport: def test_v1_tool_role_rejected(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v1, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "tool", "content": "result"}, ] with pytest.raises(ValueError, match="Unexpected role 'tool' after role 'assistant'"): render_template(template, messages) @pytest.mark.parametrize( ("version", "spm"), [ (TokenizerVersion.v2, False), (TokenizerVersion.v2, True), (TokenizerVersion.v3, False), (TokenizerVersion.v3, True), (TokenizerVersion.v7, False), (TokenizerVersion.v7, True), (TokenizerVersion.v11, False), (TokenizerVersion.v13, False), (TokenizerVersion.v15, False), ], ) def test_user_after_tool_accepted(self, version: TokenizerVersion, spm: bool) -> None: template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Search for info"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "aaaaaaaaa", "type": "function", "function": {"name": "search", "arguments": '{"q": "info"}'}, }, ], }, {"role": "tool", "name": "search", "content": "result1", "tool_call_id": "aaaaaaaaa"}, {"role": "user", "content": "Now refine"}, {"role": "assistant", "content": "Here is the refined answer"}, ] tools: list[dict[str, Any]] = [ { "type": "function", "function": { "name": "search", "description": "", "parameters": {"type": "object", "properties": {"q": {"type": "string"}}}, }, } ] output = render_template(template, messages, tools=tools) assert "Now refine" in output assert "Here is the refined answer" in output @pytest.mark.parametrize( ("version", "spm"), [ (TokenizerVersion.v7, False), (TokenizerVersion.v7, True), (TokenizerVersion.v13, False), (TokenizerVersion.v15, False), ], ) def test_user_after_tool_static_matches_dynamic(self, version: TokenizerVersion, spm: bool) -> None: config = _make_config(TestConfig(version=version, spm=spm)) static_template = _load_golden_template(config) dynamic_template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Search for info"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "aaaaaaaaa", "type": "function", "function": {"name": "search", "arguments": '{"q": "info"}'}, }, ], }, {"role": "tool", "name": "search", "content": "result1", "tool_call_id": "aaaaaaaaa"}, {"role": "user", "content": "Now refine"}, {"role": "assistant", "content": "Here is the refined answer"}, ] tools: list[dict[str, Any]] = [ { "type": "function", "function": { "name": "search", "description": "", "parameters": {"type": "object", "properties": {"q": {"type": "string"}}}, }, } ] static_output = render_template(static_template, messages, tools=tools) dynamic_output = render_template(dynamic_template, messages, tools=tools) assert static_output == dynamic_output, ( f"Output mismatch for version={version}, spm={spm}\n\n" f"Static output: {static_output}\n" f"Dynamic output: {dynamic_output}" ) class TestV2ToolResultParsing: @pytest.fixture() def v2_template(self) -> str: return generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v2, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) @pytest.mark.parametrize( ("tool_name", "user_content", "arguments", "tools", "content", "assistant_content", "expected"), [ pytest.param( "add", "What is 2+2?", '{"a": 2, "b": 2}', [ { "type": "function", "function": { "name": "add", "parameters": { "type": "object", "properties": { "a": {"type": "integer"}, "b": {"type": "integer"}, }, }, }, } ], "4", "4", '"content": 4', id="integer", ), pytest.param( "pi", "What is pi?", "{}", [{"type": "function", "function": {"name": "pi", "parameters": {}}}], "3.14", "Pi is 3.14", '"content": 3.14', id="float", ), ], ) def test_numeric_tool_result( self, v2_template: str, tool_name: str, user_content: str, arguments: str, tools: list[dict[str, Any]], content: str, assistant_content: str, expected: str, ) -> None: messages: list[dict[str, Any]] = [ {"role": "user", "content": user_content}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": tool_name, "arguments": arguments}, } ], }, {"role": "tool", "content": content, "name": tool_name}, {"role": "assistant", "content": assistant_content}, ] output = render_template(v2_template, messages, tools=tools) assert expected in output def test_v2_tool_message_missing_name_raises(self, v2_template: str) -> None: messages: list[dict[str, Any]] = [ {"role": "user", "content": "Add 2+2"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "add", "arguments": '{"a": 2, "b": 2}'}, } ], }, {"role": "tool", "content": "4"}, {"role": "assistant", "content": "4"}, ] with pytest.raises(ValueError, match="Tool message must have a name"): render_template( v2_template, messages, tools=[ { "type": "function", "function": { "name": "add", "parameters": { "type": "object", "properties": { "a": {"type": "integer"}, "b": {"type": "integer"}, }, }, }, } ], ) class TestV3ToolResultParsing: @pytest.fixture() def v3_template(self) -> str: return generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v3, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) @pytest.mark.parametrize( ("tool_name", "user_content", "arguments", "tools", "content", "assistant_content", "expected"), [ pytest.param( "add", "What is 2+2?", '{"a": 2, "b": 2}', [{"type": "function", "function": {"name": "add", "parameters": {}}}], "4", "4", '"content": 4', id="integer", ), pytest.param( "pi", "What is pi?", "{}", [{"type": "function", "function": {"name": "pi", "parameters": {}}}], "3.14", "3.14", '"content": 3.14', id="float", ), ], ) def test_numeric_tool_result( self, v3_template: str, tool_name: str, user_content: str, arguments: str, tools: list[dict[str, Any]], content: str, assistant_content: str, expected: str, ) -> None: messages: list[dict[str, Any]] = [ {"role": "user", "content": user_content}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": tool_name, "arguments": arguments}, } ], }, {"role": "tool", "content": content, "tool_call_id": "abc123def", "name": tool_name}, {"role": "assistant", "content": assistant_content}, ] output = render_template(v3_template, messages, tools=tools) assert expected in output @pytest.mark.parametrize("tool_call_id", [None, "short"]) def test_invalid_call_id(self, v3_template: str, tool_call_id: str | None) -> None: messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "greet", "arguments": "{}"}, } ], }, ] tool_msg: dict[str, Any] = {"role": "tool", "content": "hi", "name": "greet"} if tool_call_id is not None: tool_msg["tool_call_id"] = tool_call_id messages.append(tool_msg) messages.append({"role": "assistant", "content": "hi"}) with pytest.raises(ValueError, match="call_id or tool_call_id"): render_template( v3_template, messages, tools=[{"type": "function", "function": {"name": "greet", "parameters": {}}}], ) class TestV7ToolCalls: def test_v7_content_and_tool_calls_accepted(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v7, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "What's the weather?"}, { "role": "assistant", "content": "Let me check the weather for you.", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}, } ], }, {"role": "tool", "content": "Sunny, 25C", "tool_call_id": "abc123def", "name": "get_weather"}, {"role": "assistant", "content": "It's sunny and 25C in Paris."}, ] output = render_template( template, messages, tools=[ { "type": "function", "function": { "name": "get_weather", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, }, } ], ) assert "Let me check the weather for you." in output assert "[TOOL_CALLS]" in output @pytest.mark.parametrize( ("version", "spm"), [ (TokenizerVersion.v7, False), (TokenizerVersion.v7, True), (TokenizerVersion.v13, False), (TokenizerVersion.v15, False), ], ) def test_aggregation_consecutive_assistants_both_tool_calls(self, version: TokenizerVersion, spm: bool) -> None: template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Weather?"}, { "role": "assistant", "content": "Checking Paris.", "tool_calls": [ { "id": "123456789", "type": "function", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}, }, ], }, { "role": "assistant", "content": "And London.", "tool_calls": [ { "id": "023456789", "type": "function", "function": {"name": "get_weather", "arguments": '{"city": "London"}'}, }, ], }, {"role": "tool", "name": "get_weather", "content": "22", "tool_call_id": "123456789"}, {"role": "tool", "name": "get_weather", "content": "15", "tool_call_id": "023456789"}, {"role": "assistant", "content": "Paris: 22, London: 15"}, {"role": "user", "content": "Thanks"}, {"role": "assistant", "content": "Welcome"}, ] tools: list[dict[str, Any]] = [ { "type": "function", "function": { "name": "get_weather", "description": "", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, }, } ] output = render_template(template, messages, tools=tools) # Both assistant messages should be merged: content joined with \n\n assert "Checking Paris.\n\nAnd London." in output # Both tool calls should be present in the output assert '"city": "Paris"' in output assert '"city": "London"' in output @pytest.mark.parametrize( ("version", "spm"), [ (TokenizerVersion.v7, False), (TokenizerVersion.v13, False), (TokenizerVersion.v15, False), ], ) def test_tool_call_empty_string_arguments_defaults_to_empty_object( self, version: TokenizerVersion, spm: bool ) -> None: template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "greet", "arguments": ""}, } ], }, {"role": "tool", "content": "hi", "tool_call_id": "abc123def", "name": "greet"}, {"role": "assistant", "content": "Done"}, ] output = render_template( template, messages, tools=[{"type": "function", "function": {"name": "greet", "parameters": {}}}], ) assert "{}" in output assert "greet" in output class TestV11ToolCalls: def test_v11_tool_call_uses_call_id_token(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v11, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Weather?"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}, } ], }, {"role": "tool", "content": "Sunny", "tool_call_id": "abc123def"}, {"role": "assistant", "content": "Sunny in Paris."}, ] output = render_template( template, messages, tools=[ { "type": "function", "function": { "name": "get_weather", "parameters": { "type": "object", "properties": {"city": {"type": "string"}}, }, }, } ], ) assert "[TOOL_CALLS]get_weather[CALL_ID]abc123def[ARGS]" in output assert '"city": "Paris"' in output @pytest.mark.parametrize( ("version", "spm"), [ (TokenizerVersion.v3, False), (TokenizerVersion.v7, False), (TokenizerVersion.v11, False), ], ) def test_tool_result_call_id_alias(self, version: TokenizerVersion, spm: bool) -> None: template = generate_chat_template( spm=spm, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Add 2+2"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "abc123def", "type": "function", "function": {"name": "add", "arguments": '{"a": 2, "b": 2}'}, } ], }, {"role": "tool", "content": "4", "call_id": "abc123def", "name": "add"}, {"role": "assistant", "content": "4"}, ] output = render_template( template, messages, tools=[ { "type": "function", "function": { "name": "add", "parameters": { "type": "object", "properties": { "a": {"type": "integer"}, "b": {"type": "integer"}, }, }, }, } ], ) assert "abc123def" in output assert "[TOOL_RESULTS]" in output class TestV13PlusToolCalls: @pytest.mark.parametrize("version", [TokenizerVersion.v13, TokenizerVersion.v15]) def test_v13_v15_accept_short_tool_call_id(self, version: TokenizerVersion) -> None: template = generate_chat_template( spm=False, tokenizer_version=version, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Hello"}, { "role": "assistant", "content": "", "tool_calls": [ { "id": "x", "type": "function", "function": {"name": "greet", "arguments": "{}"}, } ], }, {"role": "tool", "content": "hi", "tool_call_id": "x", "name": "greet"}, {"role": "assistant", "content": "Done"}, ] output = render_template( template, messages, tools=[{"type": "function", "function": {"name": "greet", "parameters": {}}}], ) assert "greet" in output assert "Done" in output python-mistral-common-1.11.7/tests/integrations/chat_templates/unit/test_v15.py000066400000000000000000000470161523035502500277410ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.integrations.chat_templates.chat_templates import generate_chat_template from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _load_golden_template, _make_config, render_template class TestV15ModelSettings: @pytest.fixture() def v15_template(self) -> str: return generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) def test_v15_template_contains_model_settings(self, v15_template: str) -> None: assert "[MODEL_SETTINGS]" in v15_template assert "reasoning_effort" in v15_template @pytest.mark.parametrize( ("reasoning_effort", "expected_value"), [(None, "none"), ("high", "high"), ("none", "none")], ) def test_v15_reasoning_effort_rendering( self, v15_template: str, reasoning_effort: str | None, expected_value: str ) -> None: messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] output = render_template(v15_template, messages, reasoning_effort=reasoning_effort) assert f'[MODEL_SETTINGS]{{"reasoning_effort": "{expected_value}"}}[/MODEL_SETTINGS]' in output def test_v15_static_dynamic_parity(self, v15_template: str) -> None: config = _make_config(TestConfig(version=TokenizerVersion.v15)) static_template = _load_golden_template(config) messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] static_output = render_template(static_template, messages, reasoning_effort="high") dynamic_output = render_template(v15_template, messages, reasoning_effort="high") assert static_output == dynamic_output def test_v15_with_image_support(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages = [ { "role": "user", "content": [ {"type": "text", "text": "What is this?"}, {"type": "image_url", "image_url": "http://example.com/image.png"}, ], }, {"role": "assistant", "content": "It's an image."}, ] output = render_template(template, messages, reasoning_effort="high") assert '[MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' in output assert "[IMG]" in output def test_v15_with_thinking_support(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages = [ {"role": "user", "content": "Solve this problem"}, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Let me think..."}, {"type": "text", "text": "The answer is 42."}, ], }, ] output = render_template(template, messages, reasoning_effort="none") assert '[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' in output assert "[THINK]Let me think...[/THINK]" in output def test_v15_with_tools(self) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": {"location": {"type": "string", "description": "The city and state"}}, "required": ["location"], }, }, } ] messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What's the weather in Paris?"}, ] output = render_template(template, messages, tools=tools, reasoning_effort="high") assert '[MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' in output assert "[AVAILABLE_TOOLS]" in output assert "get_weather" in output @pytest.mark.parametrize( ("has_system", "has_tools", "reasoning_effort"), [ (True, True, "high"), (True, False, "high"), (False, True, "high"), (False, False, "high"), (True, True, "none"), (False, False, "none"), (True, True, None), (False, False, None), ], ) def test_v15_available_tools_and_settings_ordering( self, has_system: bool, has_tools: bool, reasoning_effort: str | None ) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [] if has_system: messages.append({"role": "system", "content": "You are helpful."}) messages.extend( [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, ] ) tools: list[dict[str, Any]] | None = None if has_tools: tools = [ { "type": "function", "function": { "name": "tool1", "description": "", "parameters": {"type": "object", "properties": {}}, }, } ] output = render_template(template, messages, tools=tools, reasoning_effort=reasoning_effort) if has_system: assert "[SYSTEM_PROMPT]You are helpful.[/SYSTEM_PROMPT]" in output sp_pos = output.index("[SYSTEM_PROMPT]") else: assert "[SYSTEM_PROMPT]" not in output sp_pos = -1 if has_tools: assert "[AVAILABLE_TOOLS]" in output tools_pos = output.index("[AVAILABLE_TOOLS]") assert tools_pos > sp_pos else: assert "[AVAILABLE_TOOLS]" not in output tools_pos = sp_pos assert "[MODEL_SETTINGS]" in output settings_pos = output.index("[MODEL_SETTINGS]") assert settings_pos > tools_pos inst_pos = output.index("[INST]") assert inst_pos > settings_pos @pytest.mark.parametrize("image", [False, True]) def test_v15_think_template_rejects_think_in_system(self, image: bool) -> None: template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=image, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ { "role": "system", "content": [ {"type": "text", "text": "System text."}, {"type": "thinking", "thinking": "System thinking."}, ], }, {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, ] with pytest.raises(ValueError, match="Only text chunks are supported in system message contents"): render_template(template, messages) class TestV15MultimodalContent: def test_v15_tool_message_with_image_content(self) -> None: r"""V15 image template renders tool message with image content using render_content.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Use tool"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "test12345", "function": {"name": "fn", "arguments": "{}"}}], }, { "role": "tool", "content": [ {"type": "text", "text": "result"}, {"type": "image_url", "image_url": "http://example.com/img.png"}, ], "tool_call_id": "test12345", }, {"role": "assistant", "content": "Done"}, ] tools = [{"type": "function", "function": {"name": "fn", "description": "test", "parameters": {}}}] output = render_template(template, messages, tools=tools, reasoning_effort="none") assert "[TOOL_RESULTS]" in output assert "[IMG]" in output assert "result" in output def test_v15_tool_message_with_audio_content(self) -> None: r"""V15 audio template renders tool message with audio content.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Use tool"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "test12345", "function": {"name": "fn", "arguments": "{}"}}], }, { "role": "tool", "content": [ {"type": "text", "text": "result"}, {"type": "input_audio", "input_audio": {"data": "abc", "format": "wav"}}, ], "tool_call_id": "test12345", }, {"role": "assistant", "content": "Done"}, ] tools = [{"type": "function", "function": {"name": "fn", "description": "test", "parameters": {}}}] output = render_template(template, messages, tools=tools, reasoning_effort="none") assert "[TOOL_RESULTS]" in output assert "[AUDIO]" in output assert "result" in output def test_v15_system_message_with_audio_content(self) -> None: r"""V15 audio template renders system message with audio content.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ { "role": "system", "content": [ {"type": "text", "text": "Listen to context"}, {"type": "input_audio", "input_audio": {"data": "abc", "format": "wav"}}, ], }, {"role": "user", "content": "Summarize"}, {"role": "assistant", "content": "Done"}, ] output = render_template(template, messages, reasoning_effort="none") assert "[SYSTEM_PROMPT]" in output assert "Listen to context" in output assert "[AUDIO]" in output def test_pre_v15_image_template_rejects_image_in_assistant(self) -> None: r"""Pre-V15 image template rejects image chunks in assistant message.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Show me"}, { "role": "assistant", "content": [ {"type": "text", "text": "Here"}, {"type": "image_url", "image_url": "http://example.com/img.png"}, ], }, ] with pytest.raises(ValueError, match="Only text chunks are supported in assistant message contents"): render_template(template, messages) def test_pre_v15_audio_template_rejects_audio_in_assistant(self) -> None: r"""Pre-V15 audio template rejects audio chunks in assistant message.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Listen"}, { "role": "assistant", "content": [ {"type": "text", "text": "Here"}, {"type": "input_audio", "input_audio": {"data": "abc", "format": "wav"}}, ], }, ] with pytest.raises(ValueError, match="Only text chunks are supported in assistant message contents"): render_template(template, messages) def test_pre_v15_audio_template_rejects_audio_in_system(self) -> None: r"""Pre-V15 audio template rejects audio chunks in system message.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=False, audio_support=True, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ { "role": "system", "content": [ {"type": "text", "text": "Context"}, {"type": "input_audio", "input_audio": {"data": "abc", "format": "wav"}}, ], }, {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, ] with pytest.raises(ValueError, match="Only text chunks are supported in system message contents"): render_template(template, messages) def test_v15_image_think_template_rejects_think_in_tool(self) -> None: r"""V15 image+think template raises when a thinking chunk appears in tool message content.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v15, image_support=True, audio_support=False, thinking_support=True, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) tools = [{"type": "function", "function": {"name": "fn", "description": "test", "parameters": {}}}] messages_with_think: list[dict[str, Any]] = [ {"role": "user", "content": "Use tool"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "test12345", "function": {"name": "fn", "arguments": "{}"}}], }, { "role": "tool", "content": [ {"type": "thinking", "thinking": "secret reasoning"}, {"type": "text", "text": "result"}, ], "tool_call_id": "test12345", }, ] with pytest.raises(ValueError, match="tool message contents") as excinfo: render_template(template, messages_with_think, tools=tools) assert "secret reasoning" not in str(excinfo.value) messages_text_only: list[dict[str, Any]] = [ {"role": "user", "content": "Use tool"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "test12345", "function": {"name": "fn", "arguments": "{}"}}], }, { "role": "tool", "content": [ {"type": "text", "text": "result"}, {"type": "image_url", "image_url": "http://example.com/img.png"}, ], "tool_call_id": "test12345", }, ] output = render_template(template, messages_text_only, tools=tools) assert "result" in output def test_pre_v15_image_template_rejects_image_in_tool(self) -> None: r"""Pre-V15 image template rejects image chunks in tool message content.""" template = generate_chat_template( spm=False, tokenizer_version=TokenizerVersion.v13, image_support=True, audio_support=False, thinking_support=False, default_system_prompt=None, plain_thinking_support=False, use_special_token_variables=True, ) messages: list[dict[str, Any]] = [ {"role": "user", "content": "Use tool"}, { "role": "assistant", "content": "", "tool_calls": [{"id": "test12345", "function": {"name": "fn", "arguments": "{}"}}], }, { "role": "tool", "content": [ {"type": "text", "text": "result"}, {"type": "image_url", "image_url": "http://example.com/img.png"}, ], "tool_call_id": "test12345", }, {"role": "assistant", "content": "Done"}, ] tools = [{"type": "function", "function": {"name": "fn", "description": "test", "parameters": {}}}] # Pre-V15 uses message['content']|string which coerces list to string representation # rather than rendering through render_content — no [IMG] token produced output = render_template(template, messages, tools=tools) assert "[IMG]" not in output python-mistral-common-1.11.7/tests/test_audio.py000066400000000000000000000333301523035502500217370ustar00rootroot00000000000000import subprocess import sys import tempfile import textwrap import warnings from typing import Literal from unittest.mock import MagicMock, patch import numpy as np import pytest import requests import soundfile as sf from pydantic import ValidationError from mistral_common.audio import hertz_to_mel, mel_filter_bank from mistral_common.protocol.instruct.chunk import AudioChunk, RawAudio from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio, AudioConfig, AudioSpectrogramConfig def _make_dummy_base64() -> str: """Create a minimal valid base64-encoded WAV for testing.""" audio = Audio(audio_array=np.zeros(1600, dtype=np.float32), sampling_rate=16000, format="wav") return audio.to_base64("wav") def sin_wave(sampling_rate: int, duration: float) -> np.ndarray: return np.sin(np.ones([int(duration * sampling_rate)])) def test_audio_config_rejects_sampling_rate_below_frame_rate() -> None: encoding_config = AudioSpectrogramConfig(num_mel_bins=80, hop_length=160, window_size=400) with pytest.raises(AssertionError): AudioConfig(sampling_rate=1, frame_rate=2.0, encoding_config=encoding_config) def test_audio_resample() -> None: sampling_rate = 44_000 original_array = sin_wave(sampling_rate, 1) audio = Audio( audio_array=original_array, sampling_rate=sampling_rate, format="wav", ) audio.resample(sampling_rate) assert np.allclose(audio.audio_array, original_array, atol=1e-5) audio.resample(sampling_rate // 2) assert audio.sampling_rate == sampling_rate // 2 assert len(audio.audio_array) == len(original_array) // 2 # fmt: off expected_resampled_array = [0.622, 0.907, 0.802, 0.870, 0.819, 0.860, 0.826, 0.854, 0.831, 0.850] # fmt: on assert np.allclose(audio.audio_array[:10], expected_resampled_array, atol=1e-3) def test_from_file() -> None: sampling_rate = 44100 original_array = sin_wave(sampling_rate, 1) # Test with a local path with tempfile.NamedTemporaryFile(suffix=".wav") as tmp: with sf.SoundFile(tmp.name, "w", samplerate=sampling_rate, channels=1) as f: f.write(original_array) audio_local = Audio.from_file(tmp.name) assert isinstance(audio_local, Audio) assert np.allclose(audio_local.audio_array, original_array, atol=1e-5) assert audio_local.sampling_rate == sampling_rate def test_from_url() -> None: # Test with an invalid URL with patch("mistral_common.tokens.tokenizers.audio._requests_lib.get") as mock_get: mock_get.side_effect = requests.RequestException("connection failed") with pytest.raises( ValueError, match="Failed to download audio from URL: https://example.com/invalid_audio.wav", ): Audio.from_url("https://example.com/invalid_audio.wav") # Test with an invalid content with patch("mistral_common.tokens.tokenizers.audio._requests_lib.get") as mock_get: mock_response = MagicMock() mock_response.status_code = 200 mock_response.content = b"this is not audio" mock_get.return_value = mock_response with pytest.raises( ValueError, match=r"Failed to create Audio instance from URL: https://example\.com \.", ): Audio.from_url("https://example.com") # Test valid URL sampling_rate = 16_000 audio_array = sin_wave(sampling_rate, 1) with tempfile.NamedTemporaryFile(suffix=".wav") as tmp: with sf.SoundFile(tmp.name, "w", samplerate=sampling_rate, channels=1) as f: f.write(audio_array) with open(tmp.name, "rb") as f: wav_bytes = f.read() with patch("mistral_common.tokens.tokenizers.audio._requests_lib.get") as mock_get: mock_response = MagicMock() mock_response.status_code = 200 mock_response.content = wav_bytes mock_get.return_value = mock_response audio_url = Audio.from_url("https://fake-audio-url.com/sample.wav", strict=False) assert isinstance(audio_url, Audio) @pytest.mark.parametrize("prefix", [True, False]) def test_audio_base64(prefix: bool) -> None: sampling_rate = 16_000 original_array = sin_wave(sampling_rate, 3.3) LOSSY = ["mp3"] LOSSLESS = ["wav"] for format in LOSSY + LOSSLESS: audio = Audio( audio_array=original_array, sampling_rate=sampling_rate, format=format, ) base64_str = audio.to_base64(format, prefix) new_audio = Audio.from_base64(base64_str) if prefix: assert base64_str.startswith(f"data:audio/{format};base64,") assert audio.sampling_rate == new_audio.sampling_rate if format in LOSSLESS: assert np.allclose(audio.audio_array, new_audio.audio_array, atol=1e-5) elif format in LOSSY: def rmse(a: np.ndarray, b: np.ndarray) -> float: return float(np.sqrt(np.mean((a - b) ** 2))) assert rmse(audio.audio_array, new_audio.audio_array) < 5e-3 else: raise ValueError(f"Unknown format {format}") @pytest.mark.parametrize( "freq, expected_mel", [ (100.0, 1.5), # Linear region (1000.0, 15.0), # Boundary of linear and log regions (4000.0, 15.0 + np.log(4000.0 / 1000.0) * 27.0 / np.log(6.4)), # Log region (10000.0, 15.0 + np.log(10000.0 / 1000.0) * 27.0 / np.log(6.4)), # Log region ], ) def test_hertz_to_mel_single_value(freq: float, expected_mel: float) -> None: mel = hertz_to_mel(freq) assert isinstance(mel, float) assert abs(mel - expected_mel) < 1e-6 def test_hertz_to_mel_array() -> None: # Test with an array of frequency values freq_array = np.array([100.0, 1000.0, 4000.0]) expected_mel_array = np.array( [ 3.0 * 100.0 / 200.0, # Linear region 15.0, # min_log_mel 15.0 + np.log(4000.0 / 1000.0) * 27.0 / np.log(6.4), # Log region ] ) np.testing.assert_array_equal(hertz_to_mel(freq_array), expected_mel_array) def test_mel_filter_bank() -> None: # Test mel_filter_bank function num_frequency_bins = 256 num_mel_bins = 20 min_frequency = 0 max_frequency = 8000 sampling_rate = 16000 mel_filters = mel_filter_bank(num_frequency_bins, num_mel_bins, min_frequency, max_frequency, sampling_rate) # Check the shape of the output assert mel_filters.shape == (num_frequency_bins, num_mel_bins) # Check that all filters sum to 1 (approximately) assert np.allclose(mel_filters.sum(axis=0), 0.032, atol=0.005) # Check that there are no negative values assert (mel_filters >= 0).all() # integration test expected_array = np.array( [ 0.03160475, 0.03200728, 0.03184327, 0.03176877, 0.03208179, 0.03160917, 0.0320748, 0.03180101, 0.03186299, 0.03190694, 0.0319008, 0.03182839, 0.03191571, 0.03184625, 0.03188972, 0.03185817, 0.0318928, 0.03186744, 0.03187269, 0.0318759, ] ) diff = np.abs(mel_filters.sum(0) - expected_array) assert (diff < 1e-5).all() class TestDeprecationWarnings: """Each test runs in a fresh subprocess to avoid module-level _warned flags.""" @staticmethod def _run_code(code: str) -> subprocess.CompletedProcess[str]: return subprocess.run( [sys.executable, "-W", "all", "-c", textwrap.dedent(code)], capture_output=True, text=True, timeout=30, ) def test_rawaudio_instantiation_warns_once(self) -> None: result = self._run_code("""\ import warnings with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") from mistral_common.protocol.instruct.chunk import RawAudio _ = RawAudio(data="abc", format="wav") _ = RawAudio(data="xyz", format="wav") dep = [x for x in w if issubclass(x.category, DeprecationWarning) and "RawAudio" in str(x.message)] assert len(dep) == 1, f"Expected 1 DeprecationWarning, got {len(dep)}: {dep}" print("OK") """) assert result.returncode == 0, result.stderr assert "OK" in result.stdout def test_audio_from_raw_audio_warns(self) -> None: result = self._run_code("""\ import warnings import numpy as np with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") from mistral_common.tokens.tokenizers.audio import Audio from mistral_common.protocol.instruct.chunk import RawAudio audio_obj = Audio(audio_array=np.zeros(1600, dtype=np.float32), sampling_rate=16000, format="wav") b64 = audio_obj.to_base64("wav") ra = RawAudio(data=b64, format="wav") result = Audio.from_raw_audio(ra) dep = [x for x in w if issubclass(x.category, DeprecationWarning) and "from_raw_audio" in str(x.message)] assert len(dep) == 1, f"Expected 1 from_raw_audio warning, got {len(dep)}: {dep}" assert hasattr(result, "audio_array") assert result.sampling_rate > 0 print("OK") """) assert result.returncode == 0, result.stderr assert "OK" in result.stdout @pytest.mark.parametrize("deprecated_name", ["Audio", "AudioFormat", "EXPECTED_FORMAT_VALUES"]) def test_audio_import_from_old_location_warns( self, deprecated_name: Literal["Audio", "AudioFormat", "EXPECTED_FORMAT_VALUES"] ) -> None: code = ( "import warnings\n" "import numpy as np\n" "with warnings.catch_warnings(record=True) as w:\n" ' warnings.simplefilter("always")\n' f" from mistral_common.audio import {deprecated_name}\n" "dep = [\n" " x for x in w\n" " if issubclass(x.category, DeprecationWarning)\n" ' and "mistral_common.audio" in str(x.message)\n' "]\n" 'assert len(dep) == 1, f"Expected 1 import warning, got {len(dep)}: {dep}"\n' "# Verify the class is usable\n" ) if deprecated_name == "Audio": code += ( 'a = Audio(audio_array=np.zeros(1600, dtype=np.float32), sampling_rate=16000, format="wav")\n' "assert a.sampling_rate == 16000\n" ) elif deprecated_name == "AudioFormat": code += "assert isinstance(AudioFormat.MP3, AudioFormat)\n" else: code += 'assert "mp3" in EXPECTED_FORMAT_VALUES\n' code += 'print("OK")\n' result = self._run_code(code) assert result.returncode == 0, result.stderr assert "OK" in result.stdout def test_rawaudio_import_from_chunk_still_works(self) -> None: result = self._run_code("""\ import warnings with warnings.catch_warnings(record=True): warnings.simplefilter("always") from mistral_common.protocol.instruct.chunk import RawAudio ra_str = RawAudio(data="hello", format="wav") assert ra_str.data == "hello" ra_bytes = RawAudio(data=b"world", format="wav") assert ra_bytes.data == b"world" print("OK") """) assert result.returncode == 0, result.stderr assert "OK" in result.stdout class TestRawAudioBackwardCompat: def test_rawaudio_still_works(self) -> None: with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) ra = RawAudio(data="some_data", format="wav") assert ra.data == "some_data" def test_rawaudio_format_accepted_silently(self) -> None: with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) ra = RawAudio(data="data", format="wav") assert ra.format == "wav" def test_audiochunk_with_rawaudio_backward_compat(self) -> None: b64 = _make_dummy_base64() with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) ra = RawAudio(data=b64, format="wav") chunk = AudioChunk(input_audio=ra) # type: ignore[arg-type] assert chunk.input_audio == b64 def test_audiochunk_with_rawaudio_dict_backward_compat(self) -> None: b64 = _make_dummy_base64() chunk = AudioChunk.model_validate({"input_audio": {"data": b64, "format": "wav"}}) assert chunk.input_audio == b64 def test_audiochunk_with_str_new_api(self) -> None: chunk = AudioChunk(input_audio="base64data") assert chunk.input_audio == "base64data" def test_audiochunk_with_bytes_new_api(self) -> None: chunk = AudioChunk(input_audio=b"raw_bytes") assert chunk.input_audio == b"raw_bytes" @pytest.mark.parametrize("empty_audio", ["", " ", b""]) def test_audiochunk_empty_input_raises_validation_error(self, empty_audio: str | bytes) -> None: with pytest.raises(ValidationError, match="should not be empty"): AudioChunk(input_audio=empty_audio) def test_transcription_request_with_rawaudio_backward_compat() -> None: b64 = _make_dummy_base64() with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) ra = RawAudio(data=b64, format="wav") req = TranscriptionRequest(audio=ra, language=None, target_streaming_delay_ms=None) # type: ignore[arg-type] assert isinstance(req.audio, str) assert req.audio == b64 python-mistral-common-1.11.7/tests/test_base.py000066400000000000000000000023021523035502500215430ustar00rootroot00000000000000import pytest from pydantic import ValidationError from mistral_common.base import MistralBase from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import UserMessage def test_filter_cls_fields() -> None: assert MistralBase._filter_cls_fields({}) == {} filtered = UserMessage._filter_cls_fields({"role": "user", "content": "hi", "name": "u1"}) assert filtered == {"role": "user", "content": "hi"} filtered = TextChunk._filter_cls_fields({"type": "text", "text": "hi", "annotations": []}) assert filtered == {"type": "text", "text": "hi"} def test_model_validate_ignore_extra_filters_and_validates() -> None: message = UserMessage.model_validate_ignore_extra({"role": "user", "content": "hi", "name": "u1"}) assert message == UserMessage(content="hi") def test_model_validate_ignore_extra_no_extra_keys() -> None: chunk = TextChunk.model_validate_ignore_extra({"type": "text", "text": "hello"}) assert chunk == TextChunk(text="hello") def test_model_validate_ignore_extra_raises_on_missing_required() -> None: with pytest.raises(ValidationError): UserMessage.model_validate_ignore_extra({"role": "user"}) python-mistral-common-1.11.7/tests/test_converters.py000066400000000000000000001526061523035502500230400ustar00rootroot00000000000000import base64 import copy import io import warnings from pathlib import Path from typing import Any import numpy as np import pytest import soundfile as sf from openai.types.audio.transcription_create_params import TranscriptionCreateParamsBase as OpenAITranscriptionRequest from openai.types.chat.chat_completion_assistant_message_param import ( ChatCompletionAssistantMessageParam as OpenAIAssistantMessage, ) from openai.types.chat.chat_completion_content_part_image_param import ( ChatCompletionContentPartImageParam as OpenAIImageChunk, ) from openai.types.chat.chat_completion_content_part_input_audio_param import ( ChatCompletionContentPartInputAudioParam as OpenAIInputAudioChunk, ) from openai.types.chat.chat_completion_content_part_text_param import ( ChatCompletionContentPartTextParam as OpenAITextChunk, ) from openai.types.chat.chat_completion_message_tool_call_param import ( ChatCompletionMessageToolCallParam as OpenAIToolCall, ) from openai.types.chat.chat_completion_system_message_param import ( ChatCompletionSystemMessageParam as OpenAISystemMessage, ) from openai.types.chat.chat_completion_tool_message_param import ChatCompletionToolMessageParam as OpenAIToolMessage from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam as OpenAITool from openai.types.chat.chat_completion_user_message_param import ChatCompletionUserMessageParam as OpenAIUserMessage from PIL import Image from pydantic_extra_types.language_code import LanguageAlpha2 from mistral_common.exceptions import InvalidAssistantMessageException from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURL, AudioURLChunk, ContentChunk, ImageChunk, ImageURL, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, ReasoningFieldFormat, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ( ChatCompletionRequest, ReasoningEffort, ) from mistral_common.protocol.instruct.tool_calls import ( Function, FunctionCall, FunctionName, NamedToolChoice, Tool, ToolCall, ToolChoiceEnum, ) from mistral_common.protocol.speech.request import SpeechRequest from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import Audio from .test_tokenizer_v7_audio_tts import _make_fake_audio CURRENT_FILE_PATH = Path(__file__).resolve() ROOT_PATH = CURRENT_FILE_PATH.parents[1] LOGO_PATH = ROOT_PATH / "docs" / "assets" / "logo_favicon.png" AUDIO_SAMPLE_URL = "https://freetestdata.com/wp-content/uploads/2021/09/Free_Test_Data_100KB_MP3.mp3" def _get_audio_chunk() -> AudioChunk: sample_rate = 44100 # Sample rate in Hz duration = 3 # Duration in seconds frequency = 440 # Frequency of the sine wave in Hz # Time array t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) audio_data = 0.5 * np.sin(2 * np.pi * frequency * t) # Write to in-memory buffer buffer = io.BytesIO() sf.write(buffer, audio_data, sample_rate, format="WAV") buffer.seek(0) data, sr = sf.read(buffer) audio = Audio(audio_array=data, sampling_rate=sr, format="wav") return AudioChunk.from_audio(audio) DUMMY_AUDIO_CHUNK = _get_audio_chunk() assert isinstance(DUMMY_AUDIO_CHUNK.input_audio, str) DUMMY_AUDIO_URL_CHUNK_BASE64 = AudioURLChunk(audio_url=AudioURL(url=DUMMY_AUDIO_CHUNK.input_audio)) DUMMY_AUDIO_URL_CHUNK_BASE64_STR = AudioURLChunk(audio_url=DUMMY_AUDIO_CHUNK.input_audio) DUMMY_AUDIO_URL_CHUNK_BASE64_PREFIX = AudioURLChunk( audio_url=AudioURL(url=f"data:audio/wav;base64,{DUMMY_AUDIO_CHUNK.input_audio}") ) DUMMY_AUDIO_URL_CHUNK_URL = AudioURLChunk(audio_url=AudioURL(url=AUDIO_SAMPLE_URL)) def test_convert_image_chunk() -> None: image = Image.open(LOGO_PATH.as_posix()) chunk = ImageChunk(image=image) openai_image = chunk.to_openai() assert openai_image["type"] == "image_url" assert isinstance(openai_image["image_url"], dict) assert openai_image["image_url"]["url"].startswith("data:image/png;base64,") assert isinstance(ImageChunk.from_openai(openai_image), ImageChunk) typeddict_openai = OpenAIImageChunk(**openai_image) # type: ignore[typeddict-item] assert isinstance(ImageChunk.from_openai(typeddict_openai), ImageChunk) def test_convert_image_chunk_from_openai_does_not_mutate_input() -> None: image = Image.open(LOGO_PATH.as_posix()) original_chunk = ImageChunk(image=image) openai_chunk = original_chunk.to_openai() original_url = openai_chunk["image_url"]["url"] ImageChunk.from_openai(openai_chunk) assert openai_chunk["image_url"]["url"] == original_url def test_convert_text_chunk() -> None: chunk = TextChunk(text="Hello") text_openai = chunk.to_openai() assert text_openai == {"type": "text", "text": "Hello"} assert TextChunk.from_openai(text_openai) == chunk typeddict_openai = OpenAITextChunk(**chunk.to_openai()) # type: ignore[typeddict-item] assert TextChunk.from_openai(typeddict_openai) == chunk def test_convert_input_audio_chunk() -> None: chunk = DUMMY_AUDIO_CHUNK openai_dict = chunk.to_openai() # Verify OpenAI-compliant shape assert openai_dict["type"] == "input_audio" assert isinstance(openai_dict["input_audio"], dict) assert "data" in openai_dict["input_audio"] assert "format" in openai_dict["input_audio"] assert openai_dict["input_audio"]["format"] in ("wav", "mp3", "flac", "ogg") # Roundtrip assert AudioChunk.from_openai(openai_dict) == chunk typeddict_openai = OpenAIInputAudioChunk(**openai_dict) # type: ignore[typeddict-item] assert AudioChunk.from_openai(typeddict_openai) == chunk @pytest.mark.parametrize( ["openai_image_url_chunk", "image_url_chunk"], [ ( OpenAIImageChunk( type="image_url", image_url={ "url": "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg", "detail": "auto", }, ), ImageURLChunk( image_url=ImageURL( url="https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg", detail="auto", ) ), ), ( OpenAIImageChunk( type="image_url", image_url={ "url": "data:image/png;base64,iVBORw0", }, ), ImageURLChunk( image_url="data:image/png;base64,iVBORw0", ), ), ], ) def test_convert_image_url_chunk(openai_image_url_chunk: dict, image_url_chunk: ImageURLChunk) -> None: assert image_url_chunk.to_openai() == openai_image_url_chunk if not isinstance(image_url_chunk.image_url, ImageURL): image_url_from_openai = ImageURLChunk.from_openai(openai_image_url_chunk) assert isinstance(image_url_from_openai.image_url, ImageURL) assert image_url_from_openai.image_url.url == image_url_chunk.image_url assert image_url_from_openai.type == image_url_chunk.type else: assert ImageURLChunk.from_openai(openai_image_url_chunk) == image_url_chunk typeddict_openai = OpenAIImageChunk(**openai_image_url_chunk) # type: ignore[typeddict-item] if not isinstance(image_url_chunk.image_url, ImageURL): image_url_chunk.image_url = ImageURL(url=image_url_chunk.image_url, detail=None) assert ImageURLChunk.from_openai(typeddict_openai) == image_url_chunk @pytest.mark.parametrize( ["vllm_audio_url_chunk", "audio_url_chunk"], [ ( { "type": "audio_url", "audio_url": {"url": AUDIO_SAMPLE_URL}, }, DUMMY_AUDIO_URL_CHUNK_URL, ), ( { "type": "audio_url", "audio_url": {"url": DUMMY_AUDIO_CHUNK.input_audio}, }, DUMMY_AUDIO_URL_CHUNK_BASE64, ), ( { "type": "audio_url", "audio_url": {"url": DUMMY_AUDIO_CHUNK.input_audio}, }, DUMMY_AUDIO_URL_CHUNK_BASE64_STR, ), ( { "type": "audio_url", "audio_url": {"url": f"data:audio/wav;base64,{DUMMY_AUDIO_CHUNK.input_audio}"}, }, DUMMY_AUDIO_URL_CHUNK_BASE64_PREFIX, ), ], ) def test_convert_audio_url_chunk(vllm_audio_url_chunk: dict, audio_url_chunk: AudioURLChunk) -> None: assert audio_url_chunk.to_openai() == vllm_audio_url_chunk if not isinstance(audio_url_chunk.audio_url, AudioURL): audio_url_from_openai = AudioURLChunk.from_openai(vllm_audio_url_chunk) assert isinstance(audio_url_from_openai.audio_url, AudioURL) assert audio_url_from_openai.audio_url.url == audio_url_chunk.audio_url assert audio_url_from_openai.type == audio_url_chunk.type else: assert AudioURLChunk.from_openai(vllm_audio_url_chunk) == audio_url_chunk def test_convert_function_from_openai_missing_parameters_and_description_and_unk_args() -> None: openai_function: dict[str, Any] = {"name": "do_nothing", "unk_field": "1"} assert Function.from_openai(openai_function) == Function(name="do_nothing", description="", parameters={}) def test_convert_tool() -> None: tool = Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, strict=True, ) ) tool_openai = tool.to_openai() assert tool_openai == ( { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, "strict": True, }, } ) assert Tool.from_openai(tool.to_openai()) == tool typeddict_openai = OpenAITool(**tool.to_openai()) # type: ignore[typeddict-item] assert Tool.from_openai(typeddict_openai) == tool def test_convert_tool_from_openai_missing_parameters_description_and_unknown_field() -> None: openai_tool: dict[str, Any] = { "type": "function", "function": { "name": "do_nothing", "unknown_field": "should be ignored", }, } original_openai_tool = copy.deepcopy(openai_tool) tool = Tool.from_openai(openai_tool) assert tool == Tool(function=Function(name="do_nothing", description="", parameters={})) assert openai_tool == original_openai_tool def test_convert_tool_call() -> None: tool_call = ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) tool_call_openai = tool_call.to_openai() assert tool_call_openai == ( { "id": "VvvODy9mT", "type": "function", "function": { "name": "get_current_weather", "arguments": '{"location": "Paris, France", "format": "celsius"}', }, } ) assert ToolCall.from_openai(tool_call.to_openai()) == tool_call typeddict_openai = OpenAIToolCall(**tool_call.to_openai()) # type: ignore[typeddict-item] assert ToolCall.from_openai(typeddict_openai) == tool_call def test_tool_call_from_openai_ignores_index() -> None: openai_tool_call = { "id": "call_123", "index": 0, "type": "function", "function": {"name": "foo", "arguments": "{}"}, } assert ToolCall.from_openai(openai_tool_call) == ToolCall( id="call_123", function=FunctionCall(name="foo", arguments="{}") ) def test_convert_think_chunk() -> None: chunk = ThinkChunk(thinking="Hello", closed=False) text_openai = chunk.to_openai() assert ThinkChunk.from_openai(text_openai) == chunk assert text_openai == {"type": "thinking", "thinking": "Hello", "closed": False} @pytest.mark.parametrize( ["openai_message", "message"], [ ({"role": "user", "content": "Hello"}, UserMessage(content="Hello")), ( {"role": "user", "content": [{"type": "text", "text": "Hello"}]}, UserMessage(content=[TextChunk(text="Hello")]), ), ( OpenAIUserMessage( role="user", content=[ {"type": "text", "text": "Describe this image"}, { "type": "image_url", "image_url": { "url": "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg", "detail": "auto", }, }, ], ), UserMessage( content=[ TextChunk(text="Describe this image"), ImageURLChunk( image_url=ImageURL( url="https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg", detail="auto", ) ), ] ), ), (OpenAIAssistantMessage(role="assistant", content="Hi"), AssistantMessage(content="Hi")), ( { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Hello", "closed": True}, {"type": "thinking", "thinking": "Hello", "closed": False}, {"type": "text", "text": "Hi"}, ], }, AssistantMessage( content=[ ThinkChunk(thinking="Hello", closed=True), ThinkChunk(thinking="Hello", closed=False), TextChunk(text="Hi"), ] ), ), ( OpenAIAssistantMessage( role="assistant", content="Hi", tool_calls=[ { "id": "VvvODy9mT", "type": "function", "function": { "name": "get_current_weather", "arguments": '{"location": "Paris, France", "format": "celsius"}', }, } ], ), AssistantMessage( content="Hi", tool_calls=[ ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) ], ), ), ( OpenAIToolMessage(role="tool", content="22", tool_call_id="VvvODy9mT"), ToolMessage(tool_call_id="VvvODy9mT", content="22"), ), ( OpenAIToolMessage( role="tool", content=[{"type": "text", "text": "22"}, {"type": "text", "text": "23"}], tool_call_id="VvvODy9mT", ), ToolMessage(tool_call_id="VvvODy9mT", content=[TextChunk(text="22"), TextChunk(text="23")]), ), ( OpenAISystemMessage(role="system", content="You are a helpful assistant."), SystemMessage(content="You are a helpful assistant."), ), ( { "role": "system", "content": [ {"type": "text", "text": "You are a helpful assistant."}, {"type": "thinking", "thinking": "Hello", "closed": False}, ], }, SystemMessage( content=[TextChunk(text="You are a helpful assistant."), ThinkChunk(thinking="Hello", closed=False)] ), ), ], ) def test_convert_openai_message_to_message_and_back(openai_message: dict, message: ChatMessage) -> None: assert type(message).from_openai(openai_message) == message assert message.to_openai() == openai_message @pytest.mark.parametrize( ["openai_message", "expected"], [ ( {"role": "assistant", "content": "Hi", "reasoning": "Let me think..."}, AssistantMessage(content=[ThinkChunk(thinking="Let me think...", closed=True), TextChunk(text="Hi")]), ), ( { "role": "assistant", "content": None, "reasoning": "Thinking aloud", "reasoning_content": "Thinking aloud", }, AssistantMessage(content=[ThinkChunk(thinking="Thinking aloud", closed=True)]), ), ( {"role": "assistant", "reasoning": "Thinking aloud"}, AssistantMessage(content=[ThinkChunk(thinking="Thinking aloud", closed=True)]), ), ( { "role": "assistant", "content": [{"type": "text", "text": "Hello"}], "reasoning": "Deep thought", }, AssistantMessage(content=[ThinkChunk(thinking="Deep thought", closed=True), TextChunk(text="Hello")]), ), ( {"role": "assistant", "content": "Hi", "reasoning_content": "Only reasoning"}, AssistantMessage(content=[ThinkChunk(thinking="Only reasoning", closed=True), TextChunk(text="Hi")]), ), ], ) def test_from_openai_reasoning_in_assistant_message(openai_message: dict[str, Any], expected: AssistantMessage) -> None: assert AssistantMessage.from_openai(openai_message) == expected def test_from_openai_reasoning_differ_reasoning_content_in_assistant_message() -> None: openai_message = {"role": "assistant", "content": "Hi", "reasoning": "Primary", "reasoning_content": "Fallback"} with pytest.raises(ValueError, match=r"`reasoning_content` and `reasoning` should be equal"): AssistantMessage.from_openai(openai_message) @pytest.mark.parametrize( "openai_message", [ { "role": "assistant", "content": [{"type": "thinking", "thinking": "hmm", "closed": True}, {"type": "text", "text": "Hi"}], "reasoning": "also thinking", }, { "role": "assistant", "content": [{"type": "thinking", "thinking": "hmm", "closed": True}], "reasoning_content": "also thinking", }, { "role": "assistant", "content": [{"type": "thinking", "thinking": "hmm", "closed": True}], "reasoning": "also thinking", "reasoning_content": "also thinking", }, ], ) def test_from_openai_thinking_chunks_and_reasoning_raises(openai_message: dict[str, Any]) -> None: with pytest.raises(InvalidAssistantMessageException): AssistantMessage.from_openai(openai_message) def test_non_leading_think_chunks_construction_ok() -> None: """Non-leading ThinkChunks are allowed at construction time.""" msg = AssistantMessage( content=[ ThinkChunk(thinking="First", closed=True), TextChunk(text="Reply"), ThinkChunk(thinking="Third", closed=False), ] ) assert msg.content is not None @pytest.mark.parametrize( "content", [ [ThinkChunk(thinking="First", closed=True), TextChunk(text="Reply"), ThinkChunk(thinking="Third")], [TextChunk(text="Reply"), ThinkChunk(thinking="After", closed=True)], [TextChunk(text="A"), TextChunk(text="B"), ThinkChunk(thinking="End", closed=True)], ], ) def test_non_leading_think_chunks_to_openai_raises(content: list[ContentChunk]) -> None: """to_openai raises when ThinkChunks are not leading.""" msg = AssistantMessage(content=content) with pytest.raises(InvalidAssistantMessageException, match="ThinkChunks must be leading"): msg.to_openai() @pytest.mark.parametrize( ["message", "convert_thinking_format", "expected"], [ # thinking: chunks stay inline ( AssistantMessage(content=[ThinkChunk(thinking="Deep thought", closed=True), TextChunk(text="Answer")]), ReasoningFieldFormat.thinking_chunks, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Deep thought", "closed": True}, {"type": "text", "text": "Answer"}, ], }, ), # thinking: multiple leading ThinkChunks stay as-is (no aggregation) ( AssistantMessage( content=[ ThinkChunk(thinking="First", closed=True), ThinkChunk(thinking="Second", closed=False), TextChunk(text="Reply"), ] ), ReasoningFieldFormat.thinking_chunks, { "role": "assistant", "content": [ {"type": "thinking", "thinking": "First", "closed": True}, {"type": "thinking", "thinking": "Second", "closed": False}, {"type": "text", "text": "Reply"}, ], }, ), # reasoning: single leading ThinkChunk extracted as flat string ( AssistantMessage(content=[ThinkChunk(thinking="Let me think", closed=True), TextChunk(text="Done")]), ReasoningFieldFormat.reasoning, {"role": "assistant", "reasoning": "Let me think", "content": "Done"}, ), # reasoning_content: single leading ThinkChunk extracted as flat string ( AssistantMessage(content=[ThinkChunk(thinking="Pondering", closed=True), TextChunk(text="Result")]), ReasoningFieldFormat.reasoning_content, {"role": "assistant", "reasoning_content": "Pondering", "content": "Result"}, ), # reasoning: multiple leading ThinkChunks concatenated with newline ( AssistantMessage( content=[ ThinkChunk(thinking="Part 1", closed=True), ThinkChunk(thinking="Part 2", closed=True), TextChunk(text="Final"), ] ), ReasoningFieldFormat.reasoning, {"role": "assistant", "reasoning": "Part 1\nPart 2", "content": "Final"}, ), # thinking: ThinkChunk only, no remaining content ( AssistantMessage(content=[ThinkChunk(thinking="Just thinking", closed=True)]), ReasoningFieldFormat.thinking_chunks, { "role": "assistant", "content": [{"type": "thinking", "thinking": "Just thinking", "closed": True}], }, ), # reasoning: ThinkChunk only, no remaining content ( AssistantMessage(content=[ThinkChunk(thinking="Only reasoning", closed=True)]), ReasoningFieldFormat.reasoning, {"role": "assistant", "reasoning": "Only reasoning"}, ), # reasoning: leading ThinkChunk with remaining list content (multiple chunks) ( AssistantMessage( content=[ ThinkChunk(thinking="Think", closed=True), TextChunk(text="A"), TextChunk(text="B"), ] ), ReasoningFieldFormat.reasoning, { "role": "assistant", "reasoning": "Think", "content": [{"type": "text", "text": "A"}, {"type": "text", "text": "B"}], }, ), # String content unchanged regardless of convert_thinking_format ( AssistantMessage(content="Simple text"), ReasoningFieldFormat.reasoning, {"role": "assistant", "content": "Simple text"}, ), # None content unchanged ( AssistantMessage(content=None), ReasoningFieldFormat.thinking_chunks, {"role": "assistant"}, ), ], ) def test_assistant_message_to_openai_convert_thinking_format( message: AssistantMessage, convert_thinking_format: ReasoningFieldFormat, expected: dict[str, Any], ) -> None: assert message.to_openai(reasoning_field_format=convert_thinking_format) == expected def test_assistant_message_to_openai_none_warns_with_think_chunks() -> None: message = AssistantMessage(content=[ThinkChunk(thinking="Hmm", closed=True), TextChunk(text="Answer")]) with pytest.warns(FutureWarning, match=r"convert_thinking_format.*defaults to 'thinking_chunks'"): result = message.to_openai() assert result == { "role": "assistant", "content": [ {"type": "thinking", "thinking": "Hmm", "closed": True}, {"type": "text", "text": "Answer"}, ], } def test_assistant_message_to_openai_none_no_warning_without_think_chunks() -> None: message = AssistantMessage(content="Plain text") with warnings.catch_warnings(): warnings.simplefilter("error") result = message.to_openai() assert result == {"role": "assistant", "content": "Plain text"} def test_assistant_message_to_openai_none_no_warning_with_none_content() -> None: message = AssistantMessage(content=None) with warnings.catch_warnings(): warnings.simplefilter("error") result = message.to_openai() assert result == {"role": "assistant"} def test_request_to_openai_forwards_reasoning_field_format() -> None: messages: list[ChatMessage] = [ UserMessage(content="Hi"), AssistantMessage(content=[ThinkChunk(thinking="Let me think", closed=True), TextChunk(text="Done")]), ] request = ChatCompletionRequest(messages=messages) openai_request = request.to_openai(reasoning_field_format=ReasoningFieldFormat.reasoning) assistant_msg = [m for m in openai_request["messages"] if m["role"] == "assistant"][0] assert assistant_msg == {"role": "assistant", "reasoning": "Let me think", "content": "Done"} @pytest.mark.parametrize( "reasoning_effort", [None, ReasoningEffort.none, ReasoningEffort.high], ) @pytest.mark.parametrize( ["openai_messages", "messages", "openai_tools", "tools"], [ ( [ OpenAISystemMessage({"role": "system", "content": "You are a helpful assistant."}), OpenAIUserMessage({"role": "user", "content": "What's the weather like in Paris?"}), OpenAIAssistantMessage( { "role": "assistant", "content": "Let me think...", "tool_calls": [ { "id": "VvvODy9mT", "type": "function", "function": { "name": "get_current_weather", "arguments": '{"location": "Paris, France", "format": "celsius"}', }, } ], } ), OpenAIToolMessage({"role": "tool", "content": "22", "tool_call_id": "VvvODy9mT"}), ], [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What's the weather like in Paris?"), AssistantMessage( content="Let me think...", tool_calls=[ ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) ], ), ToolMessage(tool_call_id="VvvODy9mT", name="get_current_weather", content="22"), ], [ OpenAITool( { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", # noqa: E501 }, }, "required": ["location", "format"], }, "strict": False, }, } ) ], [ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) ], ), ( [ OpenAISystemMessage({"role": "system", "content": "You are a helpful assistant."}), OpenAIUserMessage({"role": "user", "content": "What's the weather like in Paris?"}), OpenAIAssistantMessage( { "role": "assistant", "content": "How should I know?", } ), ], [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What's the weather like in Paris?"), AssistantMessage( content="How should I know?", ), ], None, None, ), ( [ OpenAIUserMessage({"role": "user", "content": "What's the weather like in Paris?"}), OpenAIAssistantMessage( { "role": "assistant", "tool_calls": [ { "id": "VvvODy9mT", "type": "function", "function": { "name": "get_current_weather", "arguments": '{"location": "Paris, France", "format": "celsius"}', }, } ], } ), OpenAIToolMessage({"role": "tool", "content": "22", "tool_call_id": "VvvODy9mT"}), ], [ UserMessage(content="What's the weather like in Paris?"), AssistantMessage( tool_calls=[ ToolCall( id="VvvODy9mT", function=FunctionCall( name="get_current_weather", arguments='{"location": "Paris, France", "format": "celsius"}', ), ) ] ), ToolMessage(tool_call_id="VvvODy9mT", name="get_current_weather", content="22"), ], [ OpenAITool( { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", # noqa: E501 }, }, "required": ["location", "format"], }, "strict": False, }, } ) ], [ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the user's location.", }, }, "required": ["location", "format"], }, ) ) ], ), ( [ OpenAIUserMessage({"role": "user", "content": "Listen to this"}), OpenAIAssistantMessage( { "role": "assistant", "content": "Pass the URL please.", } ), { "role": "user", "content": [ {"type": "text", "text": "Here it is !"}, { "type": "audio_url", "audio_url": { "url": AUDIO_SAMPLE_URL, }, }, {"type": "text", "text": "What do you think also of these ones?"}, { "type": "audio_url", "audio_url": { "url": DUMMY_AUDIO_URL_CHUNK_URL.audio_url.url, }, }, { "type": "audio_url", "audio_url": { "url": DUMMY_AUDIO_URL_CHUNK_BASE64.audio_url.url, }, }, { "type": "audio_url", "audio_url": { "url": DUMMY_AUDIO_URL_CHUNK_BASE64_PREFIX.audio_url.url, }, }, ], }, ], [ UserMessage(content="Listen to this"), AssistantMessage(content="Pass the URL please."), UserMessage( content=[ TextChunk(text="Here it is !"), AudioURLChunk(audio_url=AudioURL(url=AUDIO_SAMPLE_URL)), TextChunk(text="What do you think also of these ones?"), DUMMY_AUDIO_URL_CHUNK_URL, DUMMY_AUDIO_URL_CHUNK_BASE64, DUMMY_AUDIO_URL_CHUNK_BASE64_PREFIX, ] ), ], None, None, ), ], ) def test_convert_requests( openai_messages: list[dict[str, Any]], messages: list[ChatMessage], openai_tools: list[dict[str, Any]] | None, tools: list[Tool] | None, reasoning_effort: ReasoningEffort | None, ) -> None: request = ChatCompletionRequest( messages=messages, tools=tools, reasoning_effort=reasoning_effort, ) openai_request = request.to_openai(stream=True) assert openai_request["messages"] == openai_messages if tools is not None: assert openai_request["tools"] == openai_tools else: assert "tools" not in openai_request if reasoning_effort is not None: assert openai_request["reasoning_effort"] == reasoning_effort.value else: assert "reasoning_effort" not in openai_request assert openai_request["temperature"] == 0.7 stream = openai_request.pop("stream") assert stream is True reconstructed_request = ChatCompletionRequest.from_openai(**openai_request) for i, reconstructed_message in enumerate(reconstructed_request.messages): if isinstance(reconstructed_message, (SystemMessage, UserMessage, AssistantMessage)): assert reconstructed_message == messages[i] elif isinstance(reconstructed_message, ToolMessage): assert reconstructed_message.model_dump(exclude={"name"}) == messages[i].model_dump(exclude={"name"}) if tools is not None: reconstructed_tools = reconstructed_request.tools assert isinstance(tools, list) assert isinstance(reconstructed_tools, list) # Not using zip below because of mypy not recognizing reconstructed_tools as a list of Tools. assert len(tools) == len(reconstructed_tools) for i in range(len(tools)): assert reconstructed_tools[i] == tools[i] assert reconstructed_request.reasoning_effort == reasoning_effort @pytest.mark.parametrize( ["audio", "language", "stream"], [ (DUMMY_AUDIO_CHUNK, None, False), (DUMMY_AUDIO_CHUNK, "en", False), (DUMMY_AUDIO_CHUNK, "en", True), ], ) def test_convert_transcription(audio: AudioChunk, language: LanguageAlpha2 | None, stream: bool) -> None: def check_equality(a: TranscriptionRequest, b: TranscriptionRequest) -> bool: if a.audio != b.audio: return False if a.id != b.id: return False if a.model != b.model: return False if a.language != b.language: return False if a.strict_audio_validation != b.strict_audio_validation: return False if a.temperature != b.temperature: return False if a.top_p != b.top_p: return False if a.max_tokens != b.max_tokens: return False if a.random_seed != b.random_seed: return False return True seed: int = 43 request = TranscriptionRequest( audio=audio.input_audio, language=language, model="model", random_seed=seed, target_streaming_delay_ms=None ) openai_request = request.to_openai(stream=stream) assert check_equality(request, TranscriptionRequest.from_openai(openai_request)) openai_transcription = OpenAITranscriptionRequest(**openai_request) # type: ignore from_oai = TranscriptionRequest.from_openai(openai_transcription) assert isinstance(from_oai, TranscriptionRequest) assert check_equality(request, from_oai) def _audio_to_wav_bytes(audio: Audio) -> bytes: buffer = io.BytesIO() sf.write(buffer, audio.audio_array, audio.sampling_rate, format="wav") return buffer.getvalue() def test_convert_transcription_str_buffer_name() -> None: """Verify that the BytesIO buffer has a .name when audio is a base64 string.""" audio = _make_fake_audio(0.5) b64 = audio.to_base64("wav") request = TranscriptionRequest(audio=b64, model="model", language=None, target_streaming_delay_ms=None) openai_request = request.to_openai() buffer = openai_request["file"] assert isinstance(buffer, io.BytesIO) assert hasattr(buffer, "name") assert buffer.name == "audio.wav" def test_convert_transcription_bytes_buffer_name() -> None: """Verify that the BytesIO buffer has a .name when audio is raw bytes.""" audio = _make_fake_audio(0.5) raw_bytes = _audio_to_wav_bytes(audio) request = TranscriptionRequest(audio=raw_bytes, model="model", language=None, target_streaming_delay_ms=None) openai_request = request.to_openai() buffer = openai_request["file"] assert isinstance(buffer, io.BytesIO) assert hasattr(buffer, "name") assert buffer.name == "audio.wav" def test_convert_transcription_bytes_invalid_format() -> None: """Verify that invalid audio bytes raise a ValueError.""" request = TranscriptionRequest( audio=b"not valid audio data", model="model", language=None, target_streaming_delay_ms=None ) with pytest.raises(ValueError, match="Failed to detect audio format"): request.to_openai() @pytest.mark.parametrize("fmt", ["wav", "flac"]) def test_audio_chunk_to_openai_format_detection(fmt: str) -> None: audio = _make_fake_audio(0.5) b64 = audio.to_base64(fmt) chunk = AudioChunk(input_audio=b64) result = chunk.to_openai() assert result["input_audio"]["format"] == fmt assert result["input_audio"]["data"] == b64 assert AudioChunk.from_openai(result).input_audio == b64 @pytest.mark.parametrize("fmt", ["wav", "flac"]) def test_audio_chunk_to_openai_raw_bytes_format_detection(fmt: str) -> None: audio = _make_fake_audio(0.5) buffer = io.BytesIO() sf.write(buffer, audio.audio_array, audio.sampling_rate, format=fmt) raw_bytes = buffer.getvalue() result = AudioChunk(input_audio=raw_bytes).to_openai() assert result["input_audio"]["format"] == fmt assert result["input_audio"]["data"] == base64.b64encode(raw_bytes).decode("utf-8") assert AudioChunk.from_openai(result).input_audio == result["input_audio"]["data"] @pytest.mark.parametrize("fmt", ["wav", "flac"]) def test_audio_chunk_to_openai_strips_base64_data_url_prefix(fmt: str) -> None: audio = _make_fake_audio(0.5) b64 = audio.to_base64(fmt) chunk = AudioChunk(input_audio=f"data:audio/{fmt};base64,{b64}") result = chunk.to_openai() assert result["input_audio"]["format"] == fmt assert result["input_audio"]["data"] == b64 assert AudioChunk.from_openai(result).input_audio == b64 @pytest.mark.parametrize("fmt", ["wav", "flac"]) def test_transcription_to_openai_format_detection(fmt: str) -> None: audio = _make_fake_audio(0.5) b64 = audio.to_base64(fmt) request = TranscriptionRequest(audio=b64, model="model", language=None, target_streaming_delay_ms=None) openai_request = request.to_openai() buffer = openai_request["file"] assert isinstance(buffer, io.BytesIO) assert buffer.name == f"audio.{fmt}" recovered = Audio.from_bytes(buffer.getvalue()) assert np.allclose(recovered.audio_array, audio.audio_array, atol=1e-3) @pytest.mark.parametrize("fmt", ["wav", "flac"]) def test_transcription_to_openai_bytes_format_detection(fmt: str) -> None: audio = _make_fake_audio(0.5) buf = io.BytesIO() sf.write(buf, audio.audio_array, audio.sampling_rate, format=fmt) raw_bytes = buf.getvalue() request = TranscriptionRequest(audio=raw_bytes, model="model", language=None, target_streaming_delay_ms=None) openai_request = request.to_openai() buffer = openai_request["file"] assert isinstance(buffer, io.BytesIO) assert buffer.name == f"audio.{fmt}" def test_convert_speech_request_from_openai() -> None: audio = _make_fake_audio(0.5) raw_bytes = _audio_to_wav_bytes(audio) openai_dict: dict[str, Any] = { "input": "Hello world", "model": "tts-1", "voice": "female", "ref_audio": io.BytesIO(raw_bytes), "instructions": "Speak slowly", # OAI-only field, should be ignored } request = SpeechRequest.from_openai(openai_dict) assert request.input == "Hello world" assert request.model == "tts-1" assert request.voice == "female" assert isinstance(request.ref_audio, str) decoded_audio = Audio.from_base64(request.ref_audio) assert np.allclose(decoded_audio.audio_array, audio.audio_array, atol=1e-3) # Voice as dict with "id" (OAI format) should be normalized to string openai_dict_voice_obj: dict[str, Any] = { "input": "Hello", "voice": {"id": "custom-voice-123"}, } request_voice = SpeechRequest.from_openai(openai_dict_voice_obj) assert request_voice.voice == "custom-voice-123" @pytest.mark.parametrize( ["voice", "with_ref_audio", "random_seed"], [ ("female", True, None), ("female", False, 1234), (None, True, 0), ], ) def test_convert_speech_request_round_trip(voice: str | None, with_ref_audio: bool, random_seed: int | None) -> None: audio = _make_fake_audio(0.5) original = SpeechRequest( input="Round trip test", ref_audio=audio.to_base64("wav") if with_ref_audio else None, voice=voice, model="tts-1", random_seed=random_seed, ) openai_dict = original.to_openai() # OpenAI uses "seed"; mistral-common uses "random_seed" (parity with TranscriptionRequest). assert "random_seed" not in openai_dict assert openai_dict["seed"] == random_seed if with_ref_audio: assert isinstance(openai_dict["ref_audio"], io.BytesIO) else: assert "ref_audio" not in openai_dict restored = SpeechRequest.from_openai(openai_dict) # ref_audio is re-encoded through the OpenAI buffer, so it is not byte-identical; compare it separately. assert original.model_dump(exclude={"ref_audio"}) == restored.model_dump(exclude={"ref_audio"}) if with_ref_audio: assert isinstance(original.ref_audio, str) assert isinstance(restored.ref_audio, str) original_audio = Audio.from_base64(original.ref_audio) restored_audio = Audio.from_base64(restored.ref_audio) assert np.allclose(restored_audio.audio_array, original_audio.audio_array, atol=1e-3) else: assert restored.ref_audio is None class TestToolChoice: @pytest.mark.parametrize( ["tool_choice", "expected_openai", "expected_reconstructed"], [ (ToolChoiceEnum.auto, "auto", ToolChoiceEnum.auto.value), (ToolChoiceEnum.none, "none", ToolChoiceEnum.none.value), (ToolChoiceEnum.required, "required", ToolChoiceEnum.required.value), (ToolChoiceEnum.any, "required", ToolChoiceEnum.required.value), ( NamedToolChoice(function=FunctionName(name="get_weather")), {"type": "function", "function": {"name": "get_weather"}}, NamedToolChoice(function=FunctionName(name="get_weather")), ), ], ) def test_tool_choice_round_trip( self, tool_choice: ToolChoiceEnum | NamedToolChoice, expected_openai: str | dict[str, Any], expected_reconstructed: str | NamedToolChoice, ) -> None: request = ChatCompletionRequest(messages=[UserMessage(content="Hello")], tool_choice=tool_choice) openai_request = request.to_openai() assert openai_request["tool_choice"] == expected_openai reconstructed = ChatCompletionRequest.from_openai(**openai_request) assert reconstructed.tool_choice == expected_reconstructed @pytest.mark.parametrize( ["from_openai_call", "expected"], [ # Messages with extra fields ( lambda: UserMessage.from_openai({"role": "user", "content": "Hello", "name": "user1"}), UserMessage(content="Hello"), ), ( lambda: UserMessage.from_openai( {"role": "user", "content": [{"type": "text", "text": "Hello"}], "name": "user1"} ), UserMessage(content=[TextChunk(text="Hello")]), ), ( lambda: SystemMessage.from_openai({"role": "system", "content": "Be helpful", "name": "sys"}), SystemMessage(content="Be helpful"), ), ( lambda: ToolMessage.from_openai( {"role": "tool", "content": "42", "tool_call_id": "c1", "extra": "ignored"} ), ToolMessage(content="42", tool_call_id="c1"), ), ( lambda: AssistantMessage.from_openai( {"role": "assistant", "content": "Hi", "refusal": None, "audio": None} ), AssistantMessage(content="Hi"), ), # ToolCall with index ( lambda: ToolCall.from_openai( {"id": "c1", "index": 0, "type": "function", "function": {"name": "f", "arguments": "{}"}} ), ToolCall(id="c1", function=FunctionCall(name="f", arguments="{}")), ), # Tool with extra field ( lambda: Tool.from_openai( { "type": "function", "function": { "name": "get_weather", "description": "", "parameters": {"type": "object"}, "strict": False, }, "extra_openai_field": True, } ), Tool(function=Function(name="get_weather", description="", parameters={"type": "object"})), ), # Chunks with extra fields ( lambda: TextChunk.from_openai({"type": "text", "text": "Hello", "annotations": []}), TextChunk(text="Hello"), ), ( lambda: ThinkChunk.from_openai({"type": "thinking", "thinking": "hmm", "closed": True, "extra": 1}), ThinkChunk(thinking="hmm", closed=True), ), ( lambda: AudioURLChunk.from_openai( {"type": "audio_url", "audio_url": {"url": AUDIO_SAMPLE_URL}, "extra": True} ), AudioURLChunk(audio_url=AudioURL(url=AUDIO_SAMPLE_URL)), ), ( lambda: AudioChunk.from_openai({**DUMMY_AUDIO_CHUNK.to_openai(), "extra": True}), DUMMY_AUDIO_CHUNK, ), # Requests with unsupported OpenAI / unknown fields ( lambda: ChatCompletionRequest.from_openai( messages=[{"role": "user", "content": "Hello"}], temperature=0.5, stream=False, n=2, logprobs=True, frequency_penalty=0.1, unknown_field="value", ), ChatCompletionRequest(messages=[UserMessage(content="Hello")], temperature=0.5), ), ], ) def test_from_openai_drops_extra_fields(from_openai_call: Any, expected: Any) -> None: assert from_openai_call() == expected @pytest.mark.parametrize( "constructor", [ lambda: UserMessage(content="Hello", name="user1"), # type: ignore[call-arg] lambda: SystemMessage(content="Be helpful", name="sys"), # type: ignore[call-arg] lambda: TextChunk(text="Hello", extra="bad"), # type: ignore[call-arg] ], ) def test_direct_construction_still_strict(constructor: Any) -> None: with pytest.raises(Exception): constructor() python-mistral-common-1.11.7/tests/test_deprecation.py000066400000000000000000000054451523035502500231410ustar00rootroot00000000000000import os.path import warnings import pytest import mistral_common.deprecation from mistral_common.deprecation import deprecated_import, warn_once @pytest.fixture(autouse=True) def _clear_warned_keys() -> None: mistral_common.deprecation._warned_keys.clear() def test_deprecated_import_returns_correct_object() -> None: result = deprecated_import("old.module", "os.path", "join", "99.0") assert result is os.path.join def test_deprecated_import_emits_deprecation_warning() -> None: with pytest.warns(DeprecationWarning, match=r"Importing join from old\.mod") as record: deprecated_import("old.mod", "os.path", "join", "99.0") assert len(record) == 1 msg = str(record[0].message) assert "Use os.path.join instead" in msg assert "Will be removed in 99.0" in msg def test_deprecated_import_warns_only_once() -> None: with pytest.warns(DeprecationWarning): deprecated_import("old.mod", "os.path", "join", "99.0") with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") deprecated_import("old.mod", "os.path", "join", "99.0") dep_warnings = [w for w in caught if issubclass(w.category, DeprecationWarning)] assert dep_warnings == [] def test_deprecated_import_different_pairs_each_warn() -> None: with pytest.warns(DeprecationWarning, match="join"): deprecated_import("pkg.a", "os.path", "join", "1.0") with pytest.warns(DeprecationWarning, match="exists"): deprecated_import("pkg.b", "os.path", "exists", "1.0") def test_deprecated_import_raises_attribute_error() -> None: with pytest.raises(AttributeError): deprecated_import("old", "os.path", "no_such_attr_xyz", "1.0") def test_deprecated_import_raises_module_not_found_error() -> None: with pytest.raises(ModuleNotFoundError): deprecated_import("old", "no_such_module_xyz_abc", "Foo", "1.0") def test_warn_once_emits_warning() -> None: with pytest.warns(DeprecationWarning, match="something broke"): warn_once("k1", "something broke", DeprecationWarning, stacklevel=2) def test_warn_once_does_not_repeat() -> None: with pytest.warns(DeprecationWarning): warn_once("k1", "msg", DeprecationWarning, stacklevel=2) with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") warn_once("k1", "msg", DeprecationWarning, stacklevel=2) dep_warnings = [w for w in caught if issubclass(w.category, DeprecationWarning)] assert dep_warnings == [] def test_warn_once_different_keys_each_warn() -> None: with pytest.warns(DeprecationWarning, match="first"): warn_once("a", "first", DeprecationWarning, stacklevel=2) with pytest.warns(DeprecationWarning, match="second"): warn_once("b", "second", DeprecationWarning, stacklevel=2) python-mistral-common-1.11.7/tests/test_fim_tokenizer.py000066400000000000000000000007211523035502500235010ustar00rootroot00000000000000import pytest from mistral_common.tokens.tokenizers.base import FIMRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer @pytest.fixture() def tokenizer() -> MistralTokenizer: return MistralTokenizer.v3() def test_encode_fim(tokenizer: MistralTokenizer) -> None: tokenized = tokenizer.encode_fim(FIMRequest(prompt="def f(", suffix="return a + b")) assert tokenized.text == "[SUFFIX]return▁a▁+▁b[PREFIX]▁def▁f(" python-mistral-common-1.11.7/tests/test_generate_chat_template_cli.py000066400000000000000000000242061523035502500261530ustar00rootroot00000000000000import subprocess import sys from pathlib import Path import pytest from mistral_common.tokens.tokenizers.base import TokenizerVersion from tests.integrations.chat_templates.helpers import TestConfig, _build_tekken_json SCRIPT_PATH = Path(__file__).parent.parent / "scripts" / "generate_chat_template.py" @pytest.fixture(scope="session") def tekken_think_v13_path(tmp_path_factory: pytest.TempPathFactory) -> Path: """Path to a v13 think tekken.json used for auto-detect CLI tests.""" build_dir = tmp_path_factory.mktemp("tokenizer") config = TestConfig(version=TokenizerVersion.v13, think=True) return _build_tekken_json(config=config, output_dir=build_dir) def test_cli_generates_template(tmp_path: Path) -> None: """CLI generates a template file with expected content.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v15", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 assert output_path.exists() content = output_path.read_text() assert "bos_token" in content assert "[MODEL_SETTINGS]" in content def test_cli_with_image_flag(tmp_path: Path) -> None: """CLI --image flag produces template with image support.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v15", "--image", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "image" in content.lower() def test_cli_with_thinking_flag(tmp_path: Path) -> None: """CLI --thinking flag produces template with thinking support.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v15", "--thinking", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "[THINK]" in content def test_cli_with_default_system_prompt(tmp_path: Path) -> None: """CLI --default_system_prompt embeds the prompt.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--version", "v7", "--default_system_prompt", "You are helpful.", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "You are helpful." in content def test_cli_invalid_version(tmp_path: Path) -> None: """CLI rejects invalid version.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v99", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode != 0 def test_cli_invalid_config(tmp_path: Path) -> None: """CLI rejects invalid configuration (e.g., image + audio).""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v15", "--image", "--audio", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode != 0 def test_cli_missing_version(tmp_path: Path) -> None: """CLI requires --version in manual mode and emits the specific error message.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode != 0 assert "--version is required when --tokenizer_file is not provided" in result.stderr def test_cli_with_audio_flag(tmp_path: Path) -> None: """CLI --audio flag produces template with audio support.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v7", "--audio", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "[AUDIO]" in content def test_cli_with_plain_thinking_flag(tmp_path: Path) -> None: """CLI --plain_thinking flag produces template with plain text thinking.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v11", "--plain_thinking", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "" in content assert "[THINK]" not in content def test_cli_plain_thinking_invalid_version(tmp_path: Path) -> None: """CLI rejects --plain_thinking with non-v11 version.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v15", "--plain_thinking", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode != 0 def test_cli_spm_flag(tmp_path: Path) -> None: """CLI --spm flag produces SPM template.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v7", "--spm", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "bos_token" in content def test_cli_spm_with_audio_invalid(tmp_path: Path) -> None: """CLI --spm --audio together fails validation.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [sys.executable, str(SCRIPT_PATH), "--version", "v7", "--spm", "--audio", "--saving_path", str(output_path)], capture_output=True, text=True, ) assert result.returncode != 0 def test_cli_no_special_token_variables_flag(tmp_path: Path) -> None: """CLI --no_special_token_variables embeds literal BOS/EOS values.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--version", "v7", "--no_special_token_variables", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "''" in content assert "''" in content assert "bos_token" not in content assert "eos_token" not in content def test_autodetect_happy_path(tmp_path: Path, tekken_think_v13_path: Path) -> None: """Auto-detect mode with v13 think tokenizer generates template containing [THINK].""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--tokenizer_file", str(tekken_think_v13_path), "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode == 0 assert output_path.exists() content = output_path.read_text() assert "[THINK]" in content def test_autodetect_conflict_version(tmp_path: Path, tekken_think_v13_path: Path) -> None: """Auto-detect + --version exits non-zero with mutual-exclusion error.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--tokenizer_file", str(tekken_think_v13_path), "--version", "v13", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode != 0 assert "--tokenizer_file cannot be combined with manual capability flags" in result.stderr def test_autodetect_conflict_capability_flag(tmp_path: Path, tekken_think_v13_path: Path) -> None: """Auto-detect + --image exits non-zero with mutual-exclusion error.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--tokenizer_file", str(tekken_think_v13_path), "--image", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode != 0 assert "--tokenizer_file cannot be combined with manual capability flags" in result.stderr def test_autodetect_with_system_prompt(tmp_path: Path, tekken_think_v13_path: Path) -> None: """Auto-detect mode with --default_system_prompt embeds the prompt.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--tokenizer_file", str(tekken_think_v13_path), "--default_system_prompt", "You are helpful.", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "You are helpful." in content def test_autodetect_no_special_token_variables(tmp_path: Path, tekken_think_v13_path: Path) -> None: """Auto-detect + --no_special_token_variables embeds literal BOS/EOS values.""" output_path = tmp_path / "template.jinja" result = subprocess.run( [ sys.executable, str(SCRIPT_PATH), "--tokenizer_file", str(tekken_think_v13_path), "--no_special_token_variables", "--saving_path", str(output_path), ], capture_output=True, text=True, ) assert result.returncode == 0 content = output_path.read_text() assert "''" in content assert "bos_token" not in content python-mistral-common-1.11.7/tests/test_image.py000066400000000000000000000207521523035502500217240ustar00rootroot00000000000000import base64 from io import BytesIO from typing import Any from unittest.mock import Mock, patch import numpy as np import pytest import requests from PIL import Image from mistral_common.protocol.instruct.chunk import ( ImageChunk, ImageURLChunk, TextChunk, ) from mistral_common.tokens.tokenizers.image import ImageConfig, ImageEncoder, SpecialImageIDs def _create_test_image(size: tuple[int, int], color: tuple[int, int, int] = (128, 128, 128)) -> Image.Image: return Image.new("RGB", size, color) @pytest.fixture def special_token_ids() -> SpecialImageIDs: return SpecialImageIDs(img=0, img_break=1, img_end=2) @pytest.mark.parametrize("spatial_merge_size", [1, 2]) def test_image_to_num_tokens(spatial_merge_size: int, special_token_ids: SpecialImageIDs) -> None: image_config = ImageConfig( image_patch_size=16 // spatial_merge_size, max_image_size=128, spatial_merge_size=spatial_merge_size ) image_encoder = ImageEncoder(image_config, special_token_ids) for size, exp in [(4, 1), (16, 1), (128, 8), (512, 8), (2048, 8)]: img = Image.new("RGB", (size, size), "red") assert image_encoder._image_to_num_tokens(img) == (exp, exp) for size1, size2, exp1, exp2 in [(4, 2, 1, 1), (8, 16, 1, 1), (128, 64, 8, 4), (512, 1024, 4, 8)]: img = Image.new("RGB", (size1, size2), "red") assert image_encoder._image_to_num_tokens(img) == (exp1, exp2) @pytest.mark.parametrize("spatial_merge_size", [1, 2]) def test_download_image(spatial_merge_size: int, special_token_ids: SpecialImageIDs) -> None: image_config = ImageConfig( image_patch_size=16 // spatial_merge_size, max_image_size=128, spatial_merge_size=spatial_merge_size ) image_encoder = ImageEncoder(image_config, special_token_ids) test_image1 = _create_test_image((500, 300), color=(128, 128, 128)) test_image2 = _create_test_image((400, 600), color=(100, 150, 200)) def mock_get(url: str, headers: Any = None) -> Any: mock_response = Mock() if url == url1: img_byte_arr = BytesIO() test_image1.save(img_byte_arr, format="PNG") mock_response.content = img_byte_arr.getvalue() elif url == url2: img_byte_arr = BytesIO() test_image2.save(img_byte_arr, format="PNG") mock_response.content = img_byte_arr.getvalue() else: raise requests.exceptions.RequestException("Download failed") mock_response.raise_for_status = Mock() return mock_response url1 = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg" url2 = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg" with patch("mistral_common.image.requests.get", side_effect=mock_get): for url, expected_image in [(url1, test_image1), (url2, test_image2)]: content = ImageURLChunk(image_url=url) result = image_encoder(content) assert result.image is not None, "Image should be processed successfully" # Test request error. invalid_url = "https://invalid.url/image.jpg" response = requests.Response() response.status_code = 404 response._content = b"Not found" with patch("mistral_common.image.requests.get", return_value=response): with pytest.raises(RuntimeError, match="Error downloading the image"): content = ImageURLChunk(image_url=invalid_url) image_encoder(content) @pytest.mark.parametrize("spatial_merge_size", [1, 2]) def test_image_encoder(spatial_merge_size: int, special_token_ids: SpecialImageIDs) -> None: image_config = ImageConfig( image_patch_size=16 // spatial_merge_size, max_image_size=128, spatial_merge_size=spatial_merge_size ) image_encoder = ImageEncoder(image_config, special_token_ids) size = 386 img = Image.new("RGB", (size, size), "red") img_chunk = ImageChunk(image=img) text_chunk = TextChunk(text="") with pytest.raises(AttributeError): image_encoder(text_chunk) # type: ignore[arg-type] output = image_encoder(img_chunk) tokens, image = output.tokens, output.image w, h = image_encoder._image_to_num_tokens(img) # max image size 128 assert image.shape == (3, 128, 128) assert ( w * image_config.image_patch_size * spatial_merge_size, h * image_config.image_patch_size * spatial_merge_size, ) == (128, 128) assert len(tokens) == (w + 1) * h size = 111 # nearest multiple of sixteen lower than 128 is 112 img = Image.new("RGB", (size, size), "red") img_chunk = ImageChunk(image=img) text_chunk = TextChunk(text="") with pytest.raises(AttributeError): image_encoder(text_chunk) # type: ignore[arg-type] output = image_encoder(img_chunk) tokens, image = output.tokens, output.image assert image.shape == (3, 112, 112) w, h = image_encoder._image_to_num_tokens(img) assert ( w * image_config.image_patch_size * spatial_merge_size, h * image_config.image_patch_size * spatial_merge_size, ) == (112, 112) assert len(tokens) == (w + 1) * h @pytest.mark.parametrize( "size, spatial_merge_size", [ ((200, 311), 1), ((300, 212), 1), ((251, 1374), 1), ((1475, 477), 1), ((1344, 1544), 1), ((2133, 3422), 1), ((200, 311), 2), ((300, 212), 2), ((251, 1374), 2), ((1475, 477), 2), ((1344, 1544), 2), ((2133, 3422), 2), ], ) def test_image_processing(special_token_ids: SpecialImageIDs, size: tuple[int, int], spatial_merge_size: int) -> None: image_config = ImageConfig( image_patch_size=16 // spatial_merge_size, max_image_size=1024, spatial_merge_size=spatial_merge_size ) image_encoder = ImageEncoder(image_config, special_token_ids) # all images with w,h >= 1024 should be resized to 1024 # else round to nearest multiple of 16 # all while keeping the aspect ratio EXP_IMG_SIZES = { (200, 311): (208, 320), (300, 212): (304, 224), (251, 1374): (192, 1024), (1475, 477): (1024, 336), (1344, 1544): (896, 1024), (2133, 3422): (640, 1024), } # Expected sums for gray test images (RGB: 128, 128, 128) # These are manually calculated based on the normalization process and image size # The spatial_merge_size doesn't affect image sums, only token generation EXP_IMG_SUM = { (200, 311): 38949.726562, (300, 212): 39848.566406, (251, 1374): 115051.507812, (1475, 477): 201340.125000, (1344, 1544): 536907.000000, (2133, 3422): 383505.000000, } test_image = _create_test_image(size, color=(128, 128, 128)) content = ImageChunk(image=test_image) image = image_encoder(content).image expected_sum = EXP_IMG_SUM[size] assert image.transpose().shape[:2] == EXP_IMG_SIZES[size], image.transpose().shape[:2] assert np.abs(image).sum() - expected_sum < 1e-1, np.abs(image).sum() @pytest.mark.parametrize("spatial_merge_size", [1, 2]) def test_image_encoder_formats(spatial_merge_size: int, special_token_ids: SpecialImageIDs) -> None: image_config = ImageConfig( image_patch_size=16 // spatial_merge_size, max_image_size=1024, spatial_merge_size=spatial_merge_size ) image_encoder = ImageEncoder(image_config, special_token_ids) pil = _create_test_image((200, 300)) buffer = BytesIO() pil.save(buffer, "PNG") img_data = buffer.getvalue() data_url = f"data:image/jpeg;base64,{base64.b64encode(img_data).decode('utf-8')}" img_pil = ImageChunk(image=pil) img_url = ImageURLChunk(image_url="https://url.com") img_data_url = ImageURLChunk(image_url=data_url) outputs = [] for content in [img_pil, img_data_url]: assert isinstance(content, (ImageChunk, ImageURLChunk)) outputs.append(image_encoder(content)) def mock_get(url: str, headers: Any = None) -> Any: mock_response = Mock() if url == "https://url.com": mock_response.content = img_data else: raise requests.exceptions.RequestException("Download failed") mock_response.raise_fort_status = Mock() return mock_response with patch("mistral_common.image.requests.get", side_effect=mock_get): outputs.append(image_encoder(img_url)) for output in outputs[1:]: assert (output.image == outputs[0].image).all() assert output.tokens == outputs[0].tokens python-mistral-common-1.11.7/tests/test_imports.py000066400000000000000000000112311523035502500223270ustar00rootroot00000000000000import builtins from functools import _lru_cache_wrapper from types import ModuleType from typing import Any from unittest.mock import MagicMock, Mock, patch import pytest from mistral_common.imports import ( assert_hf_hub_installed, assert_jinja2_installed, assert_llguidance_installed, assert_opencv_installed, assert_package_installed, assert_sentencepiece_installed, assert_soundfile_installed, assert_soxr_installed, is_hf_hub_installed, is_jinja2_installed, is_llguidance_installed, is_opencv_installed, is_package_installed, is_sentencepiece_installed, is_soundfile_installed, is_soxr_installed, ) _IS_INSTALLED_TO_TESTS = [ is_hf_hub_installed, is_jinja2_installed, is_llguidance_installed, is_sentencepiece_installed, is_soundfile_installed, is_soxr_installed, ] _ASSERT_TO_TESTS = { ( is_hf_hub_installed, assert_hf_hub_installed, "`huggingface_hub` is not installed. Please install it with `pip install mistral-common[hf-hub]`", ), ( is_jinja2_installed, assert_jinja2_installed, "`jinja2` is not installed. Please install it with `pip install mistral-common[guidance]`", ), ( is_llguidance_installed, assert_llguidance_installed, "`llguidance` is not installed. Please install it with `pip install mistral-common[guidance]`", ), ( is_opencv_installed, assert_opencv_installed, "`opencv` is not installed. Please install it with `pip install mistral-common[opencv]`", ), ( is_sentencepiece_installed, assert_sentencepiece_installed, "`sentencepiece` is not installed. Please install it with `pip install mistral-common[sentencepiece]`", ), ( is_soundfile_installed, assert_soundfile_installed, "`soundfile` is not installed. Please install it with `pip install mistral-common[soundfile]`", ), ( is_soxr_installed, assert_soxr_installed, "`soxr` is not installed. Please install it with `pip install mistral-common[soxr]`", ), } @patch("importlib.util.find_spec") def test_is_package_installed(mock_find_spec: MagicMock) -> None: mock_find_spec.return_value = True assert is_package_installed("package_name") is True mock_find_spec.return_value = None assert is_package_installed("package_name") is False @patch("mistral_common.imports.is_package_installed") def test_assert_package_installed(mock_is_package_installed: MagicMock) -> None: mock_is_package_installed.return_value = True assert_package_installed("package_name") mock_is_package_installed.return_value = False with pytest.raises(ImportError): assert_package_installed("package_name") def test_is_opencv_installed() -> None: is_opencv_installed.cache_clear() # Module OpenCV is installed. with patch.dict("sys.modules", {"cv2": Mock()}): assert is_opencv_installed() is True is_opencv_installed.cache_clear() # Module OpenCV is missing. real_import = builtins.__import__ def fake_import(name: str, *args: Any, **kwargs: Any) -> ModuleType: if name == "cv2": raise ImportError("Simulated import error for cv2") return real_import(name, *args, **kwargs) with patch("builtins.__import__", side_effect=fake_import): assert is_opencv_installed() is False is_opencv_installed.cache_clear() @patch("mistral_common.imports.is_package_installed") @pytest.mark.parametrize("is_installed_fn", _IS_INSTALLED_TO_TESTS) def test_is_installed(mock_is_package_installed: MagicMock, is_installed_fn: _lru_cache_wrapper) -> None: is_installed_fn.cache_clear() mock_is_package_installed.return_value = True assert is_installed_fn() is True is_installed_fn.cache_clear() mock_is_package_installed.return_value = False assert is_installed_fn() is False is_installed_fn.cache_clear() @patch("mistral_common.imports.is_package_installed") @pytest.mark.parametrize("is_installed_fn, assert_fn, error_message", _ASSERT_TO_TESTS) def test_assert_installed( mock_is_package_installed: MagicMock, is_installed_fn: _lru_cache_wrapper, assert_fn: _lru_cache_wrapper, error_message: str, ) -> None: is_installed_fn.cache_clear() assert_fn.cache_clear() mock_is_package_installed.return_value = True assert_fn() is_installed_fn.cache_clear() assert_fn.cache_clear() mock_is_package_installed.return_value = False with pytest.raises(ImportError) as exc_info: assert_fn() assert str(exc_info.value) == error_message is_installed_fn.cache_clear() assert_fn.cache_clear() python-mistral-common-1.11.7/tests/test_integration_samples.py000066400000000000000000000051161523035502500247060ustar00rootroot00000000000000import json from pathlib import Path from typing import Any import pytest from mistral_common.protocol.instruct.messages import ChatMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer @pytest.fixture() def samples_dir() -> Path: return Path(__file__).parent.joinpath("data").joinpath("samples") def load_sample(samples_dir: Path, sample_name: str, version: int) -> tuple[Any, str, Any]: with open(samples_dir / f"{sample_name}/sample.json", "r") as f: sample = json.load(f) with open(samples_dir / f"{sample_name}/text_v{version}.txt", "r") as f: text = f.read() with open(samples_dir / f"{sample_name}/tokens_v{version}.json", "r") as f: tokens = json.load(f) return sample, text, tokens["tokens"] def get_tokenizer(version: int) -> MistralTokenizer: if version == 1: return MistralTokenizer.v1() elif version == 2: return MistralTokenizer.v2() elif version == 3: return MistralTokenizer.v3() else: raise ValueError(f"Invalid version: {version}") @pytest.mark.parametrize( "sample", [ {"sample_name": "get_weather_full", "versions": [2, 3]}, {"sample_name": "get_weather_no_history", "versions": [2, 3]}, {"sample_name": "several_calls", "versions": [2, 3]}, {"sample_name": "no_tools", "versions": [1, 2, 3]}, {"sample_name": "get_weather_no_system_prompt", "versions": [2, 3]}, {"sample_name": "parallel_calls", "versions": [3]}, ], ids=[ "get_weather_full", "get_weather_no_history", "several_calls", "no_tools", "get_weather_no_system_prompt", "parallel_calls", ], ) @pytest.mark.parametrize("version", [1, 2, 3], ids=["v1", "v2", "v3"]) def test_samples(sample: dict[str, Any], version: int, samples_dir: Path) -> None: if version not in sample["versions"]: pytest.skip(f"Sample {sample['sample_name']} not available for version {version}") mistral_tokenizer = get_tokenizer(version) instruct_request, text, tokens = load_sample(samples_dir, sample["sample_name"], version) chat_completion_string = json.dumps( {"model": "debug", "messages": instruct_request["messages"], "tools": instruct_request["tools"]} ) chat_completion_request = ChatCompletionRequest[ChatMessage].model_validate_json(chat_completion_string) tokenized = mistral_tokenizer.encode_chat_completion(chat_completion_request) assert tokenized.text == text assert tokenized.tokens == tokens python-mistral-common-1.11.7/tests/test_messages.py000066400000000000000000000034101523035502500224410ustar00rootroot00000000000000import pytest from pydantic import ValidationError from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, ToolMessage, UserMessage, ) from tests.fixtures.chunks import get_content_chunks as _chunks class TestMessageContentChunkUnions: r"""Pydantic-level (version-independent) content-chunk unions for each message role.""" def test_user_allows_text_image_audio(self) -> None: UserMessage(content=_chunks(("text", "image", "image_url", "audio", "audio_url"))) def test_user_rejects_think(self) -> None: for name in ("think",): with pytest.raises(ValidationError): UserMessage(content=_chunks((name,))) def test_assistant_allows_text_and_think(self) -> None: AssistantMessage(content=_chunks(("text", "think"))) def test_assistant_rejects_image_and_audio(self) -> None: for name in ("image", "image_url", "audio", "audio_url"): with pytest.raises(ValidationError): AssistantMessage(content=_chunks((name,))) def test_system_allows_text_audio_think(self) -> None: SystemMessage(content=_chunks(("text", "audio", "think"))) def test_system_rejects_image_and_audio_url(self) -> None: for name in ("image", "image_url", "audio_url"): with pytest.raises(ValidationError): SystemMessage(content=_chunks((name,))) def test_tool_allows_non_thinking_chunk_types(self) -> None: ToolMessage( content=_chunks(("text", "image", "image_url", "audio", "audio_url")), tool_call_id="c1", ) def test_tool_rejects_think(self) -> None: with pytest.raises(ValidationError): ToolMessage(content=_chunks(("think",)), tool_call_id="c1") python-mistral-common-1.11.7/tests/test_mistral_tokenizer.py000066400000000000000000000164031523035502500244050ustar00rootroot00000000000000import multiprocessing from unittest.mock import patch import pytest from mistral_common.exceptions import TokenizerException from mistral_common.protocol.instruct.validator import ValidationMode from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerV1, InstructTokenizerV2, InstructTokenizerV3, ) from mistral_common.tokens.tokenizers.mistral import MistralTokenizer SPM_SPECIAL_WHITESPACE = "▁" SPM_WHITESPACE = "▁" class TestMistralToknizer: def test_from_model(self) -> None: with pytest.warns(FutureWarning, match="from_model.*deprecated"): assert isinstance( MistralTokenizer.from_model("mistral-medium-2312").instruct_tokenizer, InstructTokenizerV1 ) with pytest.warns(FutureWarning): assert isinstance(MistralTokenizer.from_model("mistral-tiny-2312").instruct_tokenizer, InstructTokenizerV2) with pytest.warns(FutureWarning): assert isinstance(MistralTokenizer.from_model("mistral-large-2402").instruct_tokenizer, InstructTokenizerV2) with pytest.warns(FutureWarning): assert isinstance( MistralTokenizer.from_model("open-mixtral-8x22b-2404").instruct_tokenizer, InstructTokenizerV3 ) with pytest.warns(FutureWarning), pytest.raises(TokenizerException) as exc_info: MistralTokenizer.from_model("unknown-model") msg = str(exc_info.value) assert "unknown-model" in msg assert "from_hf_hub" in msg assert "from_file" in msg @pytest.mark.parametrize( ["special_token_policy", "is_tekken"], [ (SpecialTokenPolicy.IGNORE, False), (SpecialTokenPolicy.IGNORE, True), (SpecialTokenPolicy.KEEP, False), (SpecialTokenPolicy.KEEP, True), (SpecialTokenPolicy.RAISE, False), (SpecialTokenPolicy.RAISE, True), ], ) def test_decode(self, special_token_policy: SpecialTokenPolicy, is_tekken: bool) -> None: tokenizer = MistralTokenizer.v3(is_tekken=is_tekken) prompt = "This is a complicated te$t, ain't it?" for bos, eos in [[False, False], [True, True]]: encoded = tokenizer.instruct_tokenizer.tokenizer.encode(prompt, bos=bos, eos=eos) if special_token_policy is None or special_token_policy == SpecialTokenPolicy.IGNORE: assert tokenizer.decode(encoded, special_token_policy) == prompt assert tokenizer.instruct_tokenizer.decode(encoded, special_token_policy) == prompt assert tokenizer.instruct_tokenizer.tokenizer.decode(encoded, special_token_policy) == prompt elif special_token_policy == SpecialTokenPolicy.KEEP: if bos: bos_piece = "" if is_tekken else f"{SPM_SPECIAL_WHITESPACE}" expected = bos_piece + prompt elif not bos and not is_tekken: expected = SPM_SPECIAL_WHITESPACE + prompt else: expected = prompt if eos: eos_piece = "" expected += eos_piece if not is_tekken: expected = expected.replace(" ", SPM_WHITESPACE) assert tokenizer.decode(encoded, special_token_policy) == expected assert tokenizer.instruct_tokenizer.decode(encoded, special_token_policy) == expected assert tokenizer.instruct_tokenizer.tokenizer.decode(encoded, special_token_policy) == expected elif special_token_policy == SpecialTokenPolicy.RAISE: if bos or eos: with pytest.raises(ValueError): tokenizer.decode(encoded, special_token_policy) def test_from_hf_hub(self) -> None: def _mocked_hf_download( repo_id: str, token: bool | str | None = None, revision: str | None = None, force_download: bool = False, local_files_only: bool = False, ) -> str: if repo_id == "mistralai/Mistral-7B-Instruct-v0.1": return str(MistralTokenizer._data_path() / "tokenizer.model.v1") elif repo_id == "mistralai/Pixtral-Large-Instruct-2411": return str(MistralTokenizer._data_path() / "tekken_240911.json") else: raise ValueError(f"Unknown repo_id: {repo_id}") with patch("mistral_common.tokens.tokenizers.mistral.download_tokenizer_from_hf_hub", _mocked_hf_download): tokenizer = MistralTokenizer.from_hf_hub("mistralai/Mistral-7B-Instruct-v0.1") assert isinstance(tokenizer.instruct_tokenizer, InstructTokenizerV1) tokenizer = MistralTokenizer.from_hf_hub("mistralai/Pixtral-Large-Instruct-2411") assert isinstance(tokenizer.instruct_tokenizer, InstructTokenizerV3) def _worker_decode_function( tokenizer_instance_and_token_ids_and_validation_mode: tuple[MistralTokenizer, list[int], ValidationMode], ) -> str: tokenizer_instance, token_ids, validation_mode = tokenizer_instance_and_token_ids_and_validation_mode assert tokenizer_instance._chat_completion_request_validator._mode == validation_mode return tokenizer_instance.decode(token_ids) @pytest.mark.parametrize( ["tokenizer_file", "validation_mode", "token_ids", "expected"], [ ( "tokenizer.model.v1", ValidationMode.test, [1, 733, 16289, 28793, 17121, 22526, 13, 13, 28708, 733, 28748, 16289, 28793], "[INST] SYSTEM\n\na [/INST]", ), ( "tekken_240911.json", ValidationMode.finetuning, [1091, 3174, 3074, 1093, 126205, 1267, 1097, 1766, 1047, 3174, 3074, 1093], "[INST] SYSTEM\n\na [/INST]", ), ], ) def test_tokenizer_is_pickleable_with_multiprocessing( tokenizer_file: str, validation_mode: ValidationMode, token_ids: list[int], expected: str ) -> None: tokenizer_path = str(MistralTokenizer._data_path() / tokenizer_file) tokenizer = MistralTokenizer.from_file(tokenizer_path, validation_mode) with multiprocessing.Pool(processes=2) as pool: results = pool.map(_worker_decode_function, [(tokenizer, token_ids, validation_mode)]) assert len(results) == 1 assert results[0] == expected def test_mistral_tokenizer_version_property() -> None: tokenizer_v1 = MistralTokenizer.v1() assert ( tokenizer_v1.version == tokenizer_v1.instruct_tokenizer.version == tokenizer_v1.instruct_tokenizer.tokenizer.version == TokenizerVersion.v1 ) tokenizer_v3 = MistralTokenizer.v3() assert ( tokenizer_v3.version == tokenizer_v3.instruct_tokenizer.version == tokenizer_v3.instruct_tokenizer.tokenizer.version == TokenizerVersion.v3 ) def test_mistral_tokenizer_mode_property() -> None: tokenizer_path = str(MistralTokenizer._data_path() / "tokenizer.model.v1") for mode in [ValidationMode.serving, ValidationMode.finetuning, ValidationMode.test]: tokenizer = MistralTokenizer.from_file(tokenizer_path, mode) assert tokenizer.mode == tokenizer._chat_completion_request_validator.mode == mode python-mistral-common-1.11.7/tests/test_model_settings.py000066400000000000000000000226721523035502500236650ustar00rootroot00000000000000import pytest from pydantic import ValidationError from mistral_common.exceptions import InvalidRequestException from mistral_common.protocol.instruct.messages import UATS from mistral_common.protocol.instruct.request import ( ChatCompletionRequest, ModelSettings, ReasoningEffort, ) from mistral_common.tokens.tokenizers.model_settings_builder import EnumBuilder, ModelSettingsBuilder class TestModelSettings: def test_none_model_settings(self) -> None: settings = ModelSettings.none() for field_name in ModelSettings.model_fields.keys(): assert getattr(settings, field_name) is None def test_none_model_settings_builder(self) -> None: settings = ModelSettingsBuilder.none() for field_name in ModelSettingsBuilder.model_fields.keys(): assert getattr(settings, field_name) is None class TestEnumBuilder: @pytest.mark.parametrize(("values"), [([ReasoningEffort.none, ReasoningEffort.high, ReasoningEffort.none])]) def test_duplication(self, values: list[ReasoningEffort]) -> None: """Test that duplicate enum values are invalid during initialization.""" if len(set(values)) != len(values): with pytest.raises(ValidationError, match="Duplicate values"): EnumBuilder[ReasoningEffort](values=values, accepts_none=False, default=None) else: builder = EnumBuilder[ReasoningEffort](values=values, accepts_none=False, default=None) assert set(builder.values) == set(values) def test_empty_values_list(self) -> None: EnumBuilder[ReasoningEffort](values=[], accepts_none=True, default=None) with pytest.raises(ValidationError, match="Empty list of values"): EnumBuilder[ReasoningEffort](values=[], accepts_none=False, default=None) class TestModelSettingsBuilder: @pytest.mark.parametrize( ("builder", "chat_completion_request", "settings"), [ ( ModelSettingsBuilder(reasoning_effort=None), ChatCompletionRequest(messages=[], reasoning_effort=None), ModelSettings.none(), ), ( ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none, ReasoningEffort.high], accepts_none=False, default=None ) ), ChatCompletionRequest(messages=[], reasoning_effort=ReasoningEffort.none), ModelSettings(reasoning_effort=ReasoningEffort.none), ), ( ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.high], accepts_none=False, default=None ) ), ChatCompletionRequest(messages=[], reasoning_effort=ReasoningEffort.high), ModelSettings(reasoning_effort=ReasoningEffort.high), ), ( ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.high], accepts_none=True, default=None ), ), ChatCompletionRequest(messages=[], reasoning_effort=None), ModelSettings(reasoning_effort=None), ), ], ) def test_encode_successful_validation_scenarios( self, builder: ModelSettingsBuilder, chat_completion_request: ChatCompletionRequest, settings: ModelSettings ) -> None: assert settings == builder.build_settings(chat_completion_request) def test_encode_invalid_enum_value_raises_error(self) -> None: builder_high_only = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.high], accepts_none=False, default=None ) ) chat_completion_request = ChatCompletionRequest[UATS](messages=[], reasoning_effort=ReasoningEffort.none) with pytest.raises( InvalidRequestException, match=r"reasoning_effort should be one of \['high'\], got none.", ): builder_high_only.build_settings(chat_completion_request) def test_encode_missing_required_field_raises_error(self) -> None: builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none, ReasoningEffort.high], accepts_none=False, default=None ) ) chat_completion_request = ChatCompletionRequest[UATS](messages=[], reasoning_effort=None) with pytest.raises( InvalidRequestException, match=r"reasoning_effort should be set.", ): builder.build_settings(chat_completion_request) def test_encode_unexpected_field_raises_error(self) -> None: builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort](values=[], accepts_none=True, default=None) ) chat_completion_request = ChatCompletionRequest[UATS](messages=[], reasoning_effort=ReasoningEffort.high) with pytest.raises(InvalidRequestException, match=r"reasoning_effort not supported for this model."): builder.build_settings(chat_completion_request) def test_ignore_field(self) -> None: builder = ModelSettingsBuilder(reasoning_effort=None) chat_completion_request = ChatCompletionRequest[UATS](messages=[], reasoning_effort=ReasoningEffort.high) assert builder.build_settings(chat_completion_request) == ModelSettings.none() def test_reasoning_effort_does_not_exist(self) -> None: with pytest.raises(ValidationError, match=r"[type=enum, input_value='blabla', input_type=str]"): ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none, "blabla"], # type: ignore[list-item] accepts_none=False, default=None, ) ) def test_compatibility_accept_none_default(self) -> None: with pytest.raises(ValidationError, match=r"Default values can only be defined for accepts_none fields"): ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none], accepts_none=False, default=ReasoningEffort.none ) ) def test_default_in_values(self) -> None: with pytest.raises(ValidationError, match=r"Default value self.default='high' is not in self.values"): ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none], accepts_none=True, default=ReasoningEffort.high ) ) def test_default_works(self) -> None: builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none], accepts_none=True, default=ReasoningEffort.none ) ) assert builder.build_settings(ChatCompletionRequest(messages=[], reasoning_effort=None)) == ModelSettings( reasoning_effort=ReasoningEffort.none ) @pytest.mark.parametrize( ("reasoning_effort"), ([None, ReasoningEffort.none, ReasoningEffort.high]), ) def test_validator_no_settings(self, reasoning_effort: ReasoningEffort | None) -> None: builder = ModelSettingsBuilder() settings = ModelSettings(reasoning_effort=reasoning_effort) if reasoning_effort is not None: with pytest.raises(InvalidRequestException, match=r"reasoning_effort not supported for this model"): builder.validate_settings(settings) else: builder.validate_settings(settings) @pytest.mark.parametrize( ("reasoning_effort"), ([None, ReasoningEffort.none, ReasoningEffort.high]), ) def test_validator_unavailable_setting(self, reasoning_effort: ReasoningEffort | None) -> None: builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none], accepts_none=True, default=ReasoningEffort.none ) ) settings = ModelSettings(reasoning_effort=reasoning_effort) if reasoning_effort != ReasoningEffort.none: with pytest.raises(InvalidRequestException, match=r"reasoning_effort should be"): builder.validate_settings(settings) else: builder.validate_settings(settings) @pytest.mark.parametrize(("accepts_none"), ([False, True])) def test_validator_accepts_none(self, accepts_none: bool) -> None: builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort.none], accepts_none=accepts_none, default=None ) ) settings = ModelSettings(reasoning_effort=None) if accepts_none: builder.validate_settings(settings) else: with pytest.raises(InvalidRequestException, match=r"reasoning_effort should be set for this model"): builder.validate_settings(settings) def test_all_model_settings_and_builder_fields_match() -> None: model_fields = set(ModelSettings.model_fields.keys()) builder_fields = set(ModelSettingsBuilder.model_fields.keys()) assert model_fields == builder_fields python-mistral-common-1.11.7/tests/test_normalization.py000066400000000000000000001733011523035502500235270ustar00rootroot00000000000000import json import pytest from mistral_common.protocol.instruct.chunk import ( AudioChunk, ChunkTypes, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, FinetuningAssistantMessage, FinetuningMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import ( InstructRequestNormalizer, InstructRequestNormalizerV7, InstructRequestNormalizerV13, InstructRequestNormalizerV15, get_normalizer, ) from mistral_common.protocol.instruct.request import ( ChatCompletionRequest, InstructRequest, ModelSettings, ReasoningEffort, ) from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.model_settings_builder import EnumBuilder, ModelSettingsBuilder def mock_chat_completion(messages: list[ChatMessage]) -> ChatCompletionRequest: return ChatCompletionRequest( model="test", messages=messages, top_p=1.0, temperature=0.7, ) class TestChatCompletionRequestNormalization: @pytest.fixture(autouse=True) def normalizer(self) -> InstructRequestNormalizer: return InstructRequestNormalizer( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ) def test_user_system_user(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ UserMessage(content="U"), SystemMessage(content="S"), UserMessage(content="U"), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="U"), UserMessage(content="U")], system_prompt="S", ) def test_multiple_system(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ SystemMessage(content="S"), SystemMessage(content="S"), SystemMessage(content="S"), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="")], system_prompt="S\n\nS\n\nS", ) def test_single_system(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ SystemMessage(content="S"), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="")], system_prompt="S", ) def test_system_assistant_user(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ SystemMessage(content="S"), AssistantMessage(content="A"), UserMessage(content="U"), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content=""), AssistantMessage(content="A"), UserMessage(content="U")], system_prompt="S", ) def test_assistant_content_with_tool_calls(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ AssistantMessage( content="A", tool_calls=[ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "11"}'))], ) ] ) with pytest.raises(ValueError, match="Tool calls and content cannot be used together in the same message"): normalizer.from_chat_completion_request(chat_completion_request) def test_assistant_system_user_adds_user(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ AssistantMessage(content="A"), SystemMessage(content="S"), UserMessage(content="U"), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content=""), AssistantMessage(content="A"), UserMessage(content="U")], system_prompt="S", ) def test_message_aggregation_system_then_user(self, normalizer: InstructRequestNormalizer) -> None: parsed = normalizer.from_chat_completion_request( mock_chat_completion( [ SystemMessage(content="s"), SystemMessage(content="s"), SystemMessage(content="s"), UserMessage(content="u"), ] ) ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="u")], system_prompt="s\n\ns\n\ns" ) def test_message_aggregation_system_then_users(self, normalizer: InstructRequestNormalizer) -> None: parsed = normalizer.from_chat_completion_request( mock_chat_completion( [ SystemMessage(content="s"), SystemMessage(content="s"), SystemMessage(content="s"), UserMessage(content="u"), UserMessage(content="u"), ] ) ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="u\n\nu")], system_prompt="s\n\ns\n\ns" ) def test_message_aggregation_mixed_with_middle_system(self, normalizer: InstructRequestNormalizer) -> None: parsed = normalizer.from_chat_completion_request( mock_chat_completion( [ SystemMessage(content="s"), SystemMessage(content="s"), SystemMessage(content="s"), UserMessage(content="u"), UserMessage(content="u"), SystemMessage(content="s"), AssistantMessage(content="a"), UserMessage(content="u"), ] ) ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="u\n\nu"), AssistantMessage(content="a"), UserMessage(content="u")], system_prompt="s\n\ns\n\ns\n\ns", ) def test_message_aggregation_consecutive_assistants(self, normalizer: InstructRequestNormalizer) -> None: parsed = normalizer.from_chat_completion_request( mock_chat_completion( [ SystemMessage(content="s"), SystemMessage(content="s"), SystemMessage(content="s"), UserMessage(content="u"), UserMessage(content="u"), AssistantMessage(content="a"), AssistantMessage(content="a"), UserMessage(content="u"), ] ) ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="u\n\nu"), AssistantMessage(content="a\n\na"), UserMessage(content="u")], system_prompt="s\n\ns\n\ns", ) def test_message_aggregation_system_assistant_user(self, normalizer: InstructRequestNormalizer) -> None: parsed = normalizer.from_chat_completion_request( mock_chat_completion([SystemMessage(content="s"), AssistantMessage(content="a"), UserMessage(content="u")]) ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content=""), AssistantMessage(content="a"), UserMessage(content="u")], system_prompt="s", ) def test_tool_chunk_aggregation(self, normalizer: InstructRequestNormalizer) -> None: messages = [ ToolMessage(content="C", tool_call_id="1"), ToolMessage(content=[TextChunk(text='{"a": 2}')], tool_call_id="2"), ToolMessage(content=[TextChunk(text="B"), TextChunk(text="A")], tool_call_id="3"), ] expected = [ ToolMessage(content="C", tool_call_id="1"), ToolMessage(content=json.dumps({"a": 2}), tool_call_id="2"), ToolMessage(content="B\n\nA", tool_call_id="3"), ] assert ( normalizer._aggregate_tool_messages( messages, [tool_message.tool_call_id for tool_message in messages if tool_message.tool_call_id] ) == expected ) def test_normalize_chunks(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ UserMessage(content="foo"), UserMessage( content=[TextChunk(type=ChunkTypes.text, text="chunk")], ), UserMessage(content="foo"), UserMessage( content=[TextChunk(type=ChunkTypes.text, text="chunk")], ), ], ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="foo\n\nchunk\n\nfoo\n\nchunk")] ) def test_many_chunks_in_user_message(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ UserMessage(content="foo"), UserMessage( content=[ TextChunk(type=ChunkTypes.text, text="chunk1"), TextChunk(type=ChunkTypes.text, text="chunk2"), TextChunk(type=ChunkTypes.text, text="chunk3"), ], ), ], ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="foo\n\nchunk1\n\nchunk2\n\nchunk3")] ) def test_ignore_middle_empty_text_chunks(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = mock_chat_completion( messages=[ UserMessage( content=[ TextChunk(text="U"), TextChunk(text=""), TextChunk(text="V"), ], ), AssistantMessage( content=[ TextChunk(text="A"), TextChunk(text=""), TextChunk(text="B"), ], ), ] ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="U\n\nV"), AssistantMessage(content="A\n\nB")], ) def test_safety_prompt_aggregated(self, normalizer: InstructRequestNormalizer) -> None: chat_completion_request = ChatCompletionRequest[ChatMessage]( model="test", messages=[ UserMessage(content="user"), SystemMessage(content="system"), ], top_p=1.0, temperature=0.7, ) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="user")], system_prompt="system", ) def test_normalize_tools(self, normalizer: InstructRequestNormalizer) -> None: """ Test doesn't really "normalize" anything but it checks that the tools are added to the InstructRequest during from_chat_completion_request """ tools = [ Tool(function=Function(name="tool1", description="1", parameters={})), Tool(function=Function(name="tool2", description="2", parameters={})), ] request = ChatCompletionRequest[ChatMessage]( model="triton", messages=[ SystemMessage(content="helpful assistant"), UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), ], tools=tools, ) gt = InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c")], available_tools=tools, system_prompt="helpful assistant", ) normalized = normalizer.from_chat_completion_request(request) assert normalized == gt def test_normalize_funcalls(self, normalizer: InstructRequestNormalizer) -> None: request = ChatCompletionRequest[ChatMessage]( model="triton", messages=[ SystemMessage(content="helpful assistant"), UserMessage(content="a"), AssistantMessage( tool_calls=[ ToolCall( function=FunctionCall(name="tool1", arguments='\n\n{\n"input":\n "11"\n}\n\n') ), # dodgy input json string ], ), ToolMessage( name="tool1", content='{\n"output":\n "11"\n}', # dodgy input json string ), ], tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], ) gt = InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="a"), AssistantMessage( content="", tool_calls=[ ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "11"}')), # clean json string ], ), ToolMessage( name="tool1", content='{"output": "11"}', # clean json string ), ], system_prompt="helpful assistant", available_tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], ) normalized = normalizer.from_chat_completion_request(request) assert normalized == gt def test_assert_parsed_settings( self, normalizer: InstructRequestNormalizer, ) -> None: chat_completion_request = ChatCompletionRequest(messages=[UserMessage(content="B")]) parsed_request = normalizer.from_chat_completion_request(chat_completion_request) assert parsed_request == InstructRequest[ChatMessage, Tool](messages=[UserMessage(content="B")]) def test_continue_final_message_forwarded(self, normalizer: InstructRequestNormalizer) -> None: request = ChatCompletionRequest[ChatMessage]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) result = normalizer.from_chat_completion_request(request) assert result == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) def test_json_normalizes_tool_content(self, normalizer: InstructRequestNormalizer) -> None: r"""Base normalizer (v1-v3) JSON-normalizes tool message content.""" messy_json = '{"key" : "value" , "num": 1}' request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")]), ToolMessage(content=messy_json, tool_call_id="c1"), ], ) parsed = normalizer.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")], ), ToolMessage(content='{"key": "value", "num": 1}', tool_call_id="c1"), ], ) class TestChatCompletionRequestNormalizationV7: @pytest.fixture(autouse=True) def normalizer_v7(self) -> InstructRequestNormalizerV7: return InstructRequestNormalizerV7( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ) def test_system_assistant_user_v7(self, normalizer_v7: InstructRequestNormalizerV7) -> None: chat_completion_request = mock_chat_completion( messages=[ SystemMessage(content="S"), AssistantMessage(content="A"), UserMessage(content="U"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[SystemMessage(content="S"), AssistantMessage(content="A"), UserMessage(content="U")], ) def test_assistant_assistant_system_v7(self, normalizer_v7: InstructRequestNormalizerV7) -> None: chat_completion_request = mock_chat_completion( messages=[ AssistantMessage(content="A"), SystemMessage(content="S"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[AssistantMessage(content="A"), SystemMessage(content="S")], ) def test_assistant_content_with_tool_calls(self, normalizer_v7: InstructRequestNormalizerV7) -> None: chat_completion_request = mock_chat_completion( messages=[ AssistantMessage( content="A", tool_calls=[ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "11"}'))], ) ] ) normalized_chat_req: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert normalized_chat_req == InstructRequest[ChatMessage, Tool]( messages=[ AssistantMessage( content="A", tool_calls=[ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "11"}'))], ), ], ) def test_assistant_content_with_more_tool_calls(self, normalizer_v7: InstructRequestNormalizerV7) -> None: chat_completion_request = mock_chat_completion( messages=[ UserMessage(content="A1"), AssistantMessage(content="B1"), AssistantMessage( content="B2", tool_calls=[ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "1"}'))], ), AssistantMessage(content="B3"), AssistantMessage( content="B4", tool_calls=[ ToolCall(function=FunctionCall(name="tool21", arguments='{"input": "21"}')), ToolCall(function=FunctionCall(name="tool22", arguments='{"input": "22"}')), ], ), AssistantMessage(content="B5"), UserMessage(content="C1"), ] ) normalized_chat_req: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert normalized_chat_req == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A1"), AssistantMessage( content="B1\n\nB2\n\nB3\n\nB4\n\nB5", tool_calls=[ ToolCall(function=FunctionCall(name="tool1", arguments='{"input": "1"}')), ToolCall(function=FunctionCall(name="tool21", arguments='{"input": "21"}')), ToolCall(function=FunctionCall(name="tool22", arguments='{"input": "22"}')), ], ), UserMessage(content="C1"), ], ) def test_assert_parsed_settings( self, normalizer_v7: InstructRequestNormalizerV7, ) -> None: chat_completion_request = ChatCompletionRequest(messages=[UserMessage(content="B")]) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool](messages=[UserMessage(content="B")]) def test_continue_final_message_forwarded(self, normalizer_v7: InstructRequestNormalizerV7) -> None: request = ChatCompletionRequest[ChatMessage]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) result: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request(request) assert result == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) @pytest.mark.parametrize("num_empty", [0, 1, 2]) def test_only_empty_text_chunks(self, normalizer_v7: InstructRequestNormalizerV7, num_empty: int) -> None: """Messages with only empty TextChunks produce empty content.""" chat_completion_request = mock_chat_completion( messages=[ UserMessage(content=[TextChunk(text="") for _ in range(num_empty)]), AssistantMessage(content=[TextChunk(text="") for _ in range(num_empty)]), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content=""), AssistantMessage(content="")], ) def test_complex_user_aggregation(self, normalizer_v7: InstructRequestNormalizerV7) -> None: """Complex multi-user-message aggregation with mixed str, chunks, empty, and non-text chunks.""" chat_completion_request = mock_chat_completion( messages=[ UserMessage(content=""), UserMessage(content="A"), UserMessage(content=""), UserMessage(content=[TextChunk(text="B")]), UserMessage(content=[TextChunk(text="")]), UserMessage( content=[ TextChunk(text="C"), TextChunk(text="D"), ImageURLChunk(image_url="E"), TextChunk(text="G"), TextChunk(text=""), TextChunk(text="H"), ] ), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage( content=[ TextChunk(text="A\n\nB\n\nC\n\nD"), ImageURLChunk(image_url="E"), TextChunk(text="G\n\nH"), ] ), ], ) def test_complex_assistant_aggregation(self, normalizer_v7: InstructRequestNormalizerV7) -> None: """Complex multi-assistant-message aggregation with mixed str, chunks, and empty content.""" chat_completion_request = mock_chat_completion( messages=[ AssistantMessage(content=""), AssistantMessage(content="A"), AssistantMessage(content=""), AssistantMessage(content=[TextChunk(text="B")]), AssistantMessage(content=[TextChunk(text="")]), AssistantMessage( content=[ TextChunk(text="C"), TextChunk(text="D"), ] ), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[AssistantMessage(content="A\n\nB\n\nC\n\nD")], ) def test_accepts_string_content(self, normalizer_v7: InstructRequestNormalizerV7) -> None: r"""V7 normalizer accepts string content in assistant messages.""" request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(content="plain text"), ], ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="query"), AssistantMessage(content="plain text")], ) def test_skips_json_normalization_on_tool_content(self, normalizer_v7: InstructRequestNormalizerV7) -> None: r"""V7+ normalizers do not JSON-normalize tool message content.""" messy_json = '{"key" : "value" , "num": 1}' request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")]), ToolMessage(content=messy_json, tool_call_id="c1"), ], ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")], ), ToolMessage(content=messy_json, tool_call_id="c1"), ], ) def test_preserves_audio_in_system_message(self, normalizer_v7: InstructRequestNormalizerV7) -> None: r"""V7 normalizer preserves AudioChunk in system messages.""" request = mock_chat_completion( messages=[ SystemMessage(content=[TextChunk(text="hello"), AudioChunk(input_audio=b"fake_audio_data")]), UserMessage(content="test"), ] ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v7.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ SystemMessage( content=[TextChunk(text="hello"), AudioChunk(input_audio=b"fake_audio_data")], ), UserMessage(content="test"), ], ) class TestFineTuningNormalizer: @pytest.fixture(autouse=True) def normalizer(self) -> InstructRequestNormalizer: return InstructRequestNormalizer( UserMessage, FinetuningAssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ) def test_normalize_weighted_assistant(self, normalizer: InstructRequestNormalizer) -> None: request = ChatCompletionRequest[FinetuningMessage]( messages=[ SystemMessage(content="helpful assistant"), UserMessage(content="a"), FinetuningAssistantMessage(content="he", weight=0), UserMessage(content="b"), FinetuningAssistantMessage(content="ho", weight=1), FinetuningAssistantMessage(content="lla", weight=1), ], ) expected = InstructRequest[FinetuningMessage, Tool]( messages=[ UserMessage(content="a"), FinetuningAssistantMessage(content="he", weight=0), UserMessage(content="b"), FinetuningAssistantMessage(content="ho\n\nlla", weight=1), ], system_prompt="helpful assistant", ) normalized = normalizer.from_chat_completion_request(request) assert expected == normalized def test_should_not_aggregate_if_weight_is_different(self, normalizer: InstructRequestNormalizer) -> None: request = ChatCompletionRequest[FinetuningMessage]( messages=[ SystemMessage(content="helpful assistant"), UserMessage(content="a"), FinetuningAssistantMessage(content="he", weight=0), UserMessage(content="b"), FinetuningAssistantMessage(content="ho", weight=0), FinetuningAssistantMessage(content="lla", weight=1), ], ) expected = InstructRequest[FinetuningMessage, Tool]( messages=[ UserMessage(content="a"), FinetuningAssistantMessage(content="he", weight=0), UserMessage(content="b"), FinetuningAssistantMessage(content="ho", weight=0), FinetuningAssistantMessage(content="lla", weight=1), ], system_prompt="helpful assistant", ) normalized = normalizer.from_chat_completion_request(request) assert normalized == expected class TestChatCompletionRequestNormalizationV13: @pytest.fixture(autouse=True) def normalizer_v13(self) -> InstructRequestNormalizerV13: return InstructRequestNormalizerV13( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, None ) def _mock_chat_completion(self, messages: list[ChatMessage]) -> ChatCompletionRequest: return ChatCompletionRequest( model="test", messages=messages, top_p=1.0, temperature=0.7, ) def test_no_reorder_tool_messages(self, normalizer_v13: InstructRequestNormalizerV13) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), ], ) def test_reorder_last_tool_messages(self, normalizer_v13: InstructRequestNormalizerV13) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="D", tool_call_id="2"), ToolMessage(content="C", tool_call_id="1"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), ], ) def test_reorder_internal_tool_messages(self, normalizer_v13: InstructRequestNormalizerV13) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="D", tool_call_id="2"), ToolMessage(content="C", tool_call_id="1"), AssistantMessage(content="E"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), AssistantMessage(content="E"), ], ) def test_reorder_extra_tool_messages(self, normalizer_v13: InstructRequestNormalizerV13) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="D", tool_call_id="2"), ToolMessage(content="C", tool_call_id="1"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), ], ) def test_reorder_only_from_latest_assistant_message(self, normalizer_v13: InstructRequestNormalizerV13) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), AssistantMessage( content="E", tool_calls=[ ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="A"), AssistantMessage( content="B", tool_calls=[ ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="C", tool_call_id="1"), ToolMessage(content="D", tool_call_id="2"), AssistantMessage( content="E", tool_calls=[ ToolCall(id="2", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="1", function=FunctionCall(name="foo", arguments="{}")), ], ), ToolMessage(content="D", tool_call_id="2"), ToolMessage(content="C", tool_call_id="1"), ], ) @pytest.mark.parametrize( ["system_message", "expected_system_message"], [ ( SystemMessage(content="A"), SystemMessage(content="A"), ), ( SystemMessage(content=[TextChunk(text="A")]), SystemMessage(content="A"), ), ( SystemMessage(content=[TextChunk(text="A"), TextChunk(text="B")]), SystemMessage(content="A\n\nB"), ), ( SystemMessage(content=[TextChunk(text="A"), TextChunk(text="B"), ThinkChunk(thinking="C")]), SystemMessage(content=[TextChunk(text="A\n\nB"), ThinkChunk(thinking="C")]), ), ( SystemMessage( content=[ TextChunk(text="A"), TextChunk(text="B"), ThinkChunk(thinking="C"), ThinkChunk(thinking="D"), ] ), SystemMessage(content=[TextChunk(text="A\n\nB"), ThinkChunk(thinking="C"), ThinkChunk(thinking="D")]), ), ], ) def test_aggregate_system_prompt_content( self, normalizer_v13: InstructRequestNormalizerV13, system_message: SystemMessage, expected_system_message: SystemMessage, ) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion( messages=[system_message, UserMessage(content="B")] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[expected_system_message, UserMessage(content="B")] ) def test_system_messages_no_aggregation(self, normalizer_v13: InstructRequestNormalizerV13) -> None: """Consecutive system messages are NOT aggregated into one in V7+.""" chat_completion_request = self._mock_chat_completion( messages=[ SystemMessage(content="A"), SystemMessage(content="B"), UserMessage(content="C"), SystemMessage(content="D"), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ SystemMessage(content="A"), SystemMessage(content="B"), UserMessage(content="C"), SystemMessage(content="D"), ], ) def test_system_messages_normalization(self, normalizer_v13: InstructRequestNormalizerV13) -> None: """System message chunks within the same message are aggregated with no separator.""" chat_completion_request = self._mock_chat_completion( messages=[ SystemMessage(content=[TextChunk(text="A"), TextChunk(text="B")]), SystemMessage(content=[TextChunk(text="C")]), ] ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[ SystemMessage(content="A\n\nB"), SystemMessage(content="C"), ], ) def test_assert_parsed_settings( self, normalizer_v13: InstructRequestNormalizerV13, ) -> None: chat_completion_request: ChatCompletionRequest = self._mock_chat_completion(messages=[UserMessage(content="B")]) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool](messages=[UserMessage(content="B")]) def test_continue_final_message_forwarded(self, normalizer_v13: InstructRequestNormalizerV13) -> None: request = ChatCompletionRequest[ChatMessage]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) result: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request(request) assert result == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) def test_accepts_text_and_think_chunks(self, normalizer_v13: InstructRequestNormalizerV13) -> None: r"""V13 normalizer accepts TextChunk and ThinkChunk in assistant messages.""" request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(content=[ThinkChunk(thinking="reasoning"), TextChunk(text="answer")]), ], ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage(content=[ThinkChunk(thinking="reasoning"), TextChunk(text="answer")]), ], ) def test_accepts_string_content(self, normalizer_v13: InstructRequestNormalizerV13) -> None: r"""V13 normalizer accepts string content in assistant messages.""" request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(content="plain text"), ], ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="query"), AssistantMessage(content="plain text")], ) def test_assistant_think_chunk_inter_message_aggregation( self, normalizer_v13: InstructRequestNormalizerV13 ) -> None: r"""V13 normalizer preserves ThinkChunks across multiple assistant messages.""" chat_completion_request = mock_chat_completion( messages=[ AssistantMessage(content="A"), AssistantMessage(content=[TextChunk(text="B")]), AssistantMessage( content=[ ThinkChunk(thinking="T"), TextChunk(text="C"), TextChunk(text="D"), ] ), ] ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ AssistantMessage( content=[ TextChunk(text="A\n\nB"), ThinkChunk(thinking="T"), TextChunk(text="C\n\nD"), ] ), ], ) def test_assistant_think_chunk_intra_message_aggregation( self, normalizer_v13: InstructRequestNormalizerV13 ) -> None: r"""V13 normalizer coalesces TextChunks and preserves multiple ThinkChunks within a single message.""" chat_completion_request = mock_chat_completion( messages=[ UserMessage(content="u"), AssistantMessage( content=[ ThinkChunk(thinking="t1"), ThinkChunk(thinking="t2"), TextChunk(text="a1"), TextChunk(text="a2"), TextChunk(text="a3"), ] ), UserMessage(content="u"), ] ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request( chat_completion_request ) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="u"), AssistantMessage( content=[ ThinkChunk(thinking="t1"), ThinkChunk(thinking="t2"), TextChunk(text="a1\n\na2\n\na3"), ] ), UserMessage(content="u"), ], ) def test_aggregates_text_tool_content(self, normalizer_v13: InstructRequestNormalizerV13) -> None: r"""V13 normalizer aggregates TextChunks in tool messages to a string.""" request = mock_chat_completion( messages=[ UserMessage(content="query"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")]), ToolMessage(content=[TextChunk(text="hello"), TextChunk(text="world")], tool_call_id="c1"), ], ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v13.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")], ), ToolMessage(content="hello\n\nworld", tool_call_id="c1"), ], ) class TestChatCompletionRequestNormalizationV15: @pytest.fixture(autouse=True) def normalizer_v15(self) -> InstructRequestNormalizerV15: return InstructRequestNormalizerV15( UserMessage, AssistantMessage, ToolMessage, SystemMessage, InstructRequest, ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=list(ReasoningEffort), accepts_none=False, default=None ) ), ) @pytest.mark.parametrize("reasoning_effort", [ReasoningEffort.none, ReasoningEffort.high]) def test_assert_parsed_settings( self, normalizer_v15: InstructRequestNormalizerV15, reasoning_effort: ReasoningEffort ) -> None: chat_completion_request = ChatCompletionRequest( messages=[UserMessage(content="B")], reasoning_effort=reasoning_effort ) parsed_request: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request( chat_completion_request ) assert parsed_request == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="B")], settings=ModelSettings(reasoning_effort=reasoning_effort), ) def test_continue_final_message_forwarded(self, normalizer_v15: InstructRequestNormalizerV15) -> None: request = ChatCompletionRequest[ChatMessage]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, reasoning_effort=ReasoningEffort.high, ) result: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert result == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_v15_intra_message_chunks_joined_without_separator( self, normalizer_v15: InstructRequestNormalizerV15 ) -> None: """V15 joins TextChunks within the same message with no separator (chunk_join_str='').""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content=[TextChunk(text="A"), TextChunk(text="B")]), AssistantMessage(content=[TextChunk(text="C"), TextChunk(text="D")]), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="AB"), AssistantMessage(content="CD")], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_v15_inter_message_join_still_uses_separator(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 still joins text across different messages with '\n\n'.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content="First"), UserMessage(content="Second"), AssistantMessage(content="Reply"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="First\n\nSecond"), AssistantMessage(content="Reply")], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_v15_mixed_intra_and_inter_message(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 combines intra-message ('') and inter-message ('\n\n') joining.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content=[TextChunk(text="A"), TextChunk(text="B")]), UserMessage(content=[TextChunk(text="C"), TextChunk(text="D")]), AssistantMessage(content="Reply"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="AB\n\nCD"), AssistantMessage(content="Reply")], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_v15_mixed_intra_and_inter_assistant_messages(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 combines intra-message ('') and inter-message ('\n\n') joining for assistant messages.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content="Hello"), AssistantMessage(content=[TextChunk(text="A"), TextChunk(text="B")]), AssistantMessage(content=[TextChunk(text="C"), TextChunk(text="D")]), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="Hello"), AssistantMessage(content="AB\n\nCD")], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_v15_tool_message_text_chunks_joined_without_separator( self, normalizer_v15: InstructRequestNormalizerV15 ) -> None: """V15 tool message TextChunks are joined with no separator.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content="query"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")]), ToolMessage(content=[TextChunk(text="X"), TextChunk(text="Y")], tool_call_id="c1"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")], ), ToolMessage(content="XY", tool_call_id="c1"), ], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_accepts_text_and_think_chunks(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 normalizer accepts TextChunk and ThinkChunk in assistant messages.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content="query"), AssistantMessage(content=[ThinkChunk(thinking="reasoning"), TextChunk(text="answer")]), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage(content=[ThinkChunk(thinking="reasoning"), TextChunk(text="answer")]), ], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_accepts_string_content(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 normalizer accepts string content in assistant messages.""" request = ChatCompletionRequest[ChatMessage]( messages=[ UserMessage(content="query"), AssistantMessage(content="plain text"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[UserMessage(content="query"), AssistantMessage(content="plain text")], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_preserves_non_text_tool_content(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 normalizer preserves non-text chunks in tool messages.""" image_chunk = ImageURLChunk(image_url="https://example.com/image.png") request = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="query"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")]), ToolMessage(content=[image_chunk], tool_call_id="c1"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="c1")], ), ToolMessage(content=[image_chunk], tool_call_id="c1"), ], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_sorts_multimodal_tool_messages(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 normalizer sorts multimodal tool messages by tool call order.""" image_chunk_1 = ImageURLChunk(image_url="https://example.com/img1.png") image_chunk_2 = ImageURLChunk(image_url="https://example.com/img2.png") request = ChatCompletionRequest( # type: ignore[type-var] messages=[ UserMessage(content="query"), AssistantMessage( tool_calls=[ ToolCall(function=FunctionCall(name="fn1", arguments="{}"), id="c1"), ToolCall(function=FunctionCall(name="fn2", arguments="{}"), id="c2"), ] ), ToolMessage(content=[image_chunk_2], tool_call_id="c2"), ToolMessage(content=[image_chunk_1], tool_call_id="c1"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ UserMessage(content="query"), AssistantMessage( content="", tool_calls=[ ToolCall(function=FunctionCall(name="fn1", arguments="{}"), id="c1"), ToolCall(function=FunctionCall(name="fn2", arguments="{}"), id="c2"), ], ), ToolMessage(content=[image_chunk_1], tool_call_id="c1"), ToolMessage(content=[image_chunk_2], tool_call_id="c2"), ], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) def test_preserves_audio_in_system_message(self, normalizer_v15: InstructRequestNormalizerV15) -> None: r"""V15 normalizer preserves AudioChunk in system messages.""" request = ChatCompletionRequest[ChatMessage]( messages=[ SystemMessage(content=[TextChunk(text="hello"), AudioChunk(input_audio=b"fake_audio_data")]), UserMessage(content="test"), ], reasoning_effort=ReasoningEffort.high, ) parsed: InstructRequest[ChatMessage, Tool] = normalizer_v15.from_chat_completion_request(request) assert parsed == InstructRequest[ChatMessage, Tool]( messages=[ SystemMessage( content=[TextChunk(text="hello"), AudioChunk(input_audio=b"fake_audio_data")], ), UserMessage(content="test"), ], settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) @pytest.mark.parametrize( "version,expected_class,model_settings_builder", [ (TokenizerVersion.v1, InstructRequestNormalizer, None), (TokenizerVersion.v2, InstructRequestNormalizer, None), (TokenizerVersion.v3, InstructRequestNormalizer, None), (TokenizerVersion.v7, InstructRequestNormalizerV7, None), (TokenizerVersion.v11, InstructRequestNormalizerV7, None), (TokenizerVersion.v13, InstructRequestNormalizerV13, None), ( TokenizerVersion.v15, InstructRequestNormalizerV15, ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=list(ReasoningEffort), accepts_none=False, default=None ) ), ), ], ) def test_get_normalizer_version_mapping( version: TokenizerVersion, expected_class: type, model_settings_builder: ModelSettingsBuilder ) -> None: normalizer = get_normalizer(version, model_settings_builder) assert isinstance(normalizer, expected_class) assert normalizer._model_settings_builder == model_settings_builder python-mistral-common-1.11.7/tests/test_sentencepiece.py000066400000000000000000000057041523035502500234540ustar00rootroot00000000000000import os from pathlib import Path import numpy as np import pytest from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy from mistral_common.tokens.tokenizers.sentencepiece import SentencePieceTokenizer @pytest.fixture(scope="module") def tokenizer_v7() -> SentencePieceTokenizer: _model_path = ( Path(os.path.abspath(__file__)).parent.parent / "src" / "mistral_common" / "data" / "mistral_instruct_tokenizer_241114.model.v7" ) return SentencePieceTokenizer(model_path=_model_path) @pytest.mark.parametrize( ("token", "is_control"), [ ("", True), ("a", False), (1, True), (230, True), (1001, False), (np.int64(1), True), (np.int64(1001), False), ], ) def test_is_control(tokenizer_v7: SentencePieceTokenizer, token: str | int, is_control: bool) -> None: assert tokenizer_v7.is_special(token) is is_control def test_sentencepiece_tokenizer_special_ids_property(tokenizer_v7: SentencePieceTokenizer) -> None: special_ids = tokenizer_v7.special_ids assert isinstance(special_ids, set) assert len(special_ids) > 0 def test_sentencepiece_tokenizer_num_specials_property(tokenizer_v7: SentencePieceTokenizer) -> None: num_special = tokenizer_v7.num_special_tokens assert isinstance(num_special, int) assert num_special == 748 # Test that num_special_tokens matches the length of special_ids assert num_special == len(tokenizer_v7.special_ids) def test_decode_raise_policy_decodes_normal_tokens(tokenizer_v7: SentencePieceTokenizer) -> None: text = "Hello world, how are you?" ids = tokenizer_v7.encode(text, bos=False, eos=False) assert tokenizer_v7.decode(ids, SpecialTokenPolicy.RAISE) == text assert tokenizer_v7.decode(ids, SpecialTokenPolicy.RAISE) == tokenizer_v7.decode(ids, SpecialTokenPolicy.IGNORE) def test_decode_raise_policy_raises_on_special_tokens(tokenizer_v7: SentencePieceTokenizer) -> None: ids = tokenizer_v7.encode("Hello world", bos=True, eos=True) assert any(tokenizer_v7.is_special(tok) for tok in ids) with pytest.raises(ValueError): tokenizer_v7.decode(ids, SpecialTokenPolicy.RAISE) @pytest.mark.parametrize("policy", list(SpecialTokenPolicy)) def test_decode_string_special_token_policy_matches_enum( tokenizer_v7: SentencePieceTokenizer, policy: SpecialTokenPolicy ) -> None: include_special = policy is not SpecialTokenPolicy.RAISE ids = tokenizer_v7.encode("Hello world", bos=include_special, eos=include_special) assert tokenizer_v7.decode(ids, policy.value) == tokenizer_v7.decode(ids, policy) # type: ignore[arg-type] def test_decode_invalid_special_token_policy_string_raises(tokenizer_v7: SentencePieceTokenizer) -> None: ids = tokenizer_v7.encode("Hello world", bos=True, eos=True) with pytest.raises(ValueError, match=r"Invalid \`special_token_policy\`"): tokenizer_v7.decode(ids, "keeep") # type: ignore[arg-type] python-mistral-common-1.11.7/tests/test_tekken.py000066400000000000000000000302541523035502500221210ustar00rootroot00000000000000import base64 import json import re from pathlib import Path from typing import Sequence import numpy as np import pytest from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens, TokenizerVersion from mistral_common.tokens.tokenizers.tekken import ( ModelData, SpecialTokenInfo, TekkenConfig, Tekkenizer, TokenInfo, is_tekken, ) def quick_vocab(extra_toks: Sequence[bytes] = ()) -> list[TokenInfo]: vocab = [TokenInfo(rank=i, token_bytes=base64.b64encode(bytes([i])).decode(), token_str=chr(i)) for i in range(256)] for i, tok in enumerate(extra_toks): vocab.append( TokenInfo( rank=256 + i, token_bytes=base64.b64encode(tok).decode(), token_str=tok.decode(), ) ) return vocab def _get_deprecated_special_tokens() -> list[SpecialTokenInfo]: return list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS) @pytest.fixture(scope="module") def dummy_v3() -> Tekkenizer: vocab = quick_vocab([b"hello"]) return Tekkenizer( vocab, special_tokens=_get_deprecated_special_tokens(), pattern=r".+", # single token, whole string vocab_size=len(vocab) + len(_get_deprecated_special_tokens()), num_special_tokens=len(_get_deprecated_special_tokens()), version=TokenizerVersion.v3, ) def get_special_tokens( tokenizer_version: TokenizerVersion, add_audio: bool = False, add_think: bool = False ) -> list[SpecialTokenInfo]: special_tokens = list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS) if tokenizer_version < TokenizerVersion.v7 and add_audio: raise ValueError("Audio tokens are only supported in v7 and above") if tokenizer_version <= TokenizerVersion.v7 and not add_audio: return special_tokens # fill special tokens until 24 special_tokens += [ SpecialTokenInfo(rank=i, token_str=f"", is_control=True) for i in range(len(special_tokens), 24) ] if add_audio: # add audio tokes special_tokens += [ SpecialTokenInfo(rank=24, token_str=SpecialTokens.audio, is_control=True), SpecialTokenInfo(rank=25, token_str=SpecialTokens.begin_audio, is_control=True), ] # fill special tokens until 32 special_tokens += [ SpecialTokenInfo(rank=i, token_str=f"", is_control=True) for i in range(len(special_tokens), 32) ] if tokenizer_version > TokenizerVersion.v7: special_tokens += [ SpecialTokenInfo(rank=32, token_str=SpecialTokens.args, is_control=True), SpecialTokenInfo(rank=33, token_str=SpecialTokens.call_id, is_control=True), ] # fill special tokens until 34 special_tokens += [ SpecialTokenInfo(rank=i, token_str=f"", is_control=True) for i in range(len(special_tokens), 34) ] if add_audio: assert not add_think, f"Audio and think tokens are mutually exclusive, got {add_audio} and {add_think}" special_tokens += [ SpecialTokenInfo(rank=34, token_str=SpecialTokens.transcribe, is_control=True), SpecialTokenInfo(rank=35, token_str=SpecialTokens.text_to_audio, is_control=True), SpecialTokenInfo(rank=36, token_str=SpecialTokens.audio_to_text, is_control=True), ] if tokenizer_version < TokenizerVersion.v13: return special_tokens if not add_audio and add_think: special_tokens += [SpecialTokenInfo(rank=34, token_str=f"", is_control=True)] if add_think: special_tokens += [ SpecialTokenInfo(rank=35, token_str="[THINK]", is_control=True), SpecialTokenInfo(rank=36, token_str="[/THINK]", is_control=True), ] if tokenizer_version >= TokenizerVersion.v15: # fill until rank 37 special_tokens += [ SpecialTokenInfo(rank=i, token_str=f"", is_control=True) for i in range(len(special_tokens), 37) ] special_tokens += [ SpecialTokenInfo(rank=37, token_str=SpecialTokens.begin_model_settings, is_control=True), SpecialTokenInfo(rank=38, token_str=SpecialTokens.end_model_settings, is_control=True), ] return special_tokens def _write_tekkenizer_model( tmp_path: Path, vocab: list[TokenInfo] | None = None, special_tokens: list[SpecialTokenInfo] | None = None, pattern: str = ".", num_special_tokens: int = 100, version: str | None = "v3", ) -> None: # Create the vocab.json file if vocab is None: vocab = quick_vocab() config = { "pattern": pattern, "default_num_special_tokens": num_special_tokens, "default_vocab_size": 256 + 3 + num_special_tokens, } if version is not None: config["version"] = version model = ModelData( vocab=vocab if vocab else quick_vocab(), config=TekkenConfig(**config), # type: ignore special_tokens=special_tokens, version=1, type="Tekken", ) with open(tmp_path, "w") as f: json.dump(model, f) def test_roundtrip() -> None: tekkenizer = Tekkenizer( quick_vocab(extra_toks=[b"beau", b"My", b"unused"]), list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS), pattern=".", vocab_size=256 + 3 + 100, num_special_tokens=100, version=TokenizerVersion.v3, ) inputs = "My very beautiful string" encoded = tekkenizer.encode(inputs, False, False) decoded = tekkenizer.decode(encoded) assert inputs == decoded @pytest.mark.parametrize("policy", list(SpecialTokenPolicy)) def test_decode_string_special_token_policy_matches_enum(dummy_v3: Tekkenizer, policy: SpecialTokenPolicy) -> None: include_special = policy is not SpecialTokenPolicy.RAISE ids = dummy_v3.encode("hello", bos=include_special, eos=include_special) assert dummy_v3.decode(ids, policy.value) == dummy_v3.decode(ids, policy) # type: ignore[arg-type] def test_decode_invalid_special_token_policy_string_raises(dummy_v3: Tekkenizer) -> None: ids = dummy_v3.encode("hello", bos=True, eos=True) with pytest.raises(ValueError, match=r"Invalid \`special_token_policy\`"): dummy_v3.decode(ids, "keeep") # type: ignore[arg-type] def test_version(tmp_path: Path) -> None: tokpath = tmp_path / "tekken.json" vocab = quick_vocab(extra_toks=[b"beau", b"My", b"unused"]) pattern = "." num_special_tokens = 100 # test all versions can be loaded assert len(TokenizerVersion.__members__) > 0 for version in TokenizerVersion.__members__: special_tokens = get_special_tokens(TokenizerVersion(version)) _write_tekkenizer_model(tokpath, vocab, special_tokens, pattern, num_special_tokens, version=str(version)) tekkenizer_loaded = Tekkenizer.from_file(tokpath) assert tekkenizer_loaded.version == TokenizerVersion(version) # test `None` and other version cannot be loaded for version in [None, "dummy-v"]: # type: ignore _write_tekkenizer_model(tokpath, vocab, special_tokens, pattern, num_special_tokens, version=version) with pytest.raises(ValueError, match=re.compile("Unknown version:*")): tekkenizer_loaded = Tekkenizer.from_file(tokpath) def test_read_from_file(tmp_path: Path) -> None: inputs = "My very beatuiful string" tokpath = tmp_path / "tekken.json" vocab = quick_vocab(extra_toks=[b"beau", b"My", b"unused"]) pattern = "." num_special_tokens = 100 _write_tekkenizer_model(tokpath, vocab, None, pattern, num_special_tokens) tekkenizer_loaded = Tekkenizer.from_file(tokpath) tekkenizer = Tekkenizer( vocab, list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS), pattern, vocab_size=256 + 3 + num_special_tokens, num_special_tokens=100, version=TokenizerVersion.v3, ) encoded = tekkenizer.encode(inputs, False, False) encoded_from_loaded = tekkenizer_loaded.encode(inputs, False, False) assert encoded == encoded_from_loaded assert tekkenizer.num_special_tokens == num_special_tokens assert tekkenizer.version == TokenizerVersion.v3 def test_istekken(tmp_path: Path) -> None: # Initialize the Tekkenizer with a path common_case = tmp_path / "tekken.tokenizer.json" _write_tekkenizer_model(common_case) assert is_tekken(common_case) common_case.unlink() version_case = tmp_path / "v4.tekken.json" _write_tekkenizer_model(version_case) assert is_tekken(version_case) version_case.unlink() fancy_name = tmp_path / "tekken_the_destroyer.tekken.json" _write_tekkenizer_model(fancy_name) assert is_tekken(fancy_name) fancy_name.unlink() bad_name = tmp_path / "sentencepiece.json" _write_tekkenizer_model(bad_name) assert not is_tekken(bad_name) bad_name.unlink() not_json = tmp_path / "tekken.model" _write_tekkenizer_model(not_json) assert not is_tekken(not_json) not_json.unlink() assert not is_tekken(tmp_path / "nonexistent.tekken.json") def test_isbyte() -> None: tekkenizer = Tekkenizer( quick_vocab([b"hello"]), list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS), pattern=r".+", # single token, whole string vocab_size=256 + 1 + 100, num_special_tokens=100, version=TokenizerVersion.v3, ) tok = tekkenizer.encode("hello", False, False) assert len(tok) == 1 assert not tekkenizer.is_byte(tok[0]) byte_tok = tekkenizer.encode(chr(0), False, False) assert len(byte_tok) == 1 assert tekkenizer.is_byte(byte_tok[0]), byte_tok assert byte_tok[0] < 256 + tekkenizer.num_special_tokens <= tok[0] def test_id_to_byte_piece() -> None: vocab = quick_vocab([b"hello"]) tekkenizer = Tekkenizer( vocab, special_tokens=_get_deprecated_special_tokens(), pattern=r".+", # single token, whole string vocab_size=len(vocab) + len(_get_deprecated_special_tokens()), num_special_tokens=len(_get_deprecated_special_tokens()), version=TokenizerVersion.v3, ) with pytest.raises(ValueError, match="5 is a special token"): tekkenizer.id_to_byte_piece(5, special_token_policy=SpecialTokenPolicy.RAISE) assert tekkenizer.id_to_byte_piece(5, special_token_policy=SpecialTokenPolicy.IGNORE) == b"" assert tekkenizer.id_to_byte_piece(5, special_token_policy=SpecialTokenPolicy.KEEP) == b"[AVAILABLE_TOOLS]" assert ( tekkenizer.id_to_byte_piece( len(vocab) + len(_get_deprecated_special_tokens()) - 1, special_token_policy=SpecialTokenPolicy.KEEP ) == b"hello" ) def test_frozen_special_tokens_list() -> None: """ The `DEPRECATED_SPECIAL_TOKENS` is frozen and should not have any modifications. """ NUM_TOKENS_IN_FROZEN_LIST = 20 # DO NOT MODIFY. assert len(_get_deprecated_special_tokens()) == NUM_TOKENS_IN_FROZEN_LIST FROZEN_TOKENS_DO_NOT_MODIFY = [ "", "", "", "[INST]", "[/INST]", "[AVAILABLE_TOOLS]", "[/AVAILABLE_TOOLS]", "[TOOL_RESULTS]", "[/TOOL_RESULTS]", "[TOOL_CALLS]", "[IMG]", "", "[IMG_BREAK]", "[IMG_END]", "[PREFIX]", "[MIDDLE]", "[SUFFIX]", "[SYSTEM_PROMPT]", "[/SYSTEM_PROMPT]", "[TOOL_CONTENT]", ] # DO NOT MODIFY assert FROZEN_TOKENS_DO_NOT_MODIFY == [token["token_str"] for token in _get_deprecated_special_tokens()] @pytest.mark.parametrize( ("token", "is_special"), [ ("", True), ("a", False), (1, True), (1001, False), (np.int64(0), True), (np.int64(1), True), (np.int64(1001), False), ], ) def test_is_control(dummy_v3: Tekkenizer, token: str | int, is_special: bool) -> None: assert dummy_v3.is_special(token) is is_special def test_special_ids_property(dummy_v3: Tekkenizer) -> None: special_ids = dummy_v3.special_ids assert isinstance(special_ids, set) assert special_ids == set(t["rank"] for t in _get_deprecated_special_tokens()) def test_special_tokens_property(dummy_v3: Tekkenizer) -> None: num_special = dummy_v3.num_special_tokens assert isinstance(num_special, int) assert num_special == len(dummy_v3._all_special_tokens) python-mistral-common-1.11.7/tests/test_tokenize_v1.py000066400000000000000000000112621523035502500230740ustar00rootroot00000000000000import pytest from mistral_common.exceptions import InvalidAssistantMessageException, InvalidMessageStructureException from mistral_common.protocol.instruct.messages import AssistantMessage, UserMessage from mistral_common.protocol.instruct.request import InstructRequest from mistral_common.tokens.tokenizers.base import InstructTokenizer from mistral_common.tokens.tokenizers.mistral import MistralTokenizer @pytest.fixture() def tokenizer() -> InstructTokenizer: return MistralTokenizer.v1().instruct_tokenizer def test_normal(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ] ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "▁[INST]▁a▁[/INST]▁b▁[INST]▁c▁[/INST]▁d" assert tokens == [ 1, 733, 16289, 28793, 264, 733, 28748, 16289, 28793, 287, 2, 733, 16289, 28793, 277, 733, 28748, 16289, 28793, 281, 2, ] def test_system_singleturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct(InstructRequest(messages=[UserMessage(content="a")], system_prompt="SYSTEM")) tokens, text = tokenized.tokens, tokenized.text assert text == "▁[INST]▁SYSTEM<0x0A><0x0A>a▁[/INST]" assert tokens == [1, 733, 16289, 28793, 17121, 22526, 13, 13, 28708, 733, 28748, 16289, 28793] assert tokenizer.tokenizer.decode(tokens) == "[INST] SYSTEM\n\na [/INST]" def test_system_multiturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], system_prompt="SYSTEM", ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "▁[INST]▁SYSTEM<0x0A><0x0A>a▁[/INST]▁b▁[INST]▁c▁[/INST]▁d" assert tokens == [ 1, 733, 16289, 28793, 17121, 22526, 13, 13, 28708, 733, 28748, 16289, 28793, 287, 2, 733, 16289, 28793, 277, 733, 28748, 16289, 28793, 281, 2, ] first_eos = tokens.index(2) assert tokenizer.tokenizer.decode(tokens[first_eos:]) == "[INST] c [/INST] d" def test_continue_final_message(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "▁[INST]▁SYSTEM<0x0A><0x0A>a▁[/INST]▁b▁[INST]▁c▁[/INST]▁d" assert tokens == [ 1, 733, 16289, 28793, 17121, 22526, 13, 13, 28708, 733, 28748, 16289, 28793, 287, 2, 733, 16289, 28793, 277, 733, 28748, 16289, 28793, 281, ] with pytest.raises( InvalidMessageStructureException, match="Cannot continue final message if it is not an assistant message" ): tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): tokenizer.encode_assistant_message( # type: ignore[attr-defined] AssistantMessage( content='"blabla"', prefix=True, ), is_before_last_user_message=False, continue_message=True, ) python-mistral-common-1.11.7/tests/test_tokenize_v2.py000066400000000000000000000241231523035502500230750ustar00rootroot00000000000000import json import pytest from mistral_common.exceptions import InvalidAssistantMessageException, InvalidMessageStructureException from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import AssistantMessage, ToolMessage, UserMessage from mistral_common.protocol.instruct.request import InstructRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.tokens.tokenizers.base import InstructTokenizer from mistral_common.tokens.tokenizers.mistral import MistralTokenizer @pytest.fixture() def tokenizer() -> InstructTokenizer: return MistralTokenizer.v2().instruct_tokenizer def test_normal(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ] ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "[INST]▁a[/INST]▁b[INST]▁c[/INST]▁d" assert tokens == [1, 3, 1032, 4, 1055, 2, 3, 1045, 4, 1049, 2] def test_tools_singleturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[UserMessage(content="a")], available_tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( '[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"tool1",▁"description":▁"1",▁"parameters":▁{}}}][/AVAILABLE_TOOLS][INST]▁a[/INST]' ) # NOTE THE SPACE begin_tool, end_tool = tokens.index(6), tokens.index(7) assert tokens[:begin_tool] + tokens[end_tool + 1 :] == [1, 3, 1032, 4] json.loads(tokenizer.tokenizer.decode(tokens[begin_tool : end_tool + 1])) def test_tools_multiturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], available_tools=[ Tool(function=Function(name="tool1", description="1", parameters={})), Tool(function=Function(name="tool2", description="2", parameters={})), ], ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( "[INST]▁a[/INST]▁b" '[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"tool1",▁"description":▁"1",▁"parameters":▁{}}}' ',▁{"type":▁"function",▁"function":▁{"name":▁"tool2",▁"description":▁"2",▁"parameters":▁{}}}]' "[/AVAILABLE_TOOLS][INST]▁c[/INST]▁d" ) begin_tool, end_tool = tokens.index(6), tokens.index(7) assert tokens[:begin_tool] + tokens[end_tool + 1 :] == [ 1, 3, 1032, 4, 1055, 2, 3, 1045, 4, 1049, 2, ] json.loads(tokenizer.tokenizer.decode(tokens[begin_tool : end_tool + 1])) def test_system_singleturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct(InstructRequest(messages=[UserMessage(content="a")], system_prompt="SYSTEM")) tokens, text = tokenized.tokens, tokenized.text assert text == "[INST]▁SYSTEM<0x0A><0x0A>a[/INST]" # NOTE THE SPACE assert tokens == [1, 3, 17889, 23294, 781, 781, 29476, 4] assert tokenizer.tokenizer.decode(tokens) == "SYSTEM\n\na" def test_system_multiturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], system_prompt="SYSTEM", ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "[INST]▁a[/INST]▁b[INST]▁SYSTEM<0x0A><0x0A>c[/INST]▁d" assert tokens == [ 1, 3, 1032, 4, 1055, 2, 3, 17889, 23294, 781, 781, 29485, 4, 1049, 2, ] first_eos = tokens.index(2) assert tokenizer.tokenizer.decode(tokens[first_eos:]) == "SYSTEM\n\nc d" def test_continue_final_message(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) tokens, text = tokenized.tokens, tokenized.text assert text == "[INST]▁a[/INST]▁b[INST]▁SYSTEM<0x0A><0x0A>c[/INST]▁d" assert tokens == [ 1, 3, 1032, 4, 1055, 2, 3, 17889, 23294, 781, 781, 29485, 4, 1049, ] with pytest.raises( InvalidMessageStructureException, match="Cannot continue final message if it is not an assistant message" ): tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): tokenizer.encode_assistant_message( # type: ignore[attr-defined] AssistantMessage( content='"blabla"', prefix=True, ), is_before_last_user_message=False, continue_message=True, ) def test_system_tools_multiturn(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], available_tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], system_prompt="SYSTEM", ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( '[INST]▁a[/INST]▁b[AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"tool1",▁"description":▁"1",▁"parameters":▁{}}}][/AVAILABLE_TOOLS][INST]▁SYSTEM<0x0A><0x0A>c[/INST]▁d' ) begin_tool, end_tool = tokens.index(6), tokens.index(7) assert tokens[end_tool + 1 :].index(3) == 0 # begin_inst follows end_tool assert tokenizer.tokenizer.decode(tokens[:begin_tool]) == "a b" assert tokenizer.tokenizer.decode(tokens[end_tool + 1 :]) == "SYSTEM\n\nc d" def test_tool_response(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content="d"), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( '[INST]▁a[/INST][TOOL_CALLS]▁[{"name":▁"b",▁"arguments":▁{}}][TOOL_RESULTS]▁[{"name":▁"b",▁"content":▁"d"}][/TOOL_RESULTS]' ) tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content=None, tool_calls=[ToolCall(function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content='{"a": 1}'), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( '[INST]▁a[/INST][TOOL_CALLS]▁[{"name":▁"b",▁"arguments":▁{}}][TOOL_RESULTS]▁[{"name":▁"b",▁"content":▁{"a":▁1}}][/TOOL_RESULTS]' ) tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content=None, tool_calls=[ToolCall(function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content=[TextChunk(text="d"), TextChunk(text='{"a": 1}')]), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( '[INST]▁a[/INST][TOOL_CALLS]▁[{"name":▁"b",▁"arguments":▁{}}][TOOL_RESULTS]▁[{"name":▁"b",▁"content":▁"d{\\"a\\":▁1}"}][/TOOL_RESULTS]' ) def test_tool_message_multiple_shots_without_history(tokenizer: InstructTokenizer) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content="d"), AssistantMessage(content="e"), UserMessage(content="f"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content="d"), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( '[INST]▁a[/INST]▁e[INST]▁f[/INST][TOOL_CALLS]▁[{"name":▁"b",▁"arguments":▁{}}][TOOL_RESULTS]▁[{"name":▁"b",▁"content":▁"d"}][/TOOL_RESULTS]' ) python-mistral-common-1.11.7/tests/test_tokenize_v3.py000066400000000000000000000450301523035502500230760ustar00rootroot00000000000000import json from pathlib import Path from tempfile import NamedTemporaryFile import pytest from mistral_common.exceptions import InvalidAssistantMessageException, InvalidMessageStructureException from mistral_common.protocol.instruct.chunk import TextChunk from mistral_common.protocol.instruct.messages import AssistantMessage, ToolMessage, UserMessage from mistral_common.protocol.instruct.request import InstructRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.tokens.tokenizers.base import InstructTokenizer, TokenizerVersion from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.sentencepiece import ( SentencePieceTokenizer, is_sentencepiece, ) from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy, Tekkenizer TEKKEN_SPECIAL_WHITESPACE = "" TEKKEN_WHITESPACE = " " TEKKEN_BEGIN_TOOL_ID = 5 TEKKEN_END_TOOL_ID = 6 SPM_SPECIAL_WHITESPACE = "▁" SPM_WHITESPACE = "▁" SPM_BEGIN_TOOL_ID = 6 SPM_END_TOOL_ID = 7 def tokenizer() -> InstructTokenizer: return MistralTokenizer.v3().instruct_tokenizer def tekken_tokenizer() -> InstructTokenizer: tekken = MistralTokenizer.v3(is_tekken=True).instruct_tokenizer tekken.tokenizer.special_token_policy = SpecialTokenPolicy.IGNORE # type: ignore return tekken def test_is_spm() -> None: # this is valid for suffix in list(TokenizerVersion.__members__) + ["v3m1"]: with NamedTemporaryFile(suffix=".model." + suffix) as f: assert is_sentencepiece(f.name) with NamedTemporaryFile(suffix=".model") as f: assert is_sentencepiece(f.name) # this is not valid with NamedTemporaryFile(suffix=".model.vx") as f: assert not is_sentencepiece(f.name) def test_spm_version() -> None: directory = Path(__file__).parent.parent / "src" / "mistral_common" / "data" for file in directory.iterdir(): if not file.is_file() or str(file).endswith(".json"): continue suffix = file.suffix[1:].split("m")[0] if suffix == "model": assert SentencePieceTokenizer(str(file)).version == TokenizerVersion.v1 else: assert SentencePieceTokenizer(str(file)).version == TokenizerVersion(suffix) @pytest.mark.parametrize( "tokenizer, special_ws, ws, begin_tool_index, end_tool_index, expected_tokens_before_tool", [ ( tokenizer(), SPM_WHITESPACE, SPM_WHITESPACE, SPM_BEGIN_TOOL_ID, SPM_END_TOOL_ID, [1, 3, 1032, 4], ), ( tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE, TEKKEN_BEGIN_TOOL_ID, TEKKEN_END_TOOL_ID, [1, 3, 1097, 4], ), ], ) def test_tools_singleturn( tokenizer: InstructTokenizer, special_ws: str, ws: str, begin_tool_index: int, end_tool_index: int, expected_tokens_before_tool: list, ) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[UserMessage(content="a")], available_tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( f"[AVAILABLE_TOOLS]{special_ws}[" f'{{"type":{ws}"function",{ws}"function":{ws}{{"name":{ws}"tool1",{ws}"description":{ws}"1",{ws}"parameters":{ws}{{}}}}}}]' f"[/AVAILABLE_TOOLS][INST]{special_ws}a[/INST]" ) begin_tool, end_tool = tokens.index(begin_tool_index), tokens.index(end_tool_index) assert tokens[:begin_tool] + tokens[end_tool + 1 :] == expected_tokens_before_tool + [] json.loads(tokenizer.tokenizer.decode(tokens[begin_tool : end_tool + 1])) @pytest.mark.parametrize( "tokenizer, special_ws, ws, begin_tool_index, end_tool_index, " "expected_tokens_before_tool, expected_tokens_after_tool", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE, SPM_BEGIN_TOOL_ID, SPM_END_TOOL_ID, [1, 3, 1032, 4, 1055], [2, 3, 1045, 4, 1049, 2], ), ( tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE, TEKKEN_BEGIN_TOOL_ID, TEKKEN_END_TOOL_ID, [1, 3, 1097, 4, 1098], [2, 3, 1099, 4, 1100, 2], ), ], ) def test_tools_multiturn( tokenizer: InstructTokenizer, special_ws: str, ws: str, begin_tool_index: int, end_tool_index: int, expected_tokens_before_tool: list, expected_tokens_after_tool: list, ) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], available_tools=[ Tool(function=Function(name="tool1", description="1", parameters={})), Tool(function=Function(name="tool2", description="2", parameters={})), ], ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST]{special_ws}b[AVAILABLE_TOOLS]{special_ws}[" f'{{"type":{ws}"function",{ws}"function":{ws}{{"name":{ws}"tool1",{ws}"description":{ws}"1",{ws}"parameters":{ws}{{}}}}}},' f'{ws}{{"type":{ws}"function",{ws}"function":{ws}{{"name":{ws}"tool2",{ws}"description":{ws}"2",{ws}"parameters":{ws}{{}}}}}}]' f"[/AVAILABLE_TOOLS][INST]{special_ws}c[/INST]{special_ws}d" ) begin_tool, end_tool = tokens.index(begin_tool_index), tokens.index(end_tool_index) assert tokens[:begin_tool] + tokens[end_tool + 1 :] == expected_tokens_before_tool + expected_tokens_after_tool json.loads(tokenizer.tokenizer.decode(tokens[begin_tool : end_tool + 1])) @pytest.mark.parametrize( "tokenizer, special_ws, ws, begin_tool_index, end_tool_index, new_line, decoded_before_tool, decoded_after_tool", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE, SPM_BEGIN_TOOL_ID, SPM_END_TOOL_ID, "<0x0A><0x0A>", "a b", "SYSTEM\n\nc d", ), ( tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE, TEKKEN_BEGIN_TOOL_ID, TEKKEN_END_TOOL_ID, "\n\n", "ab", "SYSTEM\n\ncd", ), ], ) def test_system_tools_multiturn( tokenizer: InstructTokenizer, special_ws: str, ws: str, begin_tool_index: int, end_tool_index: int, new_line: str, decoded_before_tool: str, decoded_after_tool: str, ) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], available_tools=[Tool(function=Function(name="tool1", description="1", parameters={}))], system_prompt="SYSTEM", ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST]{special_ws}b[AVAILABLE_TOOLS]{special_ws}[" f'{{"type":{ws}"function",{ws}"function":{ws}{{"name":{ws}"tool1",{ws}"description":{ws}"1",{ws}"parameters":{ws}{{}}}}}}]' f"[/AVAILABLE_TOOLS][INST]{special_ws}SYSTEM{new_line}c[/INST]{special_ws}d" ) begin_tool, end_tool = tokens.index(begin_tool_index), tokens.index(end_tool_index) assert tokens[end_tool + 1 :].index(3) == 0 # begin_inst follows end_tool assert tokenizer.tokenizer.decode(tokens[:begin_tool]) == decoded_before_tool assert tokenizer.tokenizer.decode(tokens[end_tool + 1 :]) == decoded_after_tool @pytest.mark.parametrize( "tokenizer, special_ws, new_line", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, "<0x0A><0x0A>", ), ( tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, "\n\n", ), ], ) def test_continue_final_message( tokenizer: InstructTokenizer, special_ws: str, new_line: str, ) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), AssistantMessage(content="d"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) tokens, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST]{special_ws}b[INST]{special_ws}SYSTEM{new_line}c[/INST]{special_ws}d" ) if not isinstance(tokenizer.tokenizer, Tekkenizer): assert tokens == [1, 3, 1032, 4, 1055, 2, 3, 17889, 23294, 781, 781, 29485, 4, 1049] else: assert tokens == [1, 3, 1097, 4, 1098, 2, 3, 101289, 58343, 1267, 1099, 4, 1100] with pytest.raises( InvalidMessageStructureException, match="Cannot continue final message if it is not an assistant message" ): tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(content="b"), UserMessage(content="c"), ], system_prompt="SYSTEM", continue_final_message=True, ) ) with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): tokenizer.encode_assistant_message( # type: ignore[attr-defined] AssistantMessage( content='"blabla"', prefix=True, ), is_before_last_user_message=False, continue_message=True, ) @pytest.mark.parametrize( "tokenizer, special_ws, ws", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE, ), (tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE), ], ) def test_tool_message(tokenizer: InstructTokenizer, special_ws: str, ws: str) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="b", arguments="{}"))] ), ToolMessage( name="b", content="d", tool_call_id="123456789", ), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( f'[INST]{special_ws}a[/INST][TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}' f'"arguments":{ws}{{}},{ws}"id":{ws}"123456789"}}][TOOL_RESULTS]{special_ws}' f'{{"content":{ws}"d",{ws}"call_id":{ws}"123456789"}}[/TOOL_RESULTS]' ) tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage( content=None, tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="b", arguments="{}"))] ), ToolMessage( name="b", content='{"a": 1}', tool_call_id="123456789", ), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST][TOOL_CALLS]{special_ws}[" f'{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"123456789"}}][TOOL_RESULTS]{special_ws}' f'{{"content":{ws}{{"a":{ws}1}},{ws}"call_id":{ws}"123456789"}}[/TOOL_RESULTS]' ) tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage( content=None, tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="b", arguments="{}"))] ), ToolMessage( name="b", content=[TextChunk(text="d"), TextChunk(text='{"a": 1}')], tool_call_id="123456789", ), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST][TOOL_CALLS]{special_ws}[" f'{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"123456789"}}][TOOL_RESULTS]{special_ws}' f'{{"content":{ws}"d{{\\"a\\":{ws}1}}",{ws}"call_id":{ws}"123456789"}}[/TOOL_RESULTS]' ) @pytest.mark.parametrize( "tokenizer, special_ws, ws", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE, ), (tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE), ], ) def test_tool_message_no_id_fine_tuning_ok(tokenizer: InstructTokenizer, special_ws: str, ws: str) -> None: # In fine-tuning we allow passing a tool call as the last message. # We need to make sure to not parse this empty id as "null" function = FunctionCall(name="b", arguments="{}") tool_calls = [ToolCall(id="null", function=function), ToolCall(function=function)] for tool_call in tool_calls: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(tool_calls=[tool_call]), ], ) ) _, text = tokenized.tokens, tokenized.text assert ( text == f'[INST]{special_ws}a[/INST][TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}"arguments":{ws}{{}}}}]' ) @pytest.mark.parametrize( "tokenizer, special_ws, ws", [ ( tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE, ), ( tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE, ), ], ) def test_tool_message_multiple_shots_with_history(tokenizer: InstructTokenizer, special_ws: str, ws: str) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage(tool_calls=[ToolCall(id="0", function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content="d", tool_call_id="0"), AssistantMessage(content="e"), UserMessage(content="f"), AssistantMessage(tool_calls=[ToolCall(id="1", function=FunctionCall(name="b", arguments="{}"))]), ToolMessage(name="b", content="d", tool_call_id="1"), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST]" f'[TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"0"}}][TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"0"}}[/TOOL_RESULTS]' # noqa: E501 f"{special_ws}e[INST]{special_ws}f[/INST]" f'[TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"1"}}][TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"1"}}[/TOOL_RESULTS]' ) @pytest.mark.parametrize( "tokenizer, special_ws, ws", [ (tokenizer(), SPM_SPECIAL_WHITESPACE, SPM_WHITESPACE), (tekken_tokenizer(), TEKKEN_SPECIAL_WHITESPACE, TEKKEN_WHITESPACE), ], ) def test_tool_message_multiple_calls(tokenizer: InstructTokenizer, special_ws: str, ws: str) -> None: tokenized = tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage( tool_calls=[ ToolCall(id="0", function=FunctionCall(name="b", arguments="{}")), ToolCall(id="1", function=FunctionCall(name="q", arguments="{}")), ] ), ToolMessage(name="b", content="d", tool_call_id="0"), ToolMessage(name="q", content="d", tool_call_id="1"), AssistantMessage(content="e"), UserMessage(content="f"), AssistantMessage( tool_calls=[ ToolCall(id="2", function=FunctionCall(name="b", arguments="{}")), ToolCall(id="3", function=FunctionCall(name="q", arguments="{}")), ] ), ToolMessage(name="b", content="d", tool_call_id="2"), ToolMessage(name="q", content="d", tool_call_id="3"), ], ) ) _, text = tokenized.tokens, tokenized.text assert text == ( f"[INST]{special_ws}a[/INST]" f'[TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"0"}},{ws}{{"name":{ws}"q",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"1"}}]' f'[TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"0"}}[/TOOL_RESULTS]' f'[TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"1"}}[/TOOL_RESULTS]' f"{special_ws}e[INST]{special_ws}f[/INST]" f'[TOOL_CALLS]{special_ws}[{{"name":{ws}"b",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"2"}},{ws}{{"name":{ws}"q",{ws}"arguments":{ws}{{}},{ws}"id":{ws}"3"}}]' f'[TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"2"}}[/TOOL_RESULTS]' f'[TOOL_RESULTS]{special_ws}{{"content":{ws}"d",{ws}"call_id":{ws}"3"}}[/TOOL_RESULTS]' ) @pytest.mark.parametrize("tokenizer", [tokenizer(), tekken_tokenizer()]) def test_assistant_tool_call_and_content(tokenizer: InstructTokenizer) -> None: req: InstructRequest = InstructRequest( messages=[ UserMessage(content="a"), AssistantMessage( content="b", tool_calls=[ ToolCall(id="0", function=FunctionCall(name="b", arguments="{}")), ], ), ], ) with pytest.raises(ValueError, match="Cannot have tool calls and content defined in the same assistant message"): tokenizer.encode_instruct(req) python-mistral-common-1.11.7/tests/test_tokenizer_base.py000066400000000000000000000010761523035502500236440ustar00rootroot00000000000000import pytest from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy def test_special_token_policy_backward_compatibility() -> None: assert SpecialTokenPolicy(0) == SpecialTokenPolicy.IGNORE assert SpecialTokenPolicy(1) == SpecialTokenPolicy.KEEP assert SpecialTokenPolicy(2) == SpecialTokenPolicy.RAISE with pytest.raises(ValueError, match=r"3 is not a valid SpecialTokenPolicy"): SpecialTokenPolicy(3) with pytest.raises(ValueError, match=r"'invalid' is not a valid SpecialTokenPolicy"): SpecialTokenPolicy("invalid") python-mistral-common-1.11.7/tests/test_tokenizer_v11.py000066400000000000000000000106631523035502500233430ustar00rootroot00000000000000import pytest from mistral_common.exceptions import InvalidAssistantMessageException from mistral_common.protocol.instruct.messages import ( AssistantMessage, ) from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.tokens.tokenizers.base import TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV11 from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab @pytest.fixture(scope="session") def tekkenizer() -> InstructTokenizerV11: special_tokens = get_special_tokens(TokenizerVersion.v11) tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v11, ) return InstructTokenizerV11(tokenizer) def test_special_tokens(tekkenizer: InstructTokenizerV11) -> None: assert tekkenizer.ARGS == 32 assert tekkenizer.CALL_ID == 33 assert tekkenizer.TOOL_CALLS == 9 def test_tokenize_assistant_message(tekkenizer: InstructTokenizerV11) -> None: tokens = tekkenizer.encode_assistant_message( AssistantMessage( tool_calls=[ToolCall(function=FunctionCall(name="a_a_a", arguments="blabla"))], ), is_before_last_user_message=False, continue_message=False, ) assert tokens == [ tekkenizer.TOOL_CALLS, 197, 195, 197, 195, 197, tekkenizer.ARGS, 134, 198, 208, 197, 198, 208, 197, 134, 2, ] assert tekkenizer.tokenizer._to_string(tokens) == ('[TOOL_CALLS]a_a_a[ARGS]"blabla"') def test_tokenize_assistant_message_continue_message(tekkenizer: InstructTokenizerV11) -> None: tokens = tekkenizer.encode_assistant_message( AssistantMessage( content='"blabla"', ), is_before_last_user_message=False, continue_message=True, ) assert tokens == [ 134, 198, 208, 197, 198, 208, 197, 134, ] assert tekkenizer.tokenizer._to_string(tokens) == ('"blabla"') with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): tekkenizer.encode_assistant_message( AssistantMessage( content='"blabla"', prefix=True, ), is_before_last_user_message=False, continue_message=True, ) def test_tokenize_assistant_messages(tekkenizer: InstructTokenizerV11) -> None: tokens = tekkenizer.encode_assistant_message( AssistantMessage( tool_calls=[ ToolCall(function=FunctionCall(name="a_a_a", arguments="blabla")), ToolCall(function=FunctionCall(name="b", arguments="blu")), ], ), is_before_last_user_message=False, continue_message=False, ) assert tokens == [ tekkenizer.TOOL_CALLS, 197, 195, 197, 195, 197, tekkenizer.ARGS, 134, 198, 208, 197, 198, 208, 197, 134, tekkenizer.TOOL_CALLS, 198, tekkenizer.ARGS, 134, 198, 208, 217, 134, 2, ] assert tekkenizer.tokenizer._to_string(tokens) == ('[TOOL_CALLS]a_a_a[ARGS]"blabla"[TOOL_CALLS]b[ARGS]"blu"') def test_tokenize_assistant_message_train(tekkenizer: InstructTokenizerV11) -> None: tokens = tekkenizer.encode_assistant_message( AssistantMessage( tool_calls=[ToolCall(function=FunctionCall(name="a_a_a", arguments="blabla"), id="ABC")], ), is_before_last_user_message=True, continue_message=False, ) assert tokens == [ tekkenizer.TOOL_CALLS, 197, 195, 197, 195, 197, tekkenizer.CALL_ID, 165, 166, 167, tekkenizer.ARGS, 134, 198, 208, 197, 198, 208, 197, 134, 2, ] assert tekkenizer.tokenizer._to_string(tokens) == ('[TOOL_CALLS]a_a_a[CALL_ID]ABC[ARGS]"blabla"') python-mistral-common-1.11.7/tests/test_tokenizer_v13.py000066400000000000000000000375621523035502500233540ustar00rootroot00000000000000import pytest from mistral_common.exceptions import InvalidAssistantMessageException, TokenizerException from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, BaseMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import InstructRequestNormalizerV13 from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.protocol.instruct.validator import MistralRequestValidatorV13 from mistral_common.tokens.tokenizers.audio import AudioConfig, AudioEncoder, AudioSpectrogramConfig, SpecialAudioIDs from mistral_common.tokens.tokenizers.base import InstructTokenizer, SpecialTokens, Tokenized, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV13 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy, Tekkenizer from tests.fixtures.audio import get_dummy_audio_chunk, get_dummy_audio_url_chunk from tests.test_tekken import get_special_tokens, quick_vocab @pytest.fixture(scope="module") def audio_chunk() -> AudioChunk: return get_dummy_audio_chunk() @pytest.fixture(scope="module") def audio_url_chunk() -> AudioURLChunk: return get_dummy_audio_url_chunk() @pytest.fixture(scope="session") def v13_tekkenizer() -> InstructTokenizerV13: special_tokens = get_special_tokens(TokenizerVersion.v13, add_think=False) tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v13, ) return InstructTokenizerV13(tokenizer) @pytest.fixture(scope="session") def v13_tekkenizer_think() -> InstructTokenizerV13: special_tokens = get_special_tokens(TokenizerVersion.v13, add_think=True) tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v13, ) return InstructTokenizerV13(tokenizer) @pytest.fixture(scope="session") def v13_tekkenizer_audio() -> InstructTokenizerV13: special_tokens = get_special_tokens(TokenizerVersion.v13, add_think=False, add_audio=True) tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v13, ) audio_config = AudioConfig( sampling_rate=24_000, frame_rate=12.5, encoding_config=AudioSpectrogramConfig( num_mel_bins=128, window_size=400, hop_length=160, ), ) special_audio_ids = SpecialAudioIDs( audio=tokenizer.get_special_token(SpecialTokens.audio.value), begin_audio=tokenizer.get_special_token(SpecialTokens.begin_audio.value), streaming_pad=None, text_to_audio=None, audio_to_text=None, ) audio_encoder = AudioEncoder(audio_config, special_audio_ids) return InstructTokenizerV13(tokenizer, audio_encoder=audio_encoder) EXPECTED_TEXT_V13: str = ( r"[SYSTEM_PROMPT]S1[THINK]TS[/THINK]S2[/SYSTEM_PROMPT][AVAILABLE_TOOLS][{" r'"type": "function", "function": {"name": "math_interpreter", ' r'"description": "Get the value of an arithmetic expression.", ' r'"parameters": {"type": "object", "properties": {' r'"expression": {"type": "string", "description": ' r'"Math expression."}}}}}][/AVAILABLE_TOOLS][INST]U1[/INST]A1' r"[TOOL_CALLS]F1[ARGS]{}[TOOL_CALLS]F2[ARGS]{}" r"[TOOL_RESULTS]R1[/TOOL_RESULTS][TOOL_RESULTS]R2" r"[/TOOL_RESULTS][THINK]T1[/THINK]A2[INST]U2[/INST]" ) EXPECTED_TEXT_V13_FROM_WRONG_ORDER: str = ( r"[SYSTEM_PROMPT]S[/SYSTEM_PROMPT][AVAILABLE_TOOLS][{" r'"type": "function", "function": {"name": "math_interpreter", ' r'"description": "Get the value of an arithmetic expression.", ' r'"parameters": {"type": "object", "properties": {' r'"expression": {"type": "string", "description": ' r'"Math expression."}}}}}][/AVAILABLE_TOOLS][INST]U1[/INST]A1' r"[TOOL_CALLS]F1[ARGS]{}[TOOL_CALLS]F2[ARGS]{}" r"[TOOL_RESULTS]R1[/TOOL_RESULTS][TOOL_RESULTS]R2" r"[/TOOL_RESULTS]A2[INST]U2[/INST]" ) @pytest.fixture def available_tools() -> list[Tool]: return [ Tool( function=Function( name="math_interpreter", description="Get the value of an arithmetic expression.", parameters={ "type": "object", "properties": { "expression": { "type": "string", "description": "Math expression.", } }, }, ) ) ] @pytest.fixture def messages() -> list[BaseMessage]: return [ SystemMessage(content=[TextChunk(text="S1"), ThinkChunk(thinking="TS"), TextChunk(text="S2")]), UserMessage(content="U1"), AssistantMessage( content="A1", tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="F1", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="F2", arguments="{}")), ], ), ToolMessage(content="R1", tool_call_id="123456789"), ToolMessage(content="R2", tool_call_id="999999999"), AssistantMessage(content=[ThinkChunk(thinking="T1"), TextChunk(text="A2")]), UserMessage(content="U2"), ] @pytest.fixture def messages_wrong_order_results() -> list[BaseMessage]: return [ SystemMessage(content="S"), UserMessage(content="U1"), AssistantMessage( content="A1", tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="F1", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="F2", arguments="{}")), ], ), ToolMessage( content="R2", tool_call_id="999999999" ), # wrong order of results but passes validation and is later normalized ToolMessage(content="R1", tool_call_id="123456789"), AssistantMessage(content="A2"), UserMessage(content="U2"), ] def test_end_to_end_v13( v13_tekkenizer_think: InstructTokenizer, available_tools: list[Tool], messages: list[BaseMessage], ) -> None: """ Tests normalization (including reordering) and validation """ request_normalizer = InstructRequestNormalizerV13.normalizer() validator = MistralRequestValidatorV13() mistral_tokenizer_v13 = MistralTokenizer( instruct_tokenizer=v13_tekkenizer_think, validator=validator, request_normalizer=request_normalizer ) chat_completion_request: ChatCompletionRequest = ChatCompletionRequest( messages=messages, tools=available_tools, ) assert isinstance(mistral_tokenizer_v13, MistralTokenizer), type(mistral_tokenizer_v13) # This does validation, normalization and encoding tokenized_v13 = mistral_tokenizer_v13.encode_chat_completion(chat_completion_request) assert isinstance(tokenized_v13, Tokenized) assert tokenized_v13.text == EXPECTED_TEXT_V13, tokenized_v13.text def test_end_to_end_v13_wrong_order( v13_tekkenizer: InstructTokenizer, available_tools: list[Tool], messages_wrong_order_results: list[BaseMessage], ) -> None: """ Tests normalization (including reordering) and validation """ request_normalizer = InstructRequestNormalizerV13.normalizer() validator = MistralRequestValidatorV13() mistral_tokenizer_v13 = MistralTokenizer( instruct_tokenizer=v13_tekkenizer, validator=validator, request_normalizer=request_normalizer ) chat_completion_request: ChatCompletionRequest = ChatCompletionRequest( messages=messages_wrong_order_results, tools=available_tools, ) assert isinstance(mistral_tokenizer_v13, MistralTokenizer), type(mistral_tokenizer_v13) # This does validation, normalization and encoding tokenized_v13 = mistral_tokenizer_v13.encode_chat_completion(chat_completion_request) assert isinstance(tokenized_v13, Tokenized) assert tokenized_v13.text == EXPECTED_TEXT_V13_FROM_WRONG_ORDER, tokenized_v13.text def test_encode_tool_message(v13_tekkenizer: InstructTokenizerV13) -> None: tool_message = ToolMessage(content="R1", tool_call_id="123456789") assert isinstance(v13_tekkenizer, InstructTokenizerV13) encoded, images, audios = v13_tekkenizer.encode_tool_message(tool_message, is_before_last_user_message=False) assert encoded == [7, 182, 149, 8] assert images == [] assert audios == [] tool_message = ToolMessage(content=[TextChunk(text="R1"), TextChunk(text="R2")], tool_call_id="123456789") assert isinstance(v13_tekkenizer, InstructTokenizerV13) encoded, images, audios = v13_tekkenizer.encode_tool_message(tool_message, is_before_last_user_message=False) assert encoded == [7, 182, 149, 182, 150, 8] assert images == [] assert audios == [] def test_encode_think_chunk(v13_tekkenizer_think: InstructTokenizerV13) -> None: assert isinstance(v13_tekkenizer_think, InstructTokenizerV13) think_chunk = ThinkChunk( thinking="T1", ) encoded = v13_tekkenizer_think.encode_think(think_chunk) assert v13_tekkenizer_think.decode(encoded, special_token_policy=SpecialTokenPolicy.KEEP) == "[THINK]T1[/THINK]" think_chunk = ThinkChunk( thinking="T1", closed=False, ) encoded = v13_tekkenizer_think.encode_think(think_chunk) assert v13_tekkenizer_think.decode(encoded, special_token_policy=SpecialTokenPolicy.KEEP) == "[THINK]T1" @pytest.mark.parametrize( "message, expected", [ ( AssistantMessage(content="A1"), "A1", ), ( AssistantMessage(content="A1", prefix=True), "A1", ), ( AssistantMessage(content=[TextChunk(text="A1")]), "A1", ), ( AssistantMessage(content=[ThinkChunk(thinking="T1"), TextChunk(text="A1")]), "[THINK]T1[/THINK]A1", ), ( AssistantMessage( content=[ThinkChunk(thinking="R1", closed=False), TextChunk(text="A1")], tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="F1", arguments="{'a': 1}"))], ), "[THINK]R1A1[TOOL_CALLS]F1[ARGS]\"{'a': 1}\"", ), ], ) @pytest.mark.parametrize("continue_final_message", [True, False]) def test_tokenize_assistant_message( v13_tekkenizer_think: InstructTokenizerV13, message: AssistantMessage, expected: str, continue_final_message: bool ) -> None: if not continue_final_message: tokens = v13_tekkenizer_think.encode_assistant_message( message, is_before_last_user_message=False, continue_message=continue_final_message ) if not message.prefix: expected += "" else: if message.prefix: with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): v13_tekkenizer_think.encode_assistant_message( message, is_before_last_user_message=False, continue_message=continue_final_message ) return tokens = v13_tekkenizer_think.encode_assistant_message( message, is_before_last_user_message=False, continue_message=continue_final_message ) assert v13_tekkenizer_think.decode(tokens, special_token_policy=SpecialTokenPolicy.KEEP) == expected def test_tokenize_assistant_message_error(v13_tekkenizer: InstructTokenizerV13) -> None: with pytest.raises(TokenizerException, match=r"Invalid assistant message"): v13_tekkenizer.encode_assistant_message( AssistantMessage(content="", tool_calls=[]), is_before_last_user_message=False, continue_message=False ) with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): v13_tekkenizer.encode_assistant_message( AssistantMessage(content="z", tool_calls=[], prefix=True), is_before_last_user_message=False, continue_message=True, ) @pytest.mark.parametrize( "message, expected", [ ( SystemMessage(content="S1"), "[SYSTEM_PROMPT]S1[/SYSTEM_PROMPT]", ), ( SystemMessage(content=[TextChunk(text="S1"), ThinkChunk(thinking="TS"), TextChunk(text="S2")]), "[SYSTEM_PROMPT]S1[THINK]TS[/THINK]S2[/SYSTEM_PROMPT]", ), ( SystemMessage( content=[ TextChunk(text="S1"), TextChunk(text="S3"), ThinkChunk(thinking="TS", closed=True), ThinkChunk(thinking="TS", closed=True), TextChunk(text="S2"), ] ), "[SYSTEM_PROMPT]S1S3[THINK]TS[/THINK][THINK]TS[/THINK]S2[/SYSTEM_PROMPT]", ), ( SystemMessage( content=[ TextChunk(text="S1"), TextChunk(text="S3"), ThinkChunk(thinking="TS", closed=False), ] ), "[SYSTEM_PROMPT]S1S3[THINK]TS[/SYSTEM_PROMPT]", ), ], ) def test_encode_system_message( v13_tekkenizer_think: InstructTokenizerV13, message: SystemMessage, expected: str ) -> None: encoded, audios = v13_tekkenizer_think.encode_system_message(message) assert v13_tekkenizer_think.decode(encoded, special_token_policy=SpecialTokenPolicy.KEEP) == expected assert audios == [] @pytest.mark.parametrize("audio_fixture", ["audio_chunk", "audio_url_chunk"]) def test_encode_chat_completion_request_with_sp_and_audio( v13_tekkenizer_audio: InstructTokenizerV13, audio_fixture: str, request: pytest.FixtureRequest ) -> None: audio_chunk: AudioChunk | AudioURLChunk = request.getfixturevalue(audio_fixture) request_normalizer = InstructRequestNormalizerV13.normalizer() validator = MistralRequestValidatorV13() messages = [ SystemMessage(content="hello"), UserMessage(content=[audio_chunk]), ] mistral_tokenizer_v13 = MistralTokenizer( instruct_tokenizer=v13_tekkenizer_audio, validator=validator, request_normalizer=request_normalizer ) encoded = mistral_tokenizer_v13.encode_chat_completion(ChatCompletionRequest(messages=messages)) assert encoded.text == "[SYSTEM_PROMPT]hello[/SYSTEM_PROMPT][INST][BEGIN_AUDIO][AUDIO][AUDIO][/INST]" assert len(encoded.audios) == 1 def test_encode_chat_completion_continue_final_message(v13_tekkenizer: InstructTokenizerV13) -> None: request_normalizer = InstructRequestNormalizerV13.normalizer() validator = MistralRequestValidatorV13() mistral_tokenizer = MistralTokenizer( instruct_tokenizer=v13_tekkenizer, validator=validator, request_normalizer=request_normalizer ) request: ChatCompletionRequest = ChatCompletionRequest( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) encoded = mistral_tokenizer.encode_chat_completion(request) eos_id = v13_tekkenizer.tokenizer.eos_id assert encoded.tokens[-1] != eos_id python-mistral-common-1.11.7/tests/test_tokenizer_v15.py000066400000000000000000000522231523035502500233450ustar00rootroot00000000000000import base64 from collections.abc import Callable from io import BytesIO import pytest from PIL import Image from mistral_common.exceptions import InvalidRequestException, TokenizerException from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, ImageURLChunk, TextChunk, ThinkChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import get_normalizer from mistral_common.protocol.instruct.request import ( ChatCompletionRequest, InstructRequest, ModelSettings, ReasoningEffort, ) from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.protocol.instruct.validator import ValidationMode, get_validator from mistral_common.tokens.tokenizers.audio import AudioConfig, AudioEncoder, AudioSpectrogramConfig, SpecialAudioIDs from mistral_common.tokens.tokenizers.base import SpecialTokens, TokenizerVersion from mistral_common.tokens.tokenizers.image import ImageConfig, ImageEncoder, SpecialImageIDs from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV15 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.model_settings_builder import EnumBuilder, ModelSettingsBuilder from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.fixtures.audio import get_dummy_audio_chunk, get_dummy_audio_url_chunk from tests.test_tekken import get_special_tokens, quick_vocab EXPECTED_TEXT_V15: str = ( r"[SYSTEM_PROMPT]S[/SYSTEM_PROMPT]" r'[AVAILABLE_TOOLS][{"type": "function", "function": {"name": "math_interpreter",' r' "description": "Get the value of an arithmetic expression.",' r' "parameters": {"type": "object", "properties": {"expression":' r' {"type": "string", "description": "Math expression."}}}}}]' r'[/AVAILABLE_TOOLS][MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' r"[INST]U1[/INST]A1" r"[TOOL_CALLS]F1[ARGS]{}[TOOL_CALLS]F2[ARGS]{}" r"[TOOL_RESULTS]R1[/TOOL_RESULTS]" r"[TOOL_RESULTS]R2[/TOOL_RESULTS]A2" r"[INST]U2[/INST]" ) EXPECTED_TEXT_V15_NO_TOOLS: str = ( r"[SYSTEM_PROMPT]S[/SYSTEM_PROMPT]" r'[MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' r"[INST]U1[/INST]A1" r"[TOOL_CALLS]F1[ARGS]{}[TOOL_CALLS]F2[ARGS]{}" r"[TOOL_RESULTS]R1[/TOOL_RESULTS]" r"[TOOL_RESULTS]R2[/TOOL_RESULTS]A2" r"[INST]U2[/INST]" ) EXPECTED_TEXT_TOOL_AUDIO: str = ( r"" r'[AVAILABLE_TOOLS][{"type": "function", "function": {"name": "fn",' r' "description": "test", "parameters": {}}}]' r'[/AVAILABLE_TOOLS][MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' r"[INST]Use the tool[/INST]" r"[TOOL_CALLS]fn[ARGS]{}" r"[TOOL_RESULTS]result[BEGIN_AUDIO][AUDIO][AUDIO][/TOOL_RESULTS]" ) EXPECTED_TEXT_TOOL_IMAGE: str = ( r"" r'[AVAILABLE_TOOLS][{"type": "function", "function": {"name": "fn",' r' "description": "test", "parameters": {}}}]' r'[/AVAILABLE_TOOLS][MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' r"[INST]Use the tool[/INST]" r"[TOOL_CALLS]fn[ARGS]{}" r"[TOOL_RESULTS]result[IMG][IMG_END][/TOOL_RESULTS]" ) EXPECTED_TEXT_SYSTEM_AUDIO: str = ( r"[SYSTEM_PROMPT]System with content[BEGIN_AUDIO][AUDIO][AUDIO][/SYSTEM_PROMPT]" r'[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' r"[INST]Hello[/INST]" ) EXPECTED_TEXT_USER_AUDIO: str = ( r"" r'[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' r"[INST]Here is content[BEGIN_AUDIO][AUDIO][AUDIO][/INST]" ) EXPECTED_TEXT_USER_IMAGE: str = ( r"" r'[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' r"[INST][IMG][IMG_END]Here is content[/INST]" ) def _build_v15_tekkenizer(model_settings_builder: ModelSettingsBuilder | None) -> Tekkenizer: r"""Build a v15 Tekkenizer with the given model settings builder. Args: model_settings_builder: The model settings builder, or None to create a tekkenizer without model settings support. """ return Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=get_special_tokens(TokenizerVersion.v15, add_think=True), pattern=r".+", vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v15, model_settings_builder=model_settings_builder, ) def get_v15_tekkenizer( model_settings_builder: ModelSettingsBuilder | None, ) -> InstructTokenizerV15: """Build an InstructTokenizerV15 with the given model settings builder.""" return InstructTokenizerV15(_build_v15_tekkenizer(model_settings_builder)) def get_v15_mistral_tokenizer( model_settings_builder: ModelSettingsBuilder | None, ) -> MistralTokenizer: """Build a MistralTokenizer wrapping a v15 instruct tokenizer.""" tekkenizer = _build_v15_tekkenizer(model_settings_builder) request_normalizer = get_normalizer(TokenizerVersion.v15, tekkenizer.model_settings_builder) validator = get_validator(TokenizerVersion.v15, mode=ValidationMode.test) return MistralTokenizer( InstructTokenizerV15(tekkenizer), validator=validator, request_normalizer=request_normalizer, ) def _build_model_settings_builder( allowed_reasoning_effort: tuple[str, ...] | None, ) -> ModelSettingsBuilder: """Build a ModelSettingsBuilder from allowed reasoning effort values. When `allowed_reasoning_effort` is `None`, returns `ModelSettingsBuilder.none()` (all fields ignored). This matches the behavior of `Tekkenizer.from_file` when no `model_settings_builder` key is present in the JSON. """ if allowed_reasoning_effort is None: return ModelSettingsBuilder.none() return ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort]( values=[ReasoningEffort(v) for v in allowed_reasoning_effort], accepts_none=True, default=ReasoningEffort(allowed_reasoning_effort[0]) if allowed_reasoning_effort else None, ) ) def _get_dummy_image_url_chunk() -> ImageURLChunk: r"""Build a small base64-encoded image URL chunk for testing.""" img = Image.new("RGB", (4, 4), "red") buf = BytesIO() img.save(buf, "PNG") data_url = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}" return ImageURLChunk(image_url=data_url) def get_v15_mistral_tokenizer_with_audio() -> MistralTokenizer: r"""Build a V15 MistralTokenizer with audio encoder.""" builder = _build_model_settings_builder(tuple(ReasoningEffort)) tekkenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=get_special_tokens(TokenizerVersion.v15, add_audio=True), pattern=r".+", vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v15, model_settings_builder=builder, ) audio_config = AudioConfig( sampling_rate=24_000, frame_rate=12.5, encoding_config=AudioSpectrogramConfig( num_mel_bins=128, hop_length=160, window_size=400, ), ) special_audio_ids = SpecialAudioIDs( audio=tekkenizer.get_special_token(SpecialTokens.audio.value), begin_audio=tekkenizer.get_special_token(SpecialTokens.begin_audio.value), streaming_pad=None, text_to_audio=None, audio_to_text=None, ) audio_encoder = AudioEncoder(audio_config, special_audio_ids) instruct_tokenizer = InstructTokenizerV15(tekkenizer, audio_encoder=audio_encoder) request_normalizer = get_normalizer(TokenizerVersion.v15, tekkenizer.model_settings_builder) validator = get_validator(TokenizerVersion.v15, mode=ValidationMode.test) return MistralTokenizer( instruct_tokenizer=instruct_tokenizer, validator=validator, request_normalizer=request_normalizer, ) def get_v15_mistral_tokenizer_with_image() -> MistralTokenizer: r"""Build a V15 MistralTokenizer with image encoder.""" builder = _build_model_settings_builder(tuple(ReasoningEffort)) tekkenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens=get_special_tokens(TokenizerVersion.v15, add_think=True), pattern=r".+", vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v15, model_settings_builder=builder, ) image_config = ImageConfig(image_patch_size=16, max_image_size=1024) special_image_ids = SpecialImageIDs( img=tekkenizer.get_special_token(SpecialTokens.img.value), img_break=tekkenizer.get_special_token(SpecialTokens.img_break.value), img_end=tekkenizer.get_special_token(SpecialTokens.img_end.value), ) image_encoder = ImageEncoder(image_config, special_image_ids) instruct_tokenizer = InstructTokenizerV15(tekkenizer, image_encoder=image_encoder) request_normalizer = get_normalizer(TokenizerVersion.v15, tekkenizer.model_settings_builder) validator = get_validator(TokenizerVersion.v15, mode=ValidationMode.test) return MistralTokenizer( instruct_tokenizer=instruct_tokenizer, validator=validator, request_normalizer=request_normalizer, ) @pytest.fixture(scope="session") def v15_tekkenizer() -> InstructTokenizerV15: return get_v15_tekkenizer(_build_model_settings_builder(tuple(ReasoningEffort))) @pytest.fixture(scope="session") def v15_tekkenizer_no_reasoning() -> InstructTokenizerV15: return get_v15_tekkenizer(_build_model_settings_builder(None)) @pytest.fixture def available_tools() -> list[Tool]: return [ Tool( function=Function( name="math_interpreter", description="Get the value of an arithmetic expression.", parameters={ "type": "object", "properties": { "expression": { "type": "string", "description": "Math expression.", } }, }, ) ) ] @pytest.fixture def messages() -> list[ChatMessage]: return [ SystemMessage(content="S"), UserMessage(content="U1"), AssistantMessage( content="A1", tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="F1", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="F2", arguments="{}")), ], ), ToolMessage(content="R1", tool_call_id="123456789"), ToolMessage(content="R2", tool_call_id="999999999"), AssistantMessage(content="A2"), UserMessage(content="U2"), ] @pytest.fixture(scope="session") def audio_chunk() -> AudioChunk: return get_dummy_audio_chunk() # Multimodal content chunks and their corresponding tokenizer factories for parametrized tests. _TOOL_MULTIMODAL_PARAMS = [ pytest.param( get_dummy_audio_chunk(), 1, 0, get_v15_mistral_tokenizer_with_audio, EXPECTED_TEXT_TOOL_AUDIO, id="audio" ), pytest.param( get_dummy_audio_url_chunk(), 1, 0, get_v15_mistral_tokenizer_with_audio, EXPECTED_TEXT_TOOL_AUDIO, id="audio_url", ), pytest.param( _get_dummy_image_url_chunk(), 0, 1, get_v15_mistral_tokenizer_with_image, EXPECTED_TEXT_TOOL_IMAGE, id="image_url", ), ] _SYSTEM_MULTIMODAL_PARAMS = [ pytest.param( get_dummy_audio_chunk(), 1, 0, get_v15_mistral_tokenizer_with_audio, EXPECTED_TEXT_SYSTEM_AUDIO, id="audio" ), ] _USER_MULTIMODAL_PARAMS = [ pytest.param( get_dummy_audio_chunk(), 1, 0, get_v15_mistral_tokenizer_with_audio, EXPECTED_TEXT_USER_AUDIO, id="audio" ), pytest.param( get_dummy_audio_url_chunk(), 1, 0, get_v15_mistral_tokenizer_with_audio, EXPECTED_TEXT_USER_AUDIO, id="audio_url", ), pytest.param( _get_dummy_image_url_chunk(), 0, 1, get_v15_mistral_tokenizer_with_image, EXPECTED_TEXT_USER_IMAGE, id="image_url", ), ] def test_tools_and_reasoning_effort( v15_tekkenizer: InstructTokenizerV15, available_tools: list[Tool], messages: list[ChatMessage] ) -> None: request = InstructRequest( messages=messages, available_tools=available_tools, settings=ModelSettings(reasoning_effort=ReasoningEffort.high), ) tokenized = v15_tekkenizer.encode_instruct(request) assert tokenized.text == EXPECTED_TEXT_V15, tokenized.text def test_no_tools_and_reasoning_effort(v15_tekkenizer: InstructTokenizerV15, messages: list[ChatMessage]) -> None: request: InstructRequest = InstructRequest( messages=messages, available_tools=None, settings=ModelSettings(reasoning_effort=ReasoningEffort.none) ) tokenized = v15_tekkenizer.encode_instruct(request) expected_text_no_tools = EXPECTED_TEXT_V15_NO_TOOLS.replace("high", "none") assert tokenized.text == expected_text_no_tools, tokenized.text def test_no_settings_does_not_encode_model_settings( v15_tekkenizer_no_reasoning: InstructTokenizerV15, messages: list[ChatMessage] ) -> None: request: InstructRequest = InstructRequest(messages=messages, available_tools=None, settings=ModelSettings.none()) tokenized = v15_tekkenizer_no_reasoning.encode_instruct(request) assert "[MODEL_SETTINGS]" not in (tokenized.text or "") def test_system_think_chunk_raises_v15(v15_tekkenizer: InstructTokenizerV15) -> None: messages = [SystemMessage(content=[ThinkChunk(thinking="Hi")])] request: InstructRequest = InstructRequest( messages=messages, settings=ModelSettings(reasoning_effort=ReasoningEffort.high) ) with pytest.raises(TokenizerException, match="ThinkChunk in system message is not supported for this model"): v15_tekkenizer.encode_instruct(request) @pytest.mark.parametrize( ("reasoning_effort", "allowed_reasoning_effort", "raises", "match"), [ (None, ("none", "high"), None, None), ("none", ("none", "high"), None, None), ("high", ("none", "high"), None, None), ("high", ("none",), InvalidRequestException, "should be one of"), ("none", ("none",), None, None), ("none", (), InvalidRequestException, "not supported for this model"), ], ) def test_forbidden_reasoning_effort_raises( available_tools: list[Tool], messages: list[ChatMessage], reasoning_effort: str | None, allowed_reasoning_effort: tuple[str, ...] | None, raises: type[Exception] | None, match: str | None, ) -> None: builder = _build_model_settings_builder(allowed_reasoning_effort) tokenizer_v15 = get_v15_mistral_tokenizer(builder) request = ChatCompletionRequest( messages=messages, tools=available_tools, reasoning_effort=reasoning_effort, # type: ignore[arg-type] ) if raises is not None: assert match is not None with pytest.raises(raises, match=match): tokenizer_v15.encode_chat_completion(request) else: assert match is None tokenizer_v15.encode_chat_completion(request) @pytest.mark.parametrize(("reasoning_effort", "allowed_reasoning_effort"), [(None, ()), ("none", None)]) def test_encode_ignore_one_model_settings( messages: list[ChatMessage], reasoning_effort: str | None, allowed_reasoning_effort: tuple[str, ...] | None, ) -> None: builder = _build_model_settings_builder(allowed_reasoning_effort) tokenizer_v15 = get_v15_mistral_tokenizer(builder) request = ChatCompletionRequest(messages=messages, reasoning_effort=reasoning_effort) # type: ignore[arg-type] tokenized = tokenizer_v15.encode_chat_completion(request) assert "[MODEL_SETTINGS]" not in (tokenized.text or "") @pytest.mark.parametrize("reasoning_effort", [None, *list(ReasoningEffort)]) def test_end_to_end_with_default(messages: list[ChatMessage], reasoning_effort: ReasoningEffort | None) -> None: """When the builder has a default, None reasoning_effort resolves to the default.""" builder = _build_model_settings_builder(("none", "high")) tokenizer_v15 = get_v15_mistral_tokenizer(builder) request = ChatCompletionRequest(messages=messages, reasoning_effort=reasoning_effort) tokenized = tokenizer_v15.encode_chat_completion(request) text = tokenized.text or "" if reasoning_effort == ReasoningEffort.high: assert '[MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' in text else: # Both None (default applied) and explicit "none" resolve to the default "none" assert '[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' in text @pytest.mark.parametrize("reasoning_effort", [None, *list(ReasoningEffort)]) def test_end_to_end_no_default(messages: list[ChatMessage], reasoning_effort: ReasoningEffort | None) -> None: """When the builder has no default, None reasoning_effort means no model settings.""" builder = ModelSettingsBuilder( reasoning_effort=EnumBuilder[ReasoningEffort](values=list(ReasoningEffort), accepts_none=True, default=None) ) tokenizer_v15 = get_v15_mistral_tokenizer(builder) request = ChatCompletionRequest(messages=messages, reasoning_effort=reasoning_effort) tokenized = tokenizer_v15.encode_chat_completion(request) text = tokenized.text or "" if reasoning_effort == ReasoningEffort.high: assert '[MODEL_SETTINGS]{"reasoning_effort": "high"}[/MODEL_SETTINGS]' in text elif reasoning_effort == ReasoningEffort.none: assert '[MODEL_SETTINGS]{"reasoning_effort": "none"}[/MODEL_SETTINGS]' in text else: # None with no default -> no model settings encoded assert "[MODEL_SETTINGS]" not in text def test_encode_chat_completion_continue_final_message() -> None: builder = _build_model_settings_builder(("none", "high")) tokenizer_v15 = get_v15_mistral_tokenizer(builder) request: ChatCompletionRequest = ChatCompletionRequest( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) encoded = tokenizer_v15.encode_chat_completion(request) eos_id = tokenizer_v15.instruct_tokenizer.tokenizer.eos_id assert encoded.tokens[-1] != eos_id @pytest.mark.parametrize( ("content_chunk", "expected_audios", "expected_images", "tokenizer_factory", "expected_text"), _TOOL_MULTIMODAL_PARAMS, ) def test_encode_chat_completion_with_multimodal_tool( content_chunk: AudioChunk | AudioURLChunk | ImageURLChunk, expected_audios: int, expected_images: int, tokenizer_factory: Callable[[], MistralTokenizer], expected_text: str, ) -> None: mistral_tokenizer = tokenizer_factory() chat_request: ChatCompletionRequest = ChatCompletionRequest( messages=[ UserMessage(content="Use the tool"), AssistantMessage(tool_calls=[ToolCall(id="test12345", function=FunctionCall(name="fn", arguments="{}"))]), ToolMessage( content=[TextChunk(text="result"), content_chunk], tool_call_id="test12345", ), ], tools=[Tool(function=Function(name="fn", description="test", parameters={}))], ) encoded = mistral_tokenizer.encode_chat_completion(chat_request) assert encoded.text == expected_text, encoded.text assert len(encoded.audios) == expected_audios assert len(encoded.images) == expected_images @pytest.mark.parametrize( ("content_chunk", "expected_audios", "expected_images", "tokenizer_factory", "expected_text"), _SYSTEM_MULTIMODAL_PARAMS, ) def test_encode_chat_completion_with_multimodal_system( content_chunk: AudioChunk, expected_audios: int, expected_images: int, tokenizer_factory: Callable[[], MistralTokenizer], expected_text: str, ) -> None: mistral_tokenizer = tokenizer_factory() chat_request: ChatCompletionRequest = ChatCompletionRequest( messages=[ SystemMessage(content=[TextChunk(text="System with content"), content_chunk]), UserMessage(content="Hello"), ], ) encoded = mistral_tokenizer.encode_chat_completion(chat_request) assert encoded.text == expected_text, encoded.text assert len(encoded.audios) == expected_audios assert len(encoded.images) == expected_images @pytest.mark.parametrize( ("content_chunk", "expected_audios", "expected_images", "tokenizer_factory", "expected_text"), _USER_MULTIMODAL_PARAMS, ) def test_encode_chat_completion_with_multimodal_user( content_chunk: AudioChunk | AudioURLChunk | ImageURLChunk, expected_audios: int, expected_images: int, tokenizer_factory: Callable[[], MistralTokenizer], expected_text: str, ) -> None: mistral_tokenizer = tokenizer_factory() chat_request = ChatCompletionRequest( messages=[ UserMessage(content=[TextChunk(text="Here is content"), content_chunk]), ], ) encoded = mistral_tokenizer.encode_chat_completion(chat_request) assert encoded.text == expected_text, encoded.text assert len(encoded.audios) == expected_audios assert len(encoded.images) == expected_images python-mistral-common-1.11.7/tests/test_tokenizer_v3_mm.py000066400000000000000000000307571523035502500237630ustar00rootroot00000000000000import sys import pytest from PIL import Image from mistral_common.protocol.instruct.chunk import ( ContentChunk, ImageChunk, TextChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.base import Tokenized from mistral_common.tokens.tokenizers.image import ImageEncoder, SpecialImageIDs from mistral_common.tokens.tokenizers.mistral import MistralTokenizer text_alignment_requests: list[ChatCompletionRequest] = [ ChatCompletionRequest( messages=[ UserMessage(content="hello"), UserMessage(content=[TextChunk(text="bbb"), TextChunk(text="ccc")]), AssistantMessage(content="aaa"), UserMessage(content="goodbye"), ], ), ChatCompletionRequest( messages=[ UserMessage(content="hello"), ], ), ChatCompletionRequest(messages=[UserMessage(content=[TextChunk(text="")])]), ChatCompletionRequest( messages=[ SystemMessage(content="You are an AI assistant"), UserMessage(content=[TextChunk(text="aaa"), TextChunk(text="bbb")]), AssistantMessage(content="aaa"), UserMessage(content="goodbye"), ] ), ] img = Image.new("RGB", (4, 4), "red") img_requests: list[ChatCompletionRequest] = [ ChatCompletionRequest( messages=[ UserMessage(content=[TextChunk(text="a"), ImageChunk(image=img)]), ], ), ChatCompletionRequest( messages=[ SystemMessage(content="A B"), UserMessage(content=[TextChunk(text="C"), ImageChunk(image=img)]), ] ), ChatCompletionRequest( messages=[ SystemMessage(content="A B"), UserMessage(content=[ImageChunk(image=img), TextChunk(text="C")]), ] ), ChatCompletionRequest( messages=[ SystemMessage(content="A B"), UserMessage( content=[ ImageChunk(image=img), ImageChunk(image=img), TextChunk(text="C"), ] ), AssistantMessage(content="D"), UserMessage( content=[ ImageChunk(image=img), TextChunk(text="E"), ImageChunk(image=img), ] ), ] ), ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="A"), ImageChunk(image=img), TextChunk(text="B"), TextChunk(text="C"), ImageChunk(image=img), TextChunk(text="D"), TextChunk(text="E"), ] ), ] ), ] text_requests: list[ChatCompletionRequest] = [ ChatCompletionRequest( messages=[ UserMessage(content="hello"), AssistantMessage(content="aaa"), UserMessage(content="goodbye"), ], ), ChatCompletionRequest( messages=[ UserMessage(content="hello"), ], ), ChatCompletionRequest(messages=[UserMessage(content=[TextChunk(text="")])]), ChatCompletionRequest( messages=[ SystemMessage(content="You are an AI assistant"), UserMessage(content=[TextChunk(text="aaa"), TextChunk(text="bbb")]), AssistantMessage(content="aaa"), UserMessage(content="goodbye"), ] ), ] def _image_tokens(width: int, height: int, special_ids: SpecialImageIDs) -> list[int]: image_tokens = ([special_ids.img] * width + [special_ids.img_break]) * height image_tokens[-1] = special_ids.img_end return image_tokens def _set_test_image_patch_size(tokenizer: MistralTokenizer) -> ImageEncoder: image_encoder = tokenizer.instruct_tokenizer.image_encoder assert isinstance(image_encoder, ImageEncoder) image_encoder.image_config.image_patch_size = 2 return image_encoder def _image_tokenizer_spans(tokens: list[int], special_ids: SpecialImageIDs) -> list[list[int]]: spans: list[list[int]] = [] start_idx: int | None = None for idx, token in enumerate(tokens): if start_idx is None: if token == special_ids.img: start_idx = idx elif token == special_ids.img_end: spans.append(tokens[start_idx : idx + 1]) start_idx = None return spans @pytest.fixture def mm_tokenizer() -> MistralTokenizer: path = str(MistralTokenizer._data_path() / "tekken_240911.json") tokenizer = MistralTokenizer.from_file(path) return tokenizer @pytest.fixture def text_tokenizer() -> MistralTokenizer: path = str(MistralTokenizer._data_path() / "tekken_240718.json") tokenizer = MistralTokenizer.from_file(path) return tokenizer @pytest.mark.parametrize("r", text_alignment_requests) def test_agreement_with_text_only( mm_tokenizer: MistralTokenizer, text_tokenizer: MistralTokenizer, r: ChatCompletionRequest, ) -> None: text_output = text_tokenizer.encode_chat_completion(r) mm_output = mm_tokenizer.encode_chat_completion(r) assert mm_output.tokens == text_output.tokens, f"mm output: {mm_output.tokens}\nexpected: {text_output.tokens}" def test_swap_text_image_special_case(mm_tokenizer: MistralTokenizer) -> None: img = Image.new("RGB", (4, 4), "red") prompt = "What is on this image?" request_text_first: ChatCompletionRequest = ChatCompletionRequest( messages=[ UserMessage(content=[ImageChunk(image=img), TextChunk(text=prompt)]), ], ) request_img_first: ChatCompletionRequest = ChatCompletionRequest( messages=[ UserMessage(content=[TextChunk(text=prompt), ImageChunk(image=img)]), ], ) assert are_requests_same(mm_tokenizer, [request_text_first, request_img_first]) # adding one more text or image will lead to different results though prompt_2 = "more" request_text_first.messages[0].content.append(TextChunk(text=prompt_2)) request_img_first.messages[0].content.append(TextChunk(text=prompt_2)) assert not are_requests_same(mm_tokenizer, [request_text_first, request_img_first]) def are_requests_same(mm_tokenizer: MistralTokenizer, requests: list[ChatCompletionRequest]) -> bool: assert mm_tokenizer.instruct_tokenizer.image_encoder is not None outputs: list[Tokenized] = [] for request in requests: outputs.append(mm_tokenizer.encode_chat_completion(request)) token_same = outputs[0].tokens == outputs[1].tokens return token_same @pytest.mark.parametrize("r", img_requests + text_requests) def test_mm_normalizer( mm_tokenizer: MistralTokenizer, r: ChatCompletionRequest, ) -> None: r_norm = mm_tokenizer._instruct_request_normalizer.from_chat_completion_request(r) # filter system messages messages = [m for m in r.messages if not isinstance(m, SystemMessage)] norm_messages = [m for m in r_norm.messages] assert len(messages) == len(norm_messages) for message, norm_message in zip(messages, norm_messages): if all(isinstance(c, TextChunk) for c in message.content): # text-only is collapsed into a single str assert isinstance(norm_message.content, str) else: # image if not isinstance(message.content, str): assert not isinstance(message.content, str) assert count_expected_chunks(message.content) == len(norm_message.content) def count_expected_chunks(elements: list[ContentChunk]) -> int: """ Count the number of chunks in the list, treating consecutive TextChunks as a single chunk. """ count = 0 previous_was_text = False for element in elements: if isinstance(element, TextChunk): if not previous_was_text: count += 1 previous_was_text = True else: count += 1 previous_was_text = False return count def test_image_tokenization_integration(mm_tokenizer: MistralTokenizer) -> None: # we'll put the test cases inside the test body so we don't have too much PIL stuff # outside of functions # fmt: off # Test cases validated by manually comparing to what you get from the language # only tokenizer when you remove all the images from the request. The two should # match other than the difference in token ids when something follows vs doesn't # follow a \n (which is easy to check) requests = img_requests _im = 10 _im_break = 12 _im_end = 13 img_toks = [_im, _im, _im_break, _im, _im, _im_end] expected = [ [1, 3, *img_toks, 1097, 4], [1, 3, 1065, 1398, 1267, *img_toks, 1067, 4], [1, 3, 1065, 1398, 1267, *img_toks, 1067, 4], [1, 3, *img_toks, *img_toks, 1067, 4, 1068, 2, 3, 1065, 1398, 1267, *img_toks, 1069, *img_toks, 4], [1, 3, 1065, *img_toks, 1066, 1267, 1067, *img_toks, 1068, 1267, 1069, 4], ] # fmt: on image_encoder = mm_tokenizer.instruct_tokenizer.image_encoder assert isinstance(image_encoder, ImageEncoder) # hardcode image_patch_size = 2 image_encoder.image_config.image_patch_size = 2 kw_args = dict(strict=True) if sys.version_info >= (3, 10) else {} for r, expected_tokens in zip(requests, expected, **kw_args): output: Tokenized = mm_tokenizer.encode_chat_completion(r) assert output.tokens == expected_tokens, f"Incorrect tokens for request {r}" @pytest.mark.parametrize( "content", [ pytest.param( [ TextChunk(text=""), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="empty-text-then-two-images", ), pytest.param( [ TextChunk(text="x"), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="text-then-two-images", ), pytest.param( [ ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="two-images", ), ], ) def test_multi_image_order_is_preserved(mm_tokenizer: MistralTokenizer, content: list[ContentChunk]) -> None: image_encoder = _set_test_image_patch_size(mm_tokenizer) tokenized = mm_tokenizer.encode_chat_completion(ChatCompletionRequest(messages=[UserMessage(content=content)])) assert _image_tokenizer_spans(tokenized.tokens, image_encoder.special_ids) == [ _image_tokens(2, 2, image_encoder.special_ids), _image_tokens(3, 2, image_encoder.special_ids), ] def test_single_trailing_image_moves_first(mm_tokenizer: MistralTokenizer) -> None: image_encoder = _set_test_image_patch_size(mm_tokenizer) tokenized = mm_tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="x"), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ) ] ) ) assert _image_tokenizer_spans(tokenized.tokens, image_encoder.special_ids) == [ _image_tokens(2, 2, image_encoder.special_ids) ] x_token = mm_tokenizer.instruct_tokenizer.tokenizer.encode("x", bos=False, eos=False)[0] assert tokenized.tokens.index(image_encoder.special_ids.img) < tokenized.tokens.index(x_token) def test_single_leading_image_remains_first(mm_tokenizer: MistralTokenizer) -> None: image_encoder = _set_test_image_patch_size(mm_tokenizer) tokenized = mm_tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ ImageChunk(image=Image.new("RGB", (4, 4), "red")), TextChunk(text="x"), ] ) ] ) ) assert _image_tokenizer_spans(tokenized.tokens, image_encoder.special_ids) == [ _image_tokens(2, 2, image_encoder.special_ids) ] x_token = mm_tokenizer.instruct_tokenizer.tokenizer.encode("x", bos=False, eos=False)[0] assert tokenized.tokens.index(image_encoder.special_ids.img) < tokenized.tokens.index(x_token) python-mistral-common-1.11.7/tests/test_tokenizer_v7.py000066400000000000000000000557301523035502500232740ustar00rootroot00000000000000import json import pytest from PIL import Image from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidMessageStructureException, TokenizerException, ) from mistral_common.protocol.instruct.chunk import ( ContentChunk, ImageChunk, TextChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, ChatMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.normalize import InstructRequestNormalizerV7 from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.tool_calls import Function, FunctionCall, Tool, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidatorV5, ValidationMode, ) from mistral_common.tokens.tokenizers.base import InstructRequest, InstructTokenizer, Tokenized, TokenizerVersion from mistral_common.tokens.tokenizers.image import ImageEncoder from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV7 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.test_tekken import quick_vocab from tests.test_tokenizer_v7_audio import get_tekkenizer_with_audio @pytest.fixture def no_audio_tekkenizer() -> InstructTokenizerV7: tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), list(Tekkenizer.DEPRECATED_SPECIAL_TOKENS), pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v7, ) return InstructTokenizerV7(tokenizer) @pytest.fixture def with_audio_tekkenizer() -> InstructTokenizerV7: return get_tekkenizer_with_audio() @pytest.fixture(scope="session") def spm_tokenizer() -> InstructTokenizerV7: tokenizer = MistralTokenizer.v7(is_mm=True).instruct_tokenizer image_encoder = tokenizer.image_encoder assert isinstance(image_encoder, ImageEncoder) # hardcode image_patch_size = 2 for easier checks image_encoder.image_config.image_patch_size = 2 return tokenizer # type: ignore def test_tokenize_assistant_message(spm_tokenizer: InstructTokenizerV7) -> None: tokenized = spm_tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ TextChunk( text="a", ), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ), AssistantMessage(content="b"), ToolMessage(tool_call_id="b", content="f"), ], ) ) _im = 10 _im_break = 14 _im_end = 15 img_tokens = [_im, _im, _im_break, _im, _im, _im_end] assert tokenized.tokens == [ 1, # bos 3, # begin_inst *img_tokens, 1032, # a 4, # end_inst 1055, # b 2, # eos 8, # [TOOL_RESULTS] 1055, # tool_call_id b 18, # [TOOL_CONTENT] 1053, # f 9, # [/TOOL_RESULTS] ] assert ( tokenized.text == "[INST][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]▁a[/INST]▁b[TOOL_RESULTS]▁b[TOOL_CONTENT]▁f[/TOOL_RESULTS]" # noqa ) def test_tokenize_empty_content_assistant_message(spm_tokenizer: InstructTokenizerV7) -> None: for content in [None, ""]: tool_calls: list[ToolCall] | None for tool_calls in [None, [], [ToolCall(function=FunctionCall(name="test_fn", arguments="{}"))]]: instruct_request: InstructRequest = InstructRequest( messages=[AssistantMessage(content=content, tool_calls=tool_calls, prefix=True)] ) if not content and not tool_calls: with pytest.raises(TokenizerException, match="Invalid assistant message:"): spm_tokenizer.encode_instruct(instruct_request) else: assert spm_tokenizer.encode_instruct(instruct_request) == Tokenized( tokens=[ 1, 5, 1501, 7567, 1629, 2032, 1113, 2381, 29498, 6410, 1316, 1113, 17452, 2032, 1139, 1743, 29561, ], text='[TOOL_CALLS]▁[{"name":▁"test_fn",▁"arguments":▁{}}]', prefix_ids=[ 5, 1501, 7567, 1629, 2032, 1113, 2381, 29498, 6410, 1316, 1113, 17452, 2032, 1139, 1743, 29561, ], ) def test_tokenize_assistant_message_continue_final_message(spm_tokenizer: InstructTokenizerV7) -> None: tokenized = spm_tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ TextChunk( text="a", ), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ), AssistantMessage(content="b"), ], continue_final_message=True, ) ) _im = 10 _im_break = 14 _im_end = 15 img_tokens = [_im, _im, _im_break, _im, _im, _im_end] assert tokenized.tokens == [ 1, # bos 3, # begin_inst *img_tokens, 1032, # a 4, # end_inst 1055, # b ] assert tokenized.text == "[INST][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]▁a[/INST]▁b" with pytest.raises( InvalidMessageStructureException, match="Cannot continue final message if it is not an assistant message" ): spm_tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ TextChunk( text="a", ), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ), ], continue_final_message=True, ) ) with pytest.raises( InvalidAssistantMessageException, match="`continue_message` is only supported for assistant messages that have `prefix=False`.", ): spm_tokenizer.encode_assistant_message( AssistantMessage( content='"blabla"', prefix=True, ), is_before_last_user_message=False, continue_message=True, ) @pytest.mark.parametrize( "messages, expected_text", [ ( [ SystemMessage(content="a"), UserMessage(content="a"), AssistantMessage( content="b", tool_calls=[ ToolCall( function=FunctionCall( name="t", arguments=json.dumps( { "g": "h", }, ensure_ascii=False, ), ), ), ], ), ], '[SYSTEM_PROMPT]▁a[/SYSTEM_PROMPT][AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"t",▁"description":▁"",▁"parameters":▁{"type":▁"object",▁"properties":▁{"g":▁{"type":▁"string"},▁"h":▁{"type":▁"string"}}}}}][/AVAILABLE_TOOLS][INST]▁a[/INST]▁b[TOOL_CALLS]▁[{"name":▁"t",▁"arguments":▁{"g":▁"h"}}]', # noqa ), ( [ SystemMessage(content="a"), UserMessage(content="a"), UserMessage(content="c"), AssistantMessage( content="b", tool_calls=[ ToolCall( function=FunctionCall( name="t", arguments=json.dumps( { "g": "h", }, ensure_ascii=False, ), ), ), ], ), ToolMessage(content="b", tool_call_id="1234"), ], '[SYSTEM_PROMPT]▁a[/SYSTEM_PROMPT][INST]▁a[/INST][AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"t",▁"description":▁"",▁"parameters":▁{"type":▁"object",▁"properties":▁{"g":▁{"type":▁"string"},▁"h":▁{"type":▁"string"}}}}}][/AVAILABLE_TOOLS][INST]▁c[/INST]▁b[TOOL_CALLS]▁[{"name":▁"t",▁"arguments":▁{"g":▁"h"}}][TOOL_RESULTS]▁1234[TOOL_CONTENT]▁b[/TOOL_RESULTS]', # noqa ), ], ) def test_encode_spm(spm_tokenizer: InstructTokenizerV7, messages: list[ChatMessage], expected_text: str) -> None: tokenized = spm_tokenizer.encode_instruct( InstructRequest( available_tools=[ Tool( function=Function( name="t", parameters={ "type": "object", "properties": { "g": {"type": "string"}, "h": {"type": "string"}, }, }, ) ), ], messages=messages, ) ) assert tokenized.text == expected_text, f"{tokenized.text} != {expected_text}" def test_encode_chat_completion() -> None: tokenizer = MistralTokenizer.v7(is_mm=True) request: ChatCompletionRequest = ChatCompletionRequest( tools=[ Tool( function=Function( name="t", parameters={ "type": "object", "properties": { "g": {"type": "string"}, "h": {"type": "string"}, }, }, ) ), ], messages=[ SystemMessage(content="a"), UserMessage( content=[ TextChunk( text="a", ), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ), AssistantMessage(content="b"), ToolMessage(tool_call_id="123456789", content="f"), ], ) encoded = tokenizer.encode_chat_completion(request) assert len(encoded.images) == 1 assert encoded.images[0].shape == (3, 16, 16) assert ( encoded.text == '[SYSTEM_PROMPT]▁a[/SYSTEM_PROMPT][AVAILABLE_TOOLS]▁[{"type":▁"function",▁"function":▁{"name":▁"t",▁"description":▁"",▁"parameters":▁{"type":▁"object",▁"properties":▁{"g":▁{"type":▁"string"},▁"h":▁{"type":▁"string"}}}}}][/AVAILABLE_TOOLS][INST][IMG][IMG_END]▁a[/INST]▁b[TOOL_RESULTS]▁123456789[TOOL_CONTENT]▁f[/TOOL_RESULTS]' # noqa ) @pytest.mark.parametrize( "messages,truncated_text", [ # max_tokens is always set to truncate at 15 tokens pytest.param( # with the system prompts, only one user message fits, keep the last one [ SystemMessage(content="a"), UserMessage(content="c"), UserMessage(content="c"), SystemMessage(content="a"), UserMessage(content="bbbbbbb"), ], "[SYSTEM_PROMPT]a[/SYSTEM_PROMPT][SYSTEM_PROMPT]a[/SYSTEM_PROMPT][INST]bbbbbbb[/INST]", id="keep_sys_and_last_message", ), pytest.param( # drop the first assistant message - everything else fits [ AssistantMessage(content="c"), UserMessage(content="b"), UserMessage(content="a"), UserMessage(content="aaaaaaa"), ], "[INST]b[/INST][INST]a[/INST][INST]aaaaaaa[/INST]", ), pytest.param( # the result can start with a non-user message because the input did too [ AssistantMessage(content="c"), AssistantMessage(content="b"), UserMessage(content="a"), UserMessage(content="aaaaaaa"), ], "b[INST]a[/INST][INST]aaaaaaa[/INST]", ), pytest.param( # drop the first assistant message, then drop user+tool because the go together and both don't fit [ AssistantMessage(content="c"), UserMessage(content="c"), ToolMessage(content="c", tool_call_id="1234"), UserMessage(content="a"), AssistantMessage(content="bbbbbbb"), ], "[INST]a[/INST]bbbbbbb", id="drop_by_chunk_1", ), pytest.param( # drop everything but the last message, because the first chunk (3 messages) is too big [ UserMessage(content="c"), AssistantMessage(content="c"), AssistantMessage(content="c"), UserMessage(content="aaaaaaa"), ], "[INST]aaaaaaa[/INST]", id="drop_by_chunk_2", ), pytest.param( [ SystemMessage(content="a"), UserMessage(content="c"), AssistantMessage(content="c"), UserMessage(content="a"), AssistantMessage(content="a"), SystemMessage(content="b"), UserMessage(content="a"), ], "[SYSTEM_PROMPT]a[/SYSTEM_PROMPT][INST]a[/INST]a[SYSTEM_PROMPT]b[/SYSTEM_PROMPT][INST]a[/INST]", id="full_convo", ), ], ) @pytest.mark.parametrize("tekkenizer", ["no_audio_tekkenizer", "with_audio_tekkenizer"]) def test_truncation( request: pytest.FixtureRequest, tekkenizer: str, messages: list[ChatMessage], truncated_text: str ) -> None: tokenizer: InstructTokenizer = request.getfixturevalue(tekkenizer) tokenized = tokenizer.encode_instruct(InstructRequest(messages=messages, truncate_at_max_tokens=15)) assert tokenized.text == truncated_text, f"{tokenized.text} != {truncated_text}" @pytest.mark.parametrize( "messages", [ [ # system prompt doesn't fit SystemMessage(content="a" * 10), ], [ # last user msg doesn't fit UserMessage(content="a" * 10), ], ], ) @pytest.mark.parametrize("tekkenizer", ["no_audio_tekkenizer", "with_audio_tekkenizer"]) def test_truncation_failed(request: pytest.FixtureRequest, tekkenizer: str, messages: list[ChatMessage]) -> None: tokenizer = request.getfixturevalue(tekkenizer) with pytest.raises(TokenizerException): tokenizer.encode_instruct(InstructRequest(messages=messages, truncate_at_max_tokens=9)) def test_from_model() -> None: with pytest.warns(FutureWarning, match="from_model.*deprecated"): tokenizer = MistralTokenizer.from_model("ministral-8b-2410") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v3 assert tokenizer.instruct_tokenizer.image_encoder is None tokenizer = MistralTokenizer.from_model("mistral-small-2402") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v2 assert tokenizer.instruct_tokenizer.image_encoder is None tokenizer = MistralTokenizer.from_model("mistral-small-2409") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v3 assert tokenizer.instruct_tokenizer.image_encoder is None tokenizer = MistralTokenizer.from_model("mistral-large-2411") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v7 assert tokenizer.instruct_tokenizer.image_encoder is None tokenizer = MistralTokenizer.from_model("pixtral-large-2411") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v7 assert tokenizer.instruct_tokenizer.image_encoder is not None tokenizer = MistralTokenizer.from_model("pixtral-12b-2409") assert tokenizer.instruct_tokenizer.tokenizer.version == TokenizerVersion.v3 assert tokenizer.instruct_tokenizer.image_encoder is not None with pytest.warns(FutureWarning), pytest.raises(TokenizerException): MistralTokenizer.from_model("unknown-model") @pytest.mark.parametrize("tekkenizer", ["no_audio_tekkenizer", "with_audio_tekkenizer"]) def test_assistant_tool_call_and_content(request: pytest.FixtureRequest, tekkenizer: str) -> None: tokenizer = request.getfixturevalue(tekkenizer) instruct_request: InstructRequest = InstructRequest( available_tools=[ Tool(function=Function(name="t1", parameters={})), Tool(function=Function(name="t2", parameters={})), ], messages=[ UserMessage(content="a"), AssistantMessage( content="b1b2", tool_calls=[ ToolCall(id="000000000", function=FunctionCall(name="t1", arguments="{}")), ToolCall(id="111111111", function=FunctionCall(name="t2", arguments="{}")), ], ), ], ) tokenized = tokenizer.encode_instruct(instruct_request) tokens = tokenized.tokens text = tokenized.text assert text == ( '[AVAILABLE_TOOLS][{"type": "function", "function": ' '{"name": "t1", "description": "", "parameters": {}}}, ' '{"type": "function", "function": {"name": "t2", "description"' ': "", "parameters": {}}}][/AVAILABLE_TOOLS][INST]a[/INST]b1b2[TOOL_CALLS]' '[{"name": "t1", "arguments": {}, "id": "000000000"}, {"name": "t2", "arguments": {}' ', "id": "111111111"}]' ) # make sure it also works end to end tools = instruct_request.available_tools exclude = {"system_prompt", "truncate_at_max_tokens", "available_tools", "settings"} chat_completion_request = ChatCompletionRequest(**instruct_request.model_dump(exclude=exclude), tools=tools) validator = MistralRequestValidatorV5(mode=ValidationMode.finetuning) normalizer = InstructRequestNormalizerV7.normalizer() mistral_tokenizer = MistralTokenizer(tokenizer, validator, normalizer) tokens_2 = mistral_tokenizer.encode_chat_completion(chat_completion_request) assert tokens == tokens_2.tokens def test_encode_chat_completion_continue_final_message() -> None: tokenizer = MistralTokenizer.v7(is_mm=True) eos_id = tokenizer.instruct_tokenizer.tokenizer.eos_id request: ChatCompletionRequest = ChatCompletionRequest( messages=[UserMessage(content="a"), AssistantMessage(content="b")], continue_final_message=True, ) encoded = tokenizer.encode_chat_completion(request) assert encoded.tokens == [1, 3, 1032, 4, 1055] assert encoded.tokens[-1] != eos_id assert eos_id not in encoded.prefix_ids def _image_tokens(width: int, height: int) -> list[int]: _im = 10 _im_break = 14 _im_end = 15 image_tokens = ([_im] * width + [_im_break]) * height image_tokens[-1] = _im_end return image_tokens def _image_tokenizer_spans(tokens: list[int]) -> list[list[int]]: _im = 10 _im_end = 15 spans: list[list[int]] = [] start_idx: int | None = None for idx, token in enumerate(tokens): if start_idx is None: if token == _im: start_idx = idx elif token == _im_end: spans.append(tokens[start_idx : idx + 1]) start_idx = None return spans @pytest.mark.parametrize( "content", [ pytest.param( [ TextChunk(text=""), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="empty-text-then-two-images", ), pytest.param( [ TextChunk(text="x"), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="text-then-two-images", ), pytest.param( [ ImageChunk(image=Image.new("RGB", (4, 4), "red")), ImageChunk(image=Image.new("RGB", (6, 4), "blue")), ], id="two-images", ), ], ) def test_multi_image_order_is_preserved(spm_tokenizer: InstructTokenizerV7, content: list[ContentChunk]) -> None: tokenized = spm_tokenizer.encode_instruct(InstructRequest(messages=[UserMessage(content=content)])) assert _image_tokenizer_spans(tokenized.tokens) == [_image_tokens(2, 2), _image_tokens(3, 2)] def test_single_trailing_image_moves_first(spm_tokenizer: InstructTokenizerV7) -> None: tokenized = spm_tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ TextChunk(text="x"), ImageChunk(image=Image.new("RGB", (4, 4), "red")), ] ) ] ) ) assert _image_tokenizer_spans(tokenized.tokens) == [_image_tokens(2, 2)] x_token = spm_tokenizer.tokenizer.encode("x", bos=False, eos=False)[0] assert tokenized.tokens.index(10) < tokenized.tokens.index(x_token) def test_single_leading_image_remains_first(spm_tokenizer: InstructTokenizerV7) -> None: tokenized = spm_tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ ImageChunk(image=Image.new("RGB", (4, 4), "red")), TextChunk(text="x"), ] ) ] ) ) assert _image_tokenizer_spans(tokenized.tokens) == [_image_tokens(2, 2)] x_token = spm_tokenizer.tokenizer.encode("x", bos=False, eos=False)[0] assert tokenized.tokens.index(10) < tokenized.tokens.index(x_token) python-mistral-common-1.11.7/tests/test_tokenizer_v7_audio.py000066400000000000000000000333671523035502500244570ustar00rootroot00000000000000import tempfile from pathlib import Path from unittest.mock import patch import numpy as np import pytest import requests import soundfile as sf from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURL, AudioURLChunk, ContentChunk, TextChunk, ) from mistral_common.protocol.instruct.messages import ( UATS, AssistantMessage, SystemMessage, UserMessage, ) from mistral_common.tokens.tokenizers.audio import ( Audio, AudioConfig, AudioEncoder, AudioSpectrogramConfig, SpecialAudioIDs, ) from mistral_common.tokens.tokenizers.base import ( InstructRequest, SpecialTokens, TokenizerVersion, ) from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerV7, ) from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab def get_tekkenizer_with_audio() -> InstructTokenizerV7: special_tokens = get_special_tokens(tokenizer_version=TokenizerVersion.v7, add_audio=True) tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v7, ) audio_config = AudioConfig( sampling_rate=24_000, frame_rate=12.5, encoding_config=AudioSpectrogramConfig( num_mel_bins=128, window_size=400, hop_length=160, ), ) special_audio_ids = SpecialAudioIDs( audio=tokenizer.get_special_token(SpecialTokens.audio.value), begin_audio=tokenizer.get_special_token(SpecialTokens.begin_audio.value), streaming_pad=None, text_to_audio=None, audio_to_text=None, ) audio_encoder = AudioEncoder(audio_config, special_audio_ids) return InstructTokenizerV7(tokenizer, audio_encoder=audio_encoder) @pytest.fixture(scope="session") def tekkenizer() -> InstructTokenizerV7: return get_tekkenizer_with_audio() def sin_wave(sampling_rate: int, duration: float) -> np.ndarray: return np.sin(np.ones([int(duration * sampling_rate)])) @pytest.fixture(scope="session") def sample_audio() -> Audio: sampling_rate = 44100 original_array = sin_wave(sampling_rate, 1) audio = Audio( audio_array=original_array, sampling_rate=sampling_rate, format="wav", ) return audio @pytest.fixture(scope="session") def sample_audio_file(sample_audio: Audio) -> Path: with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: sf.write(f.name, sample_audio.audio_array, sample_audio.sampling_rate) return Path(f.name) @pytest.fixture(scope="session") def sample_audio_url_chunk_path(sample_audio_file: Path) -> AudioURLChunk: return AudioURLChunk(audio_url=str(sample_audio_file)) @pytest.fixture(scope="session") def sample_audio_url_chunk_path_file(sample_audio_file: Path) -> AudioURLChunk: return AudioURLChunk(audio_url=AudioURL(url=f"file://{sample_audio_file}")) @pytest.fixture(scope="session") def sample_audio_url_chunk_http() -> AudioURLChunk: return AudioURLChunk(audio_url=AudioURL(url="http://example.com/audio.wav")) @pytest.fixture(scope="session") def sample_audio_url_chunk_https() -> AudioURLChunk: return AudioURLChunk(audio_url=AudioURL(url="https://example.com/audio.wav")) @pytest.fixture(scope="session") def sample_audio_url_chunk_base64(sample_audio: Audio) -> AudioURLChunk: return AudioURLChunk(audio_url=AudioURL(url=sample_audio.to_base64("wav"))) @pytest.fixture(scope="session") def sample_audio_url_chunk_base64_prefix(sample_audio: Audio) -> AudioURLChunk: return AudioURLChunk(audio_url=AudioURL(url=f"data:audio/wav;base64,{sample_audio.to_base64('wav')}")) def _get_audio_chunk(duration: float) -> AudioChunk: format = "wav" sampling_rate = 24000 signal_length = int(duration * sampling_rate) rng = np.random.default_rng(0) audio = Audio( audio_array=rng.uniform(low=-1, high=1, size=[signal_length]), sampling_rate=sampling_rate, format=format, ) return AudioChunk(input_audio=audio.to_base64(format)) def _get_specials(tekkenizer: InstructTokenizerV7) -> tuple[int, ...]: BOS = tekkenizer.tokenizer.get_special_token(SpecialTokens.bos.value) EOS = tekkenizer.tokenizer.get_special_token(SpecialTokens.eos.value) BEGIN_INST = tekkenizer.tokenizer.get_special_token(SpecialTokens.begin_inst.value) END_INST = tekkenizer.tokenizer.get_special_token(SpecialTokens.end_inst.value) AUDIO = tekkenizer.tokenizer.get_special_token(SpecialTokens.audio.value) BEGIN_AUDIO = tekkenizer.tokenizer.get_special_token(SpecialTokens.begin_audio.value) TRANSCRIBE = tekkenizer.tokenizer.get_special_token(SpecialTokens.transcribe.value) return BOS, EOS, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, TRANSCRIBE DUMMY_AUDIO = _get_audio_chunk(1.7) def test_tokenize_user_assistant_message(tekkenizer: InstructTokenizerV7) -> None: duration = 1.7 # seconds frame_rate = 12.5 audio_chunk = _get_audio_chunk(duration) num_expected_frames = int(np.ceil(duration * frame_rate)) tokenized = tekkenizer.encode_instruct( InstructRequest( messages=[ UserMessage( content=[ TextChunk( text="a", ), audio_chunk, ] ), AssistantMessage(content="c b d"), ], ) ) BOS, EOS, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, _ = _get_specials(tekkenizer) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames assert tokenized.tokens == [ BOS, BEGIN_INST, 197, # a *audio_toks, END_INST, 199, # "c" 132, # " " 198, # "b" 132, # " " 200, # "d" EOS, ] assert tokenized.text == ("[INST]a[BEGIN_AUDIO]" + "[AUDIO]" * num_expected_frames + "[/INST]c b d") assert len(tokenized.audios) == 1 base64_str = audio_chunk.input_audio assert isinstance(base64_str, str) audio_array = Audio.from_base64(base64_str).audio_array assert np.allclose(tokenized.audios[0].audio_array, audio_array, atol=1e-3) @pytest.mark.parametrize("audio_first", [True, False]) def test_tokenize_user_message(tekkenizer: InstructTokenizerV7, audio_first: bool) -> None: duration = 1.7 # seconds frame_rate = 12.5 audio_chunk = _get_audio_chunk(duration) text_chunk = TextChunk(text="a") num_expected_frames = int(np.ceil(duration * frame_rate)) chunks: list[ContentChunk] = [audio_chunk, text_chunk] if audio_first else [text_chunk, audio_chunk] tokenized = tekkenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content=chunks), ], ) ) BOS, EOS, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, _ = _get_specials(tekkenizer) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames if audio_first: assert tokenized.tokens == [ BOS, BEGIN_INST, *audio_toks, 197, # "a" END_INST, ] assert tokenized.text == ("[INST][BEGIN_AUDIO]" + "[AUDIO]" * num_expected_frames + "a[/INST]") else: assert tokenized.tokens == [ BOS, BEGIN_INST, 197, # "a" *audio_toks, END_INST, ] assert tokenized.text == ("[INST]a[BEGIN_AUDIO]" + "[AUDIO]" * num_expected_frames + "[/INST]") assert len(tokenized.audios) == 1 base64_str = audio_chunk.input_audio assert isinstance(base64_str, str) audio_array = Audio.from_base64(base64_str).audio_array assert np.allclose(tokenized.audios[0].audio_array, audio_array, atol=1e-3) def test_tokenize_multi_turn(tekkenizer: InstructTokenizerV7) -> None: duration = 1.7 # seconds frame_rate = 12.5 audio_chunk = _get_audio_chunk(duration) text_chunk = TextChunk(text="a") num_expected_frames = int(np.ceil(duration * frame_rate)) chunks: list[ContentChunk] = [audio_chunk, text_chunk] tokenized = tekkenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content=chunks), AssistantMessage(content="c b"), UserMessage(content=[audio_chunk]), AssistantMessage(content="a f"), UserMessage(content=[text_chunk]), AssistantMessage(content="g"), UserMessage(content=chunks), ], ) ) BOS, EOS, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, _ = _get_specials(tekkenizer) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames assert tokenized.tokens == [ BOS, BEGIN_INST, *audio_toks, 197, # "a" END_INST, 199, # "c" 132, # " " 198, # "b" EOS, BEGIN_INST, *audio_toks, END_INST, 197, # "a" 132, # " " 202, # "f" EOS, BEGIN_INST, 197, # "a" END_INST, 203, # "g" EOS, BEGIN_INST, *audio_toks, 197, # "a" END_INST, ] assert len(tokenized.audios) == 3 def test_no_audio_in_system_message_before_v7() -> None: path = str(MistralTokenizer._data_path() / "tekken_240911.json") tokenizer = MistralTokenizer.from_file(path).instruct_tokenizer duration = 1.7 # seconds audio_chunk = _get_audio_chunk(duration) with pytest.raises(NotImplementedError): # we don't allow system tokenizer.encode_instruct( InstructRequest( messages=[SystemMessage(content="hello")], ) ) with pytest.raises(AssertionError): # we don't allow audio tokenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content=[audio_chunk]), ] ) ) @pytest.mark.parametrize( ("messages", "match_regex"), [ ( [ SystemMessage(content="a b c d"), UserMessage(content=[TextChunk(text="a"), DUMMY_AUDIO]), ], "System messages are not yet allowed when audio is present", ), ], ) def test_tokenize_audio_raise(tekkenizer: InstructTokenizerV7, messages: list[UATS], match_regex: str) -> None: with pytest.raises(ValueError, match=match_regex): tekkenizer.encode_instruct(InstructRequest(messages=messages)) @pytest.mark.parametrize( "audio_url_chunk", [ pytest.param("sample_audio_url_chunk_path", id="path"), pytest.param("sample_audio_url_chunk_path_file", id="path_file"), pytest.param("sample_audio_url_chunk_http", id="http"), pytest.param("sample_audio_url_chunk_https", id="https"), pytest.param("sample_audio_url_chunk_base64", id="base64"), pytest.param("sample_audio_url_chunk_base64_prefix", id="base64_prefix"), ], ) def test_tokenize_audio_url_chunk( tekkenizer: InstructTokenizerV7, request: pytest.FixtureRequest, audio_url_chunk: str ) -> None: audio_url_chunk_fixture: AudioURLChunk = request.getfixturevalue(audio_url_chunk) audio_url_chunk_path_fixture: AudioURLChunk = request.getfixturevalue("sample_audio_url_chunk_path") with patch("mistral_common.tokens.tokenizers.audio._requests_lib.get") as mock_get: mock_get.return_value.content = Path(audio_url_chunk_path_fixture.url).read_bytes() mock_get.return_value.status_code = 200 tokenized = tekkenizer.encode_instruct( InstructRequest( messages=[ UserMessage(content=[audio_url_chunk_fixture]), AssistantMessage(content="c b d"), ], ) ) BOS, EOS, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, _ = _get_specials(tekkenizer) num_expected_frames = int(np.ceil(1 * 12.5)) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames assert len(tokenized.audios) == 1 assert isinstance(tokenized.audios[0], Audio) assert tokenized.audios[0].sampling_rate == 24000 assert tokenized.audios[0].format == "wav" assert tokenized.audios[0].audio_array.shape == (24000,) assert tokenized.text == ("[INST][BEGIN_AUDIO]" + "[AUDIO]" * (len(audio_toks) - 1) + "[/INST]c b d") assert tokenized.tokens == [ BOS, BEGIN_INST, *audio_toks, END_INST, 199, # "c" 132, # " " 198, # "b" 132, # " " 200, # "d" EOS, ] def test_encode_invalid_audio_url_chunk(tekkenizer: InstructTokenizerV7) -> None: assert tekkenizer.audio_encoder is not None # Mock an invalid URL with patch("mistral_common.tokens.tokenizers.audio._requests_lib.get") as mock_get: mock_get.side_effect = requests.RequestException("connection failed") with pytest.raises( ValueError, match=r"Failed to download audio from URL: https://example.com/invalid_audio.wav" ): tekkenizer.audio_encoder(AudioURLChunk(audio_url="https://example.com/invalid_audio.wav")) # Test with an invalid base64 string with pytest.raises(ValueError, match=r"base64 decoding failed. Please check the input string is a valid base64."): tekkenizer.audio_encoder(AudioURLChunk(audio_url="data:audio/wav;base64,invalid_base64_string")) # Test with an invalide file path with pytest.raises(FileNotFoundError, match=r"file='invalid_file_path.wav' does not exist"): tekkenizer.audio_encoder(AudioURLChunk(audio_url="file://invalid_file_path.wav")) python-mistral-common-1.11.7/tests/test_tokenizer_v7_audio_streaming.py000066400000000000000000000173651523035502500265300ustar00rootroot00000000000000import math from typing import Any import numpy as np import pytest from mistral_common.protocol.transcription.request import ( StreamingMode, TranscriptionRequest, ) from mistral_common.tokens.tokenizers.audio import ( OFFLINE_STREAMING_BUFFER_TOKENS, Audio, AudioConfig, AudioSpectrogramConfig, TranscriptionFormat, ) from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens, TokenizerVersion from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerV7, ) from mistral_common.tokens.tokenizers.mistral import ( load_audio_encoder, ) from mistral_common.tokens.tokenizers.tekken import SpecialTokenInfo, Tekkenizer from tests.test_tekken import get_special_tokens, quick_vocab _audio_spectrogram_config = { "num_mel_bins": 128, "hop_length": 160, "window_size": 400, } def add_special_tokens( special_tokens: list[dict[str, Any]], tokens: list[str], start_idx: int, is_control: bool = True ) -> None: """Insert `tokens` into `special_tokens` starting at `start_idx`. Raises if the target slot is already populated to avoid silent overrides. """ for offset, token in enumerate(tokens): idx = start_idx + offset try: token_entry = special_tokens[idx] except IndexError as exc: raise ValueError(f"special_tokens is missing index {idx}") from exc existing = token_entry.get("token_str") placeholder = f"" if existing not in (None, placeholder): raise ValueError(f"special_tokens[{idx}] already set to {existing}") token_entry["token_str"] = token token_entry["is_control"] = is_control def load_audio_streaming() -> Tekkenizer: special_tokens = get_special_tokens(tokenizer_version=TokenizerVersion.v7, add_audio=True) for i in range(37, 39): special_tokens += [SpecialTokenInfo(rank=i, token_str=f"", is_control=True)] special_tokens += [SpecialTokenInfo(rank=39, token_str="[STREAMING_PAD]", is_control=True)] special_tokens += [SpecialTokenInfo(rank=40, token_str="[STREAMING_WORD]", is_control=True)] tokenizer = Tekkenizer( quick_vocab([b"a", b"b", b"c", b"f", b"de"]), special_tokens, pattern=r".+", # single token, whole string vocab_size=256 + 100, num_special_tokens=100, version=TokenizerVersion.v7, ) audio_config = AudioConfig( sampling_rate=16_000, frame_rate=12.5, encoding_config=AudioSpectrogramConfig( num_mel_bins=128, window_size=400, hop_length=160, ), transcription_delay_ms=480.0, transcription_format=TranscriptionFormat.STREAMING, streaming_look_ahead_ms=2.5, streaming_look_back_ms=52.5, streaming_n_left_pad_tokens=16, ) tokenizer._audio_config = audio_config return tokenizer @pytest.fixture(scope="session") def tokenizer() -> InstructTokenizerV7: tekkenizer = load_audio_streaming() assert tekkenizer.audio is not None audio_encoder = load_audio_encoder(tekkenizer.audio, tekkenizer) return InstructTokenizerV7(tekkenizer, image_encoder=None, audio_encoder=audio_encoder) def test_special_audio_streaming_tokens(tokenizer: InstructTokenizerV7) -> None: assert tokenizer.tokenizer.get_special_token(SpecialTokens.streaming_pad.value) == 39 assert tokenizer.tokenizer.get_special_token(SpecialTokens.streaming_word.value) == 40 decoded_ids = [211, 218, 39, 194, 189, 39, 40, 172, 39, 191, 40, 2] assert tokenizer.decode(decoded_ids) == "ov^YH[" assert ( tokenizer.decode(decoded_ids, special_token_policy=SpecialTokenPolicy.KEEP) == "ov[STREAMING_PAD]^Y[STREAMING_PAD][STREAMING_WORD]H[STREAMING_PAD][[STREAMING_WORD]" ) @pytest.mark.parametrize("target_streaming_delay_ms", [None, 0, 1, 480, 960, 2400]) @pytest.mark.parametrize( ("mode", "duration"), [ (StreamingMode.OFFLINE, 1.7), # normal (StreamingMode.ONLINE, 1.7), # normal (would correspond to long delay) (StreamingMode.OFFLINE, 8.44), (StreamingMode.OFFLINE, 0.24), # very short (StreamingMode.ONLINE, 0.24), # very short delay (StreamingMode.OFFLINE, 2.312231), # weird length can correcctly be padded (StreamingMode.ONLINE, 2.312231), # should throw error as not correctly buffered ], ) def test_tokenize_streaming_request( tokenizer: InstructTokenizerV7, mode: StreamingMode, duration: float, target_streaming_delay_ms: int | None, ) -> None: sampling_rate = 24_000 signal_length = int(duration * sampling_rate) rng = np.random.default_rng(0) audio = Audio( audio_array=rng.uniform(low=-1, high=1, size=[signal_length]), sampling_rate=sampling_rate, format="wav", ) streaming_request = TranscriptionRequest( audio=audio.to_base64("wav") if mode == StreamingMode.OFFLINE else "", streaming=mode, target_streaming_delay_ms=target_streaming_delay_ms, language=None, ) tokenized = tokenizer.encode_transcription(streaming_request) assert tokenizer.audio_encoder is not None config = tokenizer.audio_encoder.audio_config delay_n_tokens = config.get_num_delay_tokens(target_streaming_delay_ms) + config.n_left_pad_tokens BOS = tokenizer.tokenizer.get_special_token(SpecialTokens.bos.value) STREAMING_PAD = tokenizer.tokenizer.get_special_token(SpecialTokens.streaming_pad.value) assert tokenized.tokens == ([BOS] + delay_n_tokens * [STREAMING_PAD]), f"{tokenized.tokens}" assert tokenized.text == "" + delay_n_tokens * "[STREAMING_PAD]" if mode == StreamingMode.OFFLINE: rounded_duration = math.ceil(duration / (config.frame_duration_ms / 1000)) * (config.frame_duration_ms / 1000) audio_array = tokenized.audios[0].audio_array assert audio_array.shape == ( rounded_duration * config.sampling_rate + (config.n_left_pad_tokens + config.n_right_pad_tokens(target_streaming_delay_ms)) * config.raw_audio_length_per_tok, ) audio_len_in_tokens = tokenizer.audio_encoder.audio_config.num_audio_tokens(audio_array.shape[0]) assert audio_len_in_tokens == ( rounded_duration * tokenizer.audio_encoder.audio_config.frame_rate + len(tokenized.tokens) + OFFLINE_STREAMING_BUFFER_TOKENS ) elif mode == StreamingMode.ONLINE: assert len(tokenized.audios) == 2, tokenized.audios left_audio, right_audio = tokenized.audios assert left_audio.duration == config.n_left_pad_tokens / config.frame_rate assert right_audio.duration == config.n_right_pad_tokens(target_streaming_delay_ms) / config.frame_rate else: raise ValueError(f"Unknown mode {mode}") @pytest.mark.parametrize( ("rate", "delay", "num_delay_tokens"), [(12.5, 360, -1), (12.5, 560, 7), (12.5, 960, 12), (50, 360, 18), (50, 560, 28), (50, 960, 48), (30, 960, -1)], ) def test_audio_config_delay(rate: float, delay: int, num_delay_tokens: int) -> None: spectogram_config = AudioSpectrogramConfig(**_audio_spectrogram_config) def audio_config_fn() -> AudioConfig: return AudioConfig( sampling_rate=16_000, frame_rate=rate, encoding_config=spectogram_config, transcription_delay_ms=delay, transcription_format=TranscriptionFormat.STREAMING, streaming_look_ahead_ms=2.5, streaming_look_back_ms=52.5, streaming_n_left_pad_tokens=16, ) if num_delay_tokens == -1: with pytest.raises(AssertionError, match="must be a multiple"): audio_config_fn() else: assert audio_config_fn().get_num_delay_tokens() == num_delay_tokens python-mistral-common-1.11.7/tests/test_tokenizer_v7_audio_transcribe.py000066400000000000000000000055311523035502500266630ustar00rootroot00000000000000import numpy as np import pytest from pydantic_extra_types.language_code import LanguageAlpha2 from mistral_common.protocol.transcription.request import TranscriptionRequest from mistral_common.tokens.tokenizers.audio import ( Audio, ) from mistral_common.tokens.tokenizers.instruct import ( InstructTokenizerV7, ) from tests.test_tokenizer_v7_audio import ( _get_audio_chunk, _get_specials, get_tekkenizer_with_audio, ) @pytest.fixture(scope="session") def tekkenizer() -> InstructTokenizerV7: return get_tekkenizer_with_audio() def get_transcription_request(duration: float, language: LanguageAlpha2 | None = None) -> TranscriptionRequest: audio_chunk = _get_audio_chunk(duration) return TranscriptionRequest( model="dummy", audio=audio_chunk.input_audio, language=language, target_streaming_delay_ms=None ) def test_tokenize_transcribe(tekkenizer: InstructTokenizerV7) -> None: duration = 1.7 # seconds frame_rate = 12.5 num_expected_frames = int(np.ceil(duration * frame_rate)) request = get_transcription_request(duration) tokenized = tekkenizer.encode_transcription(request) BOS, _, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, TRANSCRIBE = _get_specials(tekkenizer) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames assert tokenized.tokens == [ BOS, BEGIN_INST, *audio_toks, END_INST, TRANSCRIBE, ] assert tokenized.text == ("[INST][BEGIN_AUDIO]" + "[AUDIO]" * num_expected_frames + "[/INST][TRANSCRIBE]") assert len(tokenized.audios) == 1 base64_audio = request.audio assert isinstance(base64_audio, str) audio_array = Audio.from_base64(base64_audio).audio_array assert np.allclose(tokenized.audios[0].audio_array, audio_array, atol=1e-3) def test_tokenize_transcribe_with_lang(tekkenizer: InstructTokenizerV7) -> None: duration = 1.7 # seconds frame_rate = 12.5 num_expected_frames = int(np.ceil(duration * frame_rate)) request = get_transcription_request(duration, language=LanguageAlpha2("en")) tokenized = tekkenizer.encode_transcription(request) BOS, _, BEGIN_INST, END_INST, AUDIO, BEGIN_AUDIO, TRANSCRIBE = _get_specials(tekkenizer) audio_toks = [BEGIN_AUDIO] + [AUDIO] * num_expected_frames assert tokenized.tokens == [ BOS, BEGIN_INST, *audio_toks, END_INST, 208, 197, 210, 203, 158, 201, 210, TRANSCRIBE, ] assert tokenized.text == ("[INST][BEGIN_AUDIO]" + "[AUDIO]" * num_expected_frames + "[/INST]lang:en[TRANSCRIBE]") assert len(tokenized.audios) == 1 base64_audio = request.audio assert isinstance(base64_audio, str) audio_array = Audio.from_base64(base64_audio).audio_array assert np.allclose(tokenized.audios[0].audio_array, audio_array, atol=1e-3) python-mistral-common-1.11.7/tests/test_tokenizer_v7_audio_tts.py000066400000000000000000000214631523035502500253430ustar00rootroot00000000000000import math import numpy as np import pytest from mistral_common.protocol.speech.request import SpeechRequest from mistral_common.tokens.tokenizers.audio import ( Audio, AudioConfig, AudioEncoder, AudioSpectrogramConfig, ) from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens from mistral_common.tokens.tokenizers.instruct import InstructTokenizerV7 from mistral_common.tokens.tokenizers.mistral import load_audio_encoder from mistral_common.tokens.tokenizers.tekken import Tekkenizer from .test_tokenizer_v7_audio import get_tekkenizer_with_audio @pytest.fixture(scope="session") def tts_tokenizer() -> InstructTokenizerV7: mm_tekkenizer = get_tekkenizer_with_audio().tokenizer assert isinstance(mm_tekkenizer, Tekkenizer) audio_encoder = load_audio_encoder( AudioConfig( sampling_rate=24000, frame_rate=12.5, encoding_config=AudioSpectrogramConfig( num_mel_bins=128, window_size=400, hop_length=160, ), voice_num_audio_tokens={ "female": 52, "male": 76, }, ), mm_tekkenizer, ) assert isinstance(audio_encoder, AudioEncoder), type(audio_encoder) return InstructTokenizerV7(tokenizer=mm_tekkenizer, audio_encoder=audio_encoder) def _make_fake_audio(duration: float, sampling_rate: int = 24000) -> Audio: rng = np.random.default_rng(42) audio_array = rng.uniform(low=-1, high=1, size=int(duration * sampling_rate)) return Audio(audio_array=audio_array, sampling_rate=sampling_rate, format="wav") def test_encode_speech_request_with_ref_audio(tts_tokenizer: InstructTokenizerV7) -> None: duration = 1.5 sampling_rate = 24000 audio = _make_fake_audio(duration, sampling_rate) request = SpeechRequest(input="Hello world", ref_audio=audio.to_base64("wav")) tokenized = tts_tokenizer.encode_speech_request(request) assert isinstance(tts_tokenizer.audio_encoder, AudioEncoder) frame_rate = tts_tokenizer.audio_encoder.audio_config.frame_rate num_audio_tokens = math.ceil(duration * frame_rate) + 1 # +1 for eoa BOS = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.bos.value) AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio.value) BEGIN_AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.begin_audio.value) NEXT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.text_to_audio.value) REPEAT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio_to_text.value) text_tokens = tts_tokenizer.tokenizer.encode("Hello world", bos=False, eos=False) expected = ( [BOS] + [BEGIN_AUDIO] + [AUDIO] * num_audio_tokens + [NEXT_AUDIO_TEXT] + text_tokens + [REPEAT_AUDIO_TEXT] + [BEGIN_AUDIO] ) assert tokenized.tokens == expected, f"{tokenized.tokens=} != {expected=}" assert len(tokenized.audios) == 1 assert np.allclose(tokenized.audios[0].audio_array, audio.audio_array, atol=1e-3) def test_encode_speech_request_with_voice_female(tts_tokenizer: InstructTokenizerV7) -> None: request = SpeechRequest(input="Hello world", voice="female") tokenized = tts_tokenizer.encode_speech_request(request) BOS = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.bos.value) AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio.value) BEGIN_AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.begin_audio.value) NEXT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.text_to_audio.value) REPEAT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio_to_text.value) text_tokens = tts_tokenizer.tokenizer.encode("Hello world", bos=False, eos=False) expected = ( [BOS] + [BEGIN_AUDIO] + [AUDIO] * 52 + [NEXT_AUDIO_TEXT] + text_tokens + [REPEAT_AUDIO_TEXT] + [BEGIN_AUDIO] ) assert tokenized.tokens == expected, f"{tokenized.tokens=} != {expected=}" assert tokenized.audios == [] def test_encode_speech_request_with_voice_male(tts_tokenizer: InstructTokenizerV7) -> None: request = SpeechRequest(input="Hello world", voice="male") tokenized = tts_tokenizer.encode_speech_request(request) BOS = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.bos.value) AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio.value) BEGIN_AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.begin_audio.value) NEXT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.text_to_audio.value) REPEAT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio_to_text.value) text_tokens = tts_tokenizer.tokenizer.encode("Hello world", bos=False, eos=False) expected = ( [BOS] + [BEGIN_AUDIO] + [AUDIO] * 76 + [NEXT_AUDIO_TEXT] + text_tokens + [REPEAT_AUDIO_TEXT] + [BEGIN_AUDIO] ) assert tokenized.tokens == expected, f"{tokenized.tokens=} != {expected=}" assert tokenized.audios == [] def test_encode_speech_request_ref_audio_takes_precedence_over_voice( tts_tokenizer: InstructTokenizerV7, ) -> None: audio = _make_fake_audio(1.0) request = SpeechRequest(input="text", ref_audio=audio.to_base64("wav"), voice="female") tokenized = tts_tokenizer.encode_speech_request(request) # ref_audio takes precedence: audio is not None so the audio path is used assert len(tokenized.audios) == 1 assert np.allclose(tokenized.audios[0].audio_array, audio.audio_array, atol=1e-3) # Token count should match ref_audio duration, not the preset 52 for "female" assert isinstance(tts_tokenizer.audio_encoder, AudioEncoder) frame_rate = tts_tokenizer.audio_encoder.audio_config.frame_rate num_audio_tokens = math.ceil(1.0 * frame_rate) + 1 AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio.value) audio_token_count = tokenized.tokens.count(AUDIO) assert audio_token_count == num_audio_tokens, f"{audio_token_count=} != {num_audio_tokens=}" def test_encode_speech_request_neither_audio_nor_voice_fails( tts_tokenizer: InstructTokenizerV7, ) -> None: request = SpeechRequest(input="Hello world") with pytest.raises(AssertionError, match="Either ref_audio or voice must be defined"): tts_tokenizer.encode_speech_request(request) def test_encode_speech_request_text_tokens_correct(tts_tokenizer: InstructTokenizerV7) -> None: input_text = "The quick brown fox" request = SpeechRequest(input=input_text, voice="female") tokenized = tts_tokenizer.encode_speech_request(request) NEXT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.text_to_audio.value) REPEAT_AUDIO_TEXT = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio_to_text.value) # Extract text tokens between [NEXT_AUDIO_TEXT] and [REPEAT_AUDIO_TEXT] next_idx = tokenized.tokens.index(NEXT_AUDIO_TEXT) repeat_idx = tokenized.tokens.index(REPEAT_AUDIO_TEXT) text_tokens = tokenized.tokens[next_idx + 1 : repeat_idx] decoded = tts_tokenizer.tokenizer.decode(text_tokens, special_token_policy=SpecialTokenPolicy.IGNORE) assert decoded == input_text, f"{decoded=} != {input_text=}" def test_encode_speech_request_no_audio_encoder_fails() -> None: mm_tekkenizer = get_tekkenizer_with_audio().tokenizer assert isinstance(mm_tekkenizer, Tekkenizer) tokenizer_no_encoder = InstructTokenizerV7(tokenizer=mm_tekkenizer, audio_encoder=None) request = SpeechRequest(input="Hello world", voice="female") with pytest.raises(AssertionError, match="Audio encoder must be defined"): tokenizer_no_encoder.encode_speech_request(request) def test_encode_speech_request_audio_resampled(tts_tokenizer: InstructTokenizerV7) -> None: # Create audio at 16000 Hz — different from the encoder's 24000 Hz duration = 2.0 source_sr = 16000 audio = _make_fake_audio(duration, source_sr) request = SpeechRequest(input="Resample test", ref_audio=audio.to_base64("wav")) tokenized = tts_tokenizer.encode_speech_request(request) assert isinstance(tts_tokenizer.audio_encoder, AudioEncoder) target_sr = tts_tokenizer.audio_encoder.audio_config.sampling_rate frame_rate = tts_tokenizer.audio_encoder.audio_config.frame_rate assert tokenized.audios[0].sampling_rate == target_sr, f"{frame_rate=}, {tokenized.audios[0].sampling_rate=}" # After resampling the duration stays the same, but length changes to target_sr * duration num_audio_tokens = math.ceil(duration * frame_rate) + 1 AUDIO = tts_tokenizer.tokenizer.get_special_token(SpecialTokens.audio.value) audio_token_count = tokenized.tokens.count(AUDIO) assert audio_token_count == num_audio_tokens, f"{audio_token_count=} != {num_audio_tokens=}" assert len(tokenized.audios) == 1 python-mistral-common-1.11.7/tests/test_utils.py000066400000000000000000000213061523035502500217760ustar00rootroot00000000000000import tempfile from pathlib import Path from unittest.mock import MagicMock, patch import huggingface_hub as huggingface_hub import pytest import requests from mistral_common.tokens.tokenizers.utils import ( download_tokenizer_from_hf_hub, get_one_valid_tokenizer_file, list_local_hf_repo_files, ) def _create_temporary_hf_model_cache(repo_id: str) -> Path: tmp_dir = tempfile.mkdtemp() hub_folder = Path(tmp_dir) / "hf_hub" model_folder = huggingface_hub.constants.REPO_ID_SEPARATOR.join(["models", *repo_id.split("/")]) revision_file = hub_folder / model_folder / "refs" / huggingface_hub.constants.DEFAULT_REVISION revision_file.parent.mkdir(parents=True, exist_ok=True) revision_file.write_text("RANDOM_REVISION") revision_folder = hub_folder / model_folder / "snapshots" / "RANDOM_REVISION" revision_folder.mkdir(parents=True, exist_ok=True) (revision_folder / "tekken.json").write_text("{'test': 'test'}") (revision_folder / "file2.txt").write_text("test") return hub_folder @patch("huggingface_hub.constants.HF_HUB_CACHE", "/tmp/hf_cache") def test_list_local_hf_repo_files() -> None: hf_cache = _create_temporary_hf_model_cache("mistralai/Mistral-7B-v0.1") with patch("huggingface_hub.constants.HF_HUB_CACHE", hf_cache): # Test without revision files = sorted(list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", None)) assert files == ["file2.txt", "tekken.json"] # Test with a specific revision files = sorted(list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", "RANDOM_REVISION")) assert files == ["file2.txt", "tekken.json"] # Test with a branch/tag revision that must be resolved via refs (e.g. "main") files = sorted(list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", "main")) assert files == ["file2.txt", "tekken.json"] # Test with non-existent revision assert list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", "non_existent_revision") == [] # Test huggingface_hub not installed with patch("mistral_common.tokens.tokenizers.utils._hub_installed", False): with pytest.raises(ImportError): list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", "test_revision") @pytest.mark.parametrize( ["files", "expected"], [ ([], None), (["unvalid1.txt", "/path/to/tekken.json"], "/path/to/tekken.json"), (["unvalid1.txt", "tekken.json"], "tekken.json"), (["unvalid1.txt", "/tekken.json"], "/tekken.json"), (["unvalid1.txt", "sentencepiece.model"], "sentencepiece.model"), (["unvalid1.txt", "sentencepiece.model.v1"], "sentencepiece.model.v1"), (["unvalid1.txt", "/path/to/sentencepiece.model.v1", "sentencepiece.model.v1m1"], "sentencepiece.model.v1m1"), ( ["unvalid1.txt", "/sentencepiece.model.v1", "/path/to/sentencepiece.model.v1m1"], "/path/to/sentencepiece.model.v1m1", ), ( [ "unvalid1.txt", "/sentencepiece.model.v1", "/path/to/sentencepiece.model.v1m1", "/sentencepiece.model.v1/tekken.json", ], "/sentencepiece.model.v1/tekken.json", ), (["unvalid1.txt", "sentencepiece.model", "sentencepiece.model.v1m1"], "sentencepiece.model.v1m1"), (["unvalid1.txt", "unvalid2.txt"], None), ], ) def test_get_one_valid_tokenizer_files_abs(files: list[str], expected: str | None) -> None: if expected is None: with pytest.raises(ValueError): get_one_valid_tokenizer_file(files=files) else: tokenizer = get_one_valid_tokenizer_file(files=files) assert tokenizer == expected @pytest.mark.parametrize( ["files", "expected"], [ ([], None), (["unvalid1.txt", "path/to/tekken.json"], "path/to/tekken.json"), (["unvalid1.txt", "tekken.json"], "tekken.json"), (["unvalid1.txt", "sentencepiece.model"], "sentencepiece.model"), (["unvalid1.txt", "sentencepiece.model.v1"], "sentencepiece.model.v1"), (["unvalid1.txt", "sentencepiece.model.v1", "sentencepiece.model.v1m1"], "sentencepiece.model.v1m1"), ( [ "unvalid1.txt", "sentencepiece.model.v1", "sentencepiece.model.v1m1", "sentencepiece.model.v1/tekken.json", ], "sentencepiece.model.v1/tekken.json", ), (["unvalid1.txt", "sentencepiece.model", "sentencepiece.model.v1m1"], "sentencepiece.model.v1m1"), (["unvalid1.txt", "unvalid2.txt"], None), ], ) def test_download_tokenizer_from_hf_hub(files: list[str], expected: str | None) -> None: with patch("huggingface_hub.HfApi.list_repo_files", return_value=files): if expected is None: with pytest.raises(ValueError): download_tokenizer_from_hf_hub( repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None, local_files_only=False, force_download=False, ) else: with patch("huggingface_hub.hf_hub_download", return_value=expected): tokenizer = download_tokenizer_from_hf_hub( repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None, local_files_only=False, force_download=False, ) assert tokenizer == expected # Test huggingface_hub not installed with patch("mistral_common.tokens.tokenizers.utils._hub_installed", False): with pytest.raises(ImportError): list_local_hf_repo_files("mistralai/Mistral-7B-v0.1", "test_revision") # Test with local_files_only and force_download with pytest.raises( ValueError, match="You cannot force the download of the tokenizer if you only want to use local files." ): download_tokenizer_from_hf_hub( repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None, local_files_only=True, force_download=True ) @patch("huggingface_hub.HfApi.list_repo_files") def test_download_tokenizer_from_hf_hub_without_connection(mock_list_repo_files: MagicMock) -> None: mock_list_repo_files.side_effect = requests.ConnectionError("No connection with force_download=True") hf_cache = _create_temporary_hf_model_cache("mistralai/Mistral-7B-v0.1") # Test with local cache with patch("huggingface_hub.constants.HF_HUB_CACHE", hf_cache): tokenizer = download_tokenizer_from_hf_hub(repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None) assert tokenizer == str(hf_cache / "models--mistralai--Mistral-7B-v0.1/snapshots/RANDOM_REVISION/tekken.json") # Test with force download with pytest.raises(requests.ConnectionError, match="No connection with force_download=True"): download_tokenizer_from_hf_hub( repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None, force_download=True ) # Test without local cache with patch("mistral_common.tokens.tokenizers.utils.list_local_hf_repo_files", return_value=[]): with pytest.raises( FileNotFoundError, match=( "Could not connect to the Hugging Face Hub and no local files were found for the repo ID " "mistralai/Mistral-7B-v0.1 and revision None. Please check your internet connection and try again." ), ): download_tokenizer_from_hf_hub(repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None) with pytest.raises( FileNotFoundError, match=( "No local files found for the repo ID mistralai/Mistral-7B-v0.1 and revision None. Please check the " "repo ID and the revision or try to download the tokenizer without setting `local_files_only` to " "`True`." ), ): download_tokenizer_from_hf_hub( repo_id="mistralai/Mistral-7B-v0.1", token=None, revision=None, local_files_only=True ) @patch("huggingface_hub.HfApi.list_repo_files") def test_download_tokenizer_from_hf_hub_offline_mode(mock_list_repo_files: MagicMock) -> None: mock_list_repo_files.side_effect = huggingface_hub.errors.OfflineModeIsEnabled("offline mode is enabled") hf_cache = _create_temporary_hf_model_cache("mistralai/Mistral-7B-v0.1") with patch("huggingface_hub.constants.HF_HUB_CACHE", hf_cache): tokenizer = download_tokenizer_from_hf_hub(repo_id="mistralai/Mistral-7B-v0.1", token=None, revision="main") assert tokenizer == str(hf_cache / "models--mistralai--Mistral-7B-v0.1/snapshots/RANDOM_REVISION/tekken.json") python-mistral-common-1.11.7/tests/validation/000077500000000000000000000000001523035502500213555ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/validation/__init__.py000066400000000000000000000000001523035502500234540ustar00rootroot00000000000000python-mistral-common-1.11.7/tests/validation/test_chat_validation.py000066400000000000000000001026631523035502500261270ustar00rootroot00000000000000from typing import Any import pytest from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidMessageStructureException, InvalidRequestException, InvalidSystemPromptException, InvalidToolMessageException, InvalidUserMessageException, ) from mistral_common.protocol.instruct.chunk import ( AudioChunk, AudioURLChunk, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, SystemMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.request import ( ChatCompletionRequest, ReasoningEffort, ) from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidator, MistralRequestValidatorV3, MistralRequestValidatorV5, MistralRequestValidatorV13, MistralRequestValidatorV15, ValidationMode, ) from tests.fixtures.audio import get_dummy_audio_chunk, get_dummy_audio_url_chunk from tests.fixtures.chunks import get_content_chunks _Messages = list[UserMessage | AssistantMessage | SystemMessage | ToolMessage] def _user_convo(content: "str | list[Any]") -> _Messages: return [UserMessage(content=content)] def _assistant_convo(content: "str | list[Any]") -> _Messages: return [UserMessage(content="hi"), AssistantMessage(content=content), UserMessage(content="next")] def _system_convo(content: "str | list[Any]") -> _Messages: return [SystemMessage(content=content), UserMessage(content="hi")] def _tool_convo(content: "str | list[Any]") -> _Messages: return [ UserMessage(content="hi"), AssistantMessage(tool_calls=[ToolCall(function=FunctionCall(name="fn", arguments="{}"), id="test12345")]), ToolMessage(content=content, tool_call_id="test12345"), ] @pytest.fixture(scope="module") def audio_chunk() -> AudioChunk: return get_dummy_audio_chunk() @pytest.fixture(scope="module") def audio_url_chunk() -> AudioURLChunk: return get_dummy_audio_url_chunk() @pytest.fixture( params=[ MistralRequestValidator(ValidationMode.serving), MistralRequestValidatorV3(ValidationMode.serving), MistralRequestValidatorV13(ValidationMode.serving), ] ) def validator(request: pytest.FixtureRequest) -> MistralRequestValidator: return request.param # type: ignore @pytest.fixture def validator_base() -> MistralRequestValidator: return MistralRequestValidator(ValidationMode.serving) @pytest.fixture def validator_v3() -> MistralRequestValidatorV3: return MistralRequestValidatorV3(ValidationMode.serving) @pytest.fixture def validator_v5() -> MistralRequestValidatorV5: return MistralRequestValidatorV5(ValidationMode.serving) @pytest.fixture( params=[ MistralRequestValidatorV13(ValidationMode.serving), ] ) def validator_v13(request: pytest.FixtureRequest) -> MistralRequestValidator: return request.param # type: ignore @pytest.fixture( params=[ MistralRequestValidatorV15(ValidationMode.serving), ] ) def validator_v15(request: pytest.FixtureRequest) -> MistralRequestValidator: return request.param # type: ignore class TestChatValidation: def test_multiple_system_messages_OK(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ SystemMessage(content="foo"), SystemMessage(content="foo"), SystemMessage(content="foo"), UserMessage(content="foo"), # so we don't get an error for ending with a system message ], continue_final_message=False, ) def test_system_user_messages_OK(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ SystemMessage(content="foo"), UserMessage(content="foo"), ], continue_final_message=False, ) def test_user_system_messages_OK(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), SystemMessage(content="foo"), UserMessage(content="foo"), # so we don't get an error for ending with a system message ], continue_final_message=False, ) def test_user_user_messages_OK(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), UserMessage(content="foo"), # so we don't get an error for ending with a system message ], continue_final_message=False, ) def test_empty_messages(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidMessageStructureException, match=r"Conversation must have at least one message"): validator.validate_messages( messages=[], continue_final_message=False, ) def test_starts_with_system_or_user(self, validator: MistralRequestValidator) -> None: """ This is allowed in validation, we will add a blank message in normalization """ validator.validate_messages( messages=[ AssistantMessage(content="foo"), UserMessage(content="foo"), # so we don't get an error for ending with a system message ], continue_final_message=False, ) def test_ends_with_assistant(self, validator: MistralRequestValidator) -> None: with pytest.raises( InvalidMessageStructureException, match=( r"Expected last role User or Tool \(or Assistant with prefix or continue_final_message set to " r"True\) for serving but got assistant" ), ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo"), ], continue_final_message=False, ) def test_assistant_prefix(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo", prefix=True), ], continue_final_message=False, ) with pytest.raises( InvalidAssistantMessageException, match=r"Assistant message with prefix True must be last message", ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo", prefix=True), UserMessage(content="foo"), ], continue_final_message=False, ) def test_continue_final_message(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo"), ], continue_final_message=True, ) with pytest.raises( InvalidMessageStructureException, match=( r"Expected last role Assistant with prefix False for serving with continue_final_message set to True " r"but got user" ), ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo", prefix=True), UserMessage(content="foo"), ], continue_final_message=True, ) with pytest.raises( InvalidMessageStructureException, match=( r"Expected last role User or Tool \(or Assistant with prefix or continue_final_message set to True\)" r" for serving but got assistant" ), ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo"), ], continue_final_message=False, ) with pytest.raises( InvalidMessageStructureException, match=( r"Expected last role Assistant with prefix False for serving with continue_final_message set to True " r"but got assistant" ), ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage(content="foo", prefix=True), ], continue_final_message=True, ) def test_user_tool_user(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidMessageStructureException, match=r"Unexpected role 'tool' after role 'user'"): validator.validate_messages( messages=[ UserMessage(content="foo"), ToolMessage(content="foo"), UserMessage(content="foo"), ], continue_final_message=False, ) def test_model_none_serving_mode(self) -> None: validator = MistralRequestValidator[UserMessage, AssistantMessage, ToolMessage, SystemMessage]( ValidationMode.serving ) with pytest.raises(InvalidRequestException, match=r"Model name parameter is required for serving mode"): validator.validate_request(ChatCompletionRequest(messages=[UserMessage(content="foo")])) def test_model_none_test_mode(self) -> None: validator = MistralRequestValidator[UserMessage, AssistantMessage, ToolMessage, SystemMessage]( ValidationMode.test ) validator.validate_request(ChatCompletionRequest(messages=[UserMessage(content="foo")])) def test_tool_calls_OK(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ], continue_final_message=False, ) def test_not_enough_tool_messages(self, validator: MistralRequestValidator) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Not the same number of function calls and responses" ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), # missing ToolMessage ], continue_final_message=False, ) def test_too_many_tool_messages(self, validator: MistralRequestValidator) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Not the same number of function calls and responses|Unexpected tool call id", ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ToolMessage(name="foo", content="bar", tool_call_id="999999999"), # too many tool messages ], continue_final_message=False, ) def test_tool_then_user_ok(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), UserMessage(content="continue with this context"), ], continue_final_message=False, ) def test_tool_then_user_full_loop(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="search for info"), AssistantMessage( tool_calls=[ ToolCall(id="aaaaaaaaa", function=FunctionCall(name="search", arguments="{}")), ToolCall(id="bbbbbbbbb", function=FunctionCall(name="search", arguments="{}")), ] ), ToolMessage(name="search", content="result1", tool_call_id="aaaaaaaaa"), ToolMessage(name="search", content="result2", tool_call_id="bbbbbbbbb"), UserMessage(content="now refine"), AssistantMessage( tool_calls=[ToolCall(id="ccccccccc", function=FunctionCall(name="search", arguments="{}"))] ), ToolMessage(name="search", content="result3", tool_call_id="ccccccccc"), AssistantMessage(content="here is the final answer"), UserMessage(content="thanks"), ], continue_final_message=False, ) def test_partial_tool_results_then_user_fails(self, validator: MistralRequestValidator) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Not the same number of function calls and responses" ): validator.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="bar", arguments="{}")), ] ), ToolMessage(name="foo", content="result1", tool_call_id="123456789"), UserMessage(content="stop, only need the first result"), ], continue_final_message=False, ) def test_build_settings_raises_error(self, validator: MistralRequestValidator) -> None: request = ChatCompletionRequest(messages=[UserMessage(content="Hello")], reasoning_effort=ReasoningEffort.none) with pytest.raises(InvalidRequestException, match="reasoning_effort='none' is not supported for this model"): validator._validate_model_settings(request) def test_allows_text_content_chunks(self, validator_base: MistralRequestValidator) -> None: validator_base.validate_messages(_user_convo(get_content_chunks(("text",))), continue_final_message=False) validator_base.validate_messages(_assistant_convo(get_content_chunks(("text",))), continue_final_message=False) validator_base.validate_messages(_system_convo(get_content_chunks(("text",))), continue_final_message=False) validator_base.validate_messages(_tool_convo(get_content_chunks(("text",))), continue_final_message=False) def test_rejects_non_text_in_user(self, validator_base: MistralRequestValidator) -> None: for name in ("image", "image_url", "audio", "audio_url"): with pytest.raises(InvalidUserMessageException, match="Unexpected content chunk types in user message"): validator_base.validate_messages(_user_convo(get_content_chunks((name,))), continue_final_message=False) def test_rejects_non_text_in_assistant(self, validator_base: MistralRequestValidator) -> None: for name in ("think",): with pytest.raises( InvalidAssistantMessageException, match="Unexpected content chunk types in assistant message" ): validator_base.validate_messages( _assistant_convo(get_content_chunks((name,))), continue_final_message=False ) def test_rejects_non_text_in_system(self, validator_base: MistralRequestValidator) -> None: for name in ("audio", "think"): with pytest.raises(InvalidSystemPromptException, match="Unexpected content chunk types in system message"): validator_base.validate_messages( _system_convo(get_content_chunks((name,))), continue_final_message=False ) def test_rejects_non_text_in_tool(self, validator_base: MistralRequestValidator) -> None: # "think" is rejected at the Pydantic model level (ToolMessage construction), not by the validator. for name in ("image", "image_url", "audio", "audio_url"): with pytest.raises(InvalidToolMessageException, match="Unexpected content chunk types in tool message"): validator_base.validate_messages(_tool_convo(get_content_chunks((name,))), continue_final_message=False) def test_reports_sorted_unique_invalid_chunk_types(self, validator_base: MistralRequestValidator) -> None: content = get_content_chunks(("audio", "image_url")) with pytest.raises( InvalidUserMessageException, match=r"Unexpected content chunk types in user message: \['AudioChunk', 'ImageURLChunk'\]", ): validator_base.validate_messages(_user_convo(content), continue_final_message=False) class TestChatValidationV3: def test_allows_text_and_image_in_user(self, validator_v3: MistralRequestValidatorV3) -> None: content = get_content_chunks(("text", "image", "image_url")) validator_v3.validate_messages(_user_convo(content), continue_final_message=False) def test_rejects_audio_in_user(self, validator_v3: MistralRequestValidatorV3) -> None: for name in ("audio", "audio_url"): with pytest.raises(InvalidUserMessageException, match="Unexpected content chunk types in user message"): validator_v3.validate_messages(_user_convo(get_content_chunks((name,))), continue_final_message=False) class TestChatValidationV5: @pytest.mark.parametrize("audio_fixture", ["audio_chunk", "audio_url_chunk"]) def test_audio_with_system_prompt_raises_error( self, validator_v5: MistralRequestValidatorV5, audio_fixture: str, request: pytest.FixtureRequest ) -> None: audio_chunk: AudioChunk | AudioURLChunk = request.getfixturevalue(audio_fixture) with pytest.raises( ValueError, match=( r"Found system messages at indexes \[0\] and audio chunks in messages at indexes \[1\]\. This is not " r"allowed prior to the tokenizer version 13\." ), ): validator_v5.validate_messages( messages=[ SystemMessage(content="This is a system prompt"), UserMessage(content=[audio_chunk]), ], continue_final_message=False, ) @pytest.mark.parametrize("audio_fixture", ["audio_chunk", "audio_url_chunk"]) def test_audio_without_system_prompt_ok( self, validator_v5: MistralRequestValidatorV5, audio_fixture: str, request: pytest.FixtureRequest ) -> None: audio_chunk: AudioChunk | AudioURLChunk = request.getfixturevalue(audio_fixture) validator_v5.validate_messages( messages=[ UserMessage(content=[audio_chunk]), UserMessage(content="User message after audio"), ], continue_final_message=False, ) def test_system_prompt_without_audio_ok(self, validator_v5: MistralRequestValidatorV5) -> None: validator_v5.validate_messages( messages=[ SystemMessage(content="This is a system prompt"), UserMessage(content="User message after system"), ], continue_final_message=False, ) def test_build_settings_raises_error(self, validator: MistralRequestValidator) -> None: request = ChatCompletionRequest(messages=[UserMessage(content="Hello")], reasoning_effort=ReasoningEffort.none) with pytest.raises(InvalidRequestException, match="reasoning_effort='none' is not supported for this model"): validator._validate_model_settings(request) def test_allows_text_image_audio_in_user(self, validator_v5: MistralRequestValidatorV5) -> None: content = get_content_chunks(("text", "image", "image_url", "audio", "audio_url")) validator_v5.validate_messages(_user_convo(content), continue_final_message=False) def test_allows_text_audio_think_in_system(self, validator_v5: MistralRequestValidatorV5) -> None: content = get_content_chunks(("text", "audio", "think")) validator_v5.validate_messages(_system_convo(content), continue_final_message=False) def test_rejects_think_in_assistant(self, validator_v5: MistralRequestValidatorV5) -> None: for name in ("think",): with pytest.raises( InvalidAssistantMessageException, match="Unexpected content chunk types in assistant message" ): validator_v5.validate_messages( _assistant_convo(get_content_chunks((name,))), continue_final_message=False ) class TestChatValidationV13: def test_right_number_results_invalid_id(self, validator_v13: MistralRequestValidatorV13) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Unexpected tool call id", ): validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="999999999"), # invalid id ], continue_final_message=False, ) def test_extra_results(self, validator_v13: MistralRequestValidatorV13) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Unexpected tool call id", ): validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ToolMessage(name="foo", content="bar", tool_call_id="999999999"), # extra results ], continue_final_message=False, ) def test_build_settings_raises_error(self, validator: MistralRequestValidator) -> None: request = ChatCompletionRequest(messages=[UserMessage(content="Hello")], reasoning_effort=ReasoningEffort.none) with pytest.raises(InvalidRequestException, match="reasoning_effort='none' is not supported for this model"): validator._validate_model_settings(request) def test_parallel_call_missing_results(self, validator_v13: MistralRequestValidatorV13) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Not the same number of function calls and responses", ): validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), # missing ToolMessage ], continue_final_message=False, ) def allow_tool_results_wrong_order(self, validator_v13: MistralRequestValidatorV13) -> None: validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="999999999", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="999999999"), # invalid order ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ], continue_final_message=False, ) def test_tool_call_duplicate_ids_same_assistant_messages(self, validator_v13: MistralRequestValidatorV13) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Duplicate", ): validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ], continue_final_message=False, ) def test_tool_call_duplicate_ids_different_assistant_messages( self, validator_v13: MistralRequestValidatorV13 ) -> None: validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ], continue_final_message=False, ) def test_multiple_assistant_messages_some_with_tool_calls(self, validator_v13: MistralRequestValidatorV13) -> None: validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), AssistantMessage(content="h"), UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}")), ] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), AssistantMessage(content="h"), UserMessage(content="foo"), ], continue_final_message=False, ) def test_tool_then_user_ok_v13(self, validator_v13: MistralRequestValidatorV13) -> None: validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ToolCall(id="123456789", function=FunctionCall(name="foo", arguments="{}"))] ), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), UserMessage(content="continue with this context"), ], continue_final_message=False, ) def test_partial_tool_results_then_user_fails_v13(self, validator_v13: MistralRequestValidatorV13) -> None: with pytest.raises( InvalidMessageStructureException, match=r"Not the same number of function calls and responses" ): validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="aaaaaaaaa", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="bbbbbbbbb", function=FunctionCall(name="bar", arguments="{}")), ToolCall(id="ccccccccc", function=FunctionCall(name="baz", arguments="{}")), ] ), ToolMessage(name="foo", content="result1", tool_call_id="aaaaaaaaa"), UserMessage(content="stop, only need the first result"), ], continue_final_message=False, ) def test_full_loop_with_user_after_tools_v13(self, validator_v13: MistralRequestValidatorV13) -> None: validator_v13.validate_messages( messages=[ UserMessage(content="foo"), AssistantMessage( tool_calls=[ ToolCall(id="aaaaaaaaa", function=FunctionCall(name="foo", arguments="{}")), ToolCall(id="bbbbbbbbb", function=FunctionCall(name="bar", arguments="{}")), ] ), ToolMessage(name="foo", content="result1", tool_call_id="aaaaaaaaa"), ToolMessage(name="bar", content="result2", tool_call_id="bbbbbbbbb"), UserMessage(content="try something else"), AssistantMessage( tool_calls=[ToolCall(id="ccccccccc", function=FunctionCall(name="baz", arguments="{}"))] ), ToolMessage(name="baz", content="result3", tool_call_id="ccccccccc"), AssistantMessage(content="final answer"), UserMessage(content="done"), ], continue_final_message=False, ) @pytest.mark.parametrize("audio_fixture", ["audio_chunk", "audio_url_chunk"]) def test_audio_with_system_prompt_raises_ok( self, validator_v13: MistralRequestValidatorV13, audio_fixture: str, request: pytest.FixtureRequest ) -> None: audio_chunk: AudioChunk | AudioURLChunk = request.getfixturevalue(audio_fixture) validator_v13.validate_messages( messages=[ SystemMessage(content="This is a system prompt"), UserMessage(content=[audio_chunk]), ], continue_final_message=False, ) def test_allows_text_and_think_in_assistant(self, validator_v13: MistralRequestValidatorV13) -> None: content = get_content_chunks(("text", "think")) validator_v13.validate_messages(_assistant_convo(content), continue_final_message=False) def test_rejects_non_text_in_tool(self, validator_v13: MistralRequestValidatorV13) -> None: # "think" is rejected at the Pydantic model level (ToolMessage construction), not by the validator. for name in ("image", "image_url", "audio", "audio_url"): with pytest.raises(InvalidToolMessageException, match="Unexpected content chunk types in tool message"): validator_v13.validate_messages(_tool_convo(get_content_chunks((name,))), continue_final_message=False) class TestChatValidationV15: @pytest.mark.parametrize("reasoning_effort", [*list(ReasoningEffort), None]) def test_build_settings_v15_reasoning_effort( self, reasoning_effort: ReasoningEffort | None, validator_v15: MistralRequestValidatorV15 ) -> None: request = ChatCompletionRequest(messages=[UserMessage(content="Hello")], reasoning_effort=reasoning_effort) validator_v15._validate_model_settings(request) def test_allows_text_and_audio_in_system(self, validator_v15: MistralRequestValidatorV15) -> None: content = get_content_chunks(("text", "audio")) validator_v15.validate_messages(_system_convo(content), continue_final_message=False) def test_rejects_think_in_system(self, validator_v15: MistralRequestValidatorV15) -> None: for name in ("think",): with pytest.raises(InvalidSystemPromptException, match="Unexpected content chunk types in system message"): validator_v15.validate_messages( _system_convo(get_content_chunks((name,))), continue_final_message=False ) def test_allows_non_thinking_chunk_types_in_tool(self, validator_v15: MistralRequestValidatorV15) -> None: # ThinkChunk is rejected at the Pydantic model level before reaching the validator. content = get_content_chunks(("text", "image", "image_url", "audio", "audio_url")) validator_v15.validate_messages(_tool_convo(content), continue_final_message=False) python-mistral-common-1.11.7/tests/validation/test_finetuning_validation.py000066400000000000000000000166331523035502500273570ustar00rootroot00000000000000import pytest from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidFunctionCallException, InvalidMessageStructureException, ) from mistral_common.protocol.instruct.messages import ( FinetuningAssistantMessage, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidator, MistralRequestValidatorV3, ValidationMode, ) @pytest.fixture( params=[MistralRequestValidator(ValidationMode.finetuning), MistralRequestValidatorV3(ValidationMode.finetuning)] ) def validator(request: pytest.FixtureRequest) -> MistralRequestValidator: return request.param # type: ignore class TestFineTuningValidation: # This fails in chat validation def test_ends_with_assistant(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(content="foo"), ], continue_final_message=False, ) def test_has_weight_in_message_assistant(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(content="foo", weight=1), ], continue_final_message=False, ) def test_has_invalid_weight_in_message_assistant(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidAssistantMessageException, match=r"Assistant message weight must be either 0 or 1"): validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(content="foo", weight=0.5), ], continue_final_message=False, ) def test_should_allow_multiple_weights(self, validator: MistralRequestValidator) -> None: validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(content="foo", weight=0), FinetuningAssistantMessage(content="foo", weight=1), FinetuningAssistantMessage(content="foo", weight=1), ], continue_final_message=False, ) def test_ends_with_tool_call_null(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") function = FunctionCall(name="foo", arguments='{"a": 1}') validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(tool_calls=[ToolCall(id="123456789", function=function)]), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), # tool_call id left "null" as final message => OK! FinetuningAssistantMessage(tool_calls=[ToolCall(function=function)]), ], continue_final_message=False, ) def test_ends_with_no_tool_call(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") function = FunctionCall(name="foo", arguments='{"a": 1}') validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage(tool_calls=[ToolCall(id="123456789", function=function)]), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), FinetuningAssistantMessage(content="foo"), ], continue_final_message=False, ) def test_middle_with_tool_call_null_raises(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") function = FunctionCall(name="foo", arguments='{"a": 1}') expected_err = r"Tool call id of assistant message that is not last has to be defined in finetuning mode." with pytest.raises(InvalidFunctionCallException, match=expected_err): validator.validate_messages( messages=[ UserMessage(content="foo"), # tool_call id left "null" as non-final message => Raise! FinetuningAssistantMessage(tool_calls=[ToolCall(function=function)]), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), FinetuningAssistantMessage(tool_calls=[ToolCall(function=function)]), ], continue_final_message=False, ) def test_one_message_with_user_is_not_valid(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidMessageStructureException) as exc: validator.validate_messages( messages=[ UserMessage(content="foo"), ], continue_final_message=False, ) assert str(exc.value) == "Expected last role Assistant for finetuning but got user" def test_parallel_tool_call(self, validator: MistralRequestValidator) -> None: function = FunctionCall(name="foo", arguments='{"a": 1}') validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage( tool_calls=[ ToolCall(id="123456789", function=function), ToolCall(id="912345678", function=function), ] ), ], continue_final_message=False, ) validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage( tool_calls=[ ToolCall(id="123456789", function=function), ToolCall(id="912345678", function=function), ] ), ToolMessage(name="foo", content="bar", tool_call_id="912345678"), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), FinetuningAssistantMessage(content="foo"), ], continue_final_message=False, ) with pytest.raises(InvalidMessageStructureException): validator.validate_messages( messages=[ UserMessage(content="foo"), FinetuningAssistantMessage( tool_calls=[ ToolCall(id="123456789", function=function), ToolCall(id="912345678", function=function), ] ), ToolMessage(name="foo", content="bar", tool_call_id="912345678"), ToolMessage(name="foo", content="bar", tool_call_id="123456789"), ToolMessage(name="foo", content="bar", tool_call_id="891234567"), FinetuningAssistantMessage(content="foo"), ], continue_final_message=False, ) python-mistral-common-1.11.7/tests/validation/test_request.py000066400000000000000000000014451523035502500244620ustar00rootroot00000000000000import pytest from pydantic import ValidationError from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest class TestValidateRequest: @pytest.fixture def chat_request_raw(self) -> dict: return {"model": "test-model", "message": [UserMessage(content="foo")]} def test_request_random_seed_negative(self, chat_request_raw: dict) -> None: with pytest.raises(ValidationError): ChatCompletionRequest(**chat_request_raw, random_seed=-1) def test_from_openai_preserves_zero_seed(self) -> None: request = ChatCompletionRequest.from_openai( messages=[{"role": "user", "content": "hello"}], seed=0, ) assert request.random_seed == 0 python-mistral-common-1.11.7/tests/validation/test_serving_validation.py000066400000000000000000000150071523035502500266600ustar00rootroot00000000000000import pytest from mistral_common.exceptions import ( InvalidAssistantMessageException, InvalidFunctionCallException, InvalidToolMessageException, ) from mistral_common.protocol.instruct.messages import ( AssistantMessage, Roles, ToolMessage, UserMessage, ) from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall from mistral_common.protocol.instruct.validator import ( MistralRequestValidator, MistralRequestValidatorV3, ValidationMode, ) @pytest.fixture( params=[ MistralRequestValidator(ValidationMode.serving), MistralRequestValidatorV3(ValidationMode.serving), ] ) def validator(request: pytest.FixtureRequest) -> MistralRequestValidator: return request.param # type: ignore class TestValidateAssistantMessage: def test_content_and_tool_calls(self, validator: MistralRequestValidator) -> None: with pytest.raises( InvalidAssistantMessageException, match=r"Assistant message must have either content or tool_calls, but not both.", ): validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, content="a", tool_calls=[ToolCall(function=FunctionCall(name="a", arguments='{"a": 1}'))], ), ) @pytest.mark.parametrize("content,tool_calls", [("", None), (None, None), ("", []), (None, [])]) def test_no_content_and_no_tool_calls( self, content: str | None, tool_calls: list | None, validator: MistralRequestValidator ) -> None: with pytest.raises( InvalidAssistantMessageException, match=r"Assistant message must have either content or tool_calls, but not both.", ): validator._validate_assistant_message( AssistantMessage(role=Roles.assistant, content=content, tool_calls=tool_calls), ) def test_no_content_and_tool_calls(self, validator: MistralRequestValidator) -> None: validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, content="", tool_calls=[ ToolCall( id="AbCd56789", function=FunctionCall(name="function_call", arguments='{"a": 1}'), ) ], ), ) def test_content_and_no_tool_calls(self, validator: MistralRequestValidator) -> None: validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, content="content", tool_calls=[], ), ) def test_bad_function_name(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidFunctionCallException, match=r"must be a-z, A-Z, 0-9"): validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, tool_calls=[ ToolCall( id="AbCd56789", function=FunctionCall(name=')_(_)~@:}>""', arguments='{"a": 1}'), ) ], ), ) @pytest.mark.skip(reason="If json parse fails we assume that its plain text like '22' or 'a'") def test_invalid_function_json(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidFunctionCallException, match=r"must be a-z, A-Z, 0-9"): validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, tool_calls=[ToolCall(function=FunctionCall(name="function_name", arguments='{"a":aewfawef 1}'))], ) ) def test_tool_id_defined(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") with pytest.raises(InvalidFunctionCallException, match=r"Tool call id has to be defined"): validator._validate_assistant_message( AssistantMessage( role=Roles.assistant, tool_calls=[ToolCall(function=FunctionCall(name="foo", arguments='{"a": 1}'))], ) ) def test_tool_calls_bad_id(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") with pytest.raises( InvalidFunctionCallException, match=r"Tool call id was bad_id but must be a-z, A-Z, 0-9, with a length of 9.", ): validator._validate_assistant_message( AssistantMessage( tool_calls=[ ToolCall( id="bad_id", function=FunctionCall(name="foo", arguments="{}"), ) ] ) ) class TestValidateUserMessage: def test_user_message_blank_content_OK(self, validator: MistralRequestValidator) -> None: validator._validate_user_message(UserMessage(content="")) class TestValidateToolMessage: def test_tool_message_bad_name(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidToolMessageException, match=r"must be a-z, A-Z, 0-9"): validator._validate_tool_message(ToolMessage(name="~@}£Q_+", content="{}")) @pytest.mark.skip(reason="If json parse fails we assume that its plain text like '22' or 'a'") def test_tool_message_invalid_json(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidToolMessageException, match=r"must be a-z, A-Z, 0-9"): validator._validate_tool_message(ToolMessage(name="function_name", content='{"afewfaef}')) def test_tool_message_bad_id(self, validator: MistralRequestValidator) -> None: if not isinstance(validator, MistralRequestValidatorV3): pytest.skip("MistralRequestValidator v1 does not validate tool call ids") with pytest.raises( InvalidToolMessageException, match=r"Tool call id was bad_id but must be a-z, A-Z, 0-9, with a length of 9.", ): validator._validate_tool_message( ToolMessage(name="foo", content="bar", tool_call_id="bad_id"), ) python-mistral-common-1.11.7/tests/validation/test_validation.py000066400000000000000000000053271523035502500251270ustar00rootroot00000000000000import pytest from mistral_common.exceptions import ( InvalidToolException, InvalidToolSchemaException, ) from mistral_common.protocol.instruct.tool_calls import Function, Tool from mistral_common.protocol.instruct.validator import ( MistralRequestValidator, MistralRequestValidatorV3, MistralRequestValidatorV5, MistralRequestValidatorV13, MistralRequestValidatorV15, ValidationMode, get_validator, ) from mistral_common.tokens.tokenizers.base import TokenizerVersion class TestValidateTools: @pytest.fixture def validator(self) -> MistralRequestValidator: return MistralRequestValidator() def test_tool_function_bad_name(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidToolException, match=r"must be a-z, A-Z, 0-9"): validator._validate_tools( tools=[ Tool( function=Function( name=')_(_)~@:}>""', parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, }, }, ) ) ] ) def test_tool_function_invalid_schema(self, validator: MistralRequestValidator) -> None: with pytest.raises(InvalidToolSchemaException, match=r"32 is not valid under any of the given schemas"): validator._validate_tools(tools=[Tool(function=Function(name="function_name", parameters={"type": 32}))]) @pytest.mark.parametrize( "version,expected_class", [ (TokenizerVersion.v1, MistralRequestValidator), (TokenizerVersion.v2, MistralRequestValidator), (TokenizerVersion.v3, MistralRequestValidatorV3), (TokenizerVersion.v7, 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