Add mistral common support (#38906)

* wip: correct docstrings

* Add mistral-common support.

* quality

* wip: add requested methods

* wip: fix tests

* wip: add internally some methods not being supported in mistral-common

* wip

* wip: add opencv dependency and update test list

* wip: add mistral-common to testing dependencies

* wip: revert some test changes

* wip: ci

* wip: ci

* clean

* check

* check

* check

* wip: add hf image format to apply_chat_template and return pixel_values

* wip: make mistral-common non-installed safe

* wip: clean zip

* fix: from_pretrained

* fix: path and base64

* fix: path and import root

* wip: add docs

* clean

* clean

* revert

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

conftest.py

@@ -28,6 +28,7 @@ from transformers.testing_utils import HfDoctestModule, HfDocTestParser
 
 NOT_DEVICE_TESTS = {
     "test_tokenization",
+    "test_tokenization_mistral_common",
     "test_processor",
     "test_processing",
     "test_beam_constraints",

docs/source/en/model_doc/mistral.md

@@ -139,6 +139,10 @@ Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/bl
 
 [[autodoc]] MistralConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## MistralModel
 
 [[autodoc]] MistralModel

docs/source/en/model_doc/mistral3.md

@@ -227,6 +227,10 @@ This example also how to use `BitsAndBytes` to load the model in 4bit quantizati
 
 [[autodoc]] Mistral3Config
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## Mistral3Model
 
 [[autodoc]] Mistral3Model

docs/source/en/model_doc/mixtral.md

@@ -197,6 +197,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] MixtralConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## MixtralModel
 
 [[autodoc]] MixtralModel

docs/source/en/model_doc/pixtral.md

@@ -86,6 +86,10 @@ output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up
 
 [[autodoc]] PixtralVisionConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## PixtralVisionModel
 
 [[autodoc]] PixtralVisionModel

setup.py

@@ -204,6 +204,7 @@ _deps = [
     "opentelemetry-api",
     "opentelemetry-exporter-otlp",
     "opentelemetry-sdk",
+    "mistral-common[opencv]>=1.6.3",
 ]
 
 
@@ -334,6 +335,7 @@ extras["video"] = deps_list("av")
 extras["num2words"] = deps_list("num2words")
 extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
 extras["tiktoken"] = deps_list("tiktoken", "blobfile")
+extras["mistral-common"] = deps_list("mistral-common[opencv]")
 extras["testing"] = (
     deps_list(
         "pytest",
@@ -363,6 +365,7 @@ extras["testing"] = (
     )
     + extras["retrieval"]
     + extras["modelcreation"]
+    + extras["mistral-common"]
 )
 
 extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"]
@@ -384,6 +387,7 @@ extras["all"] = (
     + extras["accelerate"]
     + extras["video"]
     + extras["num2words"]
+    + extras["mistral-common"]
 )
 
 

src/transformers/__init__.py

@@ -34,6 +34,7 @@ from .utils import (
     is_g2p_en_available,
     is_keras_nlp_available,
     is_librosa_available,
+    is_mistral_common_available,
     is_pretty_midi_available,
     is_scipy_available,
     is_sentencepiece_available,
@@ -310,6 +311,18 @@ else:
         "convert_slow_tokenizer",
     ]
 
+try:
+    if not (is_mistral_common_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from .utils import dummy_mistral_common_objects
+
+    _import_structure["utils.dummy_mistral_common_objects"] = [
+        name for name in dir(dummy_mistral_common_objects) if not name.startswith("_")
+    ]
+else:
+    _import_structure["tokenization_mistral_common"] = ["MistralCommonTokenizer"]
+
 # Vision-specific objects
 try:
     if not is_vision_available():

src/transformers/dependency_versions_table.py

@@ -106,4 +106,5 @@ deps = {
     "opentelemetry-api": "opentelemetry-api",
     "opentelemetry-exporter-otlp": "opentelemetry-exporter-otlp",
     "opentelemetry-sdk": "opentelemetry-sdk",
+    "mistral-common[opencv]": "mistral-common[opencv]>=1.6.3",
 }

src/transformers/models/auto/tokenization_auto.py

@@ -21,6 +21,8 @@ import warnings
 from collections import OrderedDict
 from typing import Any, Optional, Union
 
+from transformers.utils.import_utils import is_mistral_common_available
+
 from ...configuration_utils import PretrainedConfig
 from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
 from ...modeling_gguf_pytorch_utils import load_gguf_checkpoint
@@ -387,15 +389,19 @@ TOKENIZER_MAPPING_NAMES = OrderedDict[str, tuple[Optional[str], Optional[str]]](
         (
             "mistral",
             (
-                "LlamaTokenizer" if is_sentencepiece_available() else None,
-                "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                "MistralCommonTokenizer"
+                if is_mistral_common_available()
+                else ("LlamaTokenizer" if is_sentencepiece_available() else None),
+                "LlamaTokenizerFast" if is_tokenizers_available() and not is_mistral_common_available() else None,
             ),
         ),
         (
             "mixtral",
             (
-                "LlamaTokenizer" if is_sentencepiece_available() else None,
-                "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                "MistralCommonTokenizer"
+                if is_mistral_common_available()
+                else ("LlamaTokenizer" if is_sentencepiece_available() else None),
+                "LlamaTokenizerFast" if is_tokenizers_available() and not is_mistral_common_available() else None,
             ),
         ),
         ("mllama", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)),
@@ -490,7 +496,15 @@ TOKENIZER_MAPPING_NAMES = OrderedDict[str, tuple[Optional[str], Optional[str]]](
         ("phimoe", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)),
         ("phobert", ("PhobertTokenizer", None)),
         ("pix2struct", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)),
-        ("pixtral", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
+        (
+            "pixtral",
+            (
+                None,
+                "MistralCommonTokenizer"
+                if is_mistral_common_available()
+                else ("PreTrainedTokenizerFast" if is_tokenizers_available() else None),
+            ),
+        ),
         ("plbart", ("PLBartTokenizer" if is_sentencepiece_available() else None, None)),
         ("prophetnet", ("ProphetNetTokenizer", None)),
         ("qdqbert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
@@ -721,8 +735,10 @@ def tokenizer_class_from_name(class_name: str) -> Union[type[Any], None]:
     for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items():
         if class_name in tokenizers:
             module_name = model_type_to_module_name(module_name)
-
-            module = importlib.import_module(f".{module_name}", "transformers.models")
+            if module_name in ["mistral", "mixtral"] and class_name == "MistralCommonTokenizer":
+                module = importlib.import_module(".tokenization_mistral_common", "transformers")
+            else:
+                module = importlib.import_module(f".{module_name}", "transformers.models")
             try:
                 return getattr(module, class_name)
             except AttributeError:

src/transformers/testing_utils.py

@@ -108,6 +108,7 @@ from .utils import (
     is_librosa_available,
     is_liger_kernel_available,
     is_lomo_available,
+    is_mistral_common_available,
     is_natten_available,
     is_nltk_available,
     is_onnx_available,
@@ -1526,6 +1527,13 @@ def require_speech(test_case):
     return unittest.skipUnless(is_speech_available(), "test requires torchaudio")(test_case)
 
 
+def require_mistral_common(test_case):
+    """
+    Decorator marking a test that requires mistral-common. These tests are skipped when mistral-common isn't available.
+    """
+    return unittest.skipUnless(is_mistral_common_available(), "test requires mistral-common")(test_case)
+
+
 def get_gpu_count():
     """
     Return the number of available gpus (regardless of whether torch, tf or jax is used)

src/transformers/utils/__init__.py

@@ -182,6 +182,7 @@ from .import_utils import (
     is_liger_kernel_available,
     is_lomo_available,
     is_matplotlib_available,
+    is_mistral_common_available,
     is_mlx_available,
     is_natten_available,
     is_ninja_available,

src/transformers/utils/dummy_mistral_common_objects.py

@@ -0,0 +1,9 @@
+# This file is autogenerated by the command `make fix-copies`, do not edit.
+from ..utils import DummyObject, requires_backends
+
+
+class MistralCommonTokenizer(metaclass=DummyObject):
+    _backends = ["mistral-common"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["mistral-common"])

src/transformers/utils/import_utils.py

@@ -227,6 +227,7 @@ _spqr_available = _is_package_available("spqr_quant")
 _rich_available = _is_package_available("rich")
 _kernels_available = _is_package_available("kernels")
 _matplotlib_available = _is_package_available("matplotlib")
+_mistral_common_available = _is_package_available("mistral_common")
 
 _torch_version = "N/A"
 _torch_available = False
@@ -1575,6 +1576,10 @@ def is_matplotlib_available():
     return _matplotlib_available
 
 
+def is_mistral_common_available():
+    return _mistral_common_available
+
+
 def check_torch_load_is_safe():
     if not is_torch_greater_or_equal("2.6"):
         raise ValueError(
@@ -1979,6 +1984,11 @@ RICH_IMPORT_ERROR = """
 rich`. Please note that you may need to restart your runtime after installation.
 """
 
+MISTRAL_COMMON_IMPORT_ERROR = """
+{0} requires the mistral-common library but it was not found in your environment. You can install it with pip: `pip install mistral-common`. Please note that you may need to restart your runtime after installation.
+"""
+
+
 BACKENDS_MAPPING = OrderedDict(
     [
         ("av", (is_av_available, AV_IMPORT_ERROR)),
@@ -2031,6 +2041,7 @@ BACKENDS_MAPPING = OrderedDict(
         ("pydantic", (is_pydantic_available, PYDANTIC_IMPORT_ERROR)),
         ("fastapi", (is_fastapi_available, FASTAPI_IMPORT_ERROR)),
         ("uvicorn", (is_uvicorn_available, UVICORN_IMPORT_ERROR)),
+        ("mistral-common", (is_mistral_common_available, MISTRAL_COMMON_IMPORT_ERROR)),
     ]
 )
 

utils/tests_fetcher.py

@@ -1164,7 +1164,7 @@ def parse_commit_message(commit_message: str) -> dict[str, bool]:
 JOB_TO_TEST_FILE = {
     "tests_torch": r"tests/models/.*/test_modeling_(?!(?:flax_|tf_)).*",
     "tests_generate": r"tests/models/.*/test_modeling_(?!(?:flax_|tf_)).*",
-    "tests_tokenization": r"tests/models/.*/test_tokenization.*",
+    "tests_tokenization": r"tests/(?:models/.*/test_tokenization.*|test_tokenization_mistral_common\.py)",
     "tests_processors": r"tests/models/.*/test_(?!(?:modeling_|tokenization_)).*",  # takes feature extractors, image processors, processors
     "examples_torch": r"examples/pytorch/.*test_.*",
     "tests_exotic_models": r"tests/models/.*(?=layoutlmv|nat|deta|udop|nougat).*",