Merge d65c8b3e2f into df12d87d18

setup.py

@@ -204,6 +204,7 @@ _deps = [
     "opentelemetry-api",
     "opentelemetry-exporter-otlp",
     "opentelemetry-sdk",
+    "mistral-common[open-cv]>=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[open-cv]")
 extras["testing"] = (
     deps_list(
         "pytest",
@@ -384,6 +386,7 @@ extras["all"] = (
     + extras["accelerate"]
     + extras["video"]
     + extras["num2words"]
+    + extras["mistral-common"]
 )
 
 

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": "mistral-common>=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
@@ -373,15 +375,19 @@ TOKENIZER_MAPPING_NAMES = OrderedDict[str, tuple[Optional[str], Optional[str]]](
         (
             "mistral",
             (
-                "LlamaTokenizer" if is_sentencepiece_available() else None,
+                "MistralCommonTokenizer"
-                "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                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,
+                "MistralCommonTokenizer"
-                "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                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)),
@@ -476,7 +482,15 @@ 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)),
@@ -706,8 +720,10 @@ 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)
-
+            if module_name in ["mistral", "mixtral"] and class_name == "MistralCommonTokenizer":
-            module = importlib.import_module(f".{module_name}", "transformers.models")
+                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/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
@@ -1566,6 +1567,10 @@ 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(
@@ -1970,6 +1975,11 @@ 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)),
@@ -2022,6 +2032,7 @@ 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)),
     ]
 )