Release: v4.41.2

* Unit test to verify fix

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

* fix from_pretrained in offline mode when model is preloaded in cache

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

* minor: fmt

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

---------

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>

docs/source/en/_redirects.yml

@@ -1,3 +1,4 @@
 # Optimizing inference
 
 perf_infer_gpu_many: perf_infer_gpu_one
+transformers_agents: agents

examples/flax/question-answering/run_qa.py

@@ -61,7 +61,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risk.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recognition/requirements.txt")
 

examples/flax/text-classification/run_flax_glue.py

@@ -55,7 +55,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/token-classification/run_flax_ner.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/audio-classification/run_audio_classification.py

@@ -45,7 +45,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
 

examples/pytorch/contrastive-image-text/run_clip.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/image-pretraining/run_mae.py

@@ -43,7 +43,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim.py

@@ -48,7 +48,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -53,7 +53,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/language-modeling/run_clm.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_fim.py

@@ -58,7 +58,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_fim_no_trainer.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_mlm.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")

examples/pytorch/language-modeling/run_plm.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/multiple-choice/run_swag.py

@@ -47,7 +47,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = logging.getLogger(__name__)
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 # You should update this to your particular problem to have better documentation of `model_type`

examples/pytorch/object-detection/run_object_detection.py

@@ -48,7 +48,8 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.40.0.dev0")
+check_min_version("4.41.0")
+
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/object-detection/requirements.txt")
 
 

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.40.0.dev0")
+check_min_version("4.41.0")
 
 logging.basicConfig(level=logging.INFO)
 logger = get_logger(__name__)

examples/pytorch/question-answering/run_qa.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/summarization/run_summarization.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

examples/pytorch/text-classification/run_classification.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/text-classification/run_xnli.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")

examples/pytorch/translation/run_translation.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
 

examples/pytorch/translation/run_translation_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")

examples/tensorflow/contrastive-image-text/run_clip.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version(
     "datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/contrastive-image-text/requirements.txt"

examples/tensorflow/image-classification/run_image_classification.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/tensorflow/multiple-choice/run_swag.py

@@ -50,7 +50,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/question-answering/run_qa.py

@@ -62,7 +62,7 @@ except (ModuleNotFoundError, ImportError):
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/summarization/run_summarization.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 # region Checking dependencies
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/tensorflow/text-classification/run_glue.py

@@ -47,7 +47,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 task_to_keys = {
     "cola": ("sentence", None),

examples/tensorflow/translation/run_translation.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 # region Dependencies and constants
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.41.0.dev0")
+check_min_version("4.41.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

setup.py

@@ -426,7 +426,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.41.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.41.2",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
     author_email="transformers@huggingface.co",
     description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",

src/transformers/__init__.py

@@ -18,7 +18,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.41.0.dev0"
+__version__ = "4.41.2"
 
 from typing import TYPE_CHECKING
 

src/transformers/dynamic_module_utils.py

@@ -197,7 +197,10 @@ def get_class_in_module(class_name: str, module_path: Union[str, os.PathLike]) -
     Returns:
         `typing.Type`: The class looked for.
     """
-    name = os.path.normpath(module_path).rstrip(".py").replace(os.path.sep, ".")
+    name = os.path.normpath(module_path)
+    if name.endswith(".py"):
+        name = name[:-3]
+    name = name.replace(os.path.sep, ".")
     module_spec = importlib.util.spec_from_file_location(name, location=Path(HF_MODULES_CACHE) / module_path)
     module = sys.modules.get(name)
     if module is None:

src/transformers/generation/utils.py

@@ -1354,6 +1354,23 @@ class GenerationMixin:
             self._static_cache.reset()  # reset the cache for a new generation
         return self._static_cache
 
+    def _get_decoder_start_token_id(
+        self, decoder_start_token_id: Union[int, List[int]] = None, bos_token_id: int = None
+    ) -> int:
+        decoder_start_token_id = (
+            decoder_start_token_id
+            if decoder_start_token_id is not None
+            else self.generation_config.decoder_start_token_id
+        )
+        bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
+
+        if decoder_start_token_id is not None:
+            return decoder_start_token_id
+        elif bos_token_id is not None:
+            return bos_token_id
+        else:
+            return
+
     def _prepare_special_tokens(
         self,
         generation_config: GenerationConfig,
@@ -1378,11 +1395,16 @@ class GenerationMixin:
                 return token
             return torch.tensor(token, device=device, dtype=torch.long)
 
+        # for BC we also try to get `decoder_start_token_id` from model's generation config (#30892)
+        if self.config.is_encoder_decoder:
+            generation_config.decoder_start_token_id = self._get_decoder_start_token_id(
+                generation_config.decoder_start_token_id, generation_config.bos_token_id
+            )
+
         bos_token_id = _tensor_or_none(generation_config.bos_token_id, device=device)
         eos_token_id = _tensor_or_none(generation_config.eos_token_id, device=device)
         pad_token_id = _tensor_or_none(generation_config.pad_token_id, device=device)
         decoder_start_token_id = _tensor_or_none(generation_config.decoder_start_token_id, device=device)
-        decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
 
         # We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
         if eos_token_id is not None and eos_token_id.ndim == 0:

src/transformers/integrations/integration_utils.py

@@ -31,17 +31,8 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Union
 import numpy as np
 import packaging.version
 
-from .. import PreTrainedModel, TFPreTrainedModel
 from .. import __version__ as version
-from ..utils import (
-    PushToHubMixin,
-    flatten_dict,
-    is_datasets_available,
-    is_pandas_available,
-    is_tf_available,
-    is_torch_available,
-    logging,
-)
+from ..utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
 
 
 logger = logging.get_logger(__name__)
@@ -78,7 +69,6 @@ if TYPE_CHECKING and _has_neptune:
         except importlib.metadata.PackageNotFoundError:
             _has_neptune = False
 
-from .. import modelcard  # noqa: E402
 from ..trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
 from ..trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
 from ..training_args import ParallelMode  # noqa: E402
@@ -673,22 +663,6 @@ class TensorBoardCallback(TrainerCallback):
             self.tb_writer = None
 
 
-def save_model_architecture_to_file(model: Any, output_dir: str):
-    with open(f"{output_dir}/model_architecture.txt", "w+") as f:
-        if isinstance(model, PreTrainedModel):
-            print(model, file=f)
-        elif is_tf_available() and isinstance(model, TFPreTrainedModel):
-
-            def print_to_file(s):
-                print(s, file=f)
-
-            model.summary(print_fn=print_to_file)
-        elif is_torch_available() and (
-            isinstance(model, (torch.nn.Module, PushToHubMixin)) and hasattr(model, "base_model")
-        ):
-            print(model, file=f)
-
-
 class WandbCallback(TrainerCallback):
     """
     A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
@@ -754,9 +728,6 @@ class WandbCallback(TrainerCallback):
             if hasattr(model, "config") and model.config is not None:
                 model_config = model.config.to_dict()
                 combined_dict = {**model_config, **combined_dict}
-            if hasattr(model, "peft_config") and model.peft_config is not None:
-                peft_config = model.peft_config
-                combined_dict = {**{"peft_config": peft_config}, **combined_dict}
             trial_name = state.trial_name
             init_args = {}
             if trial_name is not None:
@@ -790,46 +761,6 @@ class WandbCallback(TrainerCallback):
                 self._wandb.watch(model, log=_watch_model, log_freq=max(100, state.logging_steps))
             self._wandb.run._label(code="transformers_trainer")
 
-            # add number of model parameters to wandb config
-            try:
-                self._wandb.config["model/num_parameters"] = model.num_parameters()
-            except AttributeError:
-                logger.info("Could not log the number of model parameters in Weights & Biases.")
-
-            # log the initial model and architecture to an artifact
-            with tempfile.TemporaryDirectory() as temp_dir:
-                model_name = (
-                    f"model-{self._wandb.run.id}"
-                    if (args.run_name is None or args.run_name == args.output_dir)
-                    else f"model-{self._wandb.run.name}"
-                )
-                model_artifact = self._wandb.Artifact(
-                    name=model_name,
-                    type="model",
-                    metadata={
-                        "model_config": model.config.to_dict() if hasattr(model, "config") else None,
-                        "num_parameters": self._wandb.config.get("model/num_parameters"),
-                        "initial_model": True,
-                    },
-                )
-                model.save_pretrained(temp_dir)
-                # add the architecture to a separate text file
-                save_model_architecture_to_file(model, temp_dir)
-
-                for f in Path(temp_dir).glob("*"):
-                    if f.is_file():
-                        with model_artifact.new_file(f.name, mode="wb") as fa:
-                            fa.write(f.read_bytes())
-                self._wandb.run.log_artifact(model_artifact, aliases=["base_model"])
-
-                badge_markdown = (
-                    f'[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge'
-                    f'-28.svg" alt="Visualize in Weights & Biases" width="20'
-                    f'0" height="32"/>]({self._wandb.run.get_url()})'
-                )
-
-                modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
-
     def on_train_begin(self, args, state, control, model=None, **kwargs):
         if self._wandb is None:
             return
@@ -860,25 +791,20 @@ class WandbCallback(TrainerCallback):
                     else {
                         f"eval/{args.metric_for_best_model}": state.best_metric,
                         "train/total_floss": state.total_flos,
-                        "model/num_parameters": self._wandb.config.get("model/num_parameters"),
                     }
                 )
-                metadata["final_model"] = True
                 logger.info("Logging model artifacts. ...")
                 model_name = (
                     f"model-{self._wandb.run.id}"
                     if (args.run_name is None or args.run_name == args.output_dir)
                     else f"model-{self._wandb.run.name}"
                 )
-                # add the model architecture to a separate text file
-                save_model_architecture_to_file(model, temp_dir)
-
                 artifact = self._wandb.Artifact(name=model_name, type="model", metadata=metadata)
                 for f in Path(temp_dir).glob("*"):
                     if f.is_file():
                         with artifact.new_file(f.name, mode="wb") as fa:
                             fa.write(f.read_bytes())
-                self._wandb.run.log_artifact(artifact, aliases=["final_model"])
+                self._wandb.run.log_artifact(artifact)
 
     def on_log(self, args, state, control, model=None, logs=None, **kwargs):
         single_value_scalars = [
@@ -908,30 +834,18 @@ class WandbCallback(TrainerCallback):
                 for k, v in dict(self._wandb.summary).items()
                 if isinstance(v, numbers.Number) and not k.startswith("_")
             }
-            checkpoint_metadata["model/num_parameters"] = self._wandb.config.get("model/num_parameters")
 
             ckpt_dir = f"checkpoint-{state.global_step}"
             artifact_path = os.path.join(args.output_dir, ckpt_dir)
             logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. ...")
             checkpoint_name = (
-                f"model-{self._wandb.run.id}"
+                f"checkpoint-{self._wandb.run.id}"
                 if (args.run_name is None or args.run_name == args.output_dir)
-                else f"model-{self._wandb.run.name}"
+                else f"checkpoint-{self._wandb.run.name}"
             )
             artifact = self._wandb.Artifact(name=checkpoint_name, type="model", metadata=checkpoint_metadata)
             artifact.add_dir(artifact_path)
-            self._wandb.log_artifact(
-                artifact, aliases=[f"epoch_{round(state.epoch, 2)}", f"checkpoint_global_step_{state.global_step}"]
-            )
-
-    def on_predict(self, args, state, control, metrics, **kwargs):
-        if self._wandb is None:
-            return
-        if not self._initialized:
-            self.setup(args, state, **kwargs)
-        if state.is_world_process_zero:
-            metrics = rewrite_logs(metrics)
-            self._wandb.log(metrics)
+            self._wandb.log_artifact(artifact, aliases=[f"checkpoint-{state.global_step}"])
 
 
 class CometCallback(TrainerCallback):

src/transformers/modeling_utils.py

@@ -3389,7 +3389,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                         )
                         if resolved_archive_file is not None:
                             is_sharded = True
-
+                    if not local_files_only and not is_offline_mode():
                         if resolved_archive_file is not None:
                             if filename in [WEIGHTS_NAME, WEIGHTS_INDEX_NAME]:
                                 # If the PyTorch file was found, check if there is a safetensors file on the repository

src/transformers/models/llama/tokenization_llama_fast.py

@@ -151,9 +151,6 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
         self.legacy = legacy
 
         if add_prefix_space is not None:
-            logger.warning_once(
-                "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers"
-            )
             kwargs["from_slow"] = True
 
         super().__init__(
@@ -166,6 +163,7 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
             add_bos_token=add_bos_token,
             add_eos_token=add_eos_token,
             use_default_system_prompt=use_default_system_prompt,
+            legacy=legacy,
             **kwargs,
         )
         self._add_bos_token = add_bos_token

src/transformers/models/llava_next/processing_llava_next.py

@@ -40,8 +40,8 @@ class LlavaNextProcessor(ProcessorMixin):
     """
 
     attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "LlavaNextImageProcessor"
-    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
 
     def __init__(self, image_processor=None, tokenizer=None):
         super().__init__(image_processor, tokenizer)

src/transformers/models/paligemma/modeling_paligemma.py

@@ -282,9 +282,14 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
         self.vocab_size = model_embeds.num_embeddings
         return model_embeds
 
-    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
+    def _merge_input_ids_with_image_features(
+        self, image_features, inputs_embeds, input_ids, attention_mask, labels, token_type_ids, cache_position
+    ):
         _, _, embed_dim = image_features.shape
         batch_size, sequence_length = input_ids.shape
+        dtype, device = inputs_embeds.dtype, inputs_embeds.device
+        min_dtype = torch.finfo(dtype).min
+
         scaled_image_features = image_features / (self.config.hidden_size**0.5)
         final_embedding = torch.zeros(
             batch_size, sequence_length, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
@@ -295,34 +300,56 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
         pad_mask = input_ids == self.pad_token_id
 
         # expand masks to match embedding dimension
-        text_mask_expanded = text_mask.unsqueeze(-1).expand(-1, -1, embed_dim)
-        pad_mask_expanded = pad_mask.unsqueeze(-1).expand(-1, -1, embed_dim)
+        text_mask_expanded = text_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device)
+        pad_mask_expanded = pad_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device)
         # insert padding and text token embeddings
         final_embedding = torch.where(text_mask_expanded, inputs_embeds, final_embedding)
         final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding)
         # insert image embeddings - the image mask is always less or equal to the sentence in length
         final_embedding = final_embedding.masked_scatter(
-            image_mask.unsqueeze(-1).expand_as(final_embedding), scaled_image_features
+            image_mask.unsqueeze(-1).expand_as(final_embedding).to(device=final_embedding.device),
+            scaled_image_features.to(device=final_embedding.device, dtype=final_embedding.dtype),
         )
         final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding)
+        if attention_mask is not None:
+            position_ids = (attention_mask.cumsum(-1)).masked_fill_((attention_mask == 0), 1)
+        else:
+            position_ids = None
 
-        final_attention_mask_4d = attention_mask.unsqueeze(1).unsqueeze(2) * attention_mask.unsqueeze(1).unsqueeze(-1)
-        final_attention_mask_4d = final_attention_mask_4d.float().expand(
-            -1, self.config.text_config.num_key_value_heads, -1, -1
+        if token_type_ids is not None and labels is not None:
+            # we are training thus we need to create a full mask on the image + prefix but causal on suffix
+            target_length = cache_position[-1] + 1
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(inputs_embeds.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+                    causal_mask.device
+                )
+                # unmask the prefill
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
                 )
 
-        # position_ids = torch.arange(0, sequence_length, device=input_ids.device).expand(batch_size, -1)
-        # position_ids = torch.where(input_ids == self.pad_token_id, torch.ones_like(position_ids), position_ids)
-        position_ids = (attention_mask.cumsum(-1)).masked_fill_((attention_mask == 0), 1)
-
-        if labels is not None:
             final_labels = torch.full(
                 (batch_size, sequence_length), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
             )
             final_labels = torch.where(input_ids != self.pad_token_id, labels, final_labels)
         else:
+            causal_mask = attention_mask.unsqueeze(1).unsqueeze(2) * attention_mask.unsqueeze(1).unsqueeze(-1)
+            causal_mask = causal_mask.to(dtype).expand(-1, self.config.text_config.num_key_value_heads, -1, -1)
             final_labels = None
-        return final_embedding, final_attention_mask_4d, final_labels, position_ids
+        return final_embedding, causal_mask, final_labels, position_ids
 
     @add_start_docstrings_to_model_forward(PALIGEMMA_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=PaliGemmaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
@@ -333,6 +360,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
@@ -396,8 +424,10 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
                 selected_image_feature = image_outputs.last_hidden_state
                 image_features = self.multi_modal_projector(selected_image_feature)
 
+                if cache_position is None:
+                    cache_position = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device)
                 inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
-                    image_features, inputs_embeds, input_ids, attention_mask, labels
+                    image_features, inputs_embeds, input_ids, attention_mask, labels, token_type_ids, cache_position
                 )
 
             else:
@@ -456,7 +486,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
                 # we use the input attention mask to shift the logits and labels, because it is 2D.
                 shift_attention_mask = input_attention_mask[..., 1:]
                 shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
-                shift_labels = shift_labels[shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
             else:
                 shift_logits = shift_logits.contiguous()
                 shift_labels = shift_labels.contiguous()
@@ -486,6 +516,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
         cache_position=None,
         pixel_values=None,
         attention_mask=None,
+        token_type_ids=None,
         **kwargs,
     ):
         past_length = 0
@@ -544,6 +575,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
                 "use_cache": kwargs.get("use_cache"),
                 "attention_mask": attention_mask,
                 "pixel_values": pixel_values,
+                "token_type_ids": token_type_ids,
             }
         )
         return model_inputs

src/transformers/models/video_llava/processing_video_llava.py

@@ -42,7 +42,7 @@ class VideoLlavaProcessor(ProcessorMixin):
 
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "VideoLlavaImageProcessor"
-    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    tokenizer_class = "AutoTokenizer"
 
     def __init__(self, image_processor=None, tokenizer=None):
         super().__init__(image_processor, tokenizer)

tests/generation/test_utils.py

@@ -65,6 +65,7 @@ if is_torch_available():
         GenerateBeamEncoderDecoderOutput,
         GenerateDecoderOnlyOutput,
         GenerateEncoderDecoderOutput,
+        GenerationConfig,
         GreedySearchDecoderOnlyOutput,
         GreedySearchEncoderDecoderOutput,
         LogitsProcessorList,
@@ -2478,6 +2479,35 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
 
         self.assertListEqual(outputs.tolist(), outputs_batched_ids.tolist())
 
+    def test_decoder_start_id_from_config(self):
+        # Refer to: (#30899)
+        articles = [
+            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
+            "Michael Phelps is arguably the most decorated Olympian of all time.",
+        ]
+        bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
+        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
+            torch_device
+        )
+        input_ids = bart_tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
+        decoder_start_token_id = bart_model.generation_config.decoder_start_token_id
+
+        # we should be able to take `decoder_start_token_id` from model's generation config if user passes a `GenerationConfig` type
+        outputs = bart_model.generate(input_ids, generation_config=GenerationConfig(do_sample=False))
+
+        # If the generatoin config has no `decoder_start_token_id` or `bos_token_id`, we will raise an error unless user passes it in config
+        bart_model.generation_config.decoder_start_token_id = None
+        bart_model.generation_config.bos_token_id = None
+        outputs_with_user_id = bart_model.generate(
+            input_ids,
+            generation_config=GenerationConfig(do_sample=False, decoder_start_token_id=decoder_start_token_id),
+        )
+
+        self.assertListEqual(outputs.tolist(), outputs_with_user_id.tolist())
+
+        with self.assertRaises(ValueError):
+            outputs = bart_model.generate(input_ids, generation_config=GenerationConfig(do_sample=False))
+
     def test_contrastive_search_batched(self):
         # PT-only test: TF doesn't have constrained beam search
         # Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)

tests/models/paligemma/test_modeling_paligemma.py

@@ -163,6 +163,8 @@ class PaliGemmaVisionText2TextModelTester:
             "pixel_values": pixel_values,
             "input_ids": input_ids,
             "attention_mask": attention_mask,
+            "labels": input_ids,
+            "token_type_ids": torch.zeros_like(input_ids),
         }
         return config, inputs_dict
 

tests/test_modeling_utils.py

@@ -33,6 +33,7 @@ from requests.exceptions import HTTPError
 from transformers import (
     AutoConfig,
     AutoModel,
+    AutoModelForImageClassification,
     AutoModelForSequenceClassification,
     OwlViTForObjectDetection,
     PretrainedConfig,
@@ -76,7 +77,6 @@ sys.path.append(str(Path(__file__).parent.parent / "utils"))
 
 from test_module.custom_configuration import CustomConfig, NoSuperInitConfig  # noqa E402
 
-
 if is_torch_available():
     import torch
     from safetensors.torch import save_file as safe_save_file
@@ -194,6 +194,97 @@ if is_torch_available():
             attention_mask = _prepare_4d_attention_mask(mask, dtype=inputs_embeds.dtype)
             return attention_mask
 
+    class TestOffline(unittest.TestCase):
+        def test_offline(self):
+            # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
+            from huggingface_hub import constants
+
+            from transformers.utils import hub
+
+            offlfine_env = hub._is_offline_mode
+            hub_cache_env = constants.HF_HUB_CACHE
+            hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
+            default_cache = constants.default_cache_path
+            transformers_cache = hub.TRANSFORMERS_CACHE
+
+            try:
+                hub._is_offline_mode = True
+                with tempfile.TemporaryDirectory() as tmpdir:
+                    LOG.info("Temporary cache dir %s", tmpdir)
+                    constants.HF_HUB_CACHE = tmpdir
+                    constants.HUGGINGFACE_HUB_CACHE = tmpdir
+                    constants.default_cache_path = tmpdir
+                    hub.TRANSFORMERS_CACHE = tmpdir
+                    # First offline load should fail
+                    try:
+                        AutoModelForImageClassification.from_pretrained(
+                            TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None
+                        )
+                    except OSError:
+                        LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
+                    else:
+                        self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
+
+                    # Download model -> Huggingface Hub not concerned by our offline mode
+                    LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
+                    hub_api = HfApi()
+                    local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
+                    LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
+
+                    AutoModelForImageClassification.from_pretrained(
+                        TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None
+                    )
+            finally:
+                # Tear down: reset env as it was before calling this test
+                hub._is_offline_mode = offlfine_env
+                constants.HF_HUB_CACHE = hub_cache_env
+                constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
+                constants.default_cache_path = default_cache
+                hub.TRANSFORMERS_CACHE = transformers_cache
+
+        def test_local_files_only(self):
+            # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
+            from huggingface_hub import constants
+
+            from transformers.utils import hub
+
+            hub_cache_env = constants.HF_HUB_CACHE
+            hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
+            default_cache = constants.default_cache_path
+            transformers_cache = hub.TRANSFORMERS_CACHE
+            try:
+                with tempfile.TemporaryDirectory() as tmpdir:
+                    LOG.info("Temporary cache dir %s", tmpdir)
+                    constants.HF_HUB_CACHE = tmpdir
+                    constants.HUGGINGFACE_HUB_CACHE = tmpdir
+                    constants.default_cache_path = tmpdir
+                    hub.TRANSFORMERS_CACHE = tmpdir
+                    try:
+                        AutoModelForImageClassification.from_pretrained(
+                            TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True
+                        )
+                    except OSError:
+                        LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
+                    else:
+                        self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
+
+                    LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
+                    hub_api = HfApi()
+                    local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
+                    LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
+
+                    AutoModelForImageClassification.from_pretrained(
+                        TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True
+                    )
+            finally:
+                # Tear down: reset env as it was before calling this test
+                constants.HF_HUB_CACHE = hub_cache_env
+                constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
+                constants.default_cache_path = default_cache
+                hub.TRANSFORMERS_CACHE = transformers_cache
+
 
 if is_flax_available():
     from transformers import FlaxBertModel
@@ -205,6 +296,9 @@ if is_tf_available():
 TINY_T5 = "patrickvonplaten/t5-tiny-random"
 TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
 TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
+TINY_IMAGE_CLASSIF = "hf-internal-testing/tiny-random-SiglipForImageClassification"
+
+LOG = logging.get_logger(__name__)
 
 
 def check_models_equal(model1, model2):