Use OSError (#38712)

Signed-off-by: cyy <cyyever@outlook.com>

src/transformers/generation/configuration_utils.py

@@ -1049,13 +1049,13 @@ class GenerationConfig(PushToHubMixin):
                     _commit_hash=commit_hash,
                 )
                 commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
-            except EnvironmentError:
+            except OSError:
                 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
                 # the original exception.
                 raise
             except Exception:
                 # For any other exception, we throw a generic error.
-                raise EnvironmentError(
+                raise OSError(
                     f"Can't load the configuration of '{pretrained_model_name}'. If you were trying to load it"
                     " from 'https://huggingface.co/models', make sure you don't have a local directory with the same"
                     f" name. Otherwise, make sure '{pretrained_model_name}' is the correct path to a directory"
@@ -1067,9 +1067,7 @@ class GenerationConfig(PushToHubMixin):
             config_dict = cls._dict_from_json_file(resolved_config_file)
             config_dict["_commit_hash"] = commit_hash
         except (json.JSONDecodeError, UnicodeDecodeError):
-            raise EnvironmentError(
+            raise OSError(f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file.")
-                f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file."
-            )
 
         if is_local:
             logger.info(f"loading configuration file {resolved_config_file}")

src/transformers/integrations/integration_utils.py

@@ -1623,7 +1623,7 @@ class NeptuneCallback(TrainerCallback):
                 copy_path = os.path.join(consistent_checkpoint_path, cpkt_path)
                 shutil.copytree(relative_path, copy_path)
                 target_path = consistent_checkpoint_path
-            except IOError as e:
+            except OSError as e:
                 logger.warning(
                     "NeptuneCallback was unable to made a copy of checkpoint due to I/O exception: '{}'. "
                     "Could fail trying to upload.".format(e)

src/transformers/integrations/tensor_parallel.py

@@ -48,7 +48,7 @@ def initialize_tensor_parallelism(tp_plan, tp_size=None):
         return None, None, None
 
     if not is_torch_greater_or_equal("2.5"):
-        raise EnvironmentError("Tensor parallel is only supported for `torch>=2.5`.")
+        raise OSError("Tensor parallel is only supported for `torch>=2.5`.")
 
     # Detect the accelerator on the machine. If no accelerator is available, it returns CPU.
     device_type = torch._C._get_accelerator().type
@@ -70,7 +70,7 @@ def initialize_tensor_parallelism(tp_plan, tp_size=None):
                 current_device.set_device(local_rank)
 
         except Exception as e:
-            raise EnvironmentError(
+            raise OSError(
                 "We tried to initialize torch.distributed for you, but it failed. Make "
                 "sure you init torch distributed in your script to use `tp_plan='auto'`."
             ) from e

src/transformers/modeling_flax_pytorch_utils.py

@@ -347,7 +347,7 @@ def load_flax_checkpoint_in_pytorch_model(model, flax_checkpoint_path):
             try:
                 flax_state_dict = from_bytes(flax_cls, state_f.read())
             except UnpicklingError:
-                raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")
+                raise OSError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")
 
     return load_flax_weights_in_pytorch_model(model, flax_state_dict)
 

src/transformers/modeling_flax_utils.py

@@ -435,7 +435,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                     else:
                         raise ValueError from e
             except (UnicodeDecodeError, ValueError):
-                raise EnvironmentError(f"Unable to convert {resolved_archive_file} to Flax deserializable object. ")
+                raise OSError(f"Unable to convert {resolved_archive_file} to Flax deserializable object. ")
 
         return state
 
@@ -476,7 +476,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                     else:
                         raise ValueError from e
             except (UnicodeDecodeError, ValueError):
-                raise EnvironmentError(f"Unable to convert {shard_file} to Flax deserializable object. ")
+                raise OSError(f"Unable to convert {shard_file} to Flax deserializable object. ")
 
             state = flatten_dict(state, sep="/")
             state_sharded_dict.update(state)
@@ -738,13 +738,13 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                     is_sharded = True
                     raise NotImplementedError("Support for sharded checkpoints using safetensors is coming soon!")
                 elif os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, WEIGHTS_NAME)):
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Error no file named {FLAX_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
                         "but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those "
                         "weights."
                     )
                 else:
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
                         f"{pretrained_model_name_or_path}."
                     )
@@ -820,29 +820,29 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                                 "Support for sharded checkpoints using safetensors is coming soon!"
                             )
                         elif has_file(pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {FLAX_WEIGHTS_NAME} but there is a file for PyTorch weights. Use `from_pt=True` to"
                                 " load this model from those weights."
                             )
                         elif has_file(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME, **has_file_kwargs):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {FLAX_WEIGHTS_INDEX_NAME} but there is a sharded file for PyTorch weights. Use"
                                 " `from_pt=True` to load this model from those weights."
                             )
                         else:
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}."
                             )
-                except EnvironmentError:
+                except OSError:
                     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                     # to the original exception.
                     raise
                 except Exception:
                     # For any other exception, we throw a generic error.
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                         " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                         f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"

src/transformers/modeling_tf_utils.py

@@ -2760,7 +2760,7 @@ class TFPreTrainedModel(keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushT
 
                 # At this stage we don't have a weight file so we will raise an error.
                 elif use_safetensors:
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Error no file named {SAFE_WEIGHTS_NAME} or {SAFE_WEIGHTS_INDEX_NAME} found in directory {pretrained_model_name_or_path}. "
                         f"Please make sure that the model has been saved with `safe_serialization=True` or do not "
                         f"set `use_safetensors=True`."
@@ -2768,13 +2768,13 @@ class TFPreTrainedModel(keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushT
                 elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)) or os.path.isfile(
                     os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)
                 ):
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Error no file named {TF2_WEIGHTS_NAME} or {SAFE_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
                         "but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those "
                         "weights."
                     )
                 else:
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Error no file named {TF2_WEIGHTS_NAME}, {SAFE_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
                         f"{pretrained_model_name_or_path}."
                     )
@@ -2850,25 +2850,25 @@ class TFPreTrainedModel(keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushT
                         if has_file(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME, **has_file_kwargs):
                             is_sharded = True
                         elif has_file(pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {TF2_WEIGHTS_NAME} but there is a file for PyTorch weights. Use `from_pt=True` to"
                                 " load this model from those weights."
                             )
                         else:
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME},"
                                 f" {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
                             )
 
-                except EnvironmentError:
+                except OSError:
                     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                     # to the original exception.
                     raise
                 except Exception:
                     # For any other exception, we throw a generic error.
 
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                         " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                         f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"

src/transformers/modeling_utils.py

@@ -1069,7 +1069,7 @@ def _get_resolved_checkpoint_files(
                 os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index"))
                 or os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME))
             ):
-                raise EnvironmentError(
+                raise OSError(
                     f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
                     f" {pretrained_model_name_or_path} but there is a file for TensorFlow weights. Use"
                     " `from_tf=True` to load this model from those weights."
@@ -1077,18 +1077,18 @@ def _get_resolved_checkpoint_files(
             elif not use_safetensors and os.path.isfile(
                 os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
             ):
-                raise EnvironmentError(
+                raise OSError(
                     f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
                     f" {pretrained_model_name_or_path} but there is a file for Flax weights. Use `from_flax=True`"
                     " to load this model from those weights."
                 )
             elif use_safetensors:
-                raise EnvironmentError(
+                raise OSError(
                     f"Error no file named {_add_variant(SAFE_WEIGHTS_NAME, variant)} found in directory"
                     f" {pretrained_model_name_or_path}."
                 )
             else:
-                raise EnvironmentError(
+                raise OSError(
                     f"Error no file named {_add_variant(WEIGHTS_NAME, variant)}, {_add_variant(SAFE_WEIGHTS_NAME, variant)},"
                     f" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME + '.index'} or {FLAX_WEIGHTS_NAME} found in directory"
                     f" {pretrained_model_name_or_path}."
@@ -1156,7 +1156,7 @@ def _get_resolved_checkpoint_files(
                             )
                         cached_file_kwargs["revision"] = revision
                         if resolved_archive_file is None:
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {_add_variant(SAFE_WEIGHTS_NAME, variant)} or {_add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)} "
                                 "and thus cannot be loaded with `safetensors`. Please make sure that the model has "
@@ -1222,13 +1222,13 @@ def _get_resolved_checkpoint_files(
                             "local_files_only": local_files_only,
                         }
                         if has_file(pretrained_model_name_or_path, TF2_WEIGHTS_NAME, **has_file_kwargs):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file for TensorFlow weights."
                                 " Use `from_tf=True` to load this model from those weights."
                             )
                         elif has_file(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME, **has_file_kwargs):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file for Flax weights. Use"
                                 " `from_flax=True` to load this model from those weights."
@@ -1236,25 +1236,25 @@ def _get_resolved_checkpoint_files(
                         elif variant is not None and has_file(
                             pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs
                         ):
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file without the variant"
                                 f" {variant}. Use `variant=None` to load this model from those weights."
                             )
                         else:
-                            raise EnvironmentError(
+                            raise OSError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
                                 f" {_add_variant(WEIGHTS_NAME, variant)}, {_add_variant(SAFE_WEIGHTS_NAME, variant)},"
                                 f" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or {FLAX_WEIGHTS_NAME}."
                             )
 
-            except EnvironmentError:
+            except OSError:
                 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                 # to the original exception.
                 raise
             except Exception as e:
                 # For any other exception, we throw a generic error.
-                raise EnvironmentError(
+                raise OSError(
                     f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                     " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                     f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"

src/transformers/models/auto/auto_factory.py

@@ -415,7 +415,7 @@ class _BaseAutoModelClass:
     _model_mapping = None
 
     def __init__(self, *args, **kwargs) -> None:
-        raise EnvironmentError(
+        raise OSError(
             f"{self.__class__.__name__} is designed to be instantiated "
             f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
             f"`{self.__class__.__name__}.from_config(config)` methods."

src/transformers/models/auto/configuration_auto.py

@@ -1047,7 +1047,7 @@ class AutoConfig:
     """
 
     def __init__(self) -> None:
-        raise EnvironmentError(
+        raise OSError(
             "AutoConfig is designed to be instantiated "
             "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/auto/feature_extraction_auto.py

@@ -255,7 +255,7 @@ class AutoFeatureExtractor:
     """
 
     def __init__(self):
-        raise EnvironmentError(
+        raise OSError(
             "AutoFeatureExtractor is designed to be instantiated "
             "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/auto/image_processing_auto.py

@@ -338,7 +338,7 @@ class AutoImageProcessor:
     """
 
     def __init__(self):
-        raise EnvironmentError(
+        raise OSError(
             "AutoImageProcessor is designed to be instantiated "
             "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/auto/processing_auto.py

@@ -176,7 +176,7 @@ class AutoProcessor:
     """
 
     def __init__(self):
-        raise EnvironmentError(
+        raise OSError(
             "AutoProcessor is designed to be instantiated "
             "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/auto/tokenization_auto.py

@@ -819,7 +819,7 @@ class AutoTokenizer:
     """
 
     def __init__(self):
-        raise EnvironmentError(
+        raise OSError(
             "AutoTokenizer is designed to be instantiated "
             "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/auto/video_processing_auto.py

@@ -205,7 +205,7 @@ class AutoVideoProcessor:
     """
 
     def __init__(self):
-        raise EnvironmentError(
+        raise OSError(
             "AutoVideoProcessor is designed to be instantiated "
             "using the `AutoVideoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
         )

src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py

@@ -693,7 +693,7 @@ class TransfoXLCorpus:
         # redirect to the cache, if necessary
         try:
             resolved_corpus_file = cached_file(pretrained_model_name_or_path, CORPUS_NAME, cache_dir=cache_dir)
-        except EnvironmentError:
+        except OSError:
             logger.error(
                 f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list"
                 f" ({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. We assumed '{pretrained_model_name_or_path}'"

src/transformers/models/rag/retrieval_rag.py

@@ -117,13 +117,13 @@ class LegacyIndex(Index):
         try:
             # Load from URL or cache if already cached
             resolved_archive_file = cached_file(index_path, filename)
-        except EnvironmentError:
+        except OSError:
             msg = (
                 f"Can't load '{filename}'. Make sure that:\n\n"
                 f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}\n\n"
                 f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
             )
-            raise EnvironmentError(msg)
+            raise OSError(msg)
         if is_local:
             logger.info(f"loading file {resolved_archive_file}")
         else:

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1322,7 +1322,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
 
                 state_dict = safe_load_file(weight_path)
 
-            except EnvironmentError:
+            except OSError:
                 if use_safetensors:
                     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                     # to the original exception.
@@ -1331,7 +1331,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
             except Exception:
                 # For any other exception, we throw a generic error.
                 if use_safetensors:
-                    raise EnvironmentError(
+                    raise OSError(
                         f"Can't load the model for '{model_path_or_id}'. If you were trying to load it"
                         " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                         f" same name. Otherwise, make sure '{model_path_or_id}' is the correct path to a"
@@ -1362,7 +1362,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
                     weights_only=True,
                 )
 
-            except EnvironmentError:
+            except OSError:
                 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                 # to the original exception.
                 raise
@@ -1372,7 +1372,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
 
             except Exception:
                 # For any other exception, we throw a generic error.
-                raise EnvironmentError(
+                raise OSError(
                     f"Can't load the model for '{model_path_or_id}'. If you were trying to load it"
                     " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                     f" same name. Otherwise, make sure '{model_path_or_id}' is the correct path to a"

src/transformers/onnx/features.py

@@ -669,7 +669,7 @@ class FeaturesManager:
             elif is_tf_available():
                 framework = "tf"
             else:
-                raise EnvironmentError("Neither PyTorch nor TensorFlow found in environment. Cannot export to ONNX.")
+                raise OSError("Neither PyTorch nor TensorFlow found in environment. Cannot export to ONNX.")
 
         logger.info(f"Framework not requested. Using {exporter_map[framework]} to export to ONNX.")
 

src/transformers/utils/hub.py

@@ -570,7 +570,7 @@ def cached_files(
         msg = (
             f"a file named {missing_entries[0]}" if len(missing_entries) == 1 else f"files named {(*missing_entries,)}"
         )
-        raise EnvironmentError(
+        raise OSError(
             f"{path_or_repo_id} does not appear to have {msg}. Checkout 'https://huggingface.co/{path_or_repo_id}/tree/{revision_}'"
             " for available files."
         )

src/transformers/video_processing_utils.py

@@ -579,7 +579,7 @@ class BaseVideoProcessor(BaseImageProcessorFast):
                     revision=revision,
                     subfolder=subfolder,
                 )
-            except EnvironmentError:
+            except OSError:
                 video_processor_file = "preprocessor_config.json"
                 resolved_video_processor_file = cached_file(
                     pretrained_model_name_or_path,
@@ -600,13 +600,13 @@ class BaseVideoProcessor(BaseImageProcessorFast):
                     "the file or load and save the processor back which renames it automatically. "
                     "Loading from `preprocessor.json` will be removed in v5.0."
                 )
-            except EnvironmentError:
+            except OSError:
                 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
                 # the original exception.
                 raise
             except Exception:
                 # For any other exception, we throw a generic error.
-                raise EnvironmentError(
+                raise OSError(
                     f"Can't load video processor for '{pretrained_model_name_or_path}'. If you were trying to load"
                     " it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                     f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
@@ -620,7 +620,7 @@ class BaseVideoProcessor(BaseImageProcessorFast):
             video_processor_dict = json.loads(text)
 
         except json.JSONDecodeError:
-            raise EnvironmentError(
+            raise OSError(
                 f"It looks like the config file at '{resolved_video_processor_file}' is not a valid JSON file."
             )