Merge 3afd9aceca into 2d561713f8

src/transformers/configuration_utils.py

@@ -18,7 +18,7 @@ import copy
 import json
 import os
 import warnings
-from typing import Any, Optional, Union
+from typing import Any, Optional, TypeVar, Union
 
 from packaging import version
 
@@ -42,6 +42,10 @@ from .utils.generic import is_timm_config_dict
 logger = logging.get_logger(__name__)
 
 
+# type hinting: specifying the type of config class that inherits from PretrainedConfig
+SpecificPretrainedConfigType = TypeVar("SpecificPretrainedConfigType", bound="PretrainedConfig")
+
+
 class PretrainedConfig(PushToHubMixin):
     # no-format
     r"""
@@ -191,7 +195,7 @@ class PretrainedConfig(PushToHubMixin):
 
     model_type: str = ""
     base_config_key: str = ""
-    sub_configs: dict[str, "PretrainedConfig"] = {}
+    sub_configs: dict[str, type["PretrainedConfig"]] = {}
     has_no_defaults_at_init: bool = False
     attribute_map: dict[str, str] = {}
     base_model_tp_plan: Optional[dict[str, Any]] = None
@@ -474,7 +478,7 @@ class PretrainedConfig(PushToHubMixin):
 
     @classmethod
     def from_pretrained(
-        cls,
+        cls: type[SpecificPretrainedConfigType],
         pretrained_model_name_or_path: Union[str, os.PathLike],
         cache_dir: Optional[Union[str, os.PathLike]] = None,
         force_download: bool = False,
@@ -482,7 +486,7 @@ class PretrainedConfig(PushToHubMixin):
         token: Optional[Union[str, bool]] = None,
         revision: str = "main",
         **kwargs,
-    ) -> "PretrainedConfig":
+    ) -> SpecificPretrainedConfigType:
         r"""
         Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration.
 
@@ -717,7 +721,9 @@ class PretrainedConfig(PushToHubMixin):
         return config_dict, kwargs
 
     @classmethod
-    def from_dict(cls, config_dict: dict[str, Any], **kwargs) -> "PretrainedConfig":
+    def from_dict(
+        cls: type[SpecificPretrainedConfigType], config_dict: dict[str, Any], **kwargs
+    ) -> SpecificPretrainedConfigType:
         """
         Instantiates a [`PretrainedConfig`] from a Python dictionary of parameters.
 
@@ -778,7 +784,9 @@ class PretrainedConfig(PushToHubMixin):
             return config
 
     @classmethod
-    def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
+    def from_json_file(
+        cls: type[SpecificPretrainedConfigType], json_file: Union[str, os.PathLike]
+    ) -> SpecificPretrainedConfigType:
         """
         Instantiates a [`PretrainedConfig`] from the path to a JSON file of parameters.
 

src/transformers/feature_extraction_utils.py

@@ -20,7 +20,7 @@ import json
 import os
 import warnings
 from collections import UserDict
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
 
 import numpy as np
 
@@ -55,6 +55,9 @@ logger = logging.get_logger(__name__)
 
 PreTrainedFeatureExtractor = Union["SequenceFeatureExtractor"]  # noqa: F821
 
+# type hinting: specifying the type of feature extractor class that inherits from FeatureExtractionMixin
+SpecificFeatureExtractorType = TypeVar("SpecificFeatureExtractorType", bound="FeatureExtractionMixin")
+
 
 class BatchFeature(UserDict):
     r"""
@@ -270,7 +273,7 @@ class FeatureExtractionMixin(PushToHubMixin):
 
     @classmethod
     def from_pretrained(
-        cls,
+        cls: type[SpecificFeatureExtractorType],
         pretrained_model_name_or_path: Union[str, os.PathLike],
         cache_dir: Optional[Union[str, os.PathLike]] = None,
         force_download: bool = False,
@@ -278,7 +281,7 @@ class FeatureExtractionMixin(PushToHubMixin):
         token: Optional[Union[str, bool]] = None,
         revision: str = "main",
         **kwargs,
-    ):
+    ) -> SpecificFeatureExtractorType:
         r"""
         Instantiate a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a feature extractor, *e.g.* a
         derived class of [`SequenceFeatureExtractor`].

src/transformers/modeling_utils.py

@@ -2616,7 +2616,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
         return config
 
     @classmethod
-    def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> PretrainedConfig:
+    def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False):
         """
         Checks the availability of SDPA for a given model.
 

src/transformers/models/clvp/configuration_clvp.py

@@ -131,7 +131,7 @@ class ClvpEncoderConfig(PretrainedConfig):
     @classmethod
     def from_pretrained(
         cls, pretrained_model_name_or_path: Union[str, os.PathLike], config_type: str = "text_config", **kwargs
-    ) -> "PretrainedConfig":
+    ):
         cls._set_token_in_kwargs(kwargs)
 
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

src/transformers/models/deprecated/jukebox/configuration_jukebox.py

@@ -345,9 +345,7 @@ class JukeboxPriorConfig(PretrainedConfig):
         self.zero_out = zero_out
 
     @classmethod
-    def from_pretrained(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs
-    ) -> "PretrainedConfig":
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs):
         cls._set_token_in_kwargs(kwargs)
 
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
@@ -470,7 +468,7 @@ class JukeboxVQVAEConfig(PretrainedConfig):
         self.zero_out = zero_out
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs):
         cls._set_token_in_kwargs(kwargs)
 
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

src/transformers/models/falcon/modeling_falcon.py

@@ -15,7 +15,7 @@
 """PyTorch Falcon model."""
 
 import math
-from typing import TYPE_CHECKING, Optional, Union
+from typing import Optional, Union
 
 import torch
 import torch.utils.checkpoint
@@ -47,9 +47,6 @@ from ...utils import (
 from .configuration_falcon import FalconConfig
 
 
-if TYPE_CHECKING:
-    from ...configuration_utils import PretrainedConfig
-
 if is_flash_attn_available():
     from ...modeling_flash_attention_utils import _flash_attention_forward
 
@@ -688,7 +685,7 @@ class FalconPreTrainedModel(PreTrainedModel):
 
     # Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
     @classmethod
-    def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig":
+    def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False):
         _is_bettertransformer = getattr(cls, "use_bettertransformer", False)
         if _is_bettertransformer:
             return config

src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py

@@ -1074,7 +1074,7 @@ class Qwen2_5OmniConfig(PretrainedConfig):
 
         super().__init__(**kwargs)
 
-    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+    def get_text_config(self, decoder=False):
         """
         Returns the config that is meant to be used with text IO. On most models, it is the original config instance
         itself. On specific composite models, it is under a set of valid names.

src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py

@@ -1114,7 +1114,7 @@ class Qwen2_5OmniConfig(PretrainedConfig):
 
         super().__init__(**kwargs)
 
-    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+    def get_text_config(self, decoder=False):
         """
         Returns the config that is meant to be used with text IO. On most models, it is the original config instance
         itself. On specific composite models, it is under a set of valid names.

src/transformers/models/t5gemma/configuration_t5gemma.py

@@ -324,7 +324,7 @@ class T5GemmaConfig(PretrainedConfig):
             setattr(self.decoder, key, value)
         super().__setattr__(key, value)
 
-    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+    def get_text_config(self, decoder=False):
         # Always return self, regardless of the decoder option.
         del decoder
         return self

src/transformers/models/t5gemma/modular_t5gemma.py

@@ -213,7 +213,7 @@ class T5GemmaConfig(PretrainedConfig):
             setattr(self.decoder, key, value)
         super().__setattr__(key, value)
 
-    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+    def get_text_config(self, decoder=False):
         # Always return self, regardless of the decoder option.
         del decoder
         return self

src/transformers/processing_utils.py

@@ -24,7 +24,7 @@ import typing
 import warnings
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Optional, TypedDict, Union
+from typing import Any, Optional, TypedDict, TypeVar, Union
 
 import numpy as np
 import typing_extensions
@@ -75,6 +75,9 @@ if is_torch_available():
 
 logger = logging.get_logger(__name__)
 
+# type hinting: specifying the type of processor class that inherits from ProcessorMixin
+SpecificProcessorType = TypeVar("SpecificProcessorType", bound="ProcessorMixin")
+
 # Dynamically import the Transformers module to grab the attribute classes of the processor from their names.
 transformers_module = direct_transformers_import(Path(__file__).parent)
 
@@ -1246,7 +1249,7 @@ class ProcessorMixin(PushToHubMixin):
 
     @classmethod
     def from_pretrained(
-        cls,
+        cls: type[SpecificProcessorType],
         pretrained_model_name_or_path: Union[str, os.PathLike],
         cache_dir: Optional[Union[str, os.PathLike]] = None,
         force_download: bool = False,
@@ -1254,7 +1257,7 @@ class ProcessorMixin(PushToHubMixin):
         token: Optional[Union[str, bool]] = None,
         revision: str = "main",
         **kwargs,
-    ):
+    ) -> SpecificProcessorType:
         r"""
         Instantiate a processor associated with a pretrained model.