import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE__		:       Any       						=       "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__		:       List[Any]       						=       direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE__		:       Dict       						=       transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE__		:       Tuple       						=       {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)







def      _a					(   lowercase__	:      Union[str, Any]  ,	lowercase__	:      Tuple  ,	lowercase__	:      int  ,	lowercase__	:      Tuple			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		False
     for attribute in attributes:
          for modeling_source in source_strings:
               # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
               if (
                   f'''config.{attribute}''' in modeling_source
                   or f'''getattr(config, "{attribute}"''' in modeling_source
                   or f'''getattr(self.config, "{attribute}"''' in modeling_source
               ):
                    SCREAMING_SNAKE_CASE__					:					int      =		True
               # Deal with multi-line cases
               elif (
                   re.search(
                       rf'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''  ,	lowercase__  ,	)
                   is not None
               ):
                    SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		True
               # `SequenceSummary` is called with `SequenceSummary(config)`
               elif attribute in [
                   "summary_type",
                   "summary_use_proj",
                   "summary_activation",
                   "summary_last_dropout",
                   "summary_proj_to_labels",
                   "summary_first_dropout",
               ]:
                    if "SequenceSummary" in modeling_source:
                         SCREAMING_SNAKE_CASE__					:					Optional[int]      =		True
               if attribute_used:
                    break
          if attribute_used:
               break

    # common and important attributes, even if they do not always appear in the modeling files
     SCREAMING_SNAKE_CASE__					:					str      =		[
         'bos_index',
         'eos_index',
         'pad_index',
         'unk_index',
         'mask_index',
         'image_size',
         'use_cache',
         'out_features',
         'out_indices',
     ]
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		['encoder_no_repeat_ngram_size']

     # Special cases to be allowed
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		True
     if not attribute_used:
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		False
          for attribute in attributes:
               # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
               if attribute in ["is_encoder_decoder"] and default_value is True:
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		True
               elif attribute in ["tie_word_embeddings"] and default_value is False:
                    SCREAMING_SNAKE_CASE__					:					Optional[int]      =		True

               # Allow cases without checking the default value in the configuration class
               elif attribute in attributes_to_allow + attributes_used_in_generation:
                    SCREAMING_SNAKE_CASE__					:					str      =		True
               elif attribute.endswith('_token_id'			):
                    SCREAMING_SNAKE_CASE__					:					Any      =		True

               # configuration class specific cases
               if not case_allowed:
                    SCREAMING_SNAKE_CASE__					:					Optional[int]      =		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__  ,	[]			)
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		allowed_cases is True or attribute in allowed_cases

     return attribute_used or case_allowed







def      _a					(   lowercase__	:      Union[str, Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		dict(inspect.signature(config_class.__init__			).parameters			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[x for x in list(signature.keys()			) if x not in ['self', 'kwargs']]
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[signature[param].default for param in parameter_names]

     # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
     # as one variant is used, the test should pass
     SCREAMING_SNAKE_CASE__					:					str      =		{}
     if len(config_class.attribute_map			) > 0:
          SCREAMING_SNAKE_CASE__					:					str      =		{v: k for k, v in config_class.attribute_map.items()}

     # Get the path to modeling source files
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		inspect.getsourcefile(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		os.path.dirname(lowercase__			)
     # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[os.path.join(lowercase__  ,	lowercase__			) for fn in os.listdir(lowercase__			) if fn.startswith('modeling_'			)]

     # Get the source code strings
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[]
     for path in modeling_paths:
          if os.path.isfile(lowercase__			):
               with open(lowercase__			) as fp:
                    modeling_sources.append(fp.read()			)

     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[]
     for config_param, default_value in zip(lowercase__  ,	lowercase__			):
          # `attributes` here is all the variant names for `config_param`
          SCREAMING_SNAKE_CASE__					:					int      =		[config_param]
          # some configuration classes have non-empty `attribute_map`, and both names could be used in the
          # corresponding modeling files. As long as one of them appears, it is fine.
          if config_param in reversed_attribute_map:
               attributes.append(reversed_attribute_map[config_param]			)

          if not check_attribute_being_used(lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__			):
               unused_attributes.append(attributes[0]			)

     return sorted(lowercase__			)







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{}
     for _config_class in list(CONFIG_MAPPING.values()			):
          # Skip deprecated models
          if "models.deprecated" in _config_class.__module__:
               continue
          # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
          SCREAMING_SNAKE_CASE__					:					int      =		[
              cls
              for name, cls in inspect.getmembers(
                  inspect.getmodule(_config_class			)  ,	lambda lowercase__			: inspect.isclass(lowercase__			)
                  and issubclass(lowercase__  ,	lowercase__			)
                  and inspect.getmodule(lowercase__			) == inspect.getmodule(_config_class			)  ,	)
          ]
          for config_class in config_classes_in_module:
               SCREAMING_SNAKE_CASE__					:					Any      =		check_config_attributes_being_used(lowercase__			)
               if len(lowercase__			) > 0:
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		unused_attributes

     if len(lowercase__			) > 0:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
          for name, attributes in configs_with_unused_attributes.items():
               error += f'''{name}: {attributes}\n'''

          raise ValueError(lowercase__			)


if __name__ == "__main__":
      check_config_attributes()







import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class 						lowerCamelCase_							:



					'''simple docstring'''







					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					1
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None






					def A   (					self    ) ->      "DownloadConfig":







								'''simple docstring'''

								return self.__class__(**{k: copy.deepcopy(snake_case_    ) for k, v in self.__dict__.items()}    )


