





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
snake_case_				=			'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
snake_case_				=			direct_transformers_import(PATH_TO_TRANSFORMERS)

snake_case_				=			transformers.models.auto.configuration_auto.CONFIG_MAPPING

snake_case_				=			{
    # 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 lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Any       ,      snake_case_	:  List[Any]       )      ->				Union[str, Any]:
   __snake_case							       =   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
         ):
            __snake_case							       =   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}\""""       ,      snake_case_       ,      )
             is not None
         ):
            __snake_case							       =   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:
               __snake_case							       =   True
         if attribute_used:
            break
      if attribute_used:
         break

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

   # Special cases to be allowed
   __snake_case							       =   True
   if not attribute_used:
      __snake_case							       =   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:
            __snake_case							       =   True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            __snake_case							       =   True

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

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

   return attribute_used or case_allowed






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       )      ->				Tuple:
   __snake_case							       =   dict(inspect.signature(config_class.__init__       ).parameters       )
   __snake_case							       =   [x for x in list(signature.keys()       ) if x not in ['''self''', '''kwargs''']]
   __snake_case							       =   [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
   __snake_case							       =   {}
   if len(config_class.attribute_map       ) > 0:
      __snake_case							       =   {v: k for k, v in config_class.attribute_map.items()}

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

   # Get the source code strings
   __snake_case							       =   []
   for path in modeling_paths:
      if os.path.isfile(snake_case_       ):
         with open(snake_case_       ) as fp:
            modeling_sources.append(fp.read()       )

   __snake_case							       =   []
   for config_param, default_value in zip(snake_case_       ,      snake_case_       ):
      # `attributes` here is all the variant names for `config_param`
      __snake_case							       =   [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(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ):
         unused_attributes.append(attributes[0]       )

   return sorted(snake_case_       )






def lowerCamelCase__    (							)      ->				Union[str, Any]:
   __snake_case							       =   {}
   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.)
      __snake_case							       =   [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class       )       ,      lambda snake_case_       : inspect.isclass(snake_case_       )
              and issubclass(snake_case_       ,      snake_case_       )
              and inspect.getmodule(snake_case_       ) == inspect.getmodule(_config_class       )       ,      )
      ]
      for config_class in config_classes_in_module:
         __snake_case							       =   check_config_attributes_being_used(snake_case_       )
         if len(snake_case_       ) > 0:
            __snake_case							       =   unused_attributes

   if len(snake_case_       ) > 0:
      __snake_case							       =   '''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(snake_case_       )


if __name__ == "__main__":
    check_config_attributes()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

