"""simple docstring"""
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
lowercase__					       =	'src/transformers'


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

lowercase__					       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__					       =	{
    # 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   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  a__:     str										=	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
      ):
        a__:     Dict										=	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}"'		,     _SCREAMING_SNAKE_CASE		,     )
          is not None
      ):
        a__:     str										=	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:
          a__:     List[str]										=	True
      if attribute_used:
        break
    if attribute_used:
      break

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

  # Special cases to be allowed
  a__:     Any										=	True
  if not attribute_used:
    a__:     int										=	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:
        a__:     Optional[Any]										=	True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        a__:     str										=	True

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

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

  return attribute_used or case_allowed







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Dict:
  a__:     List[str]										=	dict(inspect.signature(config_class.__init__   ).parameters   )
  a__:     Any										=	[x for x in list(signature.keys()   ) if x not in ['self', 'kwargs']]
  a__:     str										=	[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
  a__:     Union[str, Any]										=	{}
  if len(config_class.attribute_map   ) > 0:
    a__:     int										=	{v: k for k, v in config_class.attribute_map.items()}

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

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

  a__:     List[Any]										=	[]
  for config_param, default_value in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    # `attributes` here is all the variant names for `config_param`
    a__:     Dict										=	[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(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      unused_attributes.append(attributes[0]   )

  return sorted(_SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Any										=	{}
  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.)
    a__:     Optional[Any]										=	[
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class   )		,     lambda _SCREAMING_SNAKE_CASE   : inspect.isclass(_SCREAMING_SNAKE_CASE   )
            and issubclass(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
            and inspect.getmodule(_SCREAMING_SNAKE_CASE   ) == inspect.getmodule(_config_class   )		,     )
    ]
    for config_class in config_classes_in_module:
      a__:     Tuple										=	check_config_attributes_being_used(_SCREAMING_SNAKE_CASE   )
      if len(_SCREAMING_SNAKE_CASE   ) > 0:
        a__:     Any										=	unused_attributes

  if len(_SCREAMING_SNAKE_CASE   ) > 0:
    a__:     Optional[Any]										=	'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(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 check_config_attributes()



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  while a != 0:
    a__							,    a__:     List[str]										=	b % a, a
  return b







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  if gcd(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) != 1:
    a__:     Dict										=	F'mod inverse of {a!r} and {m!r} does not exist'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     Union[str, Any]										=	1, 0, a
  a__							,    a__							,    a__:     Any										=	0, 1, m
  while va != 0:
    a__:     int										=	ua // va
    a__							,    a__							,    a__							,    a__							,    a__							,    a__:     Any										=	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m


