






"""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
_UpperCamelCase		:  Optional[int]						=		'''src/transformers'''


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

_UpperCamelCase		:  Optional[int]						=		transformers.models.auto.configuration_auto.CONFIG_MAPPING

_UpperCamelCase		:  Union[str, Any]						=		{
    # 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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Dict		,					__snake_case			:  Dict		,					__snake_case			:  Any    ):





  '''simple docstring'''





  lowercase							    =       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
      ):
        lowercase							    =       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}\"'		,					_a		,					)
          is not None
      ):
        lowercase							    =       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:
          lowercase							    =       True
      if attribute_used:
        break
    if attribute_used:
      break

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

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

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

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

  return attribute_used or case_allowed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       dict(inspect.signature(config_class.__init__    ).parameters    )
  lowercase							    =       [x for x in list(signature.keys()    ) if x not in ['''self''', '''kwargs''']]
  lowercase							    =       [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
  lowercase							    =       {}
  if len(config_class.attribute_map    ) > 0:
    lowercase							    =       {v: k for k, v in config_class.attribute_map.items()}

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

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

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

  return sorted(_a    )
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       {}
  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.)
    lowercase							    =       [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class    )		,					lambda __snake_case    : inspect.isclass(_a    )
            and issubclass(_a		,					_a    )
            and inspect.getmodule(_a    ) == inspect.getmodule(_config_class    )		,					)
    ]
    for config_class in config_classes_in_module:
      lowercase							    =       check_config_attributes_being_used(_a    )
      if len(_a    ) > 0:
        lowercase							    =       unused_attributes

  if len(_a    ) > 0:
    lowercase							    =       '''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(_a    )


if __name__ == "__main__":
    check_config_attributes()







"""simple docstring"""

from functools import lru_cache




@lru_cache
def __lowercase			(							_a    ):
    if num < 0:
        raise ValueError('''Number should not be negative.'''    )

    return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





