

'''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
lowerCAmelCase__   		=      '''src/transformers'''


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

lowerCAmelCase__   		=      transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase__   		=      {
    # 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	(       A__	,							A__	,							A__	,							A__ ):






 """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 						_A	(       A__ ):






 """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 						_A	(       ):






 """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 A__ : 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'''
def 		A_(     A						:		list[int]):
							UpperCamelCase   			=     []
							if len(A) == 1:
														return [nums.copy()]
							for _ in range(len(A)):
														UpperCamelCase   			=     nums.pop(0)
														UpperCamelCase   			=     permute(A)
														for perm in permutations:
																					perm.append(A)
														result.extend(A)
														nums.append(A)
							return result







def 		A_(     A						:		str):

							def backtrack(A						:		str):
														if start == len(A) - 1:
																					output.append(nums[:])
														else:
																					for i in range(A		,     len(A)):
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]
																												backtrack(start + 1)
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]  # backtrack

							UpperCamelCase   			=     []
							backtrack(0)
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						lowerCAmelCase					:    Dict           = permutea([1, 2, 3])
						print(res)
						doctest.testmod()




