
"""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
_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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			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
																					):
																												_lowerCAmelCase  	=			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
																					):
																												_lowerCAmelCase  	=			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:
																																			_lowerCAmelCase  	=			True
																					if attribute_used:
																												break
														if attribute_used:
																					break

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

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

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

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

							return attribute_used or case_allowed






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]						):






							"""simple docstring"""



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

							# Get the path to modeling source files
							_lowerCAmelCase  	=			inspect.getsourcefile(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			os.path.dirname(SCREAMING_SNAKE_CASE						)
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							_lowerCAmelCase  	=			[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
							_lowerCAmelCase  	=			[]
							for path in modeling_paths:
														if os.path.isfile(SCREAMING_SNAKE_CASE						):
																					with open(SCREAMING_SNAKE_CASE						) as fp:
																												modeling_sources.append(fp.read()						)

							_lowerCAmelCase  	=			[]
							for config_param, default_value in zip(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														# `attributes` here is all the variant names for `config_param`
														_lowerCAmelCase  	=			[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 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			{}
							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.)
														_lowerCAmelCase  	=			[
														    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:
																					_lowerCAmelCase  	=			check_config_attributes_being_used(SCREAMING_SNAKE_CASE						)
																					if len(SCREAMING_SNAKE_CASE						) > 0:
																												_lowerCAmelCase  	=			unused_attributes

							if len(SCREAMING_SNAKE_CASE						) > 0:
														_lowerCAmelCase  	=			'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 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							if len(SCREAMING_SNAKE_CASE						) == 1:
														return [nums.copy()]
							for _ in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			nums.pop(0						)
														_lowerCAmelCase  	=			permute(SCREAMING_SNAKE_CASE						)
														for perm in permutations:
																					perm.append(SCREAMING_SNAKE_CASE						)
														result.extend(SCREAMING_SNAKE_CASE						)
														nums.append(SCREAMING_SNAKE_CASE						)
							return result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



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

							_lowerCAmelCase  	=			[]
							backtrack(0						)
							return output


if __name__ == "__main__":
	import doctest

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






