


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
A	:		Tuple    =       '''src/transformers'''


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

A	:		Any    =       transformers.models.auto.configuration_auto.CONFIG_MAPPING

A	:		str    =       {
    # 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 			UpperCamelCase (     __magic_name__					:							Optional[int]   ,   __magic_name__					:							str   ,   __magic_name__					:							int   ,   __magic_name__					:							List[Any]					)       ->  str:
		"""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 			UpperCamelCase (     __magic_name__					:							int					)       ->  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 			UpperCamelCase (     )       ->  Optional[int]:
		"""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 __magic_name__					: 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()








from functools import lru_cache






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  set:
		"""simple docstring"""




		lowercase__					      =		2
		lowercase__					      =		set()
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.add(__magic_name__					)
		if n > 1:
				factors.add(__magic_name__					)
		return factors






@lru_cache
def 			UpperCamelCase (     __magic_name__					:							int					)       ->  int:
		"""simple docstring"""




		return len(unique_prime_factors(__magic_name__					)					)






def 			UpperCamelCase (     __magic_name__					:							list					)       ->  bool:
		"""simple docstring"""




		return len(set(__magic_name__					)					) in (0, 1)






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  list:
		"""simple docstring"""




		lowercase__					      =		2

		while True:
				# Increment each value of a generated range
				lowercase__					      =		[base + i for i in range(__magic_name__					)]

				# Run elements through out unique_prime_factors function
				# Append our target number to the end.
				lowercase__					      =		[upf_len(__magic_name__					) for x in group]
				checker.append(__magic_name__					)

				# If all numbers in the list are equal, return the group variable.
				if equality(__magic_name__					):
						return group

				# Increment our base variable by 1
				base += 1






def 			UpperCamelCase (     __magic_name__					:							int = 4					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		run(__magic_name__					)
		return results[0] if len(__magic_name__					) else None


if __name__ == "__main__":
	print(solution())

