


'''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				:int        =      "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase				:Union[str, Any]        =      direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase				:Union[str, Any]        =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase				: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 lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					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
			):
				__magic_name__	:   Union[str, Any]					     =					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
			):
				__magic_name__	:   Union[str, Any]					     =					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:
					__magic_name__	:   Tuple					     =					True
			if attribute_used:
				break
		if attribute_used:
			break

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

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

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

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

	return attribute_used or case_allowed





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







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

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

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

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

	return sorted(__A		)





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					{}
	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.)
		__magic_name__	:   Dict					     =					[
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class		)		,		lambda lowerCAmelCase		: inspect.isclass(__A		)
		        and issubclass(__A		,		__A		)
		        and inspect.getmodule(__A		) == inspect.getmodule(_config_class		)		,		)
		]
		for config_class in config_classes_in_module:
			__magic_name__	:   Tuple					     =					check_config_attributes_being_used(__A		)
			if len(__A		) > 0:
				__magic_name__	:   List[Any]					     =					unused_attributes

	if len(__A		) > 0:
		__magic_name__	:   Union[str, 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(__A		)


if __name__ == "__main__":
							check_config_attributes()



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		lowerCAmelCase		) and isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					len(set_a.intersection(lowerCAmelCase		)		)

		if alternative_union:
			__magic_name__	:   List[str]					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
		else:
			__magic_name__	:   Any					     =					len(set_a.union(lowerCAmelCase		)		)

		return intersection / union

	if isinstance(lowerCAmelCase		,		(list, tuple)		) and isinstance(lowerCAmelCase		,		(list, tuple)		):
		__magic_name__	:   str					     =					[element for element in set_a if element in set_b]

		if alternative_union:
			__magic_name__	:   Dict					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
			return len(lowerCAmelCase		) / union
		else:
			__magic_name__	:   Any					     =					set_a + [element for element in set_b if element not in set_a]
			return len(lowerCAmelCase		) / len(lowerCAmelCase		)

		return len(lowerCAmelCase		) / len(lowerCAmelCase		)
	return None


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      {'''a''', '''b''', '''c''', '''d''', '''e'''}
							lowerCAmelCase				:Tuple        =      {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
							print(jaccard_similarity(set_a, set_b))