


'''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 			a      (			__a ,					__a ,					__a ,					__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :str												= 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
															):
																				UpperCamelCase__   :Dict												= 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
															):
																				UpperCamelCase__   :str												= 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:
																									UpperCamelCase__   :str												= True
															if attribute_used:
																				break
										if attribute_used:
															break

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

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

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

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

					return attribute_used or case_allowed


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






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

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

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

					UpperCamelCase__   :Optional[int]												= []
					for config_param, default_value in zip(__a ,					__a   ):
										# `attributes` here is all the variant names for `config_param`
										UpperCamelCase__   :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 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :str												= {}
					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.)
										UpperCamelCase__   :List[Any]												= [
										    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:
															UpperCamelCase__   :List[Any]												= check_config_attributes_being_used(__a   )
															if len(__a   ) > 0:
																				UpperCamelCase__   :Dict												= unused_attributes

					if len(__a   ) > 0:
										UpperCamelCase__   :Optional[int]												= '''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'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )