from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  _lowerCamelCase			(__A     ):
				_snake_case     =  """ClapFeatureExtractor"""
				_snake_case     =  ("""RobertaTokenizer""", """RobertaTokenizerFast""")







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Any						):

					"""simple docstring"""






					super().__init__(UpperCamelCase__			,					UpperCamelCase__						)







				def __call__( self    :       str			,					lowerCamelCase_    :       Dict=None			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       Dict=None			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							kwargs.pop('sampling_rate'			,					UpperCamelCase__						)

					if text is None and audios is None:
						raise ValueError('You have to specify either text or audios. Both cannot be none.'						)

					if text is not None:
						_lowercase :    Optional[int]							=							self.tokenizer(UpperCamelCase__			,					return_tensors=UpperCamelCase__			,					**UpperCamelCase__						)

					if audios is not None:
						_lowercase :    Optional[Any]							=							self.feature_extractor(
						    UpperCamelCase__			,					sampling_rate=UpperCamelCase__			,					return_tensors=UpperCamelCase__			,					**UpperCamelCase__						)

					if text is not None and audios is not None:
						_lowercase :    Dict							=							audio_features.input_features
						return encoding
					elif text is not None:
						return encoding
					else:
						return BatchEncoding(data=dict(**UpperCamelCase__						)			,					tensor_type=UpperCamelCase__						)







				def 							__UpperCAmelCase					( self    :       Tuple			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					return self.tokenizer.batch_decode(*UpperCamelCase__			,					**UpperCamelCase__						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					*lowerCamelCase_    :       Union[str, Any]			,					**lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					return self.tokenizer.decode(*UpperCamelCase__			,					**UpperCamelCase__						)







				@property
				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.tokenizer.model_input_names
					_lowercase :    Union[str, Any]							=							self.feature_extractor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names						)						)




"""simple docstring"""







from __future__ import annotations

import collections
import pprint
from pathlib import Path


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return "".join(sorted(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return word_by_signature[signature(__UpperCAmelCase			)]


SCREAMING_SNAKE_CASE									=						Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE									=						sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE									=						collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



