


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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       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:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )









from __future__ import annotations

import collections
import pprint
from pathlib import Path
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return "".join(sorted(UpperCAmelCase_						)						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return word_by_signature[signature(UpperCAmelCase_						)]


snake_case				:    str       				=     Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case				:    Optional[int]       				=     sorted({word.strip().lower() for word in data.splitlines()})

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

if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     {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))





