




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class      A       (						_a       ):
							lowercase_							     =							'Wav2Vec2FeatureExtractor'
							lowercase_							     =							'AutoTokenizer'







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.feature_extractor
														_a      	=							False







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														try:
																					return super().from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
														except OSError:
																					warnings.warn(
																					    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
																					    ''' include a `tokenizer_class` attribute is deprecated and will be '''
																					    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
																					    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
																					    '''file to suppress this warning: '''				,     lowerCAmelCase_				,     )

																					_a      	=							WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																					_a      	=							WavaVecaCTCTokenizer.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )

																					return cls(feature_extractor=lowerCAmelCase_				,     tokenizer=lowerCAmelCase_   )







							def __call__(    self			:    str				,     *lowerCAmelCase_			:    Union[str, Any]				,     **lowerCAmelCase_			:    Any   )     ->				Optional[int]:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor(*lowerCAmelCase_				,     **lowerCAmelCase_   )

														if "raw_speech" in kwargs:
																					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
																					_a      	=							kwargs.pop('''raw_speech'''   )
														else:
																					_a      	=							kwargs.pop('''audio'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''sampling_rate'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''text'''				,     lowerCAmelCase_   )
														if len(lowerCAmelCase_   ) > 0:
																					_a      	=							args[0]
																					_a      	=							args[1:]

														if audio is None and text is None:
																					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''   )

														if audio is not None:
																					_a      	=							self.feature_extractor(lowerCAmelCase_				,     *lowerCAmelCase_				,     sampling_rate=lowerCAmelCase_				,     **lowerCAmelCase_   )
														if text is not None:
																					_a      	=							self.tokenizer(lowerCAmelCase_				,     **lowerCAmelCase_   )

														if text is None:
																					return inputs
														elif audio is None:
																					return encodings
														else:
																					_a      	=							encodings['''input_ids''']
																					return inputs







							def   __lowerCAmelCase     (    self			:    str				,     *lowerCAmelCase_			:    List[str]				,     **lowerCAmelCase_			:    Any   )     ->				Any:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor.pad(*lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							kwargs.pop('''input_features'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''labels'''				,     lowerCAmelCase_   )
														if len(lowerCAmelCase_   ) > 0:
																					_a      	=							args[0]
																					_a      	=							args[1:]

														if input_features is not None:
																					_a      	=							self.feature_extractor.pad(lowerCAmelCase_				,     *lowerCAmelCase_				,     **lowerCAmelCase_   )
														if labels is not None:
																					_a      	=							self.tokenizer.pad(lowerCAmelCase_				,     **lowerCAmelCase_   )

														if labels is None:
																					return input_features
														elif input_features is None:
																					return labels
														else:
																					_a      	=							labels['''input_ids''']
																					return input_features







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     *lowerCAmelCase_			:    List[str]				,     **lowerCAmelCase_			:    List[str]   )     ->				Optional[Any]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple				,     *lowerCAmelCase_			:    Tuple				,     **lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


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







							@contextmanager
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				int:


														"""simple docstring"""


														warnings.warn(
														    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
														    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
														    '''your audio inputs, or in a separate call.'''   )
														_a      	=							True
														_a      	=							self.tokenizer
														yield
														_a      	=							self.feature_extractor
														_a      	=							False



def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
