


"""simple docstring"""





from ...processing_utils import ProcessorMixin



class 						snake_case_(				a__	):
     __UpperCamelCase     =    '''SpeechT5FeatureExtractor'''
     __UpperCamelCase     =    '''SpeechT5Tokenizer'''






     def __init__(		self     :    List[str]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    str ):
           super().__init__(UpperCamelCase_    , UpperCamelCase_ )






     def __call__(		self     :    int    , *UpperCamelCase_     :    Optional[int]    , **UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Optional[Any]				=      kwargs.pop('''audio'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      kwargs.pop('''text'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      kwargs.pop('''text_target'''    , UpperCamelCase_ )
           lowerCAmelCase    :		int				=      kwargs.pop('''audio_target'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      kwargs.pop('''sampling_rate'''    , UpperCamelCase_ )

           if audio is not None and text is not None:
                 raise ValueError(
                     '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''' )
           if audio_target is not None and text_target is not None:
                 raise ValueError(
                     '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''' )
           if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''' )

           if audio is not None:
                 lowerCAmelCase    :		Optional[int]				=      self.feature_extractor(UpperCamelCase_    , *UpperCamelCase_    , sampling_rate=UpperCamelCase_    , **UpperCamelCase_ )
           elif text is not None:
                 lowerCAmelCase    :		str				=      self.tokenizer(UpperCamelCase_    , **UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		List[str]				=      None

           if audio_target is not None:
                 lowerCAmelCase    :		Optional[int]				=      self.feature_extractor(audio_target=UpperCamelCase_    , *UpperCamelCase_    , sampling_rate=UpperCamelCase_    , **UpperCamelCase_ )
                 lowerCAmelCase    :		List[Any]				=      targets['''input_values''']
           elif text_target is not None:
                 lowerCAmelCase    :		int				=      self.tokenizer(UpperCamelCase_    , **UpperCamelCase_ )
                 lowerCAmelCase    :		List[str]				=      targets['''input_ids''']
           else:
                 lowerCAmelCase    :		List[Any]				=      None

           if inputs is None:
                 return targets

           if targets is not None:
                 lowerCAmelCase    :		Any				=      labels

                 lowerCAmelCase    :		List[Any]				=      targets.get('''attention_mask''' )
                 if decoder_attention_mask is not None:
                       lowerCAmelCase    :		Dict				=      decoder_attention_mask

           return inputs






     def    lowerCamelCase__   (		self     :    List[Any]    , *UpperCamelCase_     :    int    , **UpperCamelCase_     :    int ):
           lowerCAmelCase    :		Optional[Any]				=      kwargs.pop('''input_values'''    , UpperCamelCase_ )
           lowerCAmelCase    :		str				=      kwargs.pop('''input_ids'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      kwargs.pop('''labels'''    , UpperCamelCase_ )

           if input_values is not None and input_ids is not None:
                 raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''' )
           if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''' )

           if input_values is not None:
                 lowerCAmelCase    :		Optional[Any]				=      self.feature_extractor.pad(UpperCamelCase_    , *UpperCamelCase_    , **UpperCamelCase_ )
           elif input_ids is not None:
                 lowerCAmelCase    :		str				=      self.tokenizer.pad(UpperCamelCase_    , **UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		Any				=      None

           if labels is not None:
                 if "input_ids" in labels or (isinstance(UpperCamelCase_    , UpperCamelCase_ ) and "input_ids" in labels[0]):
                       lowerCAmelCase    :		str				=      self.tokenizer.pad(UpperCamelCase_    , **UpperCamelCase_ )
                       lowerCAmelCase    :		List[Any]				=      targets['''input_ids''']
                 else:
                       lowerCAmelCase    :		int				=      self.feature_extractor.feature_size
                       lowerCAmelCase    :		Optional[Any]				=      self.feature_extractor.num_mel_bins
                       lowerCAmelCase    :		int				=      self.feature_extractor.pad(UpperCamelCase_    , *UpperCamelCase_    , **UpperCamelCase_ )
                       lowerCAmelCase    :		Tuple				=      feature_size_hack
                       lowerCAmelCase    :		List[str]				=      targets['''input_values''']
           else:
                 lowerCAmelCase    :		Optional[int]				=      None

           if inputs is None:
                 return targets

           if targets is not None:
                 lowerCAmelCase    :		List[Any]				=      labels

                 lowerCAmelCase    :		Any				=      targets.get('''attention_mask''' )
                 if decoder_attention_mask is not None:
                       lowerCAmelCase    :		Any				=      decoder_attention_mask

           return inputs






     def    lowerCamelCase__   (		self     :    List[str]    , *UpperCamelCase_     :    Optional[int]    , **UpperCamelCase_     :    Optional[Any] ):
           return self.tokenizer.batch_decode(*UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Dict    , *UpperCamelCase_     :    int    , **UpperCamelCase_     :    Optional[Any] ):
           return self.tokenizer.decode(*UpperCamelCase_    , **UpperCamelCase_ )







'''simple docstring'''






# using dfs for finding eulerian path traversal




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = (path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = True, True
                     _UpperCAmelCase  :						List[Any]			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       return path




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = 0
       _UpperCAmelCase  :						Optional[int]			     = -1
       for i in range(lowerCAmelCase_		):
              if i not in graph.keys():
                     continue
              if len(graph[i]		) % 2 == 1:
                     odd_degree_nodes += 1
                     _UpperCAmelCase  :						Optional[int]			     = i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = [[False for _ in range(max_node + 1		)] for _ in range(max_node + 1		)]
       _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = check_circuit_or_path(lowerCAmelCase_       ,   lowerCAmelCase_		)
       if check == 3:
              print("""graph is not Eulerian"""		)
              print("""no path"""		)
              return
       _UpperCAmelCase  :						Dict			     = 1
       if check == 2:
              _UpperCAmelCase  :						Dict			     = odd_node
              print("""graph has a Euler path"""		)
       if check == 1:
              print("""graph has a Euler cycle"""		)
       _UpperCAmelCase  :						Dict			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       print(lowerCAmelCase_		)




def     snake_case_					(     )->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _UpperCAmelCase  :						int			     = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _UpperCAmelCase  :						Tuple			     = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _UpperCAmelCase  :						List[Any]			     = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _UpperCAmelCase  :						List[str]			     = {
           1: [],
           2: []
           # all degree is zero
       }
       _UpperCAmelCase  :						Union[str, Any]			     = 10
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   main()



