

"""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						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"Wav2Vec2FeatureExtractor"
    UpperCAmelCase						 =					"AutoTokenizer"

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






         """simple docstring"""


         super().__init__(UpperCamelCase    ,						UpperCamelCase       )
         A__										=  self.feature_extractor
         A__										=  False

    @classmethod
    def 				UpperCamelCase	(     cls: Union[str, Any]    ,						UpperCamelCase: str    ,						**UpperCamelCase: List[str]       ):






         """simple docstring"""


         try:
              return super().from_pretrained(UpperCamelCase    ,						**UpperCamelCase       )
         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: """    ,						UpperCamelCase    ,						)

              A__										=  WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase    ,						**UpperCamelCase       )
              A__										=  WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase    ,						**UpperCamelCase       )

              return cls(feature_extractor=UpperCamelCase    ,						tokenizer=UpperCamelCase       )

    def __call__(     self: Dict    ,						*UpperCamelCase: Tuple    ,						**UpperCamelCase: Dict       ):






         """simple docstring"""


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

         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"""    ,						UpperCamelCase       )
         A__										=  kwargs.pop("""sampling_rate"""    ,						UpperCamelCase       )
         A__										=  kwargs.pop("""text"""    ,						UpperCamelCase       )
         if len(UpperCamelCase       ) > 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(UpperCamelCase    ,						*UpperCamelCase    ,						sampling_rate=UpperCamelCase    ,						**UpperCamelCase       )
         if text is not None:
              A__										=  self.tokenizer(UpperCamelCase    ,						**UpperCamelCase       )

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              A__										=  encodings["""input_ids"""]
              return inputs

    def 				UpperCamelCase	(     self: List[Any]    ,						*UpperCamelCase: Union[str, Any]    ,						**UpperCamelCase: Tuple       ):






         """simple docstring"""


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

         A__										=  kwargs.pop("""input_features"""    ,						UpperCamelCase       )
         A__										=  kwargs.pop("""labels"""    ,						UpperCamelCase       )
         if len(UpperCamelCase       ) > 0:
              A__										=  args[0]
              A__										=  args[1:]

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

         if labels is None:
              return input_features
         elif input_features is None:
              return labels
         else:
              A__										=  labels["""input_ids"""]
              return input_features

    def 				UpperCamelCase	(     self: List[Any]    ,						*UpperCamelCase: Optional[int]    ,						**UpperCamelCase: List[str]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[Any]    ,						*UpperCamelCase: Optional[Any]    ,						**UpperCamelCase: Any       ):






         """simple docstring"""


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





    @contextmanager
    def 				UpperCamelCase	(     self: Optional[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









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(UpperCAmelCase_			)			):
          if indegree[i] == 0:
               queue.append(UpperCAmelCase_			)

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(UpperCAmelCase_			)

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






