






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''MCTCTFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     '''AutoTokenizer'''





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  self.feature_extractor
         _lowerCAmelCase 			=  False





   def __call__(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         # For backward compatibility
         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.'''			)
               _lowerCAmelCase 			=  kwargs.pop('''raw_speech'''			)
         else:
               _lowerCAmelCase 			=  kwargs.pop('''audio'''       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  kwargs.pop('''text'''       ,     _lowerCAmelCase			)
         if len(_lowerCAmelCase			) > 0:
               _lowerCAmelCase 			=  args[0]
               _lowerCAmelCase 			=  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:
               _lowerCAmelCase 			=  self.feature_extractor(_lowerCAmelCase       ,     *_lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     **_lowerCAmelCase			)
         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     **_lowerCAmelCase			)

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





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor.pad(*_lowerCAmelCase       ,     **_lowerCAmelCase			)

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

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

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





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @contextmanager
   def 	__lowerCAmelCase							(	self			):
         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.'''			)
         _lowerCAmelCase 			=  True
         _lowerCAmelCase 			=  self.tokenizer
         yield
         _lowerCAmelCase 			=  self.feature_extractor
         _lowerCAmelCase 			=  False




def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Any:  # noqa: E741
      _lowerCAmelCase 			=  len(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  [0] * n
      _lowerCAmelCase 			=  [False] * n
      _lowerCAmelCase 			=  [False] * n

      def dfs(_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	int				):
            if parent == root:
                  out_edge_count += 1
            _lowerCAmelCase 			=  True
            _lowerCAmelCase 			=  at

            for to in l[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        _lowerCAmelCase 			=  min(low[at]     ,				low[to]				)

                        # AP found via bridge
                        if at < low[to]:
                              _lowerCAmelCase 			=  True
                        # AP found via cycle
                        if at == low[to]:
                              _lowerCAmelCase 			=  True
                  else:
                        _lowerCAmelCase 			=  min(low[at]     ,				_SCREAMING_SNAKE_CASE				)
            return out_edge_count

      for i in range(_SCREAMING_SNAKE_CASE				):
            if not visited[i]:
                  _lowerCAmelCase 			=  0
                  _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				-1     ,				_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  out_edge_count > 1

      for x in range(len(_SCREAMING_SNAKE_CASE				)				):
            if is_art[x] is True:
                  print(_SCREAMING_SNAKE_CASE				)


# Adjacency list of graph
UpperCAmelCase_									=							{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}


compute_ap(data)