


"""simple docstring"""


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class   _a      (      _lowercase ):
    a_    :				Dict             =    '''MCTCTFeatureExtractor'''
    a_    :				Optional[int]             =    '''AutoTokenizer'''
    def __init__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					int				):
          super().__init__(A_							,					A_				)
          lowerCamelCase__           =		self.feature_extractor
          lowerCamelCase__           =		False
    def __call__( self							:					Any							,					*SCREAMING_SNAKE_CASE__							:					int							,					**SCREAMING_SNAKE_CASE__							:					str				):
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*A_							,					**A_				)

          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'							,					A_				)
          lowerCamelCase__           =		kwargs.pop('sampling_rate'							,					A_				)
          lowerCamelCase__           =		kwargs.pop('text'							,					A_				)
          if len(A_				) > 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(A_							,					*A_							,					sampling_rate=A_							,					**A_				)
          if text is not None:
                lowerCamelCase__           =		self.tokenizer(A_							,					**A_				)

          if text is None:
                return inputs
          elif audio is None:
                return encodings
          else:
                lowerCamelCase__           =		encodings['input_ids']
                return inputs
    def      _UpperCamelCase       ( self							:					Tuple							,					*SCREAMING_SNAKE_CASE__							:					List[str]							,					**SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          return self.tokenizer.batch_decode(*A_							,					**A_				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					*SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					**SCREAMING_SNAKE_CASE__							:					List[Any]				):
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor.pad(*A_							,					**A_				)

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

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

          if labels is None:
                return input_features
          elif input_features is None:
                return labels
          else:
                lowerCamelCase__           =		labels['input_ids']
                return input_features
    def      _UpperCamelCase       ( self							:					Tuple							,					*SCREAMING_SNAKE_CASE__							:					Dict							,					**SCREAMING_SNAKE_CASE__							:					List[str]				):
          return self.tokenizer.decode(*A_							,					**A_				)


    @contextmanager
    def      _UpperCamelCase       ( self							:					Any				):
          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







"""simple docstring"""


def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations(_a:     int							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            return sum(count_of_possible_combinations(target - item							) for item in array							)

      return count_of_possible_combinations(_a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations_with_dp_array(
          _a:     int		,   _a:     list[int]							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            if dp_array[target] != -1:
                  return dp_array[target]
            lowerCamelCase__           =		sum(
                count_of_possible_combinations_with_dp_array(target - item		,   _a							)
                for item in array							)
            lowerCamelCase__           =		answer
            return answer

      lowerCamelCase__           =		[-1] * (target + 1)
      return count_of_possible_combinations_with_dp_array(_a		,   _a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0] * (target + 1)
      lowerCamelCase__           =		1

      for i in range(1		,   target + 1							):
            for j in range(_a							):
                  if i - array[j] >= 0:
                        dp_array[i] += dp_array[i - array[j]]

      return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case          =							3
   _snake_case          =							5
   _snake_case          =							[1, 2, 5]
   print(combination_sum_iv(n, array, target))



