


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class    UpperCAmelCase__	:



     """simple docstring"""






     UpperCAmelCase__						:		int
     UpperCAmelCase__						:		TreeNode | None              =  None
     UpperCAmelCase__						:		TreeNode | None              =  None
_A			=						namedtuple('CoinsDistribResult', 'moves excess')

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):

     if root is None:
          return 0

     # Validation
     def count_nodes(SCREAMING_SNAKE_CASE__      : Optional[Any]    ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

     def count_coins(SCREAMING_SNAKE_CASE__      : int    ) -> int:
          if node is None:
               return 0

          return count_coins(node.left    ) + count_coins(node.right    ) + node.data

     if count_nodes(__SCREAMING_SNAKE_CASE    ) != count_coins(__SCREAMING_SNAKE_CASE    ):
          raise ValueError('The nodes number should be same as the number of coins'    )

     # Main calculation
     def get_distrib(SCREAMING_SNAKE_CASE__      : List[Any]    ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0      ,					1    )

          __UpperCamelCase   							=get_distrib(node.left    )
          __UpperCamelCase   							=get_distrib(node.right    )

          __UpperCamelCase   							=1 - left_distrib_excess
          __UpperCamelCase   							=1 - right_distrib_excess

          __UpperCamelCase   							=(
              left_distrib_moves
              + right_distrib_moves
              + abs(__SCREAMING_SNAKE_CASE    )
              + abs(__SCREAMING_SNAKE_CASE    )
          )
          __UpperCamelCase   							=node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE    )

     return get_distrib(__SCREAMING_SNAKE_CASE    )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()









import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  "MCTCTFeatureExtractor"
    UpperCAmelCase__						:		str              =  "AutoTokenizer"






    def __init__(      self ,       A_ ,       A_   )  ->	Dict:
         super().__init__(A_ ,       A_   )
         __UpperCamelCase   							=self.feature_extractor
         __UpperCamelCase   							=False






    def __call__(      self ,       *A_ ,       **A_   )  ->	Optional[Any]:
         # 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.'   )
              __UpperCamelCase   							=kwargs.pop('raw_speech'   )
         else:
              __UpperCamelCase   							=kwargs.pop('audio' ,       A_   )
         __UpperCamelCase   							=kwargs.pop('sampling_rate' ,       A_   )
         __UpperCamelCase   							=kwargs.pop('text' ,       A_   )
         if len(A_   ) > 0:
              __UpperCamelCase   							=args[0]
              __UpperCamelCase   							=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:
              __UpperCamelCase   							=self.feature_extractor(A_ ,       *A_ ,       sampling_rate=A_ ,       **A_   )
         if text is not None:
              __UpperCamelCase   							=self.tokenizer(A_ ,       **A_   )

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






    def _a (      self ,       *A_ ,       **A_   )  ->	str:
         return self.tokenizer.batch_decode(*A_ ,       **A_   )






    def _a (      self ,       *A_ ,       **A_   )  ->	List[Any]:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor.pad(*A_ ,       **A_   )

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

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

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






    def _a (      self ,       *A_ ,       **A_   )  ->	Optional[int]:
         return self.tokenizer.decode(*A_ ,       **A_   )






    @contextmanager
    def _a (      self   )  ->	str:
         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.'   )
         __UpperCamelCase   							=True
         __UpperCamelCase   							=self.tokenizer
         yield
         __UpperCamelCase   							=self.feature_extractor
         __UpperCamelCase   							=False





