import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "MCTCTFeatureExtractor"
    __a  =      "AutoTokenizer"
    def __init__(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     super().__init__(lowerCAmelCase , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      self.feature_extractor
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      False
    def __call__(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Dict:
     # 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.'''   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      kwargs.pop('''raw_speech'''   )
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      kwargs.pop('''audio''' , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      kwargs.pop('''sampling_rate''' , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      kwargs.pop('''text''' , lowerCAmelCase   )
     if len(lowerCAmelCase   ) > 0:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      args[0]
      SCREAMING_SNAKE_CASE__:   Optional[int]=      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:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.feature_extractor(lowerCAmelCase , *lowerCAmelCase , sampling_rate=lowerCAmelCase , **lowerCAmelCase   )
     if text is not None:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.tokenizer(lowerCAmelCase , **lowerCAmelCase   )

     if text is None:
      return inputs
     elif audio is None:
      return encodings
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      encodings['''input_ids''']
      return inputs
    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Optional[Any]:
     return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Union[str, Any]:
     # For backward compatibility
     if self._in_target_context_manager:
      return self.current_processor.pad(*lowerCAmelCase , **lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      kwargs.pop('''input_features''' , lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      kwargs.pop('''labels''' , lowerCAmelCase   )
     if len(lowerCAmelCase   ) > 0:
      SCREAMING_SNAKE_CASE__:   List[Any]=      args[0]
      SCREAMING_SNAKE_CASE__:   Tuple=      args[1:]

     if input_features is not None:
      SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extractor.pad(lowerCAmelCase , *lowerCAmelCase , **lowerCAmelCase   )
     if labels is not None:
      SCREAMING_SNAKE_CASE__:   Tuple=      self.tokenizer.pad(lowerCAmelCase , **lowerCAmelCase   )

     if labels is None:
      return input_features
     elif input_features is None:
      return labels
     else:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      labels['''input_ids''']
      return input_features
    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase   )



    @contextmanager
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     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.'''   )
     SCREAMING_SNAKE_CASE__:   Any=      True
     SCREAMING_SNAKE_CASE__:   int=      self.tokenizer
     yield
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.feature_extractor
     SCREAMING_SNAKE_CASE__:   Tuple=      False


import os







def 			A__      (	snake_case_ :		str = "matrix.txt"      ):
 with open(os.path.join(os.path.dirname(snake_case_      )       ,				snake_case_      )      ) as in_file:
  SCREAMING_SNAKE_CASE__:   Dict=      in_file.read()

 SCREAMING_SNAKE_CASE__:   Optional[int]=      [[int(snake_case_      ) for cell in row.split(''','''      )] for row in data.strip().splitlines()]
 SCREAMING_SNAKE_CASE__:   int=      [[0 for cell in row] for row in grid]
 SCREAMING_SNAKE_CASE__:   Dict=      len(grid[0]      )

 SCREAMING_SNAKE_CASE__:   Dict=      [[0 for i in range(snake_case_      )] for j in range(snake_case_      )]
 SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][0]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][i] + dp[0][i - 1]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      grid[i][0] + dp[i - 1][0]

 for i in range(1       ,				snake_case_      ):
  for j in range(1       ,				snake_case_      ):
   SCREAMING_SNAKE_CASE__:   Dict=      grid[i][j] + min(dp[i - 1][j]       ,				dp[i][j - 1]      )

 return dp[-1][-1]


if __name__ == "__main__":
      print(f'''{solution() = }''')

