






'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"MCTCTFeatureExtractor"
    lowerCAmelCase__      				=							"AutoTokenizer"

    def __init__(					self     :     Dict				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Dict     )							->							Any:

     '''simple docstring'''




     super().__init__(__lowerCamelCase				,     __lowerCamelCase     )
     lowerCamelCase__  =     self.feature_extractor
     lowerCamelCase__  =     False

    def __call__(					self     :     int				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     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    a__				(					self     :     str				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     List[str]     )							->							str:

     '''simple docstring'''




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

    def    a__				(					self     :     List[str]				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Union[str, Any]     )							->							Optional[Any]:

     '''simple docstring'''




     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    a__				(					self     :     List[Any]				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     List[Any]     )							->							int:

     '''simple docstring'''




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




    @contextmanager
    def    a__				(					self     :     Any     )							->							List[str]:

     '''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."     )
     lowerCamelCase__  =     True
     lowerCamelCase__  =     self.tokenizer
     yield
     lowerCamelCase__  =     self.feature_extractor
     lowerCamelCase__  =     False









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square(lowercase__				,     lowercase__) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowerCamelCase__  =     update_area_of_max_square(lowercase__				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 update_area_of_max_square(0				,     0)
 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square_using_dp_array(
     lowercase__				,     lowercase__				,     lowercase__) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(lowercase__				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     lowercase__				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   lowerCamelCase__  =     sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 lowerCamelCase__  =     [[-1] * cols for _ in range(lowercase__)]
 update_area_of_max_square_using_dp_array(0				,     0				,     lowercase__)

 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [[0] * (cols + 1) for _ in range(rows + 1)]
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     dp_array[row][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(dp_array[row][col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0

 return largest_square_area






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     current_row[col + 1]
   lowerCamelCase__  =     next_row[col + 1]
   lowerCamelCase__  =     next_row[col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(current_row[col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0
  lowerCamelCase__  =     current_row

 return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

