
"""simple docstring"""




def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       list[list[int]]  ) ->				Optional[int]:

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

              lowercase    :				Dict       		=							update_area_of_max_square(_SCREAMING_SNAKE_CASE     , col + 1  )
              lowercase    :				Dict       		=							update_area_of_max_square(row + 1     , col + 1  )
              lowercase    :				Optional[Any]       		=							update_area_of_max_square(row + 1     , _SCREAMING_SNAKE_CASE  )

              if mat[row][col]:
                     lowercase    :				str       		=							1 + min([right, diagonal, down]  )
                     lowercase    :				int       		=							max(largest_square_area[0]     , _SCREAMING_SNAKE_CASE  )
                     return sub_problem_sol
              else:
                     return 0

       lowercase    :				Any       		=							[0]
       update_area_of_max_square(0     , 0  )
       return largest_square_area[0]


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       list[list[int]]  ) ->				Any:

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

              lowercase    :				Tuple       		=							update_area_of_max_square_using_dp_array(_SCREAMING_SNAKE_CASE     , col + 1     , _SCREAMING_SNAKE_CASE  )
              lowercase    :				Optional[int]       		=							update_area_of_max_square_using_dp_array(row + 1     , col + 1     , _SCREAMING_SNAKE_CASE  )
              lowercase    :				Any       		=							update_area_of_max_square_using_dp_array(row + 1     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )

              if mat[row][col]:
                     lowercase    :				Union[str, Any]       		=							1 + min([right, diagonal, down]  )
                     lowercase    :				Any       		=							max(largest_square_area[0]     , _SCREAMING_SNAKE_CASE  )
                     lowercase    :				Dict       		=							sub_problem_sol
                     return sub_problem_sol
              else:
                     return 0

       lowercase    :				List[str]       		=							[0]
       lowercase    :				List[Any]       		=							[[-1] * cols for _ in range(_SCREAMING_SNAKE_CASE  )]
       update_area_of_max_square_using_dp_array(0     , 0     , _SCREAMING_SNAKE_CASE  )

       return largest_square_area[0]


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       list[list[int]]  ) ->				List[str]:
       lowercase    :				List[str]       		=							[[0] * (cols + 1) for _ in range(rows + 1  )]
       lowercase    :				List[Any]       		=							0
       for row in range(rows - 1     , -1     , -1  ):
              for col in range(cols - 1     , -1     , -1  ):
                     lowercase    :				Optional[Any]       		=							dp_array[row][col + 1]
                     lowercase    :				Union[str, Any]       		=							dp_array[row + 1][col + 1]
                     lowercase    :				int       		=							dp_array[row + 1][col]

                     if mat[row][col] == 1:
                            lowercase    :				Tuple       		=							1 + min(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
                            lowercase    :				int       		=							max(dp_array[row][col]     , _SCREAMING_SNAKE_CASE  )
                     else:
                            lowercase    :				Union[str, Any]       		=							0

       return largest_square_area


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       list[list[int]]  ) ->				str:
       lowercase    :				Tuple       		=							[0] * (cols + 1)
       lowercase    :				Dict       		=							[0] * (cols + 1)
       lowercase    :				Optional[int]       		=							0
       for row in range(rows - 1     , -1     , -1  ):
              for col in range(cols - 1     , -1     , -1  ):
                     lowercase    :				Optional[Any]       		=							current_row[col + 1]
                     lowercase    :				Union[str, Any]       		=							next_row[col + 1]
                     lowercase    :				Dict       		=							next_row[col]

                     if mat[row][col] == 1:
                            lowercase    :				int       		=							1 + min(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
                            lowercase    :				Union[str, Any]       		=							max(current_row[col]     , _SCREAMING_SNAKE_CASE  )
                     else:
                            lowercase    :				Union[str, Any]       		=							0
              lowercase    :				List[str]       		=							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]]))






'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



