


"""simple docstring"""



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
  import torch

if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm


__snake_case             =   logging.get_logger(__name__)







@dataclass
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Union[str, Any]							  =  [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]







   def __init__(     self						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               snake_case   :				str				 =   deprecated_arg[3:]
               setattr(self						,     UpperCamelCase__						,     not kwargs.pop(UpperCamelCase__						)						)
               logger.warning(
                   F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                   F' {positive_arg}={kwargs[positive_arg]}'						)

       snake_case   :				Union[str, Any]				 =   kwargs.pop("torchscript"						,     self.torchscript						)
       snake_case   :				Optional[int]				 =   kwargs.pop("torch_xla_tpu_print_metrics"						,     self.torch_xla_tpu_print_metrics						)
       snake_case   :				List[Any]				 =   kwargs.pop("fp16_opt_level"						,     self.fpaa_opt_level						)
       super().__init__(**UpperCamelCase__						)

   __UpperCAmelCase			:	bool							  =  field(default=snake_case_   ,							metadata={'''help''': '''Trace the models using torchscript'''}     )
   __UpperCAmelCase			:	bool							  =  field(default=snake_case_   ,							metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
   __UpperCAmelCase			:	str							  =  field(
       default='''O1'''   ,							metadata={
           '''help''': (
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           )
       }   ,							)







   @cached_property
   def 			lowerCamelCase		(     self						)     ->		Tuple["torch.device", int]:

       '''simple docstring'''




       requires_backends(self						,     ["torch"]						)
       logger.info("PyTorch: setting up devices"						)
       if not self.cuda:
           snake_case   :				Optional[Any]				 =   torch.device("cpu"						)
           snake_case   :				Optional[int]				 =   0
       elif is_torch_tpu_available():
           snake_case   :				Optional[int]				 =   xm.xla_device()
           snake_case   :				List[str]				 =   0
       else:
           snake_case   :				str				 =   torch.device("cuda" if torch.cuda.is_available() else "cpu"						)
           snake_case   :				List[str]				 =   torch.cuda.device_count()
       return device, n_gpu







   @property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return is_torch_tpu_available() and self.tpu







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       requires_backends(self						,     ["torch"]						)
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()







   @property
   def 			lowerCamelCase		(     self						)     ->		"torch.device":

       '''simple docstring'''




       requires_backends(self						,     ["torch"]						)
       return self._setup_devices[0]







   @property
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       requires_backends(self						,     ["torch"]						)
       return self._setup_devices[1]







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       return self.n_gpu > 0










"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





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

        snake_case   :				Dict				 =   update_area_of_max_square(lowercase						,							col + 1			)
        snake_case   :				Tuple				 =   update_area_of_max_square(row + 1						,							col + 1			)
        snake_case   :				Any				 =   update_area_of_max_square(row + 1						,							lowercase			)

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

    snake_case   :				int				 =   [0]
    update_area_of_max_square(0						,							0			)
    return largest_square_area[0]







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





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

        snake_case   :				List[str]				 =   update_area_of_max_square_using_dp_array(lowercase						,							col + 1						,							lowercase			)
        snake_case   :				Optional[int]				 =   update_area_of_max_square_using_dp_array(row + 1						,							col + 1						,							lowercase			)
        snake_case   :				Optional[int]				 =   update_area_of_max_square_using_dp_array(row + 1						,							lowercase						,							lowercase			)

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

    snake_case   :				Union[str, Any]				 =   [0]
    snake_case   :				int				 =   [[-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  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    snake_case   :				int				 =   [[0] * (cols + 1) for _ in range(rows + 1			)]
    snake_case   :				List[Any]				 =   0
    for row in range(rows - 1						,							-1						,							-1			):
        for col in range(cols - 1						,							-1						,							-1			):
            snake_case   :				Tuple				 =   dp_array[row][col + 1]
            snake_case   :				Any				 =   dp_array[row + 1][col + 1]
            snake_case   :				List[str]				 =   dp_array[row + 1][col]

            if mat[row][col] == 1:
                snake_case   :				Optional[int]				 =   1 + min(lowercase						,							lowercase						,							lowercase			)
                snake_case   :				Tuple				 =   max(dp_array[row][col]						,							lowercase			)
            else:
                snake_case   :				List[Any]				 =   0

    return largest_square_area







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    snake_case   :				Any				 =   [0] * (cols + 1)
    snake_case   :				Any				 =   [0] * (cols + 1)
    snake_case   :				Any				 =   0
    for row in range(rows - 1						,							-1						,							-1			):
        for col in range(cols - 1						,							-1						,							-1			):
            snake_case   :				Dict				 =   current_row[col + 1]
            snake_case   :				List[Any]				 =   next_row[col + 1]
            snake_case   :				Dict				 =   next_row[col]

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





