





'''simple docstring'''

import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
   from deepspeed import DeepSpeedEngine

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







def  a__    (    lowercase :					List[str]				)		->		List[str]:





     """simple docstring"""

     if is_torch_version('''<''', '''2.0.0'''				) or not hasattr(lowercase, '''_dynamo'''				):
          return False
     return isinstance(lowercase, torch._dynamo.eval_frame.OptimizedModule				)







def  a__    (    lowercase :					int, lowercase :					bool = True				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     _UpperCamelCase       =	is_compiled_module(lowercase				)
     if is_compiled:
          _UpperCamelCase       =	model
          _UpperCamelCase       =	model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase, lowercase				):
          _UpperCamelCase       =	model.module

     if not keep_fpaa_wrapper:
          _UpperCamelCase       =	getattr(lowercase, '''forward'''				)
          _UpperCamelCase       =	model.__dict__.pop('''_original_forward''', lowercase				)
          if original_forward is not None:
               while hasattr(lowercase, '''__wrapped__'''				):
                    _UpperCamelCase       =	forward.__wrapped__
                    if forward == original_forward:
                         break
               _UpperCamelCase       =	forward
          if getattr(lowercase, '''_converted_to_transformer_engine''', lowercase				):
               convert_model(lowercase, to_transformer_engine=lowercase				)

     if is_compiled:
          _UpperCamelCase       =	model
          _UpperCamelCase       =	compiled_model

     return model







def  a__    (    )		->		str:





     """simple docstring"""

     PartialState().wait_for_everyone()







def  a__    (    lowercase :					List[Any], lowercase :					List[Any]				)		->		int:





     """simple docstring"""

     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase, lowercase				)
     elif PartialState().local_process_index == 0:
          torch.save(lowercase, lowercase				)







@contextmanager
def  a__    (    **lowercase :					Any				)		->		str:





     """simple docstring"""

     for key, value in kwargs.items():
          _UpperCamelCase       =	str(lowercase				)

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def  a__    (    lowercase :					Any				)		->		List[Any]:





     """simple docstring"""

     if not hasattr(lowercase, '''__qualname__'''				) and not hasattr(lowercase, '''__name__'''				):
          _UpperCamelCase       =	getattr(lowercase, '''__class__''', lowercase				)
     if hasattr(lowercase, '''__qualname__'''				):
          return obj.__qualname__
     if hasattr(lowercase, '''__name__'''				):
          return obj.__name__
     return str(lowercase				)







def  a__    (    lowercase :					Optional[int], lowercase :					Dict				)		->		List[Any]:





     """simple docstring"""

     for key, value in source.items():
          if isinstance(lowercase, lowercase				):
               _UpperCamelCase       =	destination.setdefault(lowercase, {}				)
               merge_dicts(lowercase, lowercase				)
          else:
               _UpperCamelCase       =	value

     return destination







def  a__    (    lowercase :					int = None				)		->		bool:





     """simple docstring"""

     if port is None:
          _UpperCamelCase       =	29500
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM				) as s:
          return s.connect_ex(('''localhost''', port)				) == 0










'''simple docstring'''

def  a__    (    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

          _UpperCamelCase       =	update_area_of_max_square(lowercase, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, lowercase				)

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

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







def  a__    (    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]

          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(lowercase, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, lowercase, lowercase				)

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

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

     return largest_square_area[0]







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





     """simple docstring"""

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

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

     return largest_square_area







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





     """simple docstring"""

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

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



