




"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

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

						__UpperCAmelCase	: Any		=					update_area_of_max_square(UpperCamelCase				,       col + 1							)
						__UpperCAmelCase	: int		=					update_area_of_max_square(row + 1				,       col + 1							)
						__UpperCAmelCase	: Tuple		=					update_area_of_max_square(row + 1				,       UpperCamelCase							)

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

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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

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

						__UpperCAmelCase	: List[str]		=					update_area_of_max_square_using_dp_array(UpperCamelCase				,       col + 1				,       UpperCamelCase							)
						__UpperCAmelCase	: str		=					update_area_of_max_square_using_dp_array(row + 1				,       col + 1				,       UpperCamelCase							)
						__UpperCAmelCase	: Optional[Any]		=					update_area_of_max_square_using_dp_array(row + 1				,       UpperCamelCase				,       UpperCamelCase							)

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

			__UpperCAmelCase	: Tuple		=					[0]
			__UpperCAmelCase	: Union[str, Any]		=					[[-1] * cols for _ in range(UpperCamelCase							)]
			update_area_of_max_square_using_dp_array(0				,       0				,       UpperCamelCase							)

			return largest_square_area[0]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					[[0] * (cols + 1) for _ in range(rows + 1							)]
			__UpperCAmelCase	: str		=					0
			for row in range(rows - 1				,       -1				,       -1							):
						for col in range(cols - 1				,       -1				,       -1							):
									__UpperCAmelCase	: Optional[Any]		=					dp_array[row][col + 1]
									__UpperCAmelCase	: Optional[Any]		=					dp_array[row + 1][col + 1]
									__UpperCAmelCase	: str		=					dp_array[row + 1][col]

									if mat[row][col] == 1:
												__UpperCAmelCase	: Any		=					1 + min(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
												__UpperCAmelCase	: List[Any]		=					max(dp_array[row][col]				,       UpperCamelCase							)
									else:
												__UpperCAmelCase	: List[Any]		=					0

			return largest_square_area

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					[0] * (cols + 1)
			__UpperCAmelCase	: int		=					[0] * (cols + 1)
			__UpperCAmelCase	: List[str]		=					0
			for row in range(rows - 1				,       -1				,       -1							):
						for col in range(cols - 1				,       -1				,       -1							):
									__UpperCAmelCase	: Optional[Any]		=					current_row[col + 1]
									__UpperCAmelCase	: Optional[int]		=					next_row[col + 1]
									__UpperCAmelCase	: Dict		=					next_row[col]

									if mat[row][col] == 1:
												__UpperCAmelCase	: Any		=					1 + min(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
												__UpperCAmelCase	: Tuple		=					max(current_row[col]				,       UpperCamelCase							)
									else:
												__UpperCAmelCase	: Optional[int]		=					0
						__UpperCAmelCase	: List[Any]		=					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 json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
A   =       logging.getLogger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					git.Repo(search_parent_directories=UpperCamelCase							)
			__UpperCAmelCase	: Any		=					{
			    "repo_id": str(UpperCamelCase							),
			    "repo_sha": str(repo.head.object.hexsha							),
			    "repo_branch": str(repo.active_branch							),
			}

			with open(os.path.join(UpperCamelCase				,       "git_log.json"							)				,       "w"							) as f:
						json.dump(UpperCamelCase				,       UpperCamelCase				,       indent=4							)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			str:


			"""simple docstring"""
			if params.n_gpu <= 0:
						__UpperCAmelCase	: str		=					0
						__UpperCAmelCase	: Dict		=					-1
						__UpperCAmelCase	: Tuple		=					True
						__UpperCAmelCase	: List[str]		=					False
						return

			assert torch.cuda.is_available()

			logger.info("Initializing GPUs"							)
			if params.n_gpu > 1:
						assert params.local_rank != -1

						__UpperCAmelCase	: Optional[int]		=					int(os.environ["WORLD_SIZE"]							)
						__UpperCAmelCase	: Union[str, Any]		=					int(os.environ["N_GPU_NODE"]							)
						__UpperCAmelCase	: Optional[int]		=					int(os.environ["RANK"]							)

						# number of nodes / node ID
						__UpperCAmelCase	: int		=					params.world_size // params.n_gpu_per_node
						__UpperCAmelCase	: Optional[int]		=					params.global_rank // params.n_gpu_per_node
						__UpperCAmelCase	: Tuple		=					True

						assert params.n_nodes == int(os.environ["N_NODES"]							)
						assert params.node_id == int(os.environ["NODE_RANK"]							)

			# local job (single GPU)
			else:
						assert params.local_rank == -1

						__UpperCAmelCase	: Union[str, Any]		=					1
						__UpperCAmelCase	: Dict		=					0
						__UpperCAmelCase	: List[Any]		=					0
						__UpperCAmelCase	: Tuple		=					0
						__UpperCAmelCase	: Any		=					1
						__UpperCAmelCase	: str		=					1
						__UpperCAmelCase	: Dict		=					False

			# sanity checks
			assert params.n_nodes >= 1
			assert 0 <= params.node_id < params.n_nodes
			assert 0 <= params.local_rank <= params.global_rank < params.world_size
			assert params.world_size == params.n_nodes * params.n_gpu_per_node

			# define whether this is the master process / if we are in multi-node distributed mode
			__UpperCAmelCase	: Union[str, Any]		=					params.node_id == 0 and params.local_rank == 0
			__UpperCAmelCase	: Dict		=					params.n_nodes > 1

			# summary
			__UpperCAmelCase	: Any		=					f"--- Global rank: {params.global_rank} - "
			logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes							)
			logger.info(PREFIX + "Node ID        : %i" % params.node_id							)
			logger.info(PREFIX + "Local rank     : %i" % params.local_rank							)
			logger.info(PREFIX + "World size     : %i" % params.world_size							)
			logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node							)
			logger.info(PREFIX + "Master         : %s" % str(params.is_master							)							)
			logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node							)							)
			logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu							)							)
			logger.info(PREFIX + "Hostname       : %s" % socket.gethostname()							)

			# set GPU device
			torch.cuda.set_device(params.local_rank							)

			# initialize multi-GPU
			if params.multi_gpu:
						logger.info("Initializing PyTorch distributed"							)
						torch.distributed.init_process_group(
						    init_method="env://"				,       backend="nccl"				,       )





def 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			np.random.seed(args.seed							)
			torch.manual_seed(args.seed							)
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed							)


