
from __future__ import annotations

__lowerCAmelCase					   =       []


def    a      ( a		,						a		,						a	)     ->bool:




			'''simple docstring'''


			for i in range(len(a	)	):
						if board[row][i] == 1:
									return False
			for i in range(len(a	)	):
						if board[i][column] == 1:
									return False
			for i, j in zip(range(a		,						-1		,						-1	)		,						range(a		,						-1		,						-1	)	):
						if board[i][j] == 1:
									return False
			for i, j in zip(range(a		,						-1		,						-1	)		,						range(a		,						len(a	)	)	):
						if board[i][j] == 1:
									return False
			return True


def    a      ( a		,						a	)     ->bool:




			'''simple docstring'''


			if row >= len(a	):
						solution.append(a	)
						printboard(a	)
						print()
						return True
			for i in range(len(a	)	):
						if is_safe(a		,						a		,						a	):
									SCREAMING_SNAKE_CASE     						= 1
									solve(a		,						row + 1	)
									SCREAMING_SNAKE_CASE     						= 0
			return False


def    a      ( a	)     ->None:




			'''simple docstring'''


			for i in range(len(a	)	):
						for j in range(len(a	)	):
									if board[i][j] == 1:
												print('''Q'''		,						end=''' '''	)
									else:
												print('''.'''		,						end=''' '''	)
						print()


# n=int(input("The no. of queens"))
__lowerCAmelCase					   =       8
__lowerCAmelCase					   =       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)





print('The total no. of solutions are :', len(solution))

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()