


from __future__ import annotations
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    ):


		"""simple docstring"""




		lowercase__						=				len(SCREAMING_SNAKE_CASE					)

		# If row is equal to the size of the board it means there are a queen in each row in
		# the current board (possible_board)
		if row == n:
				# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
				# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
				boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]					)
				return

		# We iterate each column in the row to find all possible results in each row
		for col in range(SCREAMING_SNAKE_CASE					):
				# We apply that we learned previously. First we check that in the current board
				# (possible_board) there are not other same value because if there is it means
				# that there are a collision in vertical. Then we apply the two formulas we
				# learned before:
				#
				# 45º: y - x = b or 45: row - col = b
				# 135º: y + x = b or row + col = b.
				#
				# And we verify if the results of this two formulas not exist in their variables
				# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
				#
				# If any or these are True it means there is a collision so we continue to the
				# next value in the for loop.
				if (
				    col in possible_board
				    or row - col in diagonal_right_collisions
				    or row + col in diagonal_left_collisions
				):
						continue

				# If it is False we call dfs function again and we update the inputs
				depth_first_search(
				    [*possible_board, col]						,    [*diagonal_right_collisions, row - col]						,    [*diagonal_left_collisions, row + col]						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    )
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				[]
		depth_first_search([]						,    []						,    []						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		# Print all the boards
		for board in boards:
				for column in board:
						print(SCREAMING_SNAKE_CASE					)
				print(''''''					)

		print(len(SCREAMING_SNAKE_CASE					)						,    '''solutions were found.'''					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			n_queens_solution(4)






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,
)
lowerCAmelCase	=							logging.getLogger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE					)
		lowercase__						=				{
		    '''repo_id''': str(SCREAMING_SNAKE_CASE					),
		    '''repo_sha''': str(repo.head.object.hexsha					),
		    '''repo_branch''': str(repo.active_branch					),
		}

		with open(os.path.join(SCREAMING_SNAKE_CASE						,    '''git_log.json'''					)						,    '''w'''					) as f:
				json.dump(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    indent=4					)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if params.n_gpu <= 0:
				lowercase__						=				0
				lowercase__						=				-1
				lowercase__						=				True
				lowercase__						=				False
				return

		assert torch.cuda.is_available()

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

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

				# number of nodes / node ID
				lowercase__						=				params.world_size // params.n_gpu_per_node
				lowercase__						=				params.global_rank // params.n_gpu_per_node
				lowercase__						=				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

				lowercase__						=				1
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				1
				lowercase__						=				1
				lowercase__						=				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
		lowercase__						=				params.node_id == 0 and params.local_rank == 0
		lowercase__						=				params.n_nodes > 1

		# summary
		lowercase__						=				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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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


