




from __future__ import annotations



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case						, ):
							lowerCAmelCase   =		len(_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(_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]						, _snake_case						, _snake_case						, )



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		[]
							depth_first_search([]						, []						, []						, _snake_case						, _snake_case							)

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



