



from __future__ import annotations


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	)						->      None:
			A__       =    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						(       __UpperCamelCase					)						->      None:
			A__       =    []
			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)









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




