
from pathlib import Path

import fire
from tqdm import tqdm


def 							__A(lowerCAmelCase="ro"						,  lowerCAmelCase="en"						,  lowerCAmelCase="wmt16"						,  lowerCAmelCase=None )		->				None:

		"""simple docstring"""






		try:
				import datasets
		except (ModuleNotFoundError, ImportError):
				raise ImportError("""run pip install datasets""" )
		_UpperCamelCase						    =	F'{src_lang}-{tgt_lang}'
		print(F'Converting {dataset}-{pair}' )
		_UpperCamelCase						    =	datasets.load_dataset(lowerCAmelCase						,  lowerCAmelCase )
		if save_dir is None:
				_UpperCamelCase						    =	F'{dataset}-{pair}'
		_UpperCamelCase						    =	Path(lowerCAmelCase )
		save_dir.mkdir(exist_ok=lowerCAmelCase )

		for split in ds.keys():
				print(F'Splitting {split} with {ds[split].num_rows} records' )

				# to save to val.source, val.target like summary datasets
				_UpperCamelCase						    =	"""val""" if split == """validation""" else split
				_UpperCamelCase						    =	save_dir.joinpath(F'{fn}.source' )
				_UpperCamelCase						    =	save_dir.joinpath(F'{fn}.target' )
				_UpperCamelCase						    =	src_path.open("""w+""" )
				_UpperCamelCase						    =	tgt_path.open("""w+""" )

				# reader is the bottleneck so writing one record at a time doesn't slow things down
				for x in tqdm(ds[split] ):
						_UpperCamelCase						    =	x["""translation"""]
						src_fp.write(ex[src_lang] + """\n""" )
						tgt_fp.write(ex[tgt_lang] + """\n""" )

		print(F'Saved {dataset} dataset to {save_dir}' )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)





from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# 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(lowerCAmelCase ):
				# 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]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

		# Print all the boards
		for board in boards:
				for column in board:
						print(lowerCAmelCase )
				print("""""" )

		print(len(lowerCAmelCase )						,  """solutions were found.""" )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



