

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__	   = logging.get_logger(__name__)

lowerCAmelCase__	   = {'''vocab_file''': '''sentencepiece.model'''}

lowerCAmelCase__	   = {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

lowerCAmelCase__	   = {
    '''google/rembert''': 2_5_6,
}

class 					a__						(					UpperCamelCase__			):
			"""simple docstring"""







			__lowerCamelCase        =       VOCAB_FILES_NAMES
			__lowerCamelCase        =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


			def __init__(    self      ,      lowercase      ,      lowercase=False      ,      lowercase=True      ,      lowercase=True      ,      lowercase="[CLS]"      ,      lowercase="[SEP]"      ,      lowercase="[UNK]"      ,      lowercase="[SEP]"      ,      lowercase="[PAD]"      ,      lowercase="[CLS]"      ,      lowercase="[MASK]"      ,      **lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							super().__init__(
							    do_lower_case=lowercase      ,      remove_space=lowercase      ,      keep_accents=lowercase      ,      bos_token=lowercase      ,      eos_token=lowercase      ,      unk_token=lowercase      ,      sep_token=lowercase      ,      pad_token=lowercase      ,      cls_token=lowercase      ,      mask_token=lowercase      ,      **lowercase      ,      )

							A__        =			do_lower_case
							A__        =			remove_space
							A__        =			keep_accents
							A__        =			vocab_file

							A__        =			spm.SentencePieceProcessor()
							self.sp_model.Load(lowercase       )


			@property
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							return len(self.sp_model       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			{self.convert_ids_to_tokens(lowercase       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab


			def __getstate__(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.__dict__.copy()
							A__        =			None
							return state


			def __setstate__(    self      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			d
							A__        =			spm.SentencePieceProcessor()
							self.sp_model.Load(self.vocab_file       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=False       )  ->						Any:
							'''simple docstring'''






							A__        =			self.sp_model.EncodeAsPieces(lowercase       )
							return pieces


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							return self.sp_model.PieceToId(lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							return self.sp_model.IdToPiece(lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			self.sp_model.decode_pieces(lowercase       )
							return out_string


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						List[int]:
							'''simple docstring'''






							A__        =			[self.sep_token_id]
							A__        =			[self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = False       )  ->						List[int]:
							'''simple docstring'''






							if already_has_special_tokens:
											if token_ids_a is not None:
															raise ValueError(
															    "You should not supply a second sequence if the provided sequence of "
															    "ids is already formatted with special tokens for the model."       )
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
											return [1] + ([0] * len(lowercase       )) + [1] + ([0] * len(lowercase       )) + [1]
							return [1] + ([0] * len(lowercase       )) + [1]


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						List[int]:
							'''simple docstring'''






							A__        =			[self.sep_token_id]
							A__        =			[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep       ) * [0]
							return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						Tuple[str]:
							'''simple docstring'''






							if not os.path.isdir(lowercase       ):
											logger.error("Vocabulary path ({}) should be a directory".format(lowercase       )       )
											return
							A__        =			os.path.join(
							    lowercase      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase       ):
											copyfile(self.vocab_file      ,      lowercase       )

							return (out_vocab_file,)









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				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)

