
'''simple docstring'''

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

import sentencepiece as spm

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


UpperCAmelCase__ :			List[Any]											=		logging.get_logger(__name__)

UpperCAmelCase__ :			List[str]											=		"▁"

UpperCAmelCase__ :			Dict											=		{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__ :			int											=		{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

UpperCAmelCase__ :			Dict											=		{
    "facebook/xglm-564M": 20_48,
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :List[Any]   =  VOCAB_FILES_NAMES
	snake_case__   :Any   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :str   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Tuple   =  ['input_ids', 'attention_mask']





	def __init__(       self      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[int]="<s>"   ,				__magic_name__      :							int="</s>"   ,				__magic_name__      :							Optional[Any]="</s>"   ,				__magic_name__      :							List[Any]="<s>"   ,				__magic_name__      :							List[str]="<unk>"   ,				__magic_name__      :							Optional[int]="<pad>"   ,				__magic_name__      :							Optional[Dict[str, Any]] = None   ,				**__magic_name__      :							Optional[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				lowerCAmelCase__										=				7
				lowerCAmelCase__										=				[f"""<madeupword{i}>""" for i in range(self.num_madeup_words				)]

				lowerCAmelCase__										=				kwargs.get("additional_special_tokens"   ,				[]				)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=__magic_name__   ,				eos_token=__magic_name__   ,				unk_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				pad_token=__magic_name__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(str(__magic_name__				)				)
				lowerCAmelCase__										=				vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowerCAmelCase__										=				1

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowerCAmelCase__										=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				lowerCAmelCase__										=				len(self.sp_model				)
				lowerCAmelCase__										=				{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words				)}
				self.fairseq_tokens_to_ids.update(__magic_name__				)

				lowerCAmelCase__										=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def __getstate__(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.__dict__.copy()
				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				self.sp_model.serialized_model_proto()
				return state





	def __setstate__(       self      :							List[str]   ,				__magic_name__      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				d

				# for backward compatibility
				if not hasattr(self   ,				"sp_model_kwargs"				):
							lowerCAmelCase__										=				{}

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				lowerCAmelCase__										=				[self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None   ,				__magic_name__      :							bool = False				):


				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__magic_name__   ,				token_ids_a=__magic_name__   ,				already_has_special_tokens=__magic_name__				)

				if token_ids_a is None:
							return [1] + ([0] * len(__magic_name__				))
				return [1] + ([0] * len(__magic_name__				)) + [1, 1] + ([0] * len(__magic_name__				))





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				[self.sep_token_id]

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





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				return self.sp_model.encode(__magic_name__   ,				out_type=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				lowerCAmelCase__										=				self.sp_model.PieceToId(__magic_name__				)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"".join(__magic_name__				).replace(__magic_name__   ,				" "				).strip()
				return out_string







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

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

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
							copyfile(self.vocab_file   ,				__magic_name__				)
				elif not os.path.isfile(self.vocab_file				):
							with open(__magic_name__   ,				"wb"				) as fi:
										lowerCAmelCase__										=				self.sp_model.serialized_model_proto()
										fi.write(__magic_name__				)

				return (out_vocab_file,)












from __future__ import annotations






def 		lowerCAmelCase  (	UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[list[str]]       ,			UpperCamelCase__   :   int       ,			)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   len(UpperCamelCase__					)

						# 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(UpperCamelCase__					):
												# 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]       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						depth_first_search([]       ,			[]       ,			[]       ,			UpperCamelCase__       ,			UpperCamelCase__					)

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							n_queens_solution(4)



