





'''simple docstring'''






from __future__ import annotations

lowerCAmelCase       		=      []






def      __A						(							a_						:  list[list[int]]      ,a_						:  int      ,a_						:  int	):
		for i in range(len(a_	)	):
				if board[row][i] == 1:
						return False
		for i in range(len(a_	)	):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_      ,-1      ,-1	)      ,range(a_      ,-1      ,-1	)	):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_      ,-1      ,-1	)      ,range(a_      ,len(a_	)	)	):
				if board[i][j] == 1:
						return False
		return True






def      __A						(							a_						:  list[list[int]]      ,a_						:  int	):
		if row >= len(a_	):
				solution.append(a_	)
				printboard(a_	)
				print()
				return True
		for i in range(len(a_	)	):
				if is_safe(a_      ,a_      ,a_	):
						lowerCAmelCase     :    Any						=   1
						solve(a_      ,row + 1	)
						lowerCAmelCase     :    List[Any]						=   0
		return False






def      __A						(							a_						:  list[list[int]]	):
		for i in range(len(a_	)	):
				for j in range(len(a_	)	):
						if board[i][j] == 1:
								print("Q"      ,end=" "	)
						else:
								print("."      ,end=" "	)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase       		=      8
lowerCAmelCase       		=      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))










'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      """▁"""

lowerCAmelCase       		=      {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase       		=      {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase       		=      {
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   VOCAB_FILES_NAMES
	snake_case_      =   PRETRAINED_VOCAB_FILES_MAP
	snake_case_      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_      =   ["input_ids", "attention_mask"]



	def __init__(   self		,      a_		,      a_="<s>"		,      a_="</s>"		,      a_="</s>"		,      a_="<s>"		,      a_="<unk>"		,      a_="<pad>"		,      a_="<mask>"		,      a_ = None		,      **a_		,      ):
			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase     :    str						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else mask_token

			lowerCAmelCase     :    Tuple						=   {} if sp_model_kwargs is None else sp_model_kwargs

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

			lowerCAmelCase     :    Optional[int]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.Load(str(a_	)	)
			lowerCAmelCase     :    int						=   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'

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

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

			lowerCAmelCase     :    Optional[Any]						=   len(self.sp_model	) + self.fairseq_offset
			lowerCAmelCase     :    List[Any]						=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}



	def __getstate__(   self	):
			lowerCAmelCase     :    Any						=   self.__dict__.copy()
			lowerCAmelCase     :    Optional[int]						=   None
			lowerCAmelCase     :    List[str]						=   self.sp_model.serialized_model_proto()
			return state



	def __setstate__(   self		,      a_	):
			lowerCAmelCase     :    Any						=   d

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

			lowerCAmelCase     :    Optional[Any]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCAmelCase     :    Optional[Any]						=   [self.cls_token_id]
			lowerCAmelCase     :    List[Any]						=   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = False	):

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

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



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			lowerCAmelCase     :    Dict						=   [self.sep_token_id]
			lowerCAmelCase     :    List[Any]						=   [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 + sep + token_ids_a + sep	) * [0]



	@property
	def   _lowerCamelCase    (   self	):
			return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   {self.convert_ids_to_tokens(a_	): i for i in range(self.vocab_size	)}
			vocab.update(self.added_tokens_encoder	)
			return vocab



	def   _lowerCamelCase    (   self		,      a_	):
			return self.sp_model.encode(a_		,      out_type=a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			lowerCAmelCase     :    int						=   self.sp_model.PieceToId(a_	)

			# 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   _lowerCamelCase    (   self		,      a_	):
			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   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Dict						=   "".join(a_	).replace(a_		,      " "	).strip()
			return out_string





	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			if not os.path.isdir(a_	):
					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
			lowerCAmelCase     :    int						=   os.path.join(
			    a_		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

			return (out_vocab_file,)



