





from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				)		->						None:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  len(__a )

				# 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(__a ):
								# 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] ,				__a ,				__a ,				)






def      lowerCamelCase_						(				_UpperCamelCase )		->						None:






				"""simple docstring"""


				snake_case_						:    list[list[str]]				=  []
				depth_first_search([] ,				[] ,				[] ,				__a ,				__a )

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

