
'''simple docstring'''







from __future__ import annotations

_snake_case				:   Tuple     =				[]



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



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



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


# n=int(input("The no. of queens"))
_snake_case				:   int     =				8
_snake_case				:   Any     =				[[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 typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

								if text is not None:
														_SCREAMING_SNAKE_CASE				= self.tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														encoding.update(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.qformer_tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("input_ids"      )
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("attention_mask"      )

								if images is not None:
														_SCREAMING_SNAKE_CASE				= self.image_processor(UpperCamelCase					,						return_tensors=UpperCamelCase      )
														encoding.update(UpperCamelCase      )

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




		def     lowercase       (				self					,						UpperCamelCase					,						**UpperCamelCase      ):
								if os.path.isfile(UpperCamelCase      ):
														raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'      )
								os.makedirs(UpperCamelCase					,						exist_ok=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"qformer_tokenizer"      )
								self.qformer_tokenizer.save_pretrained(UpperCamelCase      )
								return super().save_pretrained(UpperCamelCase					,						**UpperCamelCase      )




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )