






"""simple docstring"""

from __future__ import annotations

lowercase    			=       []






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						for i in range(len(lowerCamelCase_   )   ):
												if board[row][i] == 1:
																		return False
						for i in range(len(lowerCamelCase_   )   ):
												if board[i][column] == 1:
																		return False
						for i, j in zip(range(lowerCamelCase_ ,						-1 ,						-1   ) ,						range(lowerCamelCase_ ,						-1 ,						-1   )   ):
												if board[i][j] == 1:
																		return False
						for i, j in zip(range(lowerCamelCase_ ,						-1 ,						-1   ) ,						range(lowerCamelCase_ ,						len(lowerCamelCase_   )   )   ):
												if board[i][j] == 1:
																		return False
						return True






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						if row >= len(lowerCamelCase_   ):
												solution.append(lowerCamelCase_   )
												printboard(lowerCamelCase_   )
												print()
												return True
						for i in range(len(lowerCamelCase_   )   ):
												if is_safe(lowerCamelCase_ ,						lowerCamelCase_ ,						lowerCamelCase_   ):
																		__magic_name__            =     1
																		solve(lowerCamelCase_ ,						row + 1   )
																		__magic_name__            =     0
						return False






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]]   ):

						'''simple docstring'''






						for i in range(len(lowerCamelCase_   )   ):
												for j in range(len(lowerCamelCase_   )   ):
																		if board[i][j] == 1:
																								print("Q" ,						end=" "   )
																		else:
																								print("." ,						end=" "   )
												print()


# n=int(input("The no. of queens"))
lowercase    			=       8
lowercase    			=       [[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"""

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										if images is None and text is None:
																raise ValueError("You have to specify either images or text."	)

										# Get only text
										if images is None:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

										if text_encoding is not None:
																encoding_image_processor.update(__lowerCamelCase	)

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



