






"""simple docstring"""
from __future__ import annotations

lowerCamelCase_							      = []

def 							snake_case (  A__      ,A__      ,A__		):
			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 							snake_case (  A__      ,A__		):
			if row >= len(A__		):
						solution.append(A__		)
						printboard(A__		)
						print()
						return True
			for i in range(len(A__		)		):
						if is_safe(A__      ,A__      ,A__		):
									UpperCAmelCase_	:					List[str]     	=   1
									solve(A__      ,row + 1		)
									UpperCAmelCase_	:					List[Any]     	=   0
			return False

def 							snake_case (  A__		):
			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'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


