


from __future__ import annotations

__UpperCamelCase       :       Tuple						       =				[]

def 			_a  (				SCREAMING_SNAKE_CASE	:				list[list[int]]    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 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     ):







 """simple docstring"""



 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     ):
   UpperCamelCase__					:    str          =   1
   solve(SCREAMING_SNAKE_CASE    , row + 1     )
   UpperCamelCase__					:    List[str]          =   0
 return False

def 			_a  (				SCREAMING_SNAKE_CASE	:				list[list[int]]     ):







 """simple docstring"""



 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"))
__UpperCamelCase       :       List[str]						       =				8
__UpperCamelCase       :       Tuple						       =				[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

       __UpperCamelCase       :       List[Any]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


