

'''simple docstring'''






from __future__ import annotations

lowerCAmelCase__   		=      []


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 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 						_A	(       A__	,							A__ ):






 """simple docstring"""



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


def 						_A	(       A__ ):






 """simple docstring"""



 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'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      if text is None and images is None:
           raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

      if text is not None:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if images is not None:
           __SCREAMING_SNAKE_CASE      = self.image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __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     )     )






