'''simple docstring'''






from __future__ import annotations

_UpperCAmelCase    :     Optional[int]         =       []





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





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





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


# n=int(input("The no. of queens"))
_UpperCAmelCase    :     Any         =       8
_UpperCAmelCase    :     Dict         =       [[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





