






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    =							logging.get_logger(__name__)

A_    =							{
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'xglm'
							snake_case_     =							['past_key_values']

							snake_case_     =							{
							    'num_attention_heads': 'attention_heads',
							    'hidden_size': 'd_model',
							    'num_hidden_layers': 'num_layers',
							}


							def __init__(			self      :						Optional[Any]			,    snake_case      :						Union[str, Any]=25_6008			,    snake_case      :						Dict=2048			,    snake_case      :						Tuple=1024			,    snake_case      :						Union[str, Any]=4096			,    snake_case      :						Any=24			,    snake_case      :						List[Any]=16			,    snake_case      :						Union[str, Any]="gelu"			,    snake_case      :						List[Any]=0.1			,    snake_case      :						List[Any]=0.1			,    snake_case      :						Optional[Any]=0.0			,    snake_case      :						List[str]=0.0			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						List[Any]=True			,    snake_case      :						str=True			,    snake_case      :						List[str]=2			,    snake_case      :						Optional[int]=1			,    snake_case      :						str=0			,    snake_case      :						Optional[Any]=2			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  List[str]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  d_model
													A__						:  Dict		  =  ffn_dim
													A__						:  Tuple		  =  num_layers
													A__						:  Any		  =  attention_heads
													A__						:  Tuple		  =  activation_function
													A__						:  List[Any]		  =  dropout
													A__						:  List[Any]		  =  attention_dropout
													A__						:  int		  =  activation_dropout
													A__						:  Optional[Any]		  =  layerdrop
													A__						:  Union[str, Any]		  =  init_std
													A__						:  Dict		  =  scale_embedding  # scale factor will be sqrt(d_model) if True
													A__						:  Union[str, Any]		  =  use_cache

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














"""simple docstring"""



from __future__ import annotations

A_    =							[]




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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int      )		->bool:
						if row >= len(UpperCAmelCase__      ):
												solution.append(UpperCAmelCase__      )
												printboard(UpperCAmelCase__      )
												print()
												return True
						for i in range(len(UpperCAmelCase__      )      ):
												if is_safe(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      ):
																		A__						:  Tuple		  =  1
																		solve(UpperCAmelCase__,						row + 1      )
																		A__						:  Optional[int]		  =  0
						return False




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]]      )		->None:
						for i in range(len(UpperCAmelCase__      )      ):
												for j in range(len(UpperCAmelCase__      )      ):
																		if board[i][j] == 1:
																								print("""Q""",						end=""" """      )
																		else:
																								print(""".""",						end=""" """      )
												print()


# n=int(input("The no. of queens"))
A_    =							8
A_    =							[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

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






