






from __future__ import annotations

lowerCamelCase__			= []

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list[list[int]]				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list[list[int]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list[list[int]]    ):
				"""simple docstring"""







				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"))
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))





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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Dict    ='mgp-str'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Dict=[32, 128]      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		str=27      ,				__lowercase		:		Any=38      ,				__lowercase		:		str=50257      ,				__lowercase		:		List[str]=30522      ,				__lowercase		:		Tuple=768      ,				__lowercase		:		str=12      ,				__lowercase		:		Optional[Any]=12      ,				__lowercase		:		str=4.0      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Union[str, Any]=1E-5      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		Union[str, Any]=0.0      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		List[Any]=0.02      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		max_token_length
											__a       =		num_character_labels
											__a       =		num_bpe_labels
											__a       =		num_wordpiece_labels
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		mlp_ratio
											__a       =		distilled
											__a       =		layer_norm_eps
											__a       =		drop_rate
											__a       =		qkv_bias
											__a       =		attn_drop_rate
											__a       =		drop_path_rate
											__a       =		output_aa_attentions
											__a       =		initializer_range
