





'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "deta"
	snake_case_      =   {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}



	def __init__(   self		,      a_=None		,      a_=900		,      a_=2_048		,      a_=6		,      a_=2_048		,      a_=8		,      a_=6		,      a_=1_024		,      a_=8		,      a_=0.0		,      a_=True		,      a_="relu"		,      a_=256		,      a_=0.1		,      a_=0.0		,      a_=0.0		,      a_=0.02		,      a_=1.0		,      a_=True		,      a_=False		,      a_="sine"		,      a_=5		,      a_=4		,      a_=4		,      a_=True		,      a_=300		,      a_=True		,      a_=True		,      a_=1		,      a_=5		,      a_=2		,      a_=1		,      a_=1		,      a_=5		,      a_=2		,      a_=0.1		,      a_=0.25		,      **a_		,      ):
			if backbone_config is None:
					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
					lowerCAmelCase     :    Dict						=   CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]	)
			else:
					if isinstance(a_		,      a_	):
							lowerCAmelCase     :    Any						=   backbone_config.pop("model_type"	)
							lowerCAmelCase     :    Dict						=   CONFIG_MAPPING[backbone_model_type]
							lowerCAmelCase     :    int						=   config_class.from_dict(a_	)

			lowerCAmelCase     :    Optional[int]						=   backbone_config
			lowerCAmelCase     :    Dict						=   num_queries
			lowerCAmelCase     :    Optional[int]						=   max_position_embeddings
			lowerCAmelCase     :    List[Any]						=   d_model
			lowerCAmelCase     :    Optional[int]						=   encoder_ffn_dim
			lowerCAmelCase     :    Optional[int]						=   encoder_layers
			lowerCAmelCase     :    Optional[Any]						=   encoder_attention_heads
			lowerCAmelCase     :    List[str]						=   decoder_ffn_dim
			lowerCAmelCase     :    Optional[Any]						=   decoder_layers
			lowerCAmelCase     :    Tuple						=   decoder_attention_heads
			lowerCAmelCase     :    Optional[int]						=   dropout
			lowerCAmelCase     :    Dict						=   attention_dropout
			lowerCAmelCase     :    Optional[int]						=   activation_dropout
			lowerCAmelCase     :    Optional[Any]						=   activation_function
			lowerCAmelCase     :    Dict						=   init_std
			lowerCAmelCase     :    Tuple						=   init_xavier_std
			lowerCAmelCase     :    List[str]						=   encoder_layerdrop
			lowerCAmelCase     :    Dict						=   auxiliary_loss
			lowerCAmelCase     :    Any						=   position_embedding_type
			# deformable attributes
			lowerCAmelCase     :    int						=   num_feature_levels
			lowerCAmelCase     :    int						=   encoder_n_points
			lowerCAmelCase     :    Optional[int]						=   decoder_n_points
			lowerCAmelCase     :    Union[str, Any]						=   two_stage
			lowerCAmelCase     :    List[Any]						=   two_stage_num_proposals
			lowerCAmelCase     :    Union[str, Any]						=   with_box_refine
			lowerCAmelCase     :    Dict						=   assign_first_stage
			if two_stage is True and with_box_refine is False:
					raise ValueError("If two_stage is True, with_box_refine must be True."	)
			# Hungarian matcher
			lowerCAmelCase     :    str						=   class_cost
			lowerCAmelCase     :    Optional[Any]						=   bbox_cost
			lowerCAmelCase     :    Tuple						=   giou_cost
			# Loss coefficients
			lowerCAmelCase     :    Dict						=   mask_loss_coefficient
			lowerCAmelCase     :    Tuple						=   dice_loss_coefficient
			lowerCAmelCase     :    int						=   bbox_loss_coefficient
			lowerCAmelCase     :    Optional[int]						=   giou_loss_coefficient
			lowerCAmelCase     :    Any						=   eos_coefficient
			lowerCAmelCase     :    Tuple						=   focal_alpha
			super().__init__(is_encoder_decoder=a_		,      **a_	)



	@property
	def   _lowerCamelCase    (   self	):
			return self.encoder_attention_heads



	@property
	def   _lowerCamelCase    (   self	):
			return self.d_model





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   copy.deepcopy(self.__dict__	)
			lowerCAmelCase     :    Tuple						=   self.backbone_config.to_dict()
			lowerCAmelCase     :    Any						=   self.__class__.model_type
			return output










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase       		=      []






def      __A						(							a_						:  list[list[int]]      ,a_						:  int      ,a_						:  int	):
		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_						:  list[list[int]]      ,a_						:  int	):
		if row >= len(a_	):
				solution.append(a_	)
				printboard(a_	)
				print()
				return True
		for i in range(len(a_	)	):
				if is_safe(a_      ,a_      ,a_	):
						lowerCAmelCase     :    Any						=   1
						solve(a_      ,row + 1	)
						lowerCAmelCase     :    List[Any]						=   0
		return False






def      __A						(							a_						:  list[list[int]]	):
		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))



