



import copy

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


__snake_case						:Dict      		=     logging.get_logger(__name__)

__snake_case						:str      		=     {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}

class     _A       (	lowercase_    ):
       UpperCamelCase__  :    Union[str, Any]											=						'''deta'''
       UpperCamelCase__  :    int											=						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Dict=900     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=6     ,		__SCREAMING_SNAKE_CASE							:     Tuple=2_048     ,		__SCREAMING_SNAKE_CASE							:     str=8     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=6     ,		__SCREAMING_SNAKE_CASE							:     Dict=1_024     ,		__SCREAMING_SNAKE_CASE							:     str=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Any=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=256     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Any=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]="sine"     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=5     ,		__SCREAMING_SNAKE_CASE							:     List[str]=4     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=4     ,		__SCREAMING_SNAKE_CASE							:     Dict=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=300     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     str=1     ,		__SCREAMING_SNAKE_CASE							:     List[str]=5     ,		__SCREAMING_SNAKE_CASE							:     Tuple=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     int=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=5     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=0.25     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
               __a						       =				CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''])
           else:
               if isinstance(__UpperCamelCase     ,		__UpperCamelCase):
                   __a						       =				backbone_config.pop('''model_type''')
                   __a						       =				CONFIG_MAPPING[backbone_model_type]
                   __a						       =				config_class.from_dict(__UpperCamelCase)

           __a						       =				backbone_config
           __a						       =				num_queries
           __a						       =				max_position_embeddings
           __a						       =				d_model
           __a						       =				encoder_ffn_dim
           __a						       =				encoder_layers
           __a						       =				encoder_attention_heads
           __a						       =				decoder_ffn_dim
           __a						       =				decoder_layers
           __a						       =				decoder_attention_heads
           __a						       =				dropout
           __a						       =				attention_dropout
           __a						       =				activation_dropout
           __a						       =				activation_function
           __a						       =				init_std
           __a						       =				init_xavier_std
           __a						       =				encoder_layerdrop
           __a						       =				auxiliary_loss
           __a						       =				position_embedding_type
           # deformable attributes
           __a						       =				num_feature_levels
           __a						       =				encoder_n_points
           __a						       =				decoder_n_points
           __a						       =				two_stage
           __a						       =				two_stage_num_proposals
           __a						       =				with_box_refine
           __a						       =				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
           __a						       =				class_cost
           __a						       =				bbox_cost
           __a						       =				giou_cost
           # Loss coefficients
           __a						       =				mask_loss_coefficient
           __a						       =				dice_loss_coefficient
           __a						       =				bbox_loss_coefficient
           __a						       =				giou_loss_coefficient
           __a						       =				eos_coefficient
           __a						       =				focal_alpha
           super().__init__(is_encoder_decoder=__UpperCamelCase     ,		**__UpperCamelCase)



       @property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return self.encoder_attention_heads



       @property
       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           return self.d_model





       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				copy.deepcopy(self.__dict__)
           __a						       =				self.backbone_config.to_dict()
           __a						       =				self.__class__.model_type
           return output












"""simple docstring"""
from __future__ import annotations

_snake_case     :		str       						= []






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							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 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							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		=  1
																					solve(UpperCamelCase					,  row + 1       )
																					A		=  0
							return False






def 					A__				(			UpperCamelCase       ):
							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"))
_snake_case     :		List[str]       						= 8
_snake_case     :		List[str]       						= [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


