

'''simple docstring'''





from __future__ import annotations







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			bool:
					lowerCamelCase_  				=      len(__UpperCamelCase	)
					# We need to create solution object to save path.
					lowerCamelCase_  				=      [[0 for _ in range(__UpperCamelCase	)] for _ in range(__UpperCamelCase	)]
					lowerCamelCase_  				=      run_maze(__UpperCamelCase    ,0    ,0    ,__UpperCamelCase	)
					if solved:
										print('\n'.join(str(__UpperCamelCase	) for row in solutions	)	)
					else:
										print('No solution exists!'	)
					return solved







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			bool:
					lowerCamelCase_  				=      len(__UpperCamelCase	)
					# Final check point.
					if i == j == (size - 1):
										lowerCamelCase_  				=      1
										return True

					lowerCamelCase_  				=      (not i < 0) and (not j < 0)  # Check lower bounds
					lowerCamelCase_  				=      (i < size) and (j < size)  # Check upper bounds

					if lower_flag and upper_flag:
										# check for already visited and block points.
										lowerCamelCase_  				=      (not solutions[i][j]) and (not maze[i][j])
										if block_flag:
															# check visited
															lowerCamelCase_  				=      1

															# check for directions
															if (
															    run_maze(__UpperCamelCase    ,i + 1    ,__UpperCamelCase    ,__UpperCamelCase	)
															    or run_maze(__UpperCamelCase    ,__UpperCamelCase    ,j + 1    ,__UpperCamelCase	)
															    or run_maze(__UpperCamelCase    ,i - 1    ,__UpperCamelCase    ,__UpperCamelCase	)
															    or run_maze(__UpperCamelCase    ,__UpperCamelCase    ,j - 1    ,__UpperCamelCase	)
															):
																				return True

															lowerCamelCase_  				=      0
															return False
					return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()









'''simple docstring'''







import copy

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


lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Dict      =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					"""deta"""
     lowerCAmelCase_  :     Any          =					{
         """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							,)      ->				int:
          if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               snake_case			:      int        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
          else:
               if isinstance(__a							,__a							):
                    snake_case			:      str        =			backbone_config.pop("""model_type"""							)
                    snake_case			:      Optional[Any]        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[Any]        =			config_class.from_dict(__a							)

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







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[str]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      List[Any]        =			self.__class__.model_type
          return output



