






import copy

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


snake_case     					=		logging.get_logger(__name__)

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


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[int]								=						'''deta'''
     UpperCamelCase_				:		Dict								=						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple=None       ,			UpperCAmelCase_							:						List[str]=900       ,			UpperCAmelCase_							:						Optional[int]=2048       ,			UpperCAmelCase_							:						Tuple=6       ,			UpperCAmelCase_							:						str=2048       ,			UpperCAmelCase_							:						Optional[Any]=8       ,			UpperCAmelCase_							:						Union[str, Any]=6       ,			UpperCAmelCase_							:						List[str]=1024       ,			UpperCAmelCase_							:						int=8       ,			UpperCAmelCase_							:						Dict=0.0       ,			UpperCAmelCase_							:						List[str]=True       ,			UpperCAmelCase_							:						Dict="relu"       ,			UpperCAmelCase_							:						Tuple=256       ,			UpperCAmelCase_							:						List[str]=0.1       ,			UpperCAmelCase_							:						str=0.0       ,			UpperCAmelCase_							:						Tuple=0.0       ,			UpperCAmelCase_							:						Tuple=0.02       ,			UpperCAmelCase_							:						List[Any]=1.0       ,			UpperCAmelCase_							:						List[Any]=True       ,			UpperCAmelCase_							:						Optional[int]=False       ,			UpperCAmelCase_							:						Tuple="sine"       ,			UpperCAmelCase_							:						Any=5       ,			UpperCAmelCase_							:						Optional[Any]=4       ,			UpperCAmelCase_							:						Dict=4       ,			UpperCAmelCase_							:						List[str]=True       ,			UpperCAmelCase_							:						Optional[int]=300       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						List[Any]=True       ,			UpperCAmelCase_							:						str=1       ,			UpperCAmelCase_							:						Optional[Any]=5       ,			UpperCAmelCase_							:						Optional[Any]=2       ,			UpperCAmelCase_							:						List[Any]=1       ,			UpperCAmelCase_							:						Tuple=1       ,			UpperCAmelCase_							:						Any=5       ,			UpperCAmelCase_							:						List[str]=2       ,			UpperCAmelCase_							:						Any=0.1       ,			UpperCAmelCase_							:						List[Any]=0.25       ,			**UpperCAmelCase_							:						Tuple       ,			):
       if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]   )
       else:
         if isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
           SCREAMING_SNAKE_CASE							:   Tuple						    =     backbone_config.pop("model_type"   )
           SCREAMING_SNAKE_CASE							:   Tuple						    =     CONFIG_MAPPING[backbone_model_type]
           SCREAMING_SNAKE_CASE							:   Dict						    =     config_class.from_dict(UpperCAmelCase_   )

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


     @property
     def 					_A							(					self							:						Dict   ):
       return self.encoder_attention_heads


     @property
     def 					_A							(					self							:						int   ):
       return self.d_model






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     copy.deepcopy(self.__dict__   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.backbone_config.to_dict()
       SCREAMING_SNAKE_CASE							:   Any						    =     self.__class__.model_type
       return output













'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i + 1][j] + matrix[i][j]							)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



