

'''simple docstring'''





import copy

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


_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)

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






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "deta"
  a     =      {
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
  }






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

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






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






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							int:
       return self.d_model







  def    lowercase_	(							self		:	Union[str, Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					copy.deepcopy(self.__dict__      )
       SCREAMING_SNAKE_CASE__					=					self.backbone_config.to_dict()
       SCREAMING_SNAKE_CASE__					=					self.__class__.model_type
       return output





def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for data in source_data:
          for i, el in enumerate(_A       ):
               if len(_A       ) < i + 1:
                    data_lists.append([]       )
               data_lists[i].append(float(_A       )       )
     return data_lists
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for dlist, weight in zip(_A   ,	_A       ):
          SCREAMING_SNAKE_CASE__					=					min(_A       )
          SCREAMING_SNAKE_CASE__					=					max(_A       )

          SCREAMING_SNAKE_CASE__					=					[]
          # for weight 0 score is 1 - actual score
          if weight == 0:
               for item in dlist:
                    try:
                         score.append(1 - ((item - mind) / (maxd - mind))       )
                    except ZeroDivisionError:
                         score.append(1       )

          elif weight == 1:
               for item in dlist:
                    try:
                         score.append((item - mind) / (maxd - mind)       )
                    except ZeroDivisionError:
                         score.append(0       )

        # weight not 0 or 1
          else:
               SCREAMING_SNAKE_CASE__					=					F'''Invalid weight of {weight:f} provided'''
               raise ValueError(_A       )

          score_lists.append(_A       )

     return score_lists
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[0 for i in range(len(score_lists[0]       )       )]

     for slist in score_lists:
          for j, ele in enumerate(_A       ):
               SCREAMING_SNAKE_CASE__					=					final_scores[j] + ele

     return final_scores







def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_data(_A       )
     SCREAMING_SNAKE_CASE__					=					calculate_each_score(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					generate_final_scores(_A       )

     # append scores to source data
     for i, ele in enumerate(_A       ):
          source_data[i].append(_A       )

     return source_data


