

from ....configuration_utils import PretrainedConfig
from ....utils import logging


_SCREAMING_SNAKE_CASE  :						str         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Tuple         =    {
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "van"






  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	Tuple=224			,     __lowerCamelCase		:	Optional[Any]=3			,     __lowerCamelCase		:	int=[7, 3, 3, 3]			,     __lowerCamelCase		:	Optional[int]=[4, 2, 2, 2]			,     __lowerCamelCase		:	str=[64, 128, 320, 512]			,     __lowerCamelCase		:	List[Any]=[3, 3, 12, 3]			,     __lowerCamelCase		:	Any=[8, 8, 4, 4]			,     __lowerCamelCase		:	List[Any]="gelu"			,     __lowerCamelCase		:	Tuple=0.02			,     __lowerCamelCase		:	Any=1e-6			,     __lowerCamelCase		:	List[Any]=1e-2			,     __lowerCamelCase		:	int=0.0			,     __lowerCamelCase		:	List[str]=0.0			,     **__lowerCamelCase		:	List[str]			,     )	->							Tuple:
       super().__init__(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					patch_sizes
       SCREAMING_SNAKE_CASE__					=					strides
       SCREAMING_SNAKE_CASE__					=					hidden_sizes
       SCREAMING_SNAKE_CASE__					=					depths
       SCREAMING_SNAKE_CASE__					=					mlp_ratios
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					layer_scale_init_value
       SCREAMING_SNAKE_CASE__					=					drop_path_rate
       SCREAMING_SNAKE_CASE__					=					dropout_rate





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


