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


__a:			Optional[Any]	       =       logging.get_logger(__name__)

__a:			int	       =       {
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}



class       UpperCAmelCase  (						_a ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   """swinv2"""

       SCREAMING_SNAKE_CASE 		=   {
           """num_attention_heads""": """num_heads""",
           """num_hidden_layers""": """num_layers""",
       }

       def __init__(						self     ,       __lowerCAmelCase=224     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=96     ,       __lowerCAmelCase=[2, 2, 6, 2]     ,       __lowerCAmelCase=[3, 6, 12, 24]     ,       __lowerCAmelCase=7     ,       __lowerCAmelCase=4.0     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=0.0     ,       __lowerCAmelCase=0.0     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase="gelu"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=0.0_2     ,       __lowerCAmelCase=1E-5     ,       __lowerCAmelCase=32     ,       **__lowerCAmelCase     ,       )  ->  Union[str, Any]:
        super().__init__(**__lowerCamelCase			)

        lowercase__    :   Optional[Any]						= image_size
        lowercase__    :   str						= patch_size
        lowercase__    :   Tuple						= num_channels
        lowercase__    :   Optional[int]						= embed_dim
        lowercase__    :   Optional[int]						= depths
        lowercase__    :   int						= len(__lowerCamelCase			)
        lowercase__    :   List[Any]						= num_heads
        lowercase__    :   Union[str, Any]						= window_size
        lowercase__    :   Any						= mlp_ratio
        lowercase__    :   Union[str, Any]						= qkv_bias
        lowercase__    :   List[Any]						= hidden_dropout_prob
        lowercase__    :   Any						= attention_probs_dropout_prob
        lowercase__    :   List[Any]						= drop_path_rate
        lowercase__    :   List[str]						= hidden_act
        lowercase__    :   Optional[int]						= use_absolute_embeddings
        lowercase__    :   Optional[int]						= layer_norm_eps
        lowercase__    :   str						= initializer_range
        lowercase__    :   List[str]						= encoder_stride
        # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowercase__    :   List[str]						= int(embed_dim * 2 ** (len(__lowerCamelCase			) - 1)			)
        lowercase__    :   Dict						= (0, 0, 0, 0)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

