





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}

class 		UpperCamelCase__			(				__snake_case							,				__snake_case       ):



     '''simple docstring'''

     __snake_case   :     Optional[Any]		     =      "dinat"

     __snake_case   :     Any		     =      {
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(							self						:   Any						,lowerCamelCase__						:   Any=4						,lowerCamelCase__						:   Union[str, Any]=3						,lowerCamelCase__						:   Union[str, Any]=64						,lowerCamelCase__						:   Optional[int]=[3, 4, 6, 5]						,lowerCamelCase__						:   int=[2, 4, 8, 16]						,lowerCamelCase__						:   Optional[int]=7						,lowerCamelCase__						:   Dict=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]						,lowerCamelCase__						:   Tuple=3.0						,lowerCamelCase__						:   Any=True						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   Optional[Any]=0.0						,lowerCamelCase__						:   Optional[int]=0.1						,lowerCamelCase__						:   Optional[int]="gelu"						,lowerCamelCase__						:   Optional[Any]=0.02						,lowerCamelCase__						:   List[Any]=1e-5						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   int=None						,lowerCamelCase__						:   str=None						,**lowerCamelCase__						:   Dict						,)					->		Union[str, Any]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase_	)

           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embed_dim
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase_	)
           SCREAMING_SNAKE_CASE		=						num_heads
           SCREAMING_SNAKE_CASE		=						kernel_size
           SCREAMING_SNAKE_CASE		=						dilations
           SCREAMING_SNAKE_CASE		=						mlp_ratio
           SCREAMING_SNAKE_CASE		=						qkv_bias
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						initializer_range
           # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           SCREAMING_SNAKE_CASE		=						int(embed_dim * 2 ** (len(lowerCamelCase_	) - 1)	)
           SCREAMING_SNAKE_CASE		=						layer_scale_init_value
           SCREAMING_SNAKE_CASE		=						["""stem"""] + [F"""stage{idx}""" for idx in range(1						,len(lowerCamelCase_	) + 1	)]
           SCREAMING_SNAKE_CASE		=						get_aligned_output_features_output_indices(
               out_features=lowerCamelCase_						,out_indices=lowerCamelCase_						,stage_names=self.stage_names	)









def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      if index == number_of_items:
            return 0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						knapsack(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						index + 1     )
      if weights[index] <= max_weight:
            SCREAMING_SNAKE_CASE		=						values[index] + knapsack(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						max_weight - weights[index]			,						index + 1     )
      return max(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


