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


lowercase  : Tuple						   =							logging.get_logger(__name__)

lowercase  : List[str]						   =							{
    """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 __snake_case		(				lowerCAmelCase ,     lowerCAmelCase					):
    _a		:						Tuple=    "dinat"

    _a		:						List[Any]=    {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }
    def __init__(  self		,snake_case=4		,snake_case=3		,snake_case=64		,snake_case=[3, 4, 6, 5]		,snake_case=[2, 4, 8, 16]		,snake_case=7		,snake_case=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]		,snake_case=3.0		,snake_case=True		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case="gelu"		,snake_case=0.02		,snake_case=1e-5		,snake_case=0.0		,snake_case=None		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           lowercase							:					Union[str, Any]	     =				patch_size
           lowercase							:					Dict	     =				num_channels
           lowercase							:					Tuple	     =				embed_dim
           lowercase							:					List[str]	     =				depths
           lowercase							:					int	     =				len(snake_case				)
           lowercase							:					Tuple	     =				num_heads
           lowercase							:					Optional[Any]	     =				kernel_size
           lowercase							:					Any	     =				dilations
           lowercase							:					Any	     =				mlp_ratio
           lowercase							:					List[Any]	     =				qkv_bias
           lowercase							:					Union[str, Any]	     =				hidden_dropout_prob
           lowercase							:					str	     =				attention_probs_dropout_prob
           lowercase							:					List[Any]	     =				drop_path_rate
           lowercase							:					Union[str, Any]	     =				hidden_act
           lowercase							:					Any	     =				layer_norm_eps
           lowercase							:					Optional[int]	     =				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
           lowercase							:					Optional[Any]	     =				int(embed_dim * 2 ** (len(snake_case				) - 1)				)
           lowercase							:					int	     =				layer_scale_init_value
           lowercase							:					Any	     =				["""stem"""] + [f"stage{idx}" for idx in range(1		,len(snake_case				) + 1				)]
           lowercase     ,      lowercase							:					Tuple	     =				get_aligned_output_features_output_indices(
               out_features=snake_case		,out_indices=snake_case		,stage_names=self.stage_names				)


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations(SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item    ) for item in array    )

       return count_of_possible_combinations(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations_with_dp_array(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              lowercase							:					Any	     =				sum(
                  count_of_possible_combinations_with_dp_array(target - item ,     SCREAMING_SNAKE_CASE__    )
                  for item in array    )
              lowercase							:					Optional[int]	     =				answer
              return answer

       lowercase							:					int	     =				[-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					str	     =				[0] * (target + 1)
       lowercase							:					Tuple	     =				1

       for i in range(1 ,     target + 1    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  lowercase  : Any						   =							3
  lowercase  : Optional[Any]						   =							5
  lowercase  : Tuple						   =							[1, 2, 5]
  print(combination_sum_iv(n, array, target))
