
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor

class __snake_case		(				__lowerCAmelCase ,     __lowerCAmelCase					):
    _a		:						Tuple=    "pixel_values"
    _a		:						Optional[Any]=    False
    _a		:						Tuple=    TimmBackboneConfig
    def __init__(  self		,snake_case		,**snake_case				):



           '''simple docstring'''







           requires_backends(self		,"""timm"""				)
           super().__init__(lowerCamelCase__				)
           lowercase							:					int	     =				config

           if config.backbone is None:
                  raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""				)

           if config.backbone not in timm.list_models():
                  raise ValueError(f"backbone {config.backbone} is not supported by timm."				)

           if hasattr(lowerCamelCase__		,"""out_features"""				) and config.out_features is not None:
                  raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""				)

           lowercase							:					Union[str, Any]	     =				getattr(lowerCamelCase__		,"""use_pretrained_backbone"""		,lowerCamelCase__				)
           if pretrained is None:
                  raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""				)

           # We just take the final layer by default. This matches the default for the transformers models.
           lowercase							:					str	     =				config.out_indices if getattr(lowerCamelCase__		,"""out_indices"""		,lowerCamelCase__				) is not None else (-1,)

           lowercase							:					Dict	     =				timm.create_model(
               config.backbone		,pretrained=lowerCamelCase__		,features_only=config.features_only		,in_chans=config.num_channels		,out_indices=lowerCamelCase__		,**lowerCamelCase__		,)
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           lowercase							:					Any	     =				self._backbone.return_layers
           lowercase							:					Any	     =				{layer['''module''']: str(lowerCamelCase__				) for i, layer in enumerate(self._backbone.feature_info.info				)}
           super()._init_backbone(lowerCamelCase__				)
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls		,snake_case		,*snake_case		,**snake_case				):



           '''simple docstring'''







           requires_backends(cls		,["""vision""", """timm"""]				)
           from ...models.timm_backbone import TimmBackboneConfig

           lowercase							:					Optional[int]	     =				kwargs.pop("""config"""		,TimmBackboneConfig()				)

           lowercase							:					Any	     =				kwargs.pop("""use_timm_backbone"""		,lowerCamelCase__				)
           if not use_timm:
                  raise ValueError("""use_timm_backbone must be True for timm backbones"""				)

           lowercase							:					Dict	     =				kwargs.pop("""num_channels"""		,config.num_channels				)
           lowercase							:					List[str]	     =				kwargs.pop("""features_only"""		,config.features_only				)
           lowercase							:					Tuple	     =				kwargs.pop("""use_pretrained_backbone"""		,config.use_pretrained_backbone				)
           lowercase							:					Union[str, Any]	     =				kwargs.pop("""out_indices"""		,config.out_indices				)
           lowercase							:					Tuple	     =				TimmBackboneConfig(
               backbone=lowerCamelCase__		,num_channels=lowerCamelCase__		,features_only=lowerCamelCase__		,use_pretrained_backbone=lowerCamelCase__		,out_indices=lowerCamelCase__		,)
           return super()._from_config(lowerCamelCase__		,**lowerCamelCase__				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=None		,snake_case=None		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				return_dict if return_dict is not None else self.config.use_return_dict
           lowercase							:					List[Any]	     =				(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:					List[str]	     =				output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                  raise ValueError("""Cannot output attentions for timm backbones at the moment"""				)

           if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  lowercase							:					List[str]	     =				self._all_layers
                  lowercase							:					int	     =				self._backbone(lowerCamelCase__		,**lowerCamelCase__				)
                  lowercase							:					int	     =				self._return_layers
                  lowercase							:					List[str]	     =				tuple(hidden_states[i] for i in self.out_indices				)
           else:
                  lowercase							:					Optional[Any]	     =				self._backbone(lowerCamelCase__		,**lowerCamelCase__				)
                  lowercase							:					List[str]	     =				None

           lowercase							:					Dict	     =				tuple(lowerCamelCase__				)
           lowercase							:					Optional[int]	     =				tuple(lowerCamelCase__				) if hidden_states is not None else None

           if not return_dict:
                  lowercase							:					Optional[int]	     =				(feature_maps,)
                  if output_hidden_states:
                         lowercase							:					Dict	     =				output + (hidden_states,)
                  return output

           return BackboneOutput(feature_maps=lowerCamelCase__		,hidden_states=lowerCamelCase__		,attentions=lowerCamelCase__				)


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))
