
"""simple docstring"""





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    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase     ):
		lowercase__      =				'pixel_values'
		lowercase__      =				False
		lowercase__      =				TimmBackboneConfig



		def __init__( self    ,		__a    ,		**__a)	->	Union[str, Any]:
							requires_backends(self    ,		'''timm''')
							super().__init__(__a)
							_UpperCamelCase										=  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(__a    ,		'''out_features''') and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''')

							_UpperCamelCase										=  getattr(__a    ,		'''use_pretrained_backbone'''    ,		__a)
							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.
							_UpperCamelCase										=  config.out_indices if getattr(__a    ,		'''out_indices'''    ,		__a) is not None else (-1,)

							_UpperCamelCase										=  timm.create_model(
							    config.backbone    ,		pretrained=__a    ,		features_only=config.features_only    ,		in_chans=config.num_channels    ,		out_indices=__a    ,		**__a    ,		)
							# 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.
							_UpperCamelCase										=  self._backbone.return_layers
							_UpperCamelCase										=  {layer['''module''']: str(__a) for i, layer in enumerate(self._backbone.feature_info.info)}
							super()._init_backbone(__a)



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		*__a    ,		**__a)	->	Optional[Any]:
							requires_backends(cls    ,		['''vision''', '''timm'''])
							from ...models.timm_backbone import TimmBackboneConfig

							_UpperCamelCase										=  kwargs.pop('''config'''    ,		TimmBackboneConfig())

							_UpperCamelCase										=  kwargs.pop('''use_timm_backbone'''    ,		__a)
							if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones''')

							_UpperCamelCase										=  kwargs.pop('''num_channels'''    ,		config.num_channels)
							_UpperCamelCase										=  kwargs.pop('''features_only'''    ,		config.features_only)
							_UpperCamelCase										=  kwargs.pop('''use_pretrained_backbone'''    ,		config.use_pretrained_backbone)
							_UpperCamelCase										=  kwargs.pop('''out_indices'''    ,		config.out_indices)
							_UpperCamelCase										=  TimmBackboneConfig(
							    backbone=__a    ,		num_channels=__a    ,		features_only=__a    ,		use_pretrained_backbone=__a    ,		out_indices=__a    ,		)
							return super()._from_config(__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	Any:
							pass




		def 						UpperCAmelCase							( self    ,		__a    ,		__a=None    ,		__a=None    ,		__a=None    ,		**__a)	->	Union[BackboneOutput, Tuple[Tensor, ...]]:
							_UpperCamelCase										=  return_dict if return_dict is not None else self.config.use_return_dict
							_UpperCamelCase										=  (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_UpperCamelCase										=  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
												_UpperCamelCase										=  self._all_layers
												_UpperCamelCase										=  self._backbone(__a    ,		**__a)
												_UpperCamelCase										=  self._return_layers
												_UpperCamelCase										=  tuple(hidden_states[i] for i in self.out_indices)
							else:
												_UpperCamelCase										=  self._backbone(__a    ,		**__a)
												_UpperCamelCase										=  None

							_UpperCamelCase										=  tuple(__a)
							_UpperCamelCase										=  tuple(__a) if hidden_states is not None else None

							if not return_dict:
												_UpperCamelCase										=  (feature_maps,)
												if output_hidden_states:
																	_UpperCamelCase										=  output + (hidden_states,)
												return output

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








"""simple docstring"""





class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






