


'''simple docstring'''






class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							str:
      _a      =							name
      _a      =							value
      _a      =							weight
 def __repr__(	self  )   ->							Any:
      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def 							__UpperCAmelCase						(	self  )   ->							str:
      return self.value
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return self.name
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      return self.weight




 def 							__UpperCAmelCase						(	self  )   ->							str:
      return self.value / self.weight






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:int						)				->					List[Any]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:List[str]						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							sorted(lowerCAmelCase__			,  key=lowerCAmelCase__			,  reverse=lowerCAmelCase__						)
     _a      =							[]
     _a ,				_a      =							0.0, 0.0
     for i in range(len(lowerCAmelCase__						)						):
          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 		_A			()				->					List[Any]:




     '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

