

from typing import TYPE_CHECKING

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


lowercase_       		=  {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  ["DeiTFeatureExtractor"]
     lowercase_       		=  ["DeiTImageProcessor"]

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "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

     lowercase_       		=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




class  A						:







    """simple docstring"""





    def __init__(      self :       Any,lowercase_ :       Tuple,lowercase_ :       Any,lowercase_ :       List[str]		)->    List[Any]:





        '''simple docstring'''







        A__          =   name
        A__          =   value
        A__          =   weight


    def __repr__(      self :       int		)->    Tuple:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        return self.value


    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        return self.name


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return self.weight


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





        '''simple docstring'''







        return self.value / self.weight




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      List[Any]:





    '''simple docstring'''






    A__          =   []
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        menu.append(Things(name[i]    ,			value[i]    ,			weight[i]      )      )
    return menu
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Any:





    '''simple docstring'''






    A__          =   sorted(SCREAMING_SNAKE_CASE__    ,			key=SCREAMING_SNAKE_CASE__    ,			reverse=SCREAMING_SNAKE_CASE__      )
    A__          =   []
    A__      ,						A__          =   0.0, 0.0
    for i in range(len(SCREAMING_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 						_snake_case(    )							->      Any:





    '''simple docstring'''








if __name__ == "__main__":
     import doctest

     doctest.testmod()

