


class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				str		,				__a				:				Dict		,				__a				:				List[str]		,				__a				:				Any							)					->Any:
          lowerCamelCase_						:   Union[str, Any]		= name
          lowerCamelCase_						:   Dict		= value
          lowerCamelCase_						:   Dict		= weight



       def __repr__(						self				:				Union[str, Any]							)					->List[str]:
          return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          return self.value



       def 				_lowerCAmelCase							(						self				:				str							)					->str:
          return self.name



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->List[str]:
          return self.weight




       def 				_lowerCAmelCase							(						self				:				Any							)					->Optional[int]:
          return self.value / self.weight







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


def    __lowerCamelCase   (      A__						: Dict						,						A__						: int						,						A__						: Optional[Any]					)     ->    Tuple:
   lowerCamelCase_						:   Any		= sorted(A__						,						key=A__						,						reverse=A__					)
   lowerCamelCase_						:   Optional[int]		= []
   lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= 0.0, 0.0
   for i in range(len(A__					)					):
      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   (      )     ->    int:
   pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





