

class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			str  		=	name
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	value
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	weight



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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.value



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return self.name



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									return self.weight




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									return self.value / self.weight







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	[]
						for i in range(len(_UpperCAmelCase   )   ):
												menu.append(Things(name[i]    ,     value[i]    ,     weight[i]   )   )
						return menu







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	sorted(_UpperCAmelCase    ,     key=_UpperCAmelCase    ,     reverse=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			str  		=	[]
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	0.0, 0.0
						for i in range(len(_UpperCAmelCase   )   ):
												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_				(   ):
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






