






'''simple docstring'''

def 							snake_case					(     a_							: list[list[float]]	)       ->				list[list[float]]:





      """simple docstring"""

      UpperCamelCase_				:   list[list[float]]					    =							[]
      for data in source_data:
            for i, el in enumerate(a_	):
                  if len(a_	) < i + 1:
                        data_lists.append([]	)
                  data_lists[i].append(float(a_	)	)
      return data_lists



def 							snake_case					(     a_							: list[list[float]]    ,   a_							: list[int]	)       ->				list[list[float]]:





      """simple docstring"""

      UpperCamelCase_				:   list[list[float]]					    =							[]
      for dlist, weight in zip(a_    ,   a_	):
            UpperCamelCase_				:   List[str]					    =							min(a_	)
            UpperCamelCase_				:   str					    =							max(a_	)

            UpperCamelCase_				:   list[float]					    =							[]
            # for weight 0 score is 1 - actual score
            if weight == 0:
                  for item in dlist:
                        try:
                              score.append(1 - ((item - mind) / (maxd - mind))	)
                        except ZeroDivisionError:
                              score.append(1	)

            elif weight == 1:
                  for item in dlist:
                        try:
                              score.append((item - mind) / (maxd - mind)	)
                        except ZeroDivisionError:
                              score.append(0	)

        # weight not 0 or 1
            else:
                  UpperCamelCase_				:   int					    =							f"Invalid weight of {weight:f} provided"
                  raise ValueError(a_	)

            score_lists.append(a_	)

      return score_lists



def 							snake_case					(     a_							: list[list[float]]	)       ->				list[float]:





      """simple docstring"""

      UpperCamelCase_				:   list[float]					    =							[0 for i in range(len(score_lists[0]	)	)]

      for slist in score_lists:
            for j, ele in enumerate(a_	):
                  UpperCamelCase_				:   Any					    =							final_scores[j] + ele

      return final_scores



def 							snake_case					(     a_							: list[list[float]]    ,   a_							: list[int]	)       ->				list[list[float]]:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							get_data(a_	)
      UpperCamelCase_				:   Union[str, Any]					    =							calculate_each_score(a_    ,   a_	)
      UpperCamelCase_				:   Optional[Any]					    =							generate_final_scores(a_	)

      # append scores to source data
      for i, ele in enumerate(a_	):
            source_data[i].append(a_	)

      return source_data














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

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

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






