

'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     str						 =			[]
 for data in source_data:
  for i, el in enumerate(SCREAMING_SNAKE_CASE_     ):
   if len(SCREAMING_SNAKE_CASE_     ) < i + 1:
    data_lists.append([]     )
   data_lists[i].append(float(SCREAMING_SNAKE_CASE_     )     )
 return data_lists




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			[]
 for dlist, weight in zip(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_     ):
  UpperCAmelCase	:     Any						 =			min(SCREAMING_SNAKE_CASE_     )
  UpperCAmelCase	:     str						 =			max(SCREAMING_SNAKE_CASE_     )

  UpperCAmelCase	:     List[str]						 =			[]
  # 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	:     Optional[Any]						 =			F"""Invalid weight of {weight:f} provided"""
   raise ValueError(SCREAMING_SNAKE_CASE_     )

  score_lists.append(SCREAMING_SNAKE_CASE_     )

 return score_lists




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			[0 for i in range(len(score_lists[0]     )     )]

 for slist in score_lists:
  for j, ele in enumerate(SCREAMING_SNAKE_CASE_     ):
   UpperCAmelCase	:     Optional[Any]						 =			final_scores[j] + ele

 return final_scores




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			get_data(SCREAMING_SNAKE_CASE_     )
 UpperCAmelCase	:     Tuple						 =			calculate_each_score(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_     )
 UpperCAmelCase	:     Union[str, Any]						 =			generate_final_scores(SCREAMING_SNAKE_CASE_     )

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

 return source_data







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




