"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[list[float]]:
   lowercase__:   list[list[float]]					=      []
   for data in source_data:
      for i, el in enumerate(__UpperCAmelCase ):
         if len(__UpperCAmelCase ) < i + 1:
            data_lists.append([] )
         data_lists[i].append(float(__UpperCAmelCase ) )
   return data_lists



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  list[list[float]]:
   lowercase__:   list[list[float]]					=      []
   for dlist, weight in zip(__UpperCAmelCase       ,    __UpperCAmelCase ):
      lowercase__:   List[Any]					=      min(__UpperCAmelCase )
      lowercase__:   Dict					=      max(__UpperCAmelCase )

      lowercase__:   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:
         lowercase__:   Union[str, Any]					=      F"""Invalid weight of {weight:f} provided"""
         raise ValueError(__UpperCAmelCase )

      score_lists.append(__UpperCAmelCase )

   return score_lists



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[float]:
   lowercase__:   list[float]					=      [0 for i in range(len(score_lists[0] ) )]

   for slist in score_lists:
      for j, ele in enumerate(__UpperCAmelCase ):
         lowercase__:   int					=      final_scores[j] + ele

   return final_scores




def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  list[list[float]]:
   lowercase__:   str					=      get_data(__UpperCAmelCase )
   lowercase__:   Dict					=      calculate_each_score(__UpperCAmelCase       ,    __UpperCAmelCase )
   lowercase__:   int					=      generate_final_scores(__UpperCAmelCase )

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

   return source_data

"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
