




def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    len(__UpperCAmelCase							)
      if n in (0, 1):
            return 0
      return sum(query * (n - i - 1) for i, query in enumerate(sorted(__UpperCAmelCase							)							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__				:     int			       =							False





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


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCAmelCase		)

       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    generator.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       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    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    'cyberpunk 2077'
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt=_lowerCAmelCase       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='numpy'       ,       ).images

       SCREAMING_SNAKE_CASE_					=    image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]		)

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