






"""simple docstring"""



import gc
import tempfile
import unittest

import numpy as np
import torch

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


_UpperCamelCase		:  Dict						=		False

class        a     (   unittest.TestCase	):
    pass


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







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'							,		torch_dtype=torch.floataa       )
      pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

      lowercase							    =       torch.manual_seed(0       )
      lowercase							    =       pipe.dual_guided(
          prompt='first prompt'							,		image=_lowerCamelCase							,		text_to_image_strength=0.7_5							,		generator=_lowerCamelCase							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type='numpy'							,		).images

      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(_lowerCamelCase       )
        lowercase							    =       VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase							,		torch_dtype=torch.floataa       )
      pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       generator.manual_seed(0       )
      lowercase							    =       pipe.dual_guided(
          prompt='first prompt'							,		image=_lowerCamelCase							,		text_to_image_strength=0.7_5							,		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 			UpperCamelCase_      (			self       ):
      lowercase							    =       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'							,		torch_dtype=torch.floataa       )
      pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       'cyberpunk 2077'
      lowercase							    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
      lowercase							    =       torch.manual_seed(0       )
      lowercase							    =       pipe.dual_guided(
          prompt=_lowerCamelCase							,		image=_lowerCamelCase							,		text_to_image_strength=0.7_5							,		generator=_lowerCamelCase							,		guidance_scale=7.5							,		num_inference_steps=5_0							,		output_type='numpy'							,		).images

      lowercase							    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      lowercase							    =       np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]       )

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

      lowercase							    =       'A painting of a squirrel eating a burger '
      lowercase							    =       torch.manual_seed(0       )
      lowercase							    =       pipe.text_to_image(
          prompt=_lowerCamelCase							,		generator=_lowerCamelCase							,		guidance_scale=7.5							,		num_inference_steps=5_0							,		output_type='numpy'       ).images

      lowercase							    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      lowercase							    =       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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

      lowercase							    =       pipe.image_variation(_lowerCamelCase							,		generator=_lowerCamelCase							,		output_type='numpy'       ).images

      lowercase							    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      lowercase							    =       np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]       )

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










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 1_00    ):





  '''simple docstring'''





  lowercase							    =       0
  lowercase							    =       0
  for i in range(1		,					n + 1    ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(F'''{solution() = }''')


