

"""simple docstring"""





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


_a				   =							False




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







 """simple docstring"""







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


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"			)
    # remove text_unet
    pipe.remove_unused_weights()
    pipe.to(UpperCAmelCase__			)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__			)

    __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(
        prompt="first prompt",      image=UpperCAmelCase__,      text_to_image_strength=0.75,      generator=UpperCAmelCase__,      guidance_scale=7.5,      num_inference_steps=2,      output_type="numpy",      ).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(UpperCAmelCase__			)
       __lowercase			=		VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__			)

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

    __lowercase			=		generator.manual_seed(0			)
    __lowercase			=		pipe(
        prompt="first prompt",      image=UpperCAmelCase__,      text_to_image_strength=0.75,      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       _lowercase				(    self							:    Dict			):
    __lowercase			=		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"			)
    pipe.remove_unused_weights()
    pipe.to(UpperCAmelCase__			)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__			)

    __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(
        prompt=UpperCAmelCase__,      image=UpperCAmelCase__,      text_to_image_strength=0.75,      generator=UpperCAmelCase__,      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([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]			)

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







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : float,    UpperCamelCase_  : float)			->		float:
   '''simple docstring'''


   __lowercase			=		float()
   try:
      __lowercase			=		mass * acceleration
   except Exception:
      return -0.0
   return force


if __name__ == "__main__":
   import doctest

   # run doctest
   doctest.testmod()

   # demo
   _a				   =							12.5
   _a				   =							10
   _a				   =							newtons_second_law_of_motion(mass, acceleration)
   print('The force is ', force, 'N')




