


"""simple docstring"""






from ..utils import DummyObject, requires_backends


class      __lowerCamelCase	(       metaclass=a__					):

     '''simple docstring'''
     A_							:						int     =		['torch', 'transformers', 'invisible_watermark']


     def __init__(   self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				List[str]:
          requires_backends(self     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)


     @classmethod
     def 				_UpperCAmelCase   (   cls     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				List[str]:
          requires_backends(cls     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)





     @classmethod
     def 				_UpperCAmelCase   (   cls     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Optional[Any]:
          requires_backends(cls     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)







class      __lowerCamelCase	(       metaclass=a__					):

     '''simple docstring'''
     A_							:						Optional[int]     =		['torch', 'transformers', 'invisible_watermark']


     def __init__(   self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Dict:
          requires_backends(self     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)


     @classmethod
     def 				_UpperCAmelCase   (   cls     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Any:
          requires_backends(cls     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)





     @classmethod
     def 				_UpperCAmelCase   (   cls     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Optional[Any]:
          requires_backends(cls     ,					['''torch''', '''transformers''', '''invisible_watermark''']				)



"""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


__snake_case												=							False


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

     '''simple docstring'''


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


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

          _a     						=					torch.manual_seed(0				)
          _a     						=					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				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

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

          _a     						=					generator.manual_seed(0				)
          _a     						=					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 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

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

          assert image.shape == (1, 512, 512, 3)
          _a     						=					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