
'''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 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Dict:
        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_     )

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

        snake_case_    							=				torch.manual_seed(0     )
        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_     )
              snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCAmelCase_     )

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

        snake_case_    							=				generator.manual_seed(0     )
        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	:			str     ) ->							Optional[int]:
        snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"     )
        pipe.remove_unused_weights()
        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

        snake_case_    							=				"cyberpunk 2077"
        snake_case_    							=				load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"     )
        snake_case_    							=				torch.manual_seed(0     )
        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

        snake_case_    							=				image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case_    							=				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









def    a__    (	A       ):
   return " ".join([word[::-1] for word in input_str.split()]       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


