

'''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							:			str      		= False



@nightly
@require_torch_gpu
class 					A__(unittest.TestCase       ):
						"""simple docstring"""






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





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	Optional[Any]          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(_lowercase						)
									pipe.set_progress_bar_config(disable=_lowercase						)

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

									a_	:	Any          =   torch.manual_seed(0						)
									a_	:	Tuple          =   pipe(
									    prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type="""numpy"""     ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(_lowercase						)
												a_	:	Dict          =   VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowercase						)

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

									a_	:	Optional[Any]          =   generator.manual_seed(0						)
									a_	:	Optional[Any]          =   pipe(
									    prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			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						)    ->   Optional[Any]:
									a_	:	Union[str, Any]          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
									pipe.remove_unused_weights()
									pipe.to(_lowercase						)
									pipe.set_progress_bar_config(disable=_lowercase						)

									a_	:	List[str]          =   """cyberpunk 2077"""
									a_	:	List[Any]          =   load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""						)
									a_	:	str          =   torch.manual_seed(0						)
									a_	:	Optional[Any]          =   pipe(
									    prompt=_lowercase     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type="""numpy"""     ,			).images

									a_	:	Dict          =   image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a_	:	Union[str, Any]          =   np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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





from cva import destroyAllWindows, imread, imshow, waitKey


def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			a_       ,				a_	:	Optional[int]          =   img.shape[0], img.shape[1]

			def to_grayscale(a__     ,  a__     ,  a__):
						return 0.2126 * red + 0.587 * green + 0.114 * blue

			def normalize(a__):
						return min(a__     ,  2_5_5)

			for i in range(a__):
						for j in range(a__):
									a_	:	Optional[Any]          =   int(to_grayscale(*img[i][j]))
									a_	:	Union[str, Any]          =   [
									    normalize(a__),
									    normalize(greyscale + factor),
									    normalize(greyscale + 2 * factor),
									]

			return img


if __name__ == "__main__":
	# read original image
	__snake_case							:			Dict      		= {
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (10, 20, 30, 40)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(F"""Original image with sepia (factor: {percentage})""", img)

	waitKey(0)
	destroyAllWindows()


