
"""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        _lowercase   (				unittest.TestCase		):




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




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Union[str, Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
			# remove text_unet
			pipe.remove_unused_weights()
			pipe.to(UpperCAmelCase_					)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			List[str]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

			lowerCamelCase     :			Tuple		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    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_					)
					lowerCamelCase     :			List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase_					)

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

			lowerCamelCase     :			List[Any]		=    generator.manual_seed(0					)
			lowerCamelCase     :			int		=    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					)		->							Tuple:
			lowerCamelCase     :			int		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
			pipe.remove_unused_weights()
			pipe.to(UpperCAmelCase_					)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    'cyberpunk 2077'
			lowerCamelCase     :			Optional[Any]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			int		=    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

			lowerCamelCase     :			List[str]		=    image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase     :			Tuple		=    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








"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		assert (
		    isinstance(a_,						a_			) and mask > 0
		), F"""mask needs to be positive integer, your input {mask}"""
		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			Tuple		=    mask

		while submask:
				all_submasks.append(a_			)
				lowerCamelCase     :			Optional[int]		=    (submask - 1) & mask

		return all_submasks


if __name__ == "__main__":
					import doctest

					doctest.testmod()






