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


_lowerCamelCase    :      List[str]	 =						False


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




							def 		__snake_case(  self      :    Union[str, Any]					)    -> Tuple:



												'''simple docstring'''

												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 		__snake_case(  self      :    str					)    -> int:



												'''simple docstring'''

												SCREAMING_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__					)

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

												SCREAMING_SNAKE_CASE				=				torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				pipe(
												    prompt="first prompt"		,							image=lowerCamelCase__		,							text_to_image_strength=0.7_5		,							generator=lowerCamelCase__		,							guidance_scale=7.5		,							num_inference_steps=2		,							output_type="numpy"		,							).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																	pipe.save_pretrained(lowerCamelCase__					)
																	SCREAMING_SNAKE_CASE				=				VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase__					)

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

												SCREAMING_SNAKE_CASE				=				generator.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				pipe(
												    prompt="first prompt"		,							image=lowerCamelCase__		,							text_to_image_strength=0.7_5		,							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 		__snake_case(  self      :    int					)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"					)
												pipe.remove_unused_weights()
												pipe.to(lowerCamelCase__					)
												pipe.set_progress_bar_config(disable=lowerCamelCase__					)

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

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

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










'''simple docstring'''
def 				snake_case_  (   a__		:	int			):



    """simple docstring"""




    if not isinstance(a__    ,a__			) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""			)

    __lowercase							=						number & ~(number - 1)
    __lowercase							=						0
    while intermediate:
        intermediate >>= 1
        index += 1
    return index - 1


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)




