

'''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


lowerCAmelCase_						   =			False







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

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

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					snake_case							 =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(lowercase_   )
					pipe.set_progress_bar_config(disable=lowercase_   )

					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=lowercase_,	text_to_image_strength=0.75,	generator=lowercase_,	guidance_scale=7.5,	num_inference_steps=2,	output_type='numpy',	).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(lowercase_   )
						snake_case							 =				VersatileDiffusionDualGuidedPipeline.from_pretrained(lowercase_   )

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

					snake_case							 =				generator.manual_seed(0   )
					snake_case							 =				pipe(
					    prompt='first prompt',	image=lowercase_,	text_to_image_strength=0.75,	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 				_lowerCamelCase						(					self   )						->       List[str]:
					snake_case							 =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
					pipe.remove_unused_weights()
					pipe.to(lowercase_   )
					pipe.set_progress_bar_config(disable=lowercase_   )

					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=lowercase_,	image=lowercase_,	text_to_image_strength=0.75,	generator=lowercase_,	guidance_scale=7.5,	num_inference_steps=50,	output_type='numpy',	).images

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

					assert image.shape == (1, 512, 512, 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








'''simple docstring'''

from itertools import compress, repeat
from math import ceil, sqrt

def     __magic_name__       ( A							)			->		list[int]:

	if num <= 2:
		return []
	if num == 3:
		return [2]

	# Odd sieve for numbers in range [3, num - 1]
	snake_case							 =				bytearray(b'\x01'							) * ((num >> 1) - 1)

	for i in range(3					,   int(sqrt(A							)							) + 1					,   2							):
		if sieve[(i >> 1) - 1]:
			snake_case							 =				i**2
			snake_case							 =				repeat(
			    0					,   ceil((num - i_squared) / (i << 1)							)							)

	return [2] + list(compress(range(3					,   A					,   2							)					,   A							)							)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





