'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 	SCREAMING_SNAKE_CASE      (    __lowercase     ,      unittest.TestCase):







		"""simple docstring"""
		lowercase :   List[Any]							=				PaintByExamplePipeline
		lowercase :   str							=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		lowercase :   str							=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowercase :   Optional[Any]							=				frozenset([])  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






		def 		UpperCamelCase__							( self					)			->  Dict:
								torch.manual_seed(0					)
								_lowerCAmelCase					    =UNetaDConditionModel(
								    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=9		,    out_channels=4		,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,    cross_attention_dim=32		,    )
								_lowerCAmelCase					    =PNDMScheduler(skip_prk_steps=__A					)
								torch.manual_seed(0					)
								_lowerCAmelCase					    =AutoencoderKL(
								    block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,    latent_channels=4		,    )
								torch.manual_seed(0					)
								_lowerCAmelCase					    =CLIPVisionConfig(
								    hidden_size=32		,    projection_dim=32		,    intermediate_size=37		,    layer_norm_eps=1E-05		,    num_attention_heads=4		,    num_hidden_layers=5		,    image_size=32		,    patch_size=4		,    )
								_lowerCAmelCase					    =PaintByExampleImageEncoder(__A		,    proj_size=32					)
								_lowerCAmelCase					    =CLIPImageProcessor(crop_size=32		,    size=32					)

								_lowerCAmelCase					    ={
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'image_encoder': image_encoder,
								    'safety_checker': None,
								    'feature_extractor': feature_extractor,
								}
								return components






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								_lowerCAmelCase					    =np.array(image.convert('RGB'					)					)
								_lowerCAmelCase					    =image[None].transpose(0		,    3		,    1		,    2					)
								_lowerCAmelCase					    =torch.from_numpy(__A					).to(dtype=torch.floataa					) / 127.5 - 1.0
								return image






		def 		UpperCamelCase__							( self		,    __A="cpu"		,    __A=0					)			->  Dict:
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								_lowerCAmelCase					    =floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__A					)					).to(__A					)
								_lowerCAmelCase					    =image.cpu().permute(0		,    2		,    3		,    1					)[0]
								_lowerCAmelCase					    =Image.fromarray(np.uinta(__A					)					).convert('RGB'					).resize((64, 64)					)
								_lowerCAmelCase					    =Image.fromarray(np.uinta(image + 4					)					).convert('RGB'					).resize((64, 64)					)
								_lowerCAmelCase					    =Image.fromarray(np.uinta(__A					)					).convert('RGB'					).resize((32, 32)					)

								if str(__A					).startswith('mps'					):
														_lowerCAmelCase					    =torch.manual_seed(__A					)
								else:
														_lowerCAmelCase					    =torch.Generator(device=__A					).manual_seed(__A					)
								_lowerCAmelCase					    ={
								    'example_image': example_image,
								    'image': init_image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'guidance_scale': 6.0,
								    'output_type': 'numpy',
								}
								return inputs






		def 		UpperCamelCase__							( self					)			->  List[str]:
								_lowerCAmelCase					    =self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								_lowerCAmelCase					    =PaintByExamplePipeline(**__A					)
								_lowerCAmelCase					    =pipe.to('cpu'					)
								pipe.set_progress_bar_config(disable=__A					)

								_lowerCAmelCase					    =self.get_dummy_inputs()
								_lowerCAmelCase					    =pipe(**__A					)
								_lowerCAmelCase					    =output.images

								_lowerCAmelCase					    =image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_lowerCAmelCase					    =np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]					)

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






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								_lowerCAmelCase					    ='cpu'
								_lowerCAmelCase					    =self.get_dummy_inputs()
								inputs.pop('mask_image'					)
								_lowerCAmelCase					    =self.convert_to_pt(inputs.pop('image'					)					)
								_lowerCAmelCase					    =image.clamp(0		,    1					) / 2

								# make sure here that pndm scheduler skips prk
								_lowerCAmelCase					    =PaintByExamplePipeline(**self.get_dummy_components()					)
								_lowerCAmelCase					    =pipe.to(__A					)
								pipe.set_progress_bar_config(disable=__A					)

								_lowerCAmelCase					    =pipe(image=__A		,    mask_image=mask_image[:, 0]		,    **__A					)
								_lowerCAmelCase					    =output.images

								_lowerCAmelCase					    =image.cpu().permute(0		,    2		,    3		,    1					)[0]
								_lowerCAmelCase					    =mask_image.cpu().permute(0		,    2		,    3		,    1					)[0]

								_lowerCAmelCase					    =Image.fromarray(np.uinta(__A					)					).convert('RGB'					)
								_lowerCAmelCase					    =Image.fromarray(np.uinta(__A					)					).convert('RGB'					)

								_lowerCAmelCase					    =pipe(**self.get_dummy_inputs()					)
								_lowerCAmelCase					    =output.images

								assert out_a.shape == (1, 64, 64, 3)
								assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3					)

@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE      (    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					)			->  str:
								# make sure here that pndm scheduler skips prk
								_lowerCAmelCase					    =load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/paint_by_example/dog_in_bucket.png'					)
								_lowerCAmelCase					    =load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/paint_by_example/mask.png'					)
								_lowerCAmelCase					    =load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/paint_by_example/panda.jpg'					)

								_lowerCAmelCase					    =PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'					)
								_lowerCAmelCase					    =pipe.to(__A					)
								pipe.set_progress_bar_config(disable=__A					)

								_lowerCAmelCase					    =torch.manual_seed(321					)
								_lowerCAmelCase					    =pipe(
								    image=__A		,    mask_image=__A		,    example_image=__A		,    generator=__A		,    guidance_scale=5.0		,    num_inference_steps=50		,    output_type='np'		,    )

								_lowerCAmelCase					    =output.images

								_lowerCAmelCase					    =image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								_lowerCAmelCase					    =np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]					)

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






'''simple docstring'''

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



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






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

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()





