
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     __A(				a						,     unittest.TestCase    ):
				snake_case_		     =					PaintByExamplePipeline
				snake_case_		     =					IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
				snake_case_		     =					IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				snake_case_		     =					frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								torch.manual_seed(0       )
								__a    							=					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	,  )
								__a    							=					PNDMScheduler(skip_prk_steps=_snake_case       )
								torch.manual_seed(0       )
								__a    							=					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       )
								__a    							=					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	,  )
								__a    							=					PaintByExampleImageEncoder(_snake_case	,  proj_size=32       )
								__a    							=					CLIPImageProcessor(crop_size=32	,  size=32       )

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								__a    							=					np.array(image.convert('''RGB'''       )       )
								__a    							=					image[None].transpose(0	,  3	,  1	,  2       )
								__a    							=					torch.from_numpy(_snake_case       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
								return image




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case="cpu"	,  _snake_case=0       )					->   int:


								'''simple docstring'''




								__a    							=					floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_snake_case       )       ).to(_snake_case       )
								__a    							=					image.cpu().permute(0	,  2	,  3	,  1       )[0]
								__a    							=					Image.fromarray(np.uinta(_snake_case       )       ).convert('''RGB'''       ).resize((64, 64)       )
								__a    							=					Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((64, 64)       )
								__a    							=					Image.fromarray(np.uinta(_snake_case       )       ).convert('''RGB'''       ).resize((32, 32)       )

								if str(_snake_case       ).startswith('''mps'''       ):
												__a    							=					torch.manual_seed(_snake_case       )
								else:
												__a    							=					torch.Generator(device=_snake_case       ).manual_seed(_snake_case       )
								__a    							=					{
								    '''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        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								__a    							=					self.get_dummy_components()

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

								__a    							=					self.get_dummy_inputs()
								__a    							=					pipe(**_snake_case       )
								__a    							=					output.images

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

								assert image.shape == (1, 64, 64, 3)
								__a    							=					np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]       )

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								__a    							=					'''cpu'''
								__a    							=					self.get_dummy_inputs()
								inputs.pop('''mask_image'''       )
								__a    							=					self.convert_to_pt(inputs.pop('''image'''       )       )
								__a    							=					image.clamp(0	,  1       ) / 2

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

								__a    							=					pipe(image=_snake_case	,  mask_image=mask_image[:, 0]	,  **_snake_case       )
								__a    							=					output.images

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

								__a    							=					Image.fromarray(np.uinta(_snake_case       )       ).convert('''RGB'''       )
								__a    							=					Image.fromarray(np.uinta(_snake_case       )       ).convert('''RGB'''       )

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

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								super().test_inference_batch_single_identical(expected_max_diff=3E-3       )







@slow
@require_torch_gpu
class     __A(				unittest.TestCase    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/dog_in_bucket.png'''       )
								__a    							=					load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/mask.png'''       )
								__a    							=					load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/panda.jpg'''       )

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

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

								__a    							=					output.images

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

								assert image.shape == (1, 512, 512, 3)
								__a    							=					np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]       )

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

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


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

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

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

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()