





'''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       UpperCamelCase__			(			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					PaintByExamplePipeline
					SCREAMING_SNAKE_CASE__						=					IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					SCREAMING_SNAKE_CASE__						=					IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					SCREAMING_SNAKE_CASE__						=					frozenset([]	)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							torch.manual_seed(0		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	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     ,		)
							SCREAMING_SNAKE_CASE				:							int						=	PNDMScheduler(skip_prk_steps=__A		)
							torch.manual_seed(0		)
							SCREAMING_SNAKE_CASE				:							int						=	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		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	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     ,		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	PaintByExampleImageEncoder(__A     ,		proj_size=32		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	CLIPImageProcessor(crop_size=32     ,		size=32		)

							SCREAMING_SNAKE_CASE				:							Dict						=	{
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components


					def lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	np.array(image.convert("""RGB"""		)		)
							SCREAMING_SNAKE_CASE				:							int						=	image[None].transpose(0     ,		3     ,		1     ,		2		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.from_numpy(__A		).to(dtype=torch.floataa		) / 127.5 - 1.0
							return image


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Any="cpu"     ,		lowerCamelCase_							:	Optional[int]=0		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__A		)		).to(__A		)
							SCREAMING_SNAKE_CASE				:							str						=	image.cpu().permute(0     ,		2     ,		3     ,		1		)[0]
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	Image.fromarray(np.uinta(__A		)		).convert("""RGB"""		).resize((64, 64)		)
							SCREAMING_SNAKE_CASE				:							int						=	Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.fromarray(np.uinta(__A		)		).convert("""RGB"""		).resize((32, 32)		)

							if str(__A		).startswith("""mps"""		):
									SCREAMING_SNAKE_CASE				:							List[str]						=	torch.manual_seed(__A		)
							else:
									SCREAMING_SNAKE_CASE				:							Any						=	torch.Generator(device=__A		).manual_seed(__A		)
							SCREAMING_SNAKE_CASE				:							Any						=	{
							    "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 lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							SCREAMING_SNAKE_CASE				:							List[Any]						=	PaintByExamplePipeline(**__A		)
							SCREAMING_SNAKE_CASE				:							int						=	pipe.to("""cpu"""		)
							pipe.set_progress_bar_config(disable=__A		)

							SCREAMING_SNAKE_CASE				:							int						=	self.get_dummy_inputs()
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	pipe(**__A		)
							SCREAMING_SNAKE_CASE				:							int						=	output.images

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	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 lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	"cpu"
							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.get_dummy_inputs()
							inputs.pop("""mask_image"""		)
							SCREAMING_SNAKE_CASE				:							Dict						=	self.convert_to_pt(inputs.pop("""image"""		)		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	image.clamp(0     ,		1		) / 2

							# make sure here that pndm scheduler skips prk
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	PaintByExamplePipeline(**self.get_dummy_components()		)
							SCREAMING_SNAKE_CASE				:							str						=	pipe.to(__A		)
							pipe.set_progress_bar_config(disable=__A		)

							SCREAMING_SNAKE_CASE				:							int						=	pipe(image=__A     ,		mask_image=mask_image[:, 0]     ,		**__A		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	output.images

							SCREAMING_SNAKE_CASE				:							Dict						=	image.cpu().permute(0     ,		2     ,		3     ,		1		)[0]
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	mask_image.cpu().permute(0     ,		2     ,		3     ,		1		)[0]

							SCREAMING_SNAKE_CASE				:							List[str]						=	Image.fromarray(np.uinta(__A		)		).convert("""RGB"""		)
							SCREAMING_SNAKE_CASE				:							Dict						=	Image.fromarray(np.uinta(__A		)		).convert("""RGB"""		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	pipe(**self.get_dummy_inputs()		)
							SCREAMING_SNAKE_CASE				:							Any						=	output.images

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



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



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







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




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/paint_by_example/dog_in_bucket.png"""		)
							SCREAMING_SNAKE_CASE				:							Any						=	load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/paint_by_example/mask.png"""		)
							SCREAMING_SNAKE_CASE				:							str						=	load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/paint_by_example/panda.jpg"""		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	pipe.to(__A		)
							pipe.set_progress_bar_config(disable=__A		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	torch.manual_seed(3_21		)
							SCREAMING_SNAKE_CASE				:							str						=	pipe(
							    image=__A     ,		mask_image=__A     ,		example_image=__A     ,		generator=__A     ,		guidance_scale=5.0     ,		num_inference_steps=50     ,		output_type="""np"""     ,		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	output.images

							SCREAMING_SNAKE_CASE				:							Tuple						=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							SCREAMING_SNAKE_CASE				:							Tuple						=	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 __future__ import annotations



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		if len(lowerCamelCase_					) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	min(lowerCamelCase_	,	lowerCamelCase_					)
		return min_num


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)



