


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    lowercase__  (						_a					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  PaintByExamplePipeline
							A_							:				int				     =  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							A_							:				str				     =  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							A_							:				Any				     =  frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






							def UpperCAmelCase_     (   self  ):
											torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				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			:	List[Any]							=				PNDMScheduler(skip_prk_steps=snake_case_  )
											torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				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[str]							=				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			:	Optional[int]							=				PaintByExampleImageEncoder(snake_case_   , proj_size=32  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				CLIPImageProcessor(crop_size=32   , size=32  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				{
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """image_encoder""": image_encoder,
											    """safety_checker""": None,
											    """feature_extractor""": feature_extractor,
											}
											return components






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.array(image.convert("""RGB"""  )  )
											_SCREAMING_SNAKE_CASE			:	Any							=				image[None].transpose(0   , 3   , 1   , 2  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.from_numpy(snake_case_  ).to(dtype=torch.floataa  ) / 127.5 - 1.0
											return image






							def UpperCAmelCase_     (   self   , __snake_case="cpu"   , __snake_case=0  ):
											# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
											_SCREAMING_SNAKE_CASE			:	Tuple							=				floats_tensor((1, 3, 32, 32)   , rng=random.Random(snake_case_  )  ).to(snake_case_  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				image.cpu().permute(0   , 2   , 3   , 1  )[0]
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				Image.fromarray(np.uinta(snake_case_  )  ).convert("""RGB"""  ).resize((64, 64)  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				Image.fromarray(np.uinta(image + 4  )  ).convert("""RGB"""  ).resize((64, 64)  )
											_SCREAMING_SNAKE_CASE			:	Any							=				Image.fromarray(np.uinta(snake_case_  )  ).convert("""RGB"""  ).resize((32, 32)  )

											if str(snake_case_  ).startswith("""mps"""  ):
															_SCREAMING_SNAKE_CASE			:	str							=				torch.manual_seed(snake_case_  )
											else:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )
											_SCREAMING_SNAKE_CASE			:	str							=				{
											    """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  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				self.get_dummy_components()

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

											_SCREAMING_SNAKE_CASE			:	int							=				self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE			:	List[str]							=				pipe(**snake_case_  )
											_SCREAMING_SNAKE_CASE			:	Any							=				output.images

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				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 UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""cpu"""
											_SCREAMING_SNAKE_CASE			:	int							=				self.get_dummy_inputs()
											inputs.pop("""mask_image"""  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.convert_to_pt(inputs.pop("""image"""  )  )
											_SCREAMING_SNAKE_CASE			:	int							=				image.clamp(0   , 1  ) / 2

											# make sure here that pndm scheduler skips prk
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				PaintByExamplePipeline(**self.get_dummy_components()  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				pipe.to(snake_case_  )
											pipe.set_progress_bar_config(disable=snake_case_  )

											_SCREAMING_SNAKE_CASE			:	str							=				pipe(image=snake_case_   , mask_image=mask_image[:, 0]   , **snake_case_  )
											_SCREAMING_SNAKE_CASE			:	str							=				output.images

											_SCREAMING_SNAKE_CASE			:	List[str]							=				image.cpu().permute(0   , 2   , 3   , 1  )[0]
											_SCREAMING_SNAKE_CASE			:	List[str]							=				mask_image.cpu().permute(0   , 2   , 3   , 1  )[0]

											_SCREAMING_SNAKE_CASE			:	Any							=				Image.fromarray(np.uinta(snake_case_  )  ).convert("""RGB"""  )
											_SCREAMING_SNAKE_CASE			:	Any							=				Image.fromarray(np.uinta(snake_case_  )  ).convert("""RGB"""  )

											_SCREAMING_SNAKE_CASE			:	str							=				pipe(**self.get_dummy_inputs()  )
											_SCREAMING_SNAKE_CASE			:	int							=				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  ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





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





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_     (   self  ):
											# make sure here that pndm scheduler skips prk
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/paint_by_example/dog_in_bucket.png"""  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/paint_by_example/mask.png"""  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/paint_by_example/panda.jpg"""  )

											_SCREAMING_SNAKE_CASE			:	str							=				PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				pipe.to(snake_case_  )
											pipe.set_progress_bar_config(disable=snake_case_  )

											_SCREAMING_SNAKE_CASE			:	str							=				torch.manual_seed(321  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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"""   , )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				output.images

											_SCREAMING_SNAKE_CASE			:	List[str]							=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											_SCREAMING_SNAKE_CASE			:	Any							=				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








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if len(str(SCREAMING_SNAKE_CASE__					)					) == 1 or len(str(SCREAMING_SNAKE_CASE__					)					) == 1:
								return a * b

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max(len(str(SCREAMING_SNAKE_CASE__					)					)							,				len(str(SCREAMING_SNAKE_CASE__					)					)					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				ma // 2

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba((aa + aa)							,				(ba + ba)					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def       snake_case_       (  ):







				"""simple docstring"""





				print(karatsuba(1_5463							,				2_3489					)					)


if __name__ == "__main__":
				main()




