






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       ):



						"""simple docstring"""


						a  :			Dict								=     PaintByExamplePipeline
						a  :			Any								=     IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						a  :			str								=     IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						a  :			int								=     frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
						def      UpperCAmelCase				(				self :      Optional[int] )							->				int:
								torch.manual_seed(0 )
								__UpperCAmelCase							:     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		,				)
								__UpperCAmelCase							:     Optional[Any]									=  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__ )
								torch.manual_seed(0 )
								__UpperCAmelCase							:     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 )
								__UpperCAmelCase							:     Optional[Any]									=  CLIPVisionConfig(
								    hidden_size=32		,				projection_dim=32		,				intermediate_size=37		,				layer_norm_eps=1e-0_5		,				num_attention_heads=4		,				num_hidden_layers=5		,				image_size=32		,				patch_size=4		,				)
								__UpperCAmelCase							:     Any									=  PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE__		,				proj_size=32 )
								__UpperCAmelCase							:     int									=  CLIPImageProcessor(crop_size=32		,				size=32 )

								__UpperCAmelCase							:     List[str]									=  {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "image_encoder": image_encoder,
								    "safety_checker": None,
								    "feature_extractor": feature_extractor,
								}
								return components
						def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      List[Any] )							->				Union[str, Any]:
								__UpperCAmelCase							:     Union[str, Any]									=  np.array(image.convert("""RGB""" ) )
								__UpperCAmelCase							:     Dict									=  image[None].transpose(0		,				3		,				1		,				2 )
								__UpperCAmelCase							:     str									=  torch.from_numpy(SCREAMING_SNAKE_CASE__ ).to(dtype=torch.floataa ) / 127.5 - 1.0
								return image
						def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Tuple="cpu"		,				__lowercase :      str=0 )							->				Union[str, Any]:
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								__UpperCAmelCase							:     str									=  floats_tensor((1, 3, 32, 32)		,				rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
								__UpperCAmelCase							:     List[str]									=  image.cpu().permute(0		,				2		,				3		,				1 )[0]
								__UpperCAmelCase							:     Dict									=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("""RGB""" ).resize((64, 64) )
								__UpperCAmelCase							:     Dict									=  Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
								__UpperCAmelCase							:     int									=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("""RGB""" ).resize((32, 32) )

								if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
										__UpperCAmelCase							:     Any									=  torch.manual_seed(SCREAMING_SNAKE_CASE__ )
								else:
										__UpperCAmelCase							:     Optional[Any]									=  torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
								__UpperCAmelCase							:     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 :      int )							->				Tuple:
								__UpperCAmelCase							:     Optional[int]									=  self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								__UpperCAmelCase							:     Optional[int]									=  PaintByExamplePipeline(**SCREAMING_SNAKE_CASE__ )
								__UpperCAmelCase							:     Optional[int]									=  pipe.to("""cpu""" )
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

								__UpperCAmelCase							:     Optional[Any]									=  self.get_dummy_inputs()
								__UpperCAmelCase							:     Dict									=  pipe(**SCREAMING_SNAKE_CASE__ )
								__UpperCAmelCase							:     Union[str, Any]									=  output.images

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

								assert image.shape == (1, 64, 64, 3)
								__UpperCAmelCase							:     Dict									=  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 :      Optional[Any] )							->				str:
								__UpperCAmelCase							:     Optional[Any]									=  "cpu"
								__UpperCAmelCase							:     Tuple									=  self.get_dummy_inputs()
								inputs.pop("""mask_image""" )
								__UpperCAmelCase							:     List[str]									=  self.convert_to_pt(inputs.pop("""image""" ) )
								__UpperCAmelCase							:     List[Any]									=  image.clamp(0		,				1 ) / 2

								# make sure here that pndm scheduler skips prk
								__UpperCAmelCase							:     Union[str, Any]									=  PaintByExamplePipeline(**self.get_dummy_components() )
								__UpperCAmelCase							:     Tuple									=  pipe.to(SCREAMING_SNAKE_CASE__ )
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

								__UpperCAmelCase							:     Union[str, Any]									=  pipe(image=SCREAMING_SNAKE_CASE__		,				mask_image=mask_image[:, 0]		,				**SCREAMING_SNAKE_CASE__ )
								__UpperCAmelCase							:     Optional[int]									=  output.images

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

								__UpperCAmelCase							:     Any									=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("""RGB""" )
								__UpperCAmelCase							:     Optional[int]									=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("""RGB""" )

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


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



						"""simple docstring"""


						def      UpperCAmelCase				(				self :      str )							->				Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




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

								__UpperCAmelCase							:     int									=  PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example""" )
								__UpperCAmelCase							:     Optional[int]									=  pipe.to(SCREAMING_SNAKE_CASE__ )
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

								__UpperCAmelCase							:     List[str]									=  torch.manual_seed(321 )
								__UpperCAmelCase							:     Optional[int]									=  pipe(
								    image=SCREAMING_SNAKE_CASE__		,				mask_image=SCREAMING_SNAKE_CASE__		,				example_image=SCREAMING_SNAKE_CASE__		,				generator=SCREAMING_SNAKE_CASE__		,				guidance_scale=5.0		,				num_inference_steps=50		,				output_type="""np"""		,				)

								__UpperCAmelCase							:     Optional[int]									=  output.images

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

								assert image.shape == (1, 512, 512, 3)
								__UpperCAmelCase							:     List[Any]									=  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










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


