

"""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			(		__a    ,     unittest.TestCase):
			__a :       int									=							PaintByExamplePipeline
			__a :       str									=							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
			__a :       Optional[int]									=							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__a :       Optional[int]									=							frozenset([])  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				torch.manual_seed(0     )
				_UpperCAmelCase    :							Union[str, Any]									=					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    :							Tuple									=					PNDMScheduler(skip_prk_steps=_A     )
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Union[str, 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-05      ,		num_attention_heads=4      ,		num_hidden_layers=5      ,		image_size=32      ,		patch_size=4      ,		)
				_UpperCAmelCase    :							Union[str, Any]									=					PaintByExampleImageEncoder(_A      ,		proj_size=32     )
				_UpperCAmelCase    :							Dict									=					CLIPImageProcessor(crop_size=32      ,		size=32     )

				_UpperCAmelCase    :							Tuple									=					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """image_encoder""": image_encoder,
				    """safety_checker""": None,
				    """feature_extractor""": feature_extractor,
				}
				return components
			def 							__snake_case (		self      ,		_A     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					np.array(image.convert("""RGB"""     )     )
				_UpperCAmelCase    :							Optional[Any]									=					image[None].transpose(0      ,		3      ,		1      ,		2     )
				_UpperCAmelCase    :							Tuple									=					torch.from_numpy(_A     ).to(dtype=torch.floataa     ) / 127.5 - 1.0
				return image
			def 							__snake_case (		self      ,		_A="cpu"      ,		_A=0     )    ->    Dict:






				'''simple docstring'''







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

				if str(_A     ).startswith("""mps"""     ):
					_UpperCAmelCase    :							List[str]									=					torch.manual_seed(_A     )
				else:
					_UpperCAmelCase    :							Tuple									=					torch.Generator(device=_A     ).manual_seed(_A     )
				_UpperCAmelCase    :							List[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 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					self.get_dummy_components()

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase    :							List[Any]									=					PaintByExamplePipeline(**_A     )
				_UpperCAmelCase    :							Union[str, Any]									=					pipe.to("""cpu"""     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							Any									=					self.get_dummy_inputs()
				_UpperCAmelCase    :							List[Any]									=					pipe(**_A     )
				_UpperCAmelCase    :							Tuple									=					output.images

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

				assert image.shape == (1, 64, 64, 3)
				_UpperCAmelCase    :							str									=					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 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







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

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase    :							Tuple									=					PaintByExamplePipeline(**self.get_dummy_components()     )
				_UpperCAmelCase    :							List[Any]									=					pipe.to(_A     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							List[str]									=					pipe(image=_A      ,		mask_image=mask_image[:, 0]      ,		**_A     )
				_UpperCAmelCase    :							Any									=					output.images

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

				_UpperCAmelCase    :							Tuple									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     )
				_UpperCAmelCase    :							str									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     )

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

				assert out_a.shape == (1, 64, 64, 3)
				assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5e-2
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







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






@slow
@require_torch_gpu
class     _UpperCAmelCase			(		unittest.TestCase):
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







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

				_UpperCAmelCase    :							Dict									=					PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""     )
				_UpperCAmelCase    :							Optional[Any]									=					pipe.to(_A     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							Any									=					torch.manual_seed(3_21     )
				_UpperCAmelCase    :							Dict									=					pipe(
				    image=_A      ,		mask_image=_A      ,		example_image=_A      ,		generator=_A      ,		guidance_scale=5.0      ,		num_inference_steps=50      ,		output_type="""np"""      ,		)

				_UpperCAmelCase    :							List[Any]									=					output.images

				_UpperCAmelCase    :							Dict									=					image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_12, 5_12, 3)
				_UpperCAmelCase    :							Union[str, 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"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor






class     _UpperCAmelCase			(		unittest.TestCase):
			def __init__(		self      ,		_A      ,		_A=7      ,		_A=3      ,		_A=18      ,		_A=30      ,		_A=4_00      ,		_A=True      ,		_A=None      ,		_A=True      ,		_A=[0.5, 0.5, 0.5]      ,		_A=[0.5, 0.5, 0.5]      ,		)    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					size if size is not None else {"""height""": 18, """width""": 18}
				_UpperCAmelCase    :							Tuple									=					parent
				_UpperCAmelCase    :							Tuple									=					batch_size
				_UpperCAmelCase    :							Optional[int]									=					num_channels
				_UpperCAmelCase    :							Any									=					image_size
				_UpperCAmelCase    :							str									=					min_resolution
				_UpperCAmelCase    :							Dict									=					max_resolution
				_UpperCAmelCase    :							Optional[Any]									=					do_resize
				_UpperCAmelCase    :							str									=					size
				_UpperCAmelCase    :							Optional[int]									=					do_normalize
				_UpperCAmelCase    :							Optional[Any]									=					image_mean
				_UpperCAmelCase    :							Any									=					image_std
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}






@require_torch
@require_vision
class     _UpperCAmelCase			(		__a    ,     unittest.TestCase):
			__a :       List[str]									=							ViTImageProcessor if is_vision_available() else None
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					ViTImageProcessingTester(self     )
			@property
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(_A      ,		"""image_mean"""     )     )
				self.assertTrue(hasattr(_A      ,		"""image_std"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_normalize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_resize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""size"""     )     )
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}     )

				_UpperCAmelCase    :							Any									=					self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42     )
				self.assertEqual(image_processor.size      ,		{"""height""": 42, """width""": 42}     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				pass
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PIL images
				_UpperCAmelCase    :							Dict									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		Image.Image     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[Any]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Union[str, Any]									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_UpperCAmelCase    :							List[Any]									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		np.ndarray     )

				# Test not batched input
				_UpperCAmelCase    :							Any									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							int									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_UpperCAmelCase    :							Tuple									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		torch.Tensor     )

				# Test not batched input
				_UpperCAmelCase    :							Optional[int]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Tuple									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)





