

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 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :List[Any]								=			PaintByExamplePipeline
     UpperCamelCase_  :Union[str, Any]								=			IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     UpperCamelCase_  :str								=			IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCamelCase_  :List[Any]								=			frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         torch.manual_seed(0  )
         UpperCamelCase_      =	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_      =	PNDMScheduler(skip_prk_steps=_lowercase  )
         torch.manual_seed(0  )
         UpperCamelCase_      =	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_      =	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_      =	PaintByExampleImageEncoder(_lowercase		,	proj_size=32  )
         UpperCamelCase_      =	CLIPImageProcessor(crop_size=32		,	size=32  )

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






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    str:
         UpperCamelCase_      =	np.array(image.convert("RGB"  )  )
         UpperCamelCase_      =	image[None].transpose(0		,	3		,	1		,	2  )
         UpperCamelCase_      =	torch.from_numpy(_lowercase  ).to(dtype=torch.floataa  ) / 127.5 - 1.0
         return image






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

         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "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  )->    Optional[int]:
         UpperCamelCase_      =	self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         UpperCamelCase_      =	PaintByExamplePipeline(**_lowercase  )
         UpperCamelCase_      =	pipe.to("cpu"  )
         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	self.get_dummy_inputs()
         UpperCamelCase_      =	pipe(**_lowercase  )
         UpperCamelCase_      =	output.images

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

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

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

         UpperCamelCase_      =	pipe(image=_lowercase		,	mask_image=mask_image[:, 0]		,	**_lowercase  )
         UpperCamelCase_      =	output.images

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

         UpperCamelCase_      =	Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  )
         UpperCamelCase_      =	Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs()  )
         UpperCamelCase_      =	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  )->    List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3  )






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






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






     def   UpperCAmelCase_							(							self  )->    Tuple:
         # make sure here that pndm scheduler skips prk
         UpperCamelCase_      =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/paint_by_example/dog_in_bucket.png"  )
         UpperCamelCase_      =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/paint_by_example/mask.png"  )
         UpperCamelCase_      =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/paint_by_example/panda.jpg"  )

         UpperCamelCase_      =	PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"  )
         UpperCamelCase_      =	pipe.to(_lowercase  )
         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	torch.manual_seed(321  )
         UpperCamelCase_      =	pipe(
             image=_lowercase		,	mask_image=_lowercase		,	example_image=_lowercase		,	generator=_lowercase		,	guidance_scale=5.0		,	num_inference_steps=50		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images

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

         assert image.shape == (1, 512, 512, 3)
         UpperCamelCase_      =	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



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
