

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_     :Tuple									=	PaintByExamplePipeline
     UpperCamelCase_     :Dict									=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     UpperCamelCase_     :List[str]									=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCamelCase_     :Optional[int]									=	frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






     def      UpperCAmelCase_							(							self     )->			List[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     )->			Optional[Any]:
         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     )->			Tuple:
         # 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     )->			Dict:
         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     )->			int:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






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






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






     def      UpperCAmelCase_							(							self     )->			str:
         # 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



def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Dict      =			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(F'''Binary tree: {binary_tree}''')
 SCREAMING_SNAKE_CASE						:str      =			binary_tree_mirror(binary_tree, 5)
 print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')
