






'''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 		SCREAMING_SNAKE_CASE__   (     lowercase_		,		unittest.TestCase    ):
		_UpperCAmelCase         =PaintByExamplePipeline
		_UpperCAmelCase         =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_UpperCAmelCase         =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_UpperCAmelCase         =frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



		def _lowerCAmelCase     (    self:    Optional[Any])  ->Union[str, Any]:






								'''simple docstring'''


								torch.manual_seed(0)
								a_				     =   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				,   )
								a_				     =   PNDMScheduler(skip_prk_steps=a)
								torch.manual_seed(0)
								a_				     =   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)
								a_				     =   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				,   )
								a_				     =   PaintByExampleImageEncoder(a				,   proj_size=32)
								a_				     =   CLIPImageProcessor(crop_size=32				,   size=32)

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



		def _lowerCAmelCase     (    self:    Any				,   a:    Dict)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   np.array(image.convert("RGB"))
								a_				     =   image[None].transpose(0				,   3				,   1				,   2)
								a_				     =   torch.from_numpy(a).to(dtype=torch.floataa) / 127.5 - 1.0
								return image



		def _lowerCAmelCase     (    self:    str				,   a:    List[str]="cpu"				,   a:    int=0)  ->Any:






								'''simple docstring'''


								a_				     =   floats_tensor((1, 3, 32, 32)				,   rng=random.Random(a)).to(a)
								a_				     =   image.cpu().permute(0				,   2				,   3				,   1)[0]
								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB").resize((64, 64))
								a_				     =   Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB").resize((32, 32))

								if str(a).startswith("mps"):
														a_				     =   torch.manual_seed(a)
								else:
														a_				     =   torch.Generator(device=a).manual_seed(a)
								a_				     =   {
								    "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 _lowerCAmelCase     (    self:    Dict)  ->int:






								'''simple docstring'''


								a_				     =   self.get_dummy_components()

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

								a_				     =   self.get_dummy_inputs()
								a_				     =   pipe(**a)
								a_				     =   output.images

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

								assert image.shape == (1, 64, 64, 3)
								a_				     =   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 _lowerCAmelCase     (    self:    Dict)  ->Any:






								'''simple docstring'''


								a_				     =   "cpu"
								a_				     =   self.get_dummy_inputs()
								inputs.pop("mask_image")
								a_				     =   self.convert_to_pt(inputs.pop("image"))
								a_				     =   image.clamp(0				,   1) / 2

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

								a_				     =   pipe(image=a				,   mask_image=mask_image[:, 0]				,   **a)
								a_				     =   output.images

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

								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB")
								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB")

								a_				     =   pipe(**self.get_dummy_inputs())
								a_				     =   output.images

								assert out_a.shape == (1, 64, 64, 3)
								assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2




		def _lowerCAmelCase     (    self:    Optional[int])  ->Any:






								'''simple docstring'''


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





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



		def _lowerCAmelCase     (    self:    str)  ->List[str]:






								'''simple docstring'''


								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




		def _lowerCAmelCase     (    self:    List[str])  ->Any:






								'''simple docstring'''


								a_				     =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/dog_in_bucket.png")
								a_				     =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/mask.png")
								a_				     =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/panda.jpg")

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

								a_				     =   torch.manual_seed(3_21)
								a_				     =   pipe(
								    image=a				,   mask_image=a				,   example_image=a				,   generator=a				,   guidance_scale=5.0				,   num_inference_steps=50				,   output_type="np"				,   )

								a_				     =   output.images

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

								assert image.shape == (1, 5_12, 5_12, 3)
								a_				     =   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'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



