

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							(							UpperCAmelCase__							,		unittest.TestCase				):
   '''simple docstring'''







   lowerCamelCase__          =PaintByExamplePipeline
   lowerCamelCase__          =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase__          =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase__          =frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Union[str, Any]:

    """simple docstring"""

    torch.manual_seed(0		)
    SCREAMING_SNAKE_CASE				: 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 , )
    SCREAMING_SNAKE_CASE				: int			       =    PNDMScheduler(skip_prk_steps=a		)
    torch.manual_seed(0		)
    SCREAMING_SNAKE_CASE				: str			       =    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		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    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 , )
    SCREAMING_SNAKE_CASE				: List[Any]			       =    PaintByExampleImageEncoder(a , proj_size=32		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    CLIPImageProcessor(crop_size=32 , size=32		)

    SCREAMING_SNAKE_CASE				: Any			       =    {
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """image_encoder""": image_encoder,
        """safety_checker""": None,
        """feature_extractor""": feature_extractor,
    }
    return components
   def 							__UpperCamelCase	(     self							:	str , a							:	Dict		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    np.array(image.convert("RGB"		)		)
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    image[None].transpose(0 , 3 , 1 , 2		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.from_numpy(a		).to(dtype=torch.floataa		) / 127.5 - 1.0
    return image
   def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	int="cpu" , a							:	Dict=0		)							->    Any:

    """simple docstring"""

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

    if str(a		).startswith("mps"		):
     SCREAMING_SNAKE_CASE				: Dict			       =    torch.manual_seed(a		)
    else:
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.Generator(device=a		).manual_seed(a		)
    SCREAMING_SNAKE_CASE				: 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							:	List[Any]		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    self.get_dummy_components()

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

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.get_dummy_inputs()
    SCREAMING_SNAKE_CASE				: int			       =    pipe(**a		)
    SCREAMING_SNAKE_CASE				: int			       =    output.images

    SCREAMING_SNAKE_CASE				: List[str]			       =    image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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 							__UpperCamelCase	(     self							:	Any		)							->    Union[str, Any]:

    """simple docstring"""

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

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

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

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

    SCREAMING_SNAKE_CASE				: str			       =    Image.fromarray(np.uinta(a		)		).convert("RGB"		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    Image.fromarray(np.uinta(a		)		).convert("RGB"		)

    SCREAMING_SNAKE_CASE				: int			       =    pipe(**self.get_dummy_inputs()		)
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    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							:	Optional[Any]		)							->    Dict:

    """simple docstring"""

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






@slow
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase				):
   '''simple docstring'''







   def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

    """simple docstring"""

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






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

    """simple docstring"""

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

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

    SCREAMING_SNAKE_CASE				: str			       =    torch.manual_seed(321		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    pipe(
        image=a , mask_image=a , example_image=a , generator=a , guidance_scale=5.0 , num_inference_steps=50 , output_type="np" , )

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    output.images

    SCREAMING_SNAKE_CASE				: Any			       =    image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    SCREAMING_SNAKE_CASE				: 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


from __future__ import annotations

from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Dict , a							:	Any , a							:	Any		)							->    None:  # noqa: A002

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    item
				SCREAMING_SNAKE_CASE				: List[Any]			       =    next






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int]		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Node | None			       =    None
				SCREAMING_SNAKE_CASE				: List[Any]			       =    0
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    Node(a , self.head		)
				self.size += 1
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

				"""simple docstring"""

				if self.head is None:
					return None
				else:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.head.item
					SCREAMING_SNAKE_CASE				: Dict			       =    self.head.next
					self.size -= 1
					return item
			def 							__UpperCamelCase	(     self							:	Any		)							->    bool:

				"""simple docstring"""

				return self.head is None
			def __str__(     self							:	Union[str, Any]		)							->    str:

				"""simple docstring"""

				if self.is_empty():
					return ""
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.head
					SCREAMING_SNAKE_CASE				: str			       =    ""
					SCREAMING_SNAKE_CASE				: list[str]			       =    []
					while iterate:
						item_list.append(str(iterate.item		)		)
						SCREAMING_SNAKE_CASE				: str			       =    iterate.next

					SCREAMING_SNAKE_CASE				: List[str]			       =    " --> ".join(a		)

					return item_str






			def __len__(     self							:	List[Any]		)							->    int:

				"""simple docstring"""

				return self.size