





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 			_lowerCamelCase	(      _a	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : Union[str, Any]				      =PaintByExamplePipeline
      UpperCAmelCase_  : int				      =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_  : str				      =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_  : Any				      =frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        torch.manual_seed(0				)
        __snake_case				:			Dict            =    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			,  )
        __snake_case				:			List[Any]            =    PNDMScheduler(skip_prk_steps=snake_case_				)
        torch.manual_seed(0				)
        __snake_case				:			List[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				)
        __snake_case				:			List[str]            =    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			,  )
        __snake_case				:			Optional[int]            =    PaintByExampleImageEncoder(snake_case_			,  proj_size=32				)
        __snake_case				:			Optional[int]            =    CLIPImageProcessor(crop_size=32			,  size=32				)

        __snake_case				:			Union[str, Any]            =    {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """image_encoder""": image_encoder,
            """safety_checker""": None,
            """feature_extractor""": feature_extractor,
        }
        return components







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    np.array(image.convert("RGB"				)				)
        __snake_case				:			Any            =    image[None].transpose(0			,  3			,  1			,  2				)
        __snake_case				:			List[str]            =    torch.from_numpy(snake_case_				).to(dtype=torch.floataa				) / 127.5 - 1.0
        return image







      def   UpperCAmelCase						(	self			,  UpperCAmelCase="cpu"			,  UpperCAmelCase=0				)    ->				List[str]:






        '''simple docstring'''






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

        if str(snake_case_				).startswith("mps"				):
          __snake_case				:			str            =    torch.manual_seed(snake_case_				)
        else:
          __snake_case				:			Tuple            =    torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
        __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				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			Dict            =    self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        __snake_case				:			Optional[Any]            =    PaintByExamplePipeline(**snake_case_				)
        __snake_case				:			List[Any]            =    pipe.to("cpu"				)
        pipe.set_progress_bar_config(disable=snake_case_				)

        __snake_case				:			int            =    self.get_dummy_inputs()
        __snake_case				:			List[str]            =    pipe(**snake_case_				)
        __snake_case				:			Any            =    output.images

        __snake_case				:			Optional[int]            =    image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        __snake_case				:			Optional[Any]            =    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				)    ->				Any:






        '''simple docstring'''






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

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

        __snake_case				:			str            =    pipe(image=snake_case_			,  mask_image=mask_image[:, 0]			,  **snake_case_				)
        __snake_case				:			str            =    output.images

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

        __snake_case				:			Any            =    Image.fromarray(np.uinta(snake_case_				)				).convert("RGB"				)
        __snake_case				:			Any            =    Image.fromarray(np.uinta(snake_case_				)				).convert("RGB"				)

        __snake_case				:			str            =    pipe(**self.get_dummy_inputs()				)
        __snake_case				:			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				)    ->				List[Any]:






        '''simple docstring'''






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




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



      """simple docstring"""






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






        '''simple docstring'''






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







      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






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

        __snake_case				:			str            =    PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"				)
        __snake_case				:			int            =    pipe.to(snake_case_				)
        pipe.set_progress_bar_config(disable=snake_case_				)

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

        __snake_case				:			List[str]            =    output.images

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

        assert image.shape == (1, 512, 512, 3)
        __snake_case				:			Any            =    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 numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			int            =    0
        # max greyscale value for #FFFFFF
        __snake_case				:			int            =    int(self.get_greyscale(255			,  255			,  255				)				)

        if not self.min_threshold < threshold < self.max_threshold:
          __snake_case				:			Tuple            =    F"""Factor value should be from 0 to {self.max_threshold}"""
          raise ValueError(UpperCAmelCase				)

        __snake_case				:			List[Any]            =    input_img
        __snake_case				:			Any            =    threshold
        __snake_case      ,				__snake_case				:			str            =    self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        __snake_case				:			Dict            =    [
            [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
        ]
        __snake_case				:			str            =    np.ones((self.width, self.height, 3)			,  np.uinta				) * 255







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def   UpperCAmelCase						(	self				)    ->				None:






        '''simple docstring'''






        for y in range(self.height				):
          for x in range(self.width				):
            __snake_case				:			Optional[int]            =    int(self.get_greyscale(*self.input_img[y][x]				)				)
            if self.threshold > greyscale + self.error_table[y][x]:
              __snake_case				:			Any            =    (0, 0, 0)
              __snake_case				:			Optional[int]            =    greyscale + self.error_table[x][y]
            else:
              __snake_case				:			str            =    (255, 255, 255)
              __snake_case				:			Dict            =    greyscale + self.error_table[x][y] - 255
            self.error_table[y][x + 1] += int(8 / 32 * current_error				)
            self.error_table[y][x + 2] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x] += int(8 / 32 * current_error				)
            self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
            self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     _UpperCamelCase    =	[
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 126, 130, 140)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              F'''Original image with dithering threshold: {burkes.threshold}''',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()

