




'''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_		(       lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      PaintByExamplePipeline
 __snake_case	:     Dict			   =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case	:     int			   =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case	:     List[str]			   =      frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Tuple						=      {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      np.array(image.convert('''RGB''' ) )
        lowercase  :		int						=      image[None].transpose(0   ,		3   ,		1   ,		2 )
        lowercase  :		Optional[int]						=      torch.from_numpy(A ).to(dtype=torch.floataa ) / 1_2_7.5 - 1.0
        return image



 def    SCREAMING_SNAKE_CASE(  self   ,		A="cpu"   ,		A=0 ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(A ) ).to(A )
        lowercase  :		int						=      image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]
        lowercase  :		List[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((64, 64) )
        lowercase  :		List[str]						=      Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
        lowercase  :		Optional[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((32, 32) )

        if str(A ).startswith('''mps''' ):
               lowercase  :		Optional[int]						=      torch.manual_seed(A )
        else:
               lowercase  :		Optional[Any]						=      torch.Generator(device=A ).manual_seed(A )
        lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        lowercase  :		Optional[int]						=      PaintByExamplePipeline(**A )
        lowercase  :		Optional[int]						=      pipe.to('''cpu''' )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      self.get_dummy_inputs()
        lowercase  :		Dict						=      pipe(**A )
        lowercase  :		Any						=      output.images

        lowercase  :		int						=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase  :		int						=      np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        # make sure here that pndm scheduler skips prk
        lowercase  :		str						=      PaintByExamplePipeline(**self.get_dummy_components() )
        lowercase  :		int						=      pipe.to(A )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      pipe(image=A   ,		mask_image=mask_image[:, 0]   ,		**A )
        lowercase  :		str						=      output.images

        lowercase  :		Dict						=      image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]
        lowercase  :		Optional[int]						=      mask_image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]

        lowercase  :		List[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' )
        lowercase  :		Optional[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' )

        lowercase  :		List[Any]						=      pipe(**self.get_dummy_inputs() )
        lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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






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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		str						=      PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''' )
        lowercase  :		Union[str, Any]						=      pipe.to(A )
        pipe.set_progress_bar_config(disable=A )

        lowercase  :		List[str]						=      torch.manual_seed(321 )
        lowercase  :		Union[str, Any]						=      pipe(
            image=A   ,		mask_image=A   ,		example_image=A   ,		generator=A   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        lowercase  :		str						=      output.images

        lowercase  :		Optional[Any]						=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase  :		Union[str, Any]						=      np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2










'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




