


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   A__							(						UpperCamelCase__      ,   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 							__UpperCamelCase (				self    :					Tuple     )							->   Any:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					Dict     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =np.array(image.convert('''RGB'''     )     )
										_SCREAMING_SNAKE_CASE	     =image[None].transpose(0 ,			3 ,			1 ,			2     )
										_SCREAMING_SNAKE_CASE	     =torch.from_numpy(_a     ).to(dtype=torch.floataa     ) / 1_27.5 - 1.0
										return image




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Tuple="cpu" ,			_a    :					Optional[int]=0     )							->   Optional[Any]:


										"""simple docstring"""





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

										if str(_a     ).startswith('''mps'''     ):
														_SCREAMING_SNAKE_CASE	     =torch.manual_seed(_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =torch.Generator(device=_a     ).manual_seed(_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''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]     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline(**_a     )
										_SCREAMING_SNAKE_CASE	     =pipe.to('''cpu'''     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =self.get_dummy_inputs()
										_SCREAMING_SNAKE_CASE	     =pipe(**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

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

										assert image.shape == (1, 64, 64, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]     )

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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Tuple:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE	     =pipe(image=_a ,			mask_image=mask_image[:, 0] ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

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

										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )

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


										"""simple docstring"""





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

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




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


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					Dict     )							->   Optional[int]:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =torch.manual_seed(321     )
										_SCREAMING_SNAKE_CASE	     =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	     =output.images

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

										assert image.shape == (1, 512, 512, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]     )

										assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCamelCase__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase				=  k_size // 2
  _UpperCamelCase	,		_UpperCamelCase				=  np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _UpperCamelCase				=  (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      _UpperCamelCase				=  gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row, col - 1]
        _UpperCamelCase				=  sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row + 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col]
        _UpperCamelCase				=  sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _UpperCamelCase				=  sobel_grad[row, col]

  return destination


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      if destination[row, col] >= threshold_high:
        _UpperCamelCase				=  strong
      elif destination[row, col] <= threshold_low:
        _UpperCamelCase				=  0
      else:
        _UpperCamelCase				=  weak


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0]      ):
    for col in range(1  ,			image_shape[1]      ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _UpperCamelCase				=  strong
        else:
          _UpperCamelCase				=  0


def 							lowerCAmelCase__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  return destination


if __name__ == "__main__":
   # read original image in gray mode
   lowerCamelCase__   = cva.imread(R'''../image_data/lena.jpg''', 0)
   # canny edge detection
   lowerCamelCase__   = canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)




