

"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   __UpperCamelCase      (   lowerCAmelCase__       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       RePaintPipeline
	SCREAMING_SNAKE_CASE__      			=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	SCREAMING_SNAKE_CASE__      			=       PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	SCREAMING_SNAKE_CASE__      			=       IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE__      			=       False




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		torch.manual_seed(0							)
		torch.manual_seed(0							)
		UpperCAmelCase_    			=     UNetaDModel(
		    block_out_channels=(32, 64)						,		layers_per_block=2						,		sample_size=32						,		in_channels=3						,		out_channels=3						,		down_block_types=("DownBlock2D", "AttnDownBlock2D")						,		up_block_types=("AttnUpBlock2D", "UpBlock2D")						,		)
		UpperCAmelCase_    			=     RePaintScheduler()
		UpperCAmelCase_    			=     {'''unet''': unet, '''scheduler''': scheduler}
		return components




	def  __A   (						self		:  int						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  str=0							):


		'''simple docstring'''







		if str(_A							).startswith("mps"							):
			UpperCAmelCase_    			=     torch.manual_seed(_A							)
		else:
			UpperCAmelCase_    			=     torch.Generator(device=_A							).manual_seed(_A							)
		UpperCAmelCase_    			=     np.random.RandomState(_A							).standard_normal((1, 3, 32, 32)							)
		UpperCAmelCase_    			=     torch.from_numpy(_A							).to(device=_A						,		dtype=torch.floataa							)
		UpperCAmelCase_    			=     (image > 0).to(device=_A						,		dtype=torch.floataa							)
		UpperCAmelCase_    			=     {
		    '''image''': image,
		    '''mask_image''': mask,
		    '''generator''': generator,
		    '''num_inference_steps''': 5,
		    '''eta''': 0.0,
		    '''jump_length''': 2,
		    '''jump_n_sample''': 2,
		    '''output_type''': '''numpy''',
		}
		return inputs




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
		UpperCAmelCase_    			=     self.get_dummy_components()
		UpperCAmelCase_    			=     RePaintPipeline(**_A							)
		UpperCAmelCase_    			=     sd_pipe.to(_A							)
		sd_pipe.set_progress_bar_config(disable=_A							)

		UpperCAmelCase_    			=     self.get_dummy_inputs(_A							)
		UpperCAmelCase_    			=     sd_pipe(**_A							).images
		UpperCAmelCase_    			=     image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		UpperCAmelCase_    			=     np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




	@skip_mps
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		return super().test_save_load_local()




	@unittest.skip("non-deterministic pipeline"							)
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		return super().test_inference_batch_single_identical()




	@skip_mps
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def  __A   (						self		:  List[str]							):


		'''simple docstring'''







		return super().test_save_load_optional_components()




	@skip_mps
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class   __UpperCamelCase      (   unittest.TestCase  ):




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







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




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
		    "repaint/celeba_hq_256.png"							)
		UpperCAmelCase_    			=     load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
		UpperCAmelCase_    			=     load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
		    "repaint/celeba_hq_256_result.npy"							)

		UpperCAmelCase_    			=     '''google/ddpm-ema-celebahq-256'''
		UpperCAmelCase_    			=     UNetaDModel.from_pretrained(_A							)
		UpperCAmelCase_    			=     RePaintScheduler.from_pretrained(_A							)

		UpperCAmelCase_    			=     RePaintPipeline(unet=_A						,		scheduler=_A							).to(_A							)
		repaint.set_progress_bar_config(disable=_A							)
		repaint.enable_attention_slicing()

		UpperCAmelCase_    			=     torch.manual_seed(0							)
		UpperCAmelCase_    			=     repaint(
		    _A						,		_A						,		num_inference_steps=250						,		eta=0.0						,		jump_length=10						,		jump_n_sample=10						,		generator=_A						,		output_type="np"						,		)
		UpperCAmelCase_    			=     output.images[0]

		assert image.shape == (256, 256, 3)
		assert np.abs(expected_image - image							).mean() < 1e-2


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def  __lowerCAmelCase (  A    ,       A=3      ):
	UpperCAmelCase_    			=     int(mask / 2      )
	# copy image size
	UpperCAmelCase_    			=     zeros_like(A      )
	for i in range(A    ,       gray_img.shape[0] - bd      ):
		for j in range(A    ,       gray_img.shape[1] - bd      ):
			# get mask according with mask
			UpperCAmelCase_    			=     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			UpperCAmelCase_    			=     sort(A      )[inta(divide((multiply(A    ,       A      ))    ,       2      ) + 1      )]
			UpperCAmelCase_    			=     median
	return median_img


if __name__ == "__main__":
				# read original image
				_a:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				_a:      Tuple								=     median_filter(gray, 3)
				_a:      Dict								=     median_filter(gray, 5)

				# show result images
				imshow("""median filter with 3x3 mask""", medianaxa)
				imshow("""median filter with 5x5 mask""", medianaxa)
				waitKey(0)