



import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()






class 	_a			(      unittest.TestCase  ):






			@property
			def 							_snake_case   (    self )	->					Tuple:
									torch.manual_seed(0 )
									lowerCAmelCase	:   Dict					     =					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""")    ,  )
									return model






			def 							_snake_case   (    self )	->					Any:
									lowerCAmelCase	:   Union[str, Any]					     =					"""cpu"""
									lowerCAmelCase	:   List[str]					     =					self.dummy_uncond_unet
									lowerCAmelCase	:   Any					     =					DDPMScheduler()

									lowerCAmelCase	:   Optional[Any]					     =					DDPMPipeline(unet=lowercase_    ,  scheduler=lowercase_ )
									ddpm.to(lowercase_ )
									ddpm.set_progress_bar_config(disable=lowercase_ )

									lowerCAmelCase	:   Dict					     =					torch.Generator(device=lowercase_ ).manual_seed(0 )
									lowerCAmelCase	:   Tuple					     =					ddpm(generator=lowercase_    ,  num_inference_steps=2    ,  output_type="""numpy""" ).images

									lowerCAmelCase	:   List[str]					     =					torch.Generator(device=lowercase_ ).manual_seed(0 )
									lowerCAmelCase	:   Tuple					     =					ddpm(generator=lowercase_    ,  num_inference_steps=2    ,  output_type="""numpy"""    ,  return_dict=lowercase_ )[0]

									lowerCAmelCase	:   List[str]					     =					image[0, -3:, -3:, -1]
									lowerCAmelCase	:   Optional[int]					     =					image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									lowerCAmelCase	:   List[str]					     =					np.array(
									    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_0_0, 1.199e-03, 2.648e-04, 5.101e-04] )

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






			def 							_snake_case   (    self )	->					List[str]:
									lowerCAmelCase	:   Tuple					     =					self.dummy_uncond_unet
									lowerCAmelCase	:   Optional[int]					     =					DDPMScheduler(prediction_type="""sample""" )

									lowerCAmelCase	:   Dict					     =					DDPMPipeline(unet=lowercase_    ,  scheduler=lowercase_ )
									ddpm.to(lowercase_ )
									ddpm.set_progress_bar_config(disable=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					torch.manual_seed(0 )
									lowerCAmelCase	:   Union[str, Any]					     =					ddpm(generator=lowercase_    ,  num_inference_steps=2    ,  output_type="""numpy""" ).images

									lowerCAmelCase	:   Any					     =					torch.manual_seed(0 )
									lowerCAmelCase	:   Optional[Any]					     =					ddpm(generator=lowercase_    ,  num_inference_steps=2    ,  output_type="""numpy""" )[0]

									lowerCAmelCase	:   Any					     =					image[0, -3:, -3:, -1]
									lowerCAmelCase	:   str					     =					image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									lowerCAmelCase	:   int					     =					1e-2 if torch_device != """mps""" else 3e-2
									assert np.abs(image_slice.flatten() - image_eps_slice.flatten() ).max() < tolerance






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






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   str					     =					"""google/ddpm-cifar10-32"""

									lowerCAmelCase	:   Union[str, Any]					     =					UNetaDModel.from_pretrained(lowercase_ )
									lowerCAmelCase	:   Dict					     =					DDPMScheduler.from_pretrained(lowercase_ )

									lowerCAmelCase	:   Tuple					     =					DDPMPipeline(unet=lowercase_    ,  scheduler=lowercase_ )
									ddpm.to(lowercase_ )
									ddpm.set_progress_bar_config(disable=lowercase_ )

									lowerCAmelCase	:   Tuple					     =					torch.manual_seed(0 )
									lowerCAmelCase	:   Tuple					     =					ddpm(generator=lowercase_    ,  output_type="""numpy""" ).images

									lowerCAmelCase	:   Dict					     =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									lowerCAmelCase	:   Optional[int]					     =					np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5] )
									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






import cva
import numpy as np







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						try:
												return int(image[x_coordinate][y_coordinate] >= center  )
						except (IndexError, TypeError):
												return 0







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Any					     =					image[x_coordinate][y_coordinate]
						lowerCAmelCase	:   int					     =					[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

						# skip get_neighbors_pixel if center is null
						if center is None:
												return 0

						# Starting from the top right, assigning value to pixels clockwise
						lowerCAmelCase	:   Union[str, Any]					     =					[
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						]

						# Converting the binary value to decimal.
						return sum(
						    binary_value * power for binary_value, power in zip(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		lowerCAmelCase					:				Dict   	=cva.imread(
		    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
		)

		# Create a numpy array as the same height and width of read image
		lowerCAmelCase					:				Dict   	=np.zeros((image.shape[0], image.shape[1]))

		# Iterating through the image and calculating the
		# local binary pattern value for each pixel.
		for i in range(0, image.shape[0]):
				for j in range(0, image.shape[1]):
						lowerCAmelCase					:				Optional[int]   	=local_binary_value(image, i, j)

		cva.imshow('local binary pattern', lbp_image)
		cva.waitKey(0)
		cva.destroyAllWindows()

