
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()



class 			UpperCamelCase_			(						unittest.TestCase   ):




       '''simple docstring'''

       @property
       def        lowerCAmelCase__ (				self) ->	Optional[Any]:
          UpperCamelCase__    :		str     =				1
          UpperCamelCase__    :		Union[str, Any]     =				3
          UpperCamelCase__    :		Dict     =				(32, 32)

          UpperCamelCase__    :		int     =				floats_tensor((batch_size, num_channels) + sizes     ,						rng=random.Random(0)).to(UpperCamelCase)
          return image
       @property
       def        lowerCAmelCase__ (				self) ->	List[Any]:
          torch.manual_seed(0)
          UpperCamelCase__    :		Dict     =				UNetaDModel(
              block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=6     ,						out_channels=3     ,						down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,						up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,						)
          return model
       @property
       def        lowerCAmelCase__ (				self) ->	Optional[Any]:
          torch.manual_seed(0)
          UpperCamelCase__    :		Tuple     =				VQModel(
              block_out_channels=[32, 64]     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,						latent_channels=3     ,						)
          return model
       def        lowerCAmelCase__ (				self) ->	Optional[Any]:
          UpperCamelCase__    :		Any     =				'cpu'
          UpperCamelCase__    :		Tuple     =				self.dummy_uncond_unet
          UpperCamelCase__    :		Tuple     =				DDIMScheduler()
          UpperCamelCase__    :		Union[str, Any]     =				self.dummy_vq_model

          UpperCamelCase__    :		Optional[int]     =				LDMSuperResolutionPipeline(unet=UpperCamelCase     ,						vqvae=UpperCamelCase     ,						scheduler=UpperCamelCase)
          ldm.to(UpperCamelCase)
          ldm.set_progress_bar_config(disable=UpperCamelCase)

          UpperCamelCase__    :		List[str]     =				self.dummy_image.to(UpperCamelCase)

          UpperCamelCase__    :		List[Any]     =				torch.Generator(device=UpperCamelCase).manual_seed(0)
          UpperCamelCase__    :		List[str]     =				ldm(image=UpperCamelCase     ,						generator=UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy').images

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

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase__    :		Dict     =				np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150])

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






       @unittest.skipIf(torch_device != 'cuda'     ,						'This test requires a GPU')
       def        lowerCAmelCase__ (				self) ->	List[Any]:
          UpperCamelCase__    :		List[str]     =				self.dummy_uncond_unet
          UpperCamelCase__    :		str     =				DDIMScheduler()
          UpperCamelCase__    :		List[str]     =				self.dummy_vq_model

          # put models in fp16
          UpperCamelCase__    :		Optional[int]     =				unet.half()
          UpperCamelCase__    :		str     =				vqvae.half()

          UpperCamelCase__    :		Optional[int]     =				LDMSuperResolutionPipeline(unet=UpperCamelCase     ,						vqvae=UpperCamelCase     ,						scheduler=UpperCamelCase)
          ldm.to(UpperCamelCase)
          ldm.set_progress_bar_config(disable=UpperCamelCase)

          UpperCamelCase__    :		Optional[int]     =				self.dummy_image.to(UpperCamelCase)

          UpperCamelCase__    :		int     =				ldm(UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy').images

          assert image.shape == (1, 64, 64, 3)



@slow
@require_torch
class 			UpperCamelCase_			(						unittest.TestCase   ):




       '''simple docstring'''

       def        lowerCAmelCase__ (				self) ->	List[Any]:
          UpperCamelCase__    :		List[Any]     =				load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/vq_diffusion/teddy_bear_pool.png')
          UpperCamelCase__    :		Union[str, Any]     =				init_image.resize((64, 64)     ,						resample=PIL_INTERPOLATION['lanczos'])

          UpperCamelCase__    :		int     =				LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'     ,						device_map='auto')
          ldm.set_progress_bar_config(disable=UpperCamelCase)

          UpperCamelCase__    :		str     =				torch.manual_seed(0)
          UpperCamelCase__    :		List[Any]     =				ldm(image=UpperCamelCase     ,						generator=UpperCamelCase     ,						num_inference_steps=20     ,						output_type='numpy').images

          UpperCamelCase__    :		List[Any]     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 2_56, 2_56, 3)
          UpperCamelCase__    :		Union[str, Any]     =				np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907])

          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

UpperCAmelCase__				:  str       					=				180



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Dict     =				k_size // 2
			UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCamelCase__    :		int     =				(
			    1
			    / (2 * np.pi * sigma)
			    * np.exp(-(np.square(__SCREAMING_SNAKE_CASE ) + np.square(__SCREAMING_SNAKE_CASE )) / (2 * np.square(__SCREAMING_SNAKE_CASE )) )
			)
			return g



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Dict     =				np.zeros(__SCREAMING_SNAKE_CASE )

			for row in range(1				,    image_shape[0] - 1 ):
						for col in range(1				,    image_shape[1] - 1 ):
									UpperCamelCase__    :		Dict     =				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__    :		List[str]     =				sobel_grad[row, col - 1]
												UpperCamelCase__    :		Dict     =				sobel_grad[row, col + 1]
												if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
															UpperCamelCase__    :		Tuple     =				sobel_grad[row, col]

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

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

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

			return destination



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			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__    :		str     =				strong
									elif destination[row, col] <= threshold_low:
												UpperCamelCase__    :		List[str]     =				0
									else:
												UpperCamelCase__    :		int     =				weak



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			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__    :		List[str]     =				strong
												else:
															UpperCamelCase__    :		Union[str, Any]     =				0



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=15				,    __SCREAMING_SNAKE_CASE=30				,    __SCREAMING_SNAKE_CASE=128				,    __SCREAMING_SNAKE_CASE=255 )	->     Any:
			# gaussian_filter
			UpperCamelCase__    :		Optional[int]     =				img_convolve(__SCREAMING_SNAKE_CASE				,    gen_gaussian_kernel(9				,    sigma=1.4 ) )
			# get the gradient and degree by sobel_filter
			UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				sobel_filter(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Tuple     =				PI + np.radadeg(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Any     =				suppress_non_maximum(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			detect_high_low_threshold(
			    image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			track_edge(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			return destination


if __name__ == "__main__":
							# read original image in gray mode
							UpperCAmelCase__				:  List[str]       					=				cva.imread(r'''../image_data/lena.jpg''', 0)
							# canny edge detection
							UpperCAmelCase__				:  Tuple       					=				canny(lena)
							cva.imshow('''canny''', canny_destination)
							cva.waitKey(0)



