



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 	__magic_name__						(    unittest.TestCase   ):







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									torch.manual_seed(0		)
									_a     :      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 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Tuple        =							self.dummy_uncond_unet
									_a     :      Any        =							KarrasVeScheduler()

									_a     :      int        =							KarrasVePipeline(unet=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a     :      int        =							torch.manual_seed(0		)
									_a     :      Optional[int]        =							pipe(num_inference_steps=2	,generator=_UpperCAmelCase	,output_type='numpy'		).images

									_a     :      Any        =							torch.manual_seed(0		)
									_a     :      int        =							pipe(num_inference_steps=2	,generator=_UpperCAmelCase	,output_type='numpy'	,return_dict=_UpperCAmelCase		)[0]

									_a     :      Dict        =							image[0, -3:, -3:, -1]
									_a     :      Dict        =							image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_a     :      Dict        =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

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

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







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Union[str, Any]        =							'google/ncsnpp-celebahq-256'
									_a     :      List[Any]        =							UNetaDModel.from_pretrained(_UpperCAmelCase		)
									_a     :      List[str]        =							KarrasVeScheduler()

									_a     :      str        =							KarrasVePipeline(unet=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a     :      List[str]        =							torch.manual_seed(0		)
									_a     :      List[str]        =							pipe(num_inference_steps=20	,generator=_UpperCAmelCase	,output_type='numpy'		).images

									_a     :      Union[str, Any]        =							image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									_a     :      Any        =							np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]		)

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









'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




