





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class 	_UpperCAmelCase						( unittest.TestCase       ):




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						1
							UpperCamelCase__								=						3
							UpperCamelCase__								=						8
							UpperCamelCase__								=						8

							UpperCamelCase__								=						torch.rand((batch_size, num_channels, height, width)							)

							return sample




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	int:
							if isinstance(image[0]     ,   PIL.Image.Image							):
									return np.stack([np.array(lowerCamelCase_							) for i in image]     ,   axis=0							)
							elif isinstance(lowerCamelCase_     ,   torch.Tensor							):
									return image.cpu().numpy().transpose(0     ,   2     ,   3     ,   1							)
							return image




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						VaeImageProcessor(do_resize=lowerCamelCase_     ,   do_normalize=lowerCamelCase_							)

							UpperCamelCase__								=						self.dummy_sample
							UpperCamelCase__								=						self.to_np(lowerCamelCase_							)

							for output_type in ["pt", "np", "pil"]:
									UpperCamelCase__								=						image_processor.postprocess(
									    image_processor.preprocess(lowerCamelCase_							)     ,   output_type=lowerCamelCase_     ,   )
									UpperCamelCase__								=						self.to_np(lowerCamelCase_							)
									UpperCamelCase__								=						(input_np * 255).round() if output_type == '''pil''' else input_np
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F"""decoded output does not match input for output_type {output_type}"""




					def 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__								=						VaeImageProcessor(do_resize=lowerCamelCase_     ,   do_normalize=lowerCamelCase_							)
							UpperCamelCase__								=						self.dummy_sample.cpu().numpy().transpose(0     ,   2     ,   3     ,   1							)

							for output_type in ["pt", "np", "pil"]:
									UpperCamelCase__								=						image_processor.postprocess(image_processor.preprocess(lowerCamelCase_							)     ,   output_type=lowerCamelCase_							)

									UpperCamelCase__								=						self.to_np(lowerCamelCase_							)
									UpperCamelCase__								=						(input_np * 255).round() if output_type == '''pil''' else input_np
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F"""decoded output does not match input for output_type {output_type}"""




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						VaeImageProcessor(do_resize=lowerCamelCase_     ,   do_normalize=lowerCamelCase_							)

							UpperCamelCase__								=						self.dummy_sample.cpu().numpy().transpose(0     ,   2     ,   3     ,   1							)
							UpperCamelCase__								=						image_processor.numpy_to_pil(lowerCamelCase_							)

							for output_type in ["pt", "np", "pil"]:
									UpperCamelCase__								=						image_processor.postprocess(image_processor.preprocess(lowerCamelCase_							)     ,   output_type=lowerCamelCase_							)
									for i, o in zip(lowerCamelCase_     ,   lowerCamelCase_							):
											UpperCamelCase__								=						np.array(lowerCamelCase_							)
											UpperCamelCase__								=						self.to_np(lowerCamelCase_							) if output_type == '''pil''' else (self.to_np(lowerCamelCase_							) * 255).round()
											assert (
											    np.abs(in_np - out_np							).max() < 1E-6
											), F"""decoded output does not match input for output_type {output_type}"""




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						VaeImageProcessor(do_resize=lowerCamelCase_     ,   do_normalize=lowerCamelCase_							)

							UpperCamelCase__								=						self.dummy_sample
							UpperCamelCase__								=						input_pt_ad.squeeze(0							)

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )
							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						self.to_np(self.dummy_sample							)
							UpperCamelCase__								=						input_np_ad.squeeze(0							)

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )
							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						VaeImageProcessor(do_resize=lowerCamelCase_     ,   do_normalize=lowerCamelCase_							)

							UpperCamelCase__								=						self.dummy_sample
							UpperCamelCase__								=						list(lowerCamelCase_							)

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						self.to_np(self.dummy_sample							)
							list(lowerCamelCase_							)

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						image_processor.postprocess(
							    image_processor.preprocess(lowerCamelCase_							)     ,   output_type="""np"""     ,   )

							assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list							).max() < 1E-6











def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					if not isinstance(__A				,__A					):
										__magic_name__				:						Dict     				=				F'''Input value of [number={number}] must be an integer'''
										raise TypeError(__A					)
					if number < 0 or number % 2 == 1:
										return False
					__magic_name__				:						Any     				=				int(number**0.5					)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



