

"""simple docstring"""






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       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   1
				_UpperCAmelCase      :							Tuple       =   3
				_UpperCAmelCase      :							Any       =   8
				_UpperCAmelCase      :							Union[str, Any]       =   8

				_UpperCAmelCase      :							Optional[Any]       =   torch.rand((batch_size, num_channels, height, width)       )

				return sample
			def 							__snake_case	(						self ,						_A       )      ->      int:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   VaeImageProcessor(do_resize=_A ,						do_normalize=_A       )

				_UpperCAmelCase      :							Dict       =   self.dummy_sample
				_UpperCAmelCase      :							Optional[Any]       =   self.to_np(_A       )

				for output_type in ["pt", "np", "pil"]:
					_UpperCAmelCase      :							List[str]       =   image_processor.postprocess(
					    image_processor.preprocess(_A       ) ,						output_type=_A ,						)
					_UpperCAmelCase      :							int       =   self.to_np(_A       )
					_UpperCAmelCase      :							Optional[Any]       =   (input_np * 2_55).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       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   VaeImageProcessor(do_resize=_A ,						do_normalize=_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   self.dummy_sample.cpu().numpy().transpose(0 ,						2 ,						3 ,						1       )

				for output_type in ["pt", "np", "pil"]:
					_UpperCAmelCase      :							str       =   image_processor.postprocess(image_processor.preprocess(_A       ) ,						output_type=_A       )

					_UpperCAmelCase      :							Tuple       =   self.to_np(_A       )
					_UpperCAmelCase      :							Tuple       =   (input_np * 2_55).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]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   VaeImageProcessor(do_resize=_A ,						do_normalize=_A       )

				_UpperCAmelCase      :							Any       =   self.dummy_sample.cpu().numpy().transpose(0 ,						2 ,						3 ,						1       )
				_UpperCAmelCase      :							List[str]       =   image_processor.numpy_to_pil(_A       )

				for output_type in ["pt", "np", "pil"]:
					_UpperCAmelCase      :							Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A       ) ,						output_type=_A       )
					for i, o in zip(_A ,						_A       ):
						_UpperCAmelCase      :							str       =   np.array(_A       )
						_UpperCAmelCase      :							Union[str, Any]       =   self.to_np(_A       ) if output_type == """pil""" else (self.to_np(_A       ) * 2_55).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       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   VaeImageProcessor(do_resize=_A ,						do_normalize=_A       )

				_UpperCAmelCase      :							Optional[int]       =   self.dummy_sample
				_UpperCAmelCase      :							Optional[Any]       =   input_pt_ad.squeeze(0       )

				_UpperCAmelCase      :							int       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)
				_UpperCAmelCase      :							Optional[Any]       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)

				_UpperCAmelCase      :							List[Any]       =   self.to_np(self.dummy_sample       )
				_UpperCAmelCase      :							Any       =   input_np_ad.squeeze(0       )

				_UpperCAmelCase      :							Any       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)
				_UpperCAmelCase      :							Tuple       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						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       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   VaeImageProcessor(do_resize=_A ,						do_normalize=_A       )

				_UpperCAmelCase      :							Any       =   self.dummy_sample
				_UpperCAmelCase      :							Optional[Any]       =   list(_A       )

				_UpperCAmelCase      :							Dict       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)

				_UpperCAmelCase      :							int       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)

				_UpperCAmelCase      :							Optional[Any]       =   self.to_np(self.dummy_sample       )
				list(_A       )

				_UpperCAmelCase      :							Any       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						output_type="""np""" ,						)

				_UpperCAmelCase      :							Optional[int]       =   image_processor.postprocess(
				    image_processor.preprocess(_A       ) ,						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








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  float,					_lowerCAmelCase       :  float,					_lowerCAmelCase       :  float	)			->     float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative"""	)
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer"""	)
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





