




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							1
						lowerCamelCase_							:		Tuple							       =							3
						lowerCamelCase_							:		int							       =							8
						lowerCamelCase_							:		Union[str, Any]							       =							8

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

						return sample

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if isinstance(image[0]			,	PIL.Image.Image ):
							return np.stack([np.array(__UpperCamelCase ) for i in image]			,	axis=0 )
						elif isinstance(__UpperCamelCase			,	torch.Tensor ):
							return image.cpu().numpy().transpose(0			,	2			,	3			,	1 )
						return image

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							VaeImageProcessor(do_resize=__UpperCamelCase			,	do_normalize=__UpperCamelCase )

						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample
						lowerCamelCase_							:		List[Any]							       =							self.to_np(__UpperCamelCase )

						for output_type in ["pt", "np", "pil"]:
							lowerCamelCase_							:		Tuple							       =							image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase )			,	output_type=__UpperCamelCase			,	)
							lowerCamelCase_							:		Any							       =							self.to_np(__UpperCamelCase )
							lowerCamelCase_							:		Tuple							       =							(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      lowercase_							(    self ):







						'''simple docstring'''




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

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

							lowerCamelCase_							:		List[str]							       =							self.to_np(__UpperCamelCase )
							lowerCamelCase_							:		Union[str, Any]							       =							(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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							VaeImageProcessor(do_resize=__UpperCamelCase			,	do_normalize=__UpperCamelCase )

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

						for output_type in ["pt", "np", "pil"]:
							lowerCamelCase_							:		List[Any]							       =							image_processor.postprocess(image_processor.preprocess(__UpperCamelCase )			,	output_type=__UpperCamelCase )
							for i, o in zip(__UpperCamelCase			,	__UpperCamelCase ):
								lowerCamelCase_							:		int							       =							np.array(__UpperCamelCase )
								lowerCamelCase_							:		Optional[Any]							       =							self.to_np(__UpperCamelCase ) if output_type == 'pil' else (self.to_np(__UpperCamelCase ) * 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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							VaeImageProcessor(do_resize=__UpperCamelCase			,	do_normalize=__UpperCamelCase )

						lowerCamelCase_							:		Union[str, Any]							       =							self.dummy_sample
						lowerCamelCase_							:		Union[str, Any]							       =							input_pt_ad.squeeze(0 )

						lowerCamelCase_							:		Tuple							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)
						lowerCamelCase_							:		List[Any]							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)

						lowerCamelCase_							:		Optional[Any]							       =							self.to_np(self.dummy_sample )
						lowerCamelCase_							:		int							       =							input_np_ad.squeeze(0 )

						lowerCamelCase_							:		Any							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)
						lowerCamelCase_							:		Optional[Any]							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							VaeImageProcessor(do_resize=__UpperCamelCase			,	do_normalize=__UpperCamelCase )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample
						lowerCamelCase_							:		str							       =							list(__UpperCamelCase )

						lowerCamelCase_							:		Optional[int]							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)

						lowerCamelCase_							:		Optional[Any]							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)

						lowerCamelCase_							:		Any							       =							self.to_np(self.dummy_sample )
						list(__UpperCamelCase )

						lowerCamelCase_							:		Optional[int]							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	output_type='np'			,	)

						lowerCamelCase_							:		int							       =							image_processor.postprocess(
						    image_processor.preprocess(__UpperCamelCase )			,	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






from typing import Callable






def  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	def wrapper(A      ):
		lowerCamelCase_							:		Dict							       =							function_with_docstring.__doc__
		return target_function

	return wrapper
