

'''simple docstring'''

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				@property
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					1
					snake_case							=					3
					snake_case							=					8
					snake_case							=					8

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

					return sample

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

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample
					snake_case							=					self.to_np(lowercase_ )

					for output_type in ["pt", "np", "pil"]:
						snake_case							=					image_processor.postprocess(
						    image_processor.preprocess(lowercase_ ),						output_type=lowercase_,						)
						snake_case							=					self.to_np(lowercase_ )
						snake_case							=					(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 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )
					snake_case							=					self.dummy_sample.cpu().numpy().transpose(0,						2,						3,						1 )

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

						snake_case							=					self.to_np(lowercase_ )
						snake_case							=					(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 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

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

					for output_type in ["pt", "np", "pil"]:
						snake_case							=					image_processor.postprocess(image_processor.preprocess(lowercase_ ),						output_type=lowercase_ )
						for i, o in zip(lowercase_,						lowercase_ ):
							snake_case							=					np.array(lowercase_ )
							snake_case							=					self.to_np(lowercase_ ) if output_type == 'pil' else (self.to_np(lowercase_ ) * 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 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

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

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)
					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

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

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)
					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						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 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample
					snake_case							=					list(lowercase_ )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					self.to_np(self.dummy_sample )
					list(lowercase_ )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						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'''

lowerCAmelCase_     =      "\n# Installation de Transformers\n! pip install transformers datasets\n# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCAmelCase_     =      [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase_     =      {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





