

'''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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor


class 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:str 		=    image_size
									UpperCAmelCase__		:Optional[Any] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Dict 		=    do_resize
									UpperCAmelCase__		:Any 		=    size
									UpperCAmelCase__		:Any 		=    do_center_crop
									UpperCAmelCase__		:Optional[int] 		=    crop_size
									UpperCAmelCase__		:Dict 		=    do_normalize
									UpperCAmelCase__		:List[Any] 		=    image_mean
									UpperCAmelCase__		:Tuple 		=    image_std


						def    A__       (  self	)  ->str:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}


@require_torch
@require_vision
class 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(A  , 'do_resize'	)	)
									self.assertTrue(hasattr(A  , 'size'	)	)
									self.assertTrue(hasattr(A  , 'do_center_crop'	)	)
									self.assertTrue(hasattr(A  , 'center_crop'	)	)
									self.assertTrue(hasattr(A  , 'do_normalize'	)	)
									self.assertTrue(hasattr(A  , 'image_mean'	)	)
									self.assertTrue(hasattr(A  , 'image_std'	)	)


						def    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}	)
									self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}	)

									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict  , size=42  , crop_size=84	)
									self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}	)
									self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}	)


						def    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , Image.Image	)

									# Test not batched input
									UpperCAmelCase__		:int 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:List[str] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A  , numpify=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , np.ndarray	)

									# Test not batched input
									UpperCAmelCase__		:List[str] 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[Any] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A  , torchify=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , torch.Tensor	)

									# Test not batched input
									UpperCAmelCase__		:List[str] 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[int] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )





