code: 
  
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__     ,						unittest.TestCase    ):
  	A						       =   PaintByExamplePipeline
  	A						       =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  	A						       =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	A						       =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	UNetaDConditionModel(
  				    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=9			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    cross_attention_dim=32			,    )
  				SCREAMING_SNAKE_CASE_			=	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE			)
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	AutoencoderKL(
  				    block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    )
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	CLIPVisionConfig(
  				    hidden_size=32			,    projection_dim=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=32			,    patch_size=4			,    )
  				SCREAMING_SNAKE_CASE_			=	PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE			,    proj_size=32			)
  				SCREAMING_SNAKE_CASE_			=	CLIPImageProcessor(crop_size=32			,    size=32			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet': unet,
  				    'scheduler': scheduler,
  				    'vae': vae,
  				    'image_encoder': image_encoder,
  				    'safety_checker': None,
  				    'feature_extractor': feature_extractor,
  				}
  				return components
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	np.array(image.convert('RGB'			)			)
  				SCREAMING_SNAKE_CASE_			=	image[None].transpose(0			,    3			,    1			,    2			)
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(dtype=torch.floataa			) / 1_2_7.5 - 1.0
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE="cpu"			,    SCREAMING_SNAKE_CASE=0			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	floats_tensor((1, 3, 32, 32)			,    rng=random.Random(SCREAMING_SNAKE_CASE			)			).to(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	image.cpu().permute(0			,    2			,    3			,    1			)[0]
  				SCREAMING_SNAKE_CASE_			=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE			)			).convert('RGB'			).resize((64, 64)			)
  				SCREAMING_SNAKE_CASE_			=	Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
  				SCREAMING_SNAKE_CASE_			=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE			)			).convert('RGB'			).resize((32, 32)			)
  
  				if str(SCREAMING_SNAKE_CASE			).startswith('mps'			):
  							SCREAMING_SNAKE_CASE_			=	torch.manual_seed(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	torch.Generator(device=SCREAMING_SNAKE_CASE			).manual_seed(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'example_image': example_image,
  				    'image': init_image,
  				    'mask_image': mask_image,
  				    'generator': generator,
  				    'num_inference_steps': 2,
  				    'guidance_scale': 6.0,
  				    'output_type': 'numpy',
  				}
  				return inputs
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  
  				# make sure here that pndm scheduler skips prk
  				SCREAMING_SNAKE_CASE_			=	PaintByExamplePipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pipe.to('cpu'			)
  				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  				SCREAMING_SNAKE_CASE_			=	pipe(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	output.images
  
  				SCREAMING_SNAKE_CASE_			=	image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  				SCREAMING_SNAKE_CASE_			=	np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	'cpu'
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  				inputs.pop('mask_image'			)
  				SCREAMING_SNAKE_CASE_			=	self.convert_to_pt(inputs.pop('image'			)			)
  				SCREAMING_SNAKE_CASE_			=	image.clamp(0			,    1			) / 2
  
  				# make sure here that pndm scheduler skips prk
  				SCREAMING_SNAKE_CASE_			=	PaintByExamplePipeline(**self.get_dummy_components()			)
  				SCREAMING_SNAKE_CASE_			=	pipe.to(SCREAMING_SNAKE_CASE			)
  				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	pipe(image=SCREAMING_SNAKE_CASE			,    mask_image=mask_image[:, 0]			,    **SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	output.images
  
  				SCREAMING_SNAKE_CASE_			=	image.cpu().permute(0			,    2			,    3			,    1			)[0]
  				SCREAMING_SNAKE_CASE_			=	mask_image.cpu().permute(0			,    2			,    3			,    1			)[0]
  
  				SCREAMING_SNAKE_CASE_			=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE			)			).convert('RGB'			)
  				SCREAMING_SNAKE_CASE_			=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE			)			).convert('RGB'			)
  
  				SCREAMING_SNAKE_CASE_			=	pipe(**self.get_dummy_inputs()			)
  				SCREAMING_SNAKE_CASE_			=	output.images
  
  				assert out_a.shape == (1, 64, 64, 3)
  				assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5e-2
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
  
  
  
  
  @slow
  @require_torch_gpu
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	load_image(
  				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  				    '/paint_by_example/dog_in_bucket.png'			)
  				SCREAMING_SNAKE_CASE_			=	load_image(
  				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  				    '/paint_by_example/mask.png'			)
  				SCREAMING_SNAKE_CASE_			=	load_image(
  				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  				    '/paint_by_example/panda.jpg'			)
  
  				SCREAMING_SNAKE_CASE_			=	PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'			)
  				SCREAMING_SNAKE_CASE_			=	pipe.to(SCREAMING_SNAKE_CASE			)
  				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(321			)
  				SCREAMING_SNAKE_CASE_			=	pipe(
  				    image=SCREAMING_SNAKE_CASE			,    mask_image=SCREAMING_SNAKE_CASE			,    example_image=SCREAMING_SNAKE_CASE			,    generator=SCREAMING_SNAKE_CASE			,    guidance_scale=5.0			,    num_inference_steps=50			,    output_type='np'			,    )
  
  				SCREAMING_SNAKE_CASE_			=	output.images
  
  				SCREAMING_SNAKE_CASE_			=	image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 512, 512, 3)
  				SCREAMING_SNAKE_CASE_			=	np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
style_context: 
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					logging.get_logger(__name__)
  
  
  if is_vision_available():
  				import PIL
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''pixel_values''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 1 / 255			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				None:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'height': 2560, 'width': 1920}
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# The previous feature extractor size parameter was in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				if (output_width < output_height and input_width > input_height) or (
  				    output_width > output_height and input_width < input_height
  				):
  							SCREAMING_SNAKE_CASE_			=	np.rotaa(SCREAMING_SNAKE_CASE			,    3			)
  
  				if data_format is not None:
  							SCREAMING_SNAKE_CASE_			=	to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	output_width - input_width
  				SCREAMING_SNAKE_CASE_			=	output_height - input_height
  
  				if random_padding:
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_height + 1			)
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_width + 1			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	delta_height // 2
  							SCREAMING_SNAKE_CASE_			=	delta_width // 2
  
  				SCREAMING_SNAKE_CASE_			=	delta_height - pad_top
  				SCREAMING_SNAKE_CASE_			=	delta_width - pad_left
  
  				SCREAMING_SNAKE_CASE_			=	((pad_top, pad_bottom), (pad_left, pad_right))
  				return pad(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
  				return self.pad_image(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				# We always resize to the smallest of either the input or output size.
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				if height == input_height and width == input_width:
  							return image
  
  				if input_height > input_width:
  							SCREAMING_SNAKE_CASE_			=	int(input_width * height / input_height			)
  				elif input_width > input_height:
  							SCREAMING_SNAKE_CASE_			=	int(input_height * width / input_width			)
  
  				return resize(
  				    SCREAMING_SNAKE_CASE			,    size=(height, width)			,    resample=SCREAMING_SNAKE_CASE			,    reducing_gap=2.0			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(size['height']			,    size['width']			)
  				SCREAMING_SNAKE_CASE_			=	get_resize_output_image_size(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    default_to_square=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resize(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  				return resized_image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return rescale(SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return normalize(SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,    **SCREAMING_SNAKE_CASE			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	do_resize if do_resize is not None else self.do_resize
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else self.size
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# Previous feature extractor had size in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resample if resample is not None else self.resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad if do_pad is not None else self.do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale if do_rescale is not None else self.do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor if rescale_factor is not None else self.rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize if do_normalize is not None else self.do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else self.image_mean
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else self.image_std
  
  				SCREAMING_SNAKE_CASE_			=	make_list_of_images(SCREAMING_SNAKE_CASE			)
  
  				if not valid_images(SCREAMING_SNAKE_CASE			):
  							raise ValueError(
  							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
  							    'torch.Tensor, tf.Tensor or jax.ndarray.'			)
  
  				if do_resize and size is None:
  							raise ValueError('Size must be specified if do_resize is True.'			)
  
  				if do_rescale and rescale_factor is None:
  							raise ValueError('Rescale factor must be specified if do_rescale is True.'			)
  
  				if do_pad and size is None:
  							raise ValueError('Size must be specified if do_pad is True.'			)
  
  				if do_normalize and (image_mean is None or image_std is None):
  							raise ValueError('Image mean and std must be specified if do_normalize is True.'			)
  
  				# All transformations expect numpy arrays.
  				SCREAMING_SNAKE_CASE_			=	[to_numpy_array(SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_align_long_axis:
  							SCREAMING_SNAKE_CASE_			=	[self.align_long_axis(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE_			=	[self.resize(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_thumbnail:
  							SCREAMING_SNAKE_CASE_			=	[self.thumbnail(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_pad:
  							SCREAMING_SNAKE_CASE_			=	[self.pad_image(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    random_padding=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE_			=	[self.rescale(image=SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	[self.normalize(image=SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	{'pixel_values': images}
  				return BatchFeature(data=SCREAMING_SNAKE_CASE			,    tensor_type=SCREAMING_SNAKE_CASE			)
  