




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[images]
				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)

				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[..., None]

				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(images.transpose(0			,    3			,    1			,    2			)			)
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				return 2.0 * images - 1.0





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				return (images / 2 + 0.5).clamp(0			,    1			)





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:




				"""simple docstring"""



				if height is None:
							SCREAMING_SNAKE_CASE_			=	image.height
				if width is None:
							SCREAMING_SNAKE_CASE_			=	image.width

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(
				    x - x % self.config.vae_scale_factor for x in (width, height)
				)  # resize to integer multiple of vae_scale_factor
				SCREAMING_SNAKE_CASE_			=	image.resize((width, height)			,    resample=PIL_INTERPOLATION[self.config.resample]			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[image]
				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
							raise ValueError(
							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)

				if isinstance(image[0]			,    PIL.Image.Image			):
							if self.config.do_convert_rgb:
										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
							if self.config.do_resize:
										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt

				elif isinstance(image[0]			,    np.ndarray			):
							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

				elif isinstance(image[0]			,    torch.Tensor			):
							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
										return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

        # expected range [0,1], normalize to [-1,1]
				SCREAMING_SNAKE_CASE_			=	self.config.do_normalize
				if image.min() < 0:
							warnings.warn(
							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
							    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'			,    SCREAMING_SNAKE_CASE			,    )
							SCREAMING_SNAKE_CASE_			=	False

				if do_normalize:
							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)

				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if output_type == "latent":
							return image

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				if output_type == "pt":
							return image

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							return image

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)




class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""simple docstring"""



				return image[:, :, 1] * 2**8 + image[:, :, 2]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
				if images.shape[-1] == 6:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
							SCREAMING_SNAKE_CASE_			=	[
							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
							]
				elif images.shape[-1] == 4:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
				else:
							raise Exception('Not supported'			)

				return pil_images





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							if image.shape[-1] == 6:
										SCREAMING_SNAKE_CASE_			=	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,    axis=0			)
							else:
										SCREAMING_SNAKE_CASE_			=	image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
				else:
							raise Exception(f'This type {output_type} is not supported'			)






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			)
