





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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_a						=   logging.get_logger(__name__)


if is_vision_available():
			import PIL






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		["""pixel_values"""]





			def __init__(					self ,						__lowerCAmelCase = True ,						__lowerCAmelCase = None ,						__lowerCAmelCase = PILImageResampling.BICUBIC ,						__lowerCAmelCase = True ,						__lowerCAmelCase = None ,						__lowerCAmelCase = True ,						__lowerCAmelCase = 1 / 2_5_5 ,						__lowerCAmelCase = True ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = True ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)
				lowerCamelCase__              =	size if size is not None else {'''shortest_edge''': 2_2_4}
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase ,						default_to_square=__lowerCAmelCase							)
				lowerCamelCase__              =	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase ,						default_to_square=__lowerCAmelCase ,						param_name='''crop_size'''							)

				lowerCamelCase__              =	do_resize
				lowerCamelCase__              =	size
				lowerCamelCase__              =	resample
				lowerCamelCase__              =	do_center_crop
				lowerCamelCase__              =	crop_size
				lowerCamelCase__              =	do_rescale
				lowerCamelCase__              =	rescale_factor
				lowerCamelCase__              =	do_normalize
				lowerCamelCase__              =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				lowerCamelCase__              =	image_std if image_std is not None else OPENAI_CLIP_STD
				lowerCamelCase__              =	do_convert_rgb





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = PILImageResampling.BICUBIC ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase ,						default_to_square=__lowerCAmelCase							)
				if "shortest_edge" not in size:
					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'							)
				lowerCamelCase__              =	get_resize_output_image_size(__lowerCAmelCase ,						size=size['''shortest_edge'''] ,						default_to_square=__lowerCAmelCase							)
				return resize(__lowerCAmelCase ,						size=__lowerCAmelCase ,						resample=__lowerCAmelCase ,						data_format=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase							)
				if "height" not in size or "width" not in size:
					raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'							)
				return center_crop(__lowerCAmelCase ,						size=(size['''height'''], size['''width''']) ,						data_format=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				return rescale(__lowerCAmelCase ,						scale=__lowerCAmelCase ,						data_format=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				return normalize(__lowerCAmelCase ,						mean=__lowerCAmelCase ,						std=__lowerCAmelCase ,						data_format=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = ChannelDimension.FIRST ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	do_resize if do_resize is not None else self.do_resize
				lowerCamelCase__              =	size if size is not None else self.size
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase ,						param_name='''size''' ,						default_to_square=__lowerCAmelCase							)
				lowerCamelCase__              =	resample if resample is not None else self.resample
				lowerCamelCase__              =	do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCamelCase__              =	crop_size if crop_size is not None else self.crop_size
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase ,						param_name='''crop_size''' ,						default_to_square=__lowerCAmelCase							)
				lowerCamelCase__              =	do_rescale if do_rescale is not None else self.do_rescale
				lowerCamelCase__              =	rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCamelCase__              =	do_normalize if do_normalize is not None else self.do_normalize
				lowerCamelCase__              =	image_mean if image_mean is not None else self.image_mean
				lowerCamelCase__              =	image_std if image_std is not None else self.image_std
				lowerCamelCase__              =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				lowerCamelCase__              =	make_list_of_images(__lowerCAmelCase							)

				if not valid_images(__lowerCAmelCase							):
					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_center_crop and crop_size is None:
					raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

				if do_rescale and rescale_factor is None:
					raise ValueError('''Rescale factor must be specified if do_rescale 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.'''							)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
					lowerCamelCase__              =	[convert_to_rgb(__lowerCAmelCase							) for image in images]

				# All transformations expect numpy arrays.
				lowerCamelCase__              =	[to_numpy_array(__lowerCAmelCase							) for image in images]

				if do_resize:
					lowerCamelCase__              =	[self.resize(image=__lowerCAmelCase ,						size=__lowerCAmelCase ,						resample=__lowerCAmelCase							) for image in images]

				if do_center_crop:
					lowerCamelCase__              =	[self.center_crop(image=__lowerCAmelCase ,						size=__lowerCAmelCase							) for image in images]

				if do_rescale:
					lowerCamelCase__              =	[self.rescale(image=__lowerCAmelCase ,						scale=__lowerCAmelCase							) for image in images]

				if do_normalize:
					lowerCamelCase__              =	[self.normalize(image=__lowerCAmelCase ,						mean=__lowerCAmelCase ,						std=__lowerCAmelCase							) for image in images]

				lowerCamelCase__              =	[to_channel_dimension_format(__lowerCAmelCase ,						__lowerCAmelCase							) for image in images]

				lowerCamelCase__              =	{'''pixel_values''': images}
				return BatchFeature(data=__lowerCAmelCase ,						tensor_type=__lowerCAmelCase							)










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if start is None:
		lowerCamelCase__              =	0

	if end is None:
		lowerCamelCase__              =	len(__snake_case							) - 1

	if start >= end:
		return

	lowerCamelCase__              =	(start + end) // 2

	slowsort(__snake_case ,__snake_case ,__snake_case							)
	slowsort(__snake_case ,mid + 1 ,__snake_case							)

	if sequence[end] < sequence[mid]:
		lowerCamelCase__       ,			lowerCamelCase__              =	sequence[mid], sequence[end]

	slowsort(__snake_case ,__snake_case ,end - 1							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



