





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase       :Dict       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     Dict							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,	a				:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,	**a				:     int							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					size if size is not None else {"shortest_edge": 2_24}
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				int   =					get_size_dict(a							,	param_name="crop_size"    )

													SCREAMING_SNAKE_CASE__     :				str   =					do_resize
													SCREAMING_SNAKE_CASE__     :				str   =					size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      A_  (      self				:     Optional[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Any							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
													if "shortest_edge" in size:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((2_56 / 2_24) * size["shortest_edge"]    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					get_resize_output_image_size(a							,	size=a							,	default_to_square=a    )
																			SCREAMING_SNAKE_CASE__     :				int   =					{"height": output_size[0], "width": output_size[1]}
													if "height" not in size_dict or "width" not in size_dict:
																			raise ValueError(
																			    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
													return resize(
													    a							,	size=(size_dict["height"], size_dict["width"])							,	resample=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     List[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				str   =					get_size_dict(a    )
													if "height" not in size or "width" not in size:
																			raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
													return center_crop(a							,	size=(size["height"], size["width"])							,	data_format=a							,	**a    )

							def      A_  (      self				:     Dict							,	a				:     np.ndarray							,	a				:     Union[int, float]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     str							,	)	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Any							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Dict[str, int]] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, Iterable[float]]] = None							,	a				:     Optional[Union[float, Iterable[float]]] = None							,	a				:     Optional[TensorType] = None							,	a				:     ChannelDimension = ChannelDimension.FIRST							,	**a				:     Tuple							,	)	->BatchFeature:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				Tuple   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				Any   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Any   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				int   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					get_size_dict(a							,	param_name="crop_size"    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					make_list_of_images(a    )

													if not valid_images(a    ):
																			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."    )

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[to_numpy_array(a    ) for image in images]

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[self.resize(a							,	a							,	a    ) for image in images]

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[self.center_crop(a							,	a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[self.rescale(a							,	a    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.normalize(a							,	a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Dict   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  int | None = None			,   _lowerCamelCase      :  int | None = None     ):





						'''simple docstring'''


						if start is None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0

						if end is None:
												SCREAMING_SNAKE_CASE__     :				Any   =					len(_lowerCamelCase     ) - 1

						if start >= end:
												return

						SCREAMING_SNAKE_CASE__     :				List[str]   =					(start + end) // 2

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						slowsort(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )

						if sequence[end] < sequence[mid]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sequence[mid], sequence[end]

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   end - 1     )


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

				testmod()