"""simple docstring"""







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


_a	:	Optional[int]=					logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 						UpperCamelCase     (       __lowerCamelCase		):
				UpperCAmelCase							: Optional[int]       =						["""pixel_values"""]



				def __init__(self							: str      ,    _A							: bool = True      ,    _A							: Dict[str, int] = None      ,    _A							: PILImageResampling = PILImageResampling.BILINEAR      ,    _A							: bool = True      ,    _A							: bool = False      ,    _A							: bool = True      ,    _A							: bool = True      ,    _A							: Union[int, float] = 1 / 2_55      ,    _A							: bool = True      ,    _A							: Optional[Union[float, List[float]]] = None      ,    _A							: Optional[Union[float, List[float]]] = None      ,    **_A							: List[str]      ,    )       ->      str:
					super().__init__(**_lowercase)

					__snake_case     :				int 						=					size if size is not None else {'height': 25_60, 'width': 19_20}
					if isinstance(_lowercase      ,    (tuple, list)):
						# The previous feature extractor size parameter was in (width, height) format
						__snake_case     :				List[str] 						=					size[::-1]
					__snake_case     :				Any 						=					get_size_dict(_lowercase)

					__snake_case     :				str 						=					do_resize
					__snake_case     :				Union[str, Any] 						=					size
					__snake_case     :				List[str] 						=					resample
					__snake_case     :				List[Any] 						=					do_thumbnail
					__snake_case     :				Optional[int] 						=					do_align_long_axis
					__snake_case     :				Union[str, Any] 						=					do_pad
					__snake_case     :				int 						=					do_rescale
					__snake_case     :				Optional[int] 						=					rescale_factor
					__snake_case     :				Dict 						=					do_normalize
					__snake_case     :				Tuple 						=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__snake_case     :				Dict 						=					image_std if image_std is not None else IMAGENET_STANDARD_STD



				def    _lowercase	(self							: str      ,    _A							: np.ndarray      ,    _A							: Dict[str, int]      ,    _A							: Optional[Union[str, ChannelDimension]] = None)       ->      Any:
					__snake_case						,							__snake_case     :				Any 						=					get_image_size(_lowercase)
					__snake_case						,							__snake_case     :				List[str] 						=					size['height'], size['width']

					if (output_width < output_height and input_width > input_height) or (
					    output_width > output_height and input_width < input_height
					):
						__snake_case     :				Dict 						=					np.rotaa(_lowercase      ,    3)

					if data_format is not None:
						__snake_case     :				int 						=					to_channel_dimension_format(_lowercase      ,    _lowercase)

					return image



				def    _lowercase	(self							: Tuple      ,    _A							: np.ndarray      ,    _A							: Dict[str, int]      ,    _A							: bool = False      ,    _A							: Optional[Union[str, ChannelDimension]] = None      ,    )       ->      List[str]:
					__snake_case						,							__snake_case     :				str 						=					size['height'], size['width']
					__snake_case						,							__snake_case     :				List[Any] 						=					get_image_size(_lowercase)

					__snake_case     :				List[str] 						=					output_width - input_width
					__snake_case     :				List[str] 						=					output_height - input_height

					if random_padding:
						__snake_case     :				int 						=					np.random.randint(low=0      ,    high=delta_height + 1)
						__snake_case     :				List[Any] 						=					np.random.randint(low=0      ,    high=delta_width + 1)
					else:
						__snake_case     :				Optional[Any] 						=					delta_height // 2
						__snake_case     :				Optional[int] 						=					delta_width // 2

					__snake_case     :				List[str] 						=					delta_height - pad_top
					__snake_case     :				List[Any] 						=					delta_width - pad_left

					__snake_case     :				int 						=					((pad_top, pad_bottom), (pad_left, pad_right))
					return pad(_lowercase      ,    _lowercase      ,    data_format=_lowercase)



				def    _lowercase	(self							: Union[str, Any]      ,    *_A							: List[str]      ,    **_A							: int)       ->      Dict:
					logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.')
					return self.pad_image(*_lowercase      ,    **_lowercase)



				def    _lowercase	(self							: Optional[int]      ,    _A							: np.ndarray      ,    _A							: Dict[str, int]      ,    _A							: PILImageResampling = PILImageResampling.BICUBIC      ,    _A							: Optional[Union[str, ChannelDimension]] = None      ,    **_A							: Dict      ,    )       ->      Tuple:
					__snake_case						,							__snake_case     :				Optional[int] 						=					get_image_size(_lowercase)
					__snake_case						,							__snake_case     :				List[str] 						=					size['height'], size['width']

					# We always resize to the smallest of either the input or output size.
					__snake_case     :				int 						=					min(_lowercase      ,    _lowercase)
					__snake_case     :				Any 						=					min(_lowercase      ,    _lowercase)

					if height == input_height and width == input_width:
						return image

					if input_height > input_width:
						__snake_case     :				Optional[int] 						=					int(input_width * height / input_height)
					elif input_width > input_height:
						__snake_case     :				Dict 						=					int(input_height * width / input_width)

					return resize(
					    _lowercase      ,    size=(height, width)      ,    resample=_lowercase      ,    reducing_gap=2.0      ,    data_format=_lowercase      ,    **_lowercase)



				def    _lowercase	(self							: List[str]      ,    _A							: np.ndarray      ,    _A							: Dict[str, int]      ,    _A							: PILImageResampling = PILImageResampling.BICUBIC      ,    _A							: Optional[Union[str, ChannelDimension]] = None      ,    **_A							: Optional[Any]      ,    )       ->      Tuple:
					__snake_case     :				List[str] 						=					get_size_dict(_lowercase)
					__snake_case     :				Dict 						=					min(size['height']      ,    size['width'])
					__snake_case     :				int 						=					get_resize_output_image_size(_lowercase      ,    size=_lowercase      ,    default_to_square=_lowercase)
					__snake_case     :				Optional[Any] 						=					resize(_lowercase      ,    size=_lowercase      ,    resample=_lowercase      ,    data_format=_lowercase      ,    **_lowercase)
					return resized_image



				def    _lowercase	(self							: int      ,    _A							: np.ndarray      ,    _A							: Union[int, float]      ,    _A							: Optional[Union[str, ChannelDimension]] = None      ,    **_A							: Union[str, Any]      ,    )       ->      List[str]:
					return rescale(_lowercase      ,    scale=_lowercase      ,    data_format=_lowercase      ,    **_lowercase)



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






				def    _lowercase	(self							: List[str]      ,    _A							: ImageInput      ,    _A							: bool = None      ,    _A							: Dict[str, int] = None      ,    _A							: PILImageResampling = None      ,    _A							: bool = None      ,    _A							: bool = None      ,    _A							: bool = None      ,    _A							: bool = False      ,    _A							: bool = None      ,    _A							: float = None      ,    _A							: bool = None      ,    _A							: Optional[Union[float, List[float]]] = None      ,    _A							: Optional[Union[float, List[float]]] = None      ,    _A							: Optional[Union[str, TensorType]] = None      ,    _A							: Optional[ChannelDimension] = ChannelDimension.FIRST      ,    **_A							: Dict      ,    )       ->      Dict:
					__snake_case     :				Any 						=					do_resize if do_resize is not None else self.do_resize
					__snake_case     :				List[str] 						=					size if size is not None else self.size
					if isinstance(_lowercase      ,    (tuple, list)):
						# Previous feature extractor had size in (width, height) format
						__snake_case     :				Dict 						=					size[::-1]
					__snake_case     :				List[Any] 						=					get_size_dict(_lowercase)
					__snake_case     :				List[Any] 						=					resample if resample is not None else self.resample
					__snake_case     :				Any 						=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
					__snake_case     :				int 						=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
					__snake_case     :				List[str] 						=					do_pad if do_pad is not None else self.do_pad
					__snake_case     :				int 						=					do_rescale if do_rescale is not None else self.do_rescale
					__snake_case     :				int 						=					rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case     :				str 						=					do_normalize if do_normalize is not None else self.do_normalize
					__snake_case     :				Dict 						=					image_mean if image_mean is not None else self.image_mean
					__snake_case     :				Any 						=					image_std if image_std is not None else self.image_std

					__snake_case     :				Any 						=					make_list_of_images(_lowercase)

					if not valid_images(_lowercase):
						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.
					__snake_case     :				List[str] 						=					[to_numpy_array(_lowercase) for image in images]

					if do_align_long_axis:
						__snake_case     :				Optional[Any] 						=					[self.align_long_axis(_lowercase      ,    size=_lowercase) for image in images]

					if do_resize:
						__snake_case     :				Union[str, Any] 						=					[self.resize(image=_lowercase      ,    size=_lowercase      ,    resample=_lowercase) for image in images]

					if do_thumbnail:
						__snake_case     :				Optional[Any] 						=					[self.thumbnail(image=_lowercase      ,    size=_lowercase) for image in images]

					if do_pad:
						__snake_case     :				Tuple 						=					[self.pad_image(image=_lowercase      ,    size=_lowercase      ,    random_padding=_lowercase) for image in images]

					if do_rescale:
						__snake_case     :				Any 						=					[self.rescale(image=_lowercase      ,    scale=_lowercase) for image in images]

					if do_normalize:
						__snake_case     :				Dict 						=					[self.normalize(image=_lowercase      ,    mean=_lowercase      ,    std=_lowercase) for image in images]

					__snake_case     :				Tuple 						=					[to_channel_dimension_format(_lowercase      ,    _lowercase) for image in images]

					__snake_case     :				Optional[Any] 						=					{'pixel_values': images}
					return BatchFeature(data=_lowercase      ,    tensor_type=_lowercase)






"""simple docstring"""







from __future__ import annotations


def 	a__   (		UpperCAmelCase__							:      list      )->						int | float:
						UpperCAmelCase__       						=		sorted(UpperCAmelCase__      )
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	a__   (		)->						Dict:
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()

