"""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 (
    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


UpperCamelCase_       :       int							   =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class 			_lowercase		(   UpperCamelCase_  ):
				lowerCamelCase__						=       ["""pixel_values"""]

				def __init__(     self       :    List[str]       ,    _lowercase       :    Optional[Any] = True       ,    _lowercase       :    Union[str, Any] = None       ,    _lowercase       :    List[Any] = PILImageResampling.BICUBIC       ,    _lowercase       :    List[Any] = True       ,    _lowercase       :    Union[str, Any] = None       ,    _lowercase       :    str = True       ,    _lowercase       :    Optional[int] = 1 / 2_5_5       ,    _lowercase       :    Union[str, Any] = True       ,    _lowercase       :    str = None       ,    _lowercase       :    Dict = None       ,    _lowercase       :    Optional[Any] = True       ,    **_lowercase       :    List[str]       ,    ):


										'''simple docstring'''
										super().__init__(**__SCREAMING_SNAKE_CASE     )
										UpperCAmelCase__						=		size if size is not None else {"""shortest_edge""": 2_2_4}
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE       ,    default_to_square=__SCREAMING_SNAKE_CASE     )
										UpperCAmelCase__						=		crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE     )

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

				def        A (     self       :    int       ,    _lowercase       :    Any       ,    _lowercase       :    Optional[Any]       ,    _lowercase       :    List[Any] = PILImageResampling.BICUBIC       ,    _lowercase       :    List[str] = None       ,    **_lowercase       :    List[Any]       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE       ,    default_to_square=__SCREAMING_SNAKE_CASE     )
										UpperCAmelCase__						=		get_resize_output_image_size(
										    __SCREAMING_SNAKE_CASE       ,    size=(size["""height"""], size["""width"""])       ,    default_to_square=__SCREAMING_SNAKE_CASE     )
										return resize(__SCREAMING_SNAKE_CASE       ,    size=__SCREAMING_SNAKE_CASE       ,    resample=__SCREAMING_SNAKE_CASE       ,    data_format=__SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE     )

				def        A (     self       :    Optional[int]       ,    _lowercase       :    List[str]       ,    _lowercase       :    Dict       ,    _lowercase       :    Tuple = None       ,    **_lowercase       :    List[Any]       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE     )
										return center_crop(__SCREAMING_SNAKE_CASE       ,    size=(size["""height"""], size["""width"""])       ,    data_format=__SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE     )

				def        A (     self       :    str       ,    _lowercase       :    int       ,    _lowercase       :    Dict       ,    _lowercase       :    Optional[Any] = None       ,    **_lowercase       :    int       ,    ):


										'''simple docstring'''
										return rescale(__SCREAMING_SNAKE_CASE       ,    scale=__SCREAMING_SNAKE_CASE       ,    data_format=__SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE     )

				def        A (     self       :    Tuple       ,    _lowercase       :    str       ,    _lowercase       :    Optional[int]       ,    _lowercase       :    List[Any]       ,    _lowercase       :    str = None       ,    **_lowercase       :    Tuple       ,    ):


										'''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       :    List[Any]       ,    _lowercase       :    Dict       ,    _lowercase       :    Optional[int] = None       ,    _lowercase       :    Optional[Any] = None       ,    _lowercase       :    Optional[Any] = None       ,    _lowercase       :    List[Any] = None       ,    _lowercase       :    int = None       ,    _lowercase       :    int = None       ,    _lowercase       :    Optional[int] = None       ,    _lowercase       :    List[str] = None       ,    _lowercase       :    Tuple = None       ,    _lowercase       :    int = None       ,    _lowercase       :    str = None       ,    _lowercase       :    Optional[Any] = None       ,    _lowercase       :    Optional[int] = ChannelDimension.FIRST       ,    **_lowercase       :    Dict       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__						=		size if size is not None else self.size
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE       ,    default_to_square=__SCREAMING_SNAKE_CASE     )
										UpperCAmelCase__						=		resample if resample is not None else self.resample
										UpperCAmelCase__						=		do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCAmelCase__						=		crop_size if crop_size is not None else self.crop_size
										UpperCAmelCase__						=		get_size_dict(__SCREAMING_SNAKE_CASE     )
										UpperCAmelCase__						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__						=		image_std if image_std is not None else self.image_std
										UpperCAmelCase__						=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										UpperCAmelCase__						=		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_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:
																UpperCAmelCase__						=		[convert_to_rgb(__SCREAMING_SNAKE_CASE     ) for image in images]

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

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

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

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

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

										UpperCAmelCase__						=		[to_channel_dimension_format(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE     ) for image in images]

										UpperCAmelCase__						=		{"""pixel_values""": images}
										return BatchFeature(data=__SCREAMING_SNAKE_CASE       ,    tensor_type=__SCREAMING_SNAKE_CASE     )


"""simple docstring"""







def   a__      (		UpperCAmelCase__       :			list[int]			)->	int:
						UpperCAmelCase__						=		len(UpperCAmelCase__			)
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

