'''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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)


if is_vision_available():
  import PIL




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""pixel_values"""]







  def __init__(							self   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = True   ,    snake_case_ = 1 / 255   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = True   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)
    __UpperCAmelCase:					str		= size if size is not None else {"""shortest_edge""": 224}
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_   ,    param_name="""crop_size"""					)

    __UpperCAmelCase:					Tuple		= do_resize
    __UpperCAmelCase:					Dict		= size
    __UpperCAmelCase:					Tuple		= resample
    __UpperCAmelCase:					Tuple		= do_center_crop
    __UpperCAmelCase:					Any		= crop_size
    __UpperCAmelCase:					Any		= do_rescale
    __UpperCAmelCase:					List[Any]		= rescale_factor
    __UpperCAmelCase:					Optional[Any]		= do_normalize
    __UpperCAmelCase:					Optional[int]		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    __UpperCAmelCase:					List[str]		= image_std if image_std is not None else OPENAI_CLIP_STD
    __UpperCAmelCase:					List[Any]		= do_convert_rgb







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= get_size_dict(snake_case_					)
    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(snake_case_   ,    size=(size["""height"""], size["""width"""])   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = ChannelDimension.FIRST   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= do_resize if do_resize is not None else self.do_resize
    __UpperCAmelCase:					List[Any]		= size if size is not None else self.size
    __UpperCAmelCase:					List[Any]		= get_size_dict(snake_case_   ,    param_name="""size"""   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= resample if resample is not None else self.resample
    __UpperCAmelCase:					Optional[Any]		= do_center_crop if do_center_crop is not None else self.do_center_crop
    __UpperCAmelCase:					Dict		= crop_size if crop_size is not None else self.crop_size
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    param_name="""crop_size"""   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					int		= do_rescale if do_rescale is not None else self.do_rescale
    __UpperCAmelCase:					Union[str, Any]		= rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCAmelCase:					Optional[Any]		= do_normalize if do_normalize is not None else self.do_normalize
    __UpperCAmelCase:					Tuple		= image_mean if image_mean is not None else self.image_mean
    __UpperCAmelCase:					List[str]		= image_std if image_std is not None else self.image_std
    __UpperCAmelCase:					Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __UpperCAmelCase:					Optional[int]		= make_list_of_images(snake_case_					)

    if not valid_images(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:					Optional[int]		= [convert_to_rgb(snake_case_					) for image in images]

    # All transformations expect numpy arrays.
    __UpperCAmelCase:					Dict		= [to_numpy_array(snake_case_					) for image in images]

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

    if do_center_crop:
      __UpperCAmelCase:					List[Any]		= [self.center_crop(image=snake_case_   ,    size=snake_case_					) for image in images]

    if do_rescale:
      __UpperCAmelCase:					Optional[int]		= [self.rescale(image=snake_case_   ,    scale=snake_case_					) for image in images]

    if do_normalize:
      __UpperCAmelCase:					List[Any]		= [self.normalize(image=snake_case_   ,    mean=snake_case_   ,    std=snake_case_					) for image in images]

    __UpperCAmelCase:					Optional[Any]		= [to_channel_dimension_format(snake_case_   ,    snake_case_					) for image in images]

    __UpperCAmelCase:					List[str]		= {"""pixel_values""": images}
    return BatchFeature(data=snake_case_   ,    tensor_type=snake_case_					)
'''simple docstring'''






from __future__ import annotations

SCREAMING_SNAKE_CASE_	   =			10






def  UpperCamelCase__					(					_lowercase    :				list[int]     )							->	list[int]:
  __UpperCAmelCase:					Union[str, Any]		= 1
  __UpperCAmelCase:					Optional[Any]		= max(_lowercase     )
  while placement <= max_digit:
    # declare and initialize empty buckets
    __UpperCAmelCase:					list[list]		= [[] for _ in range(_lowercase     )]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __UpperCAmelCase:					Optional[Any]		= int((i / placement) % RADIX     )
      buckets[tmp].append(_lowercase     )
    # put each buckets' contents into list_of_ints
    __UpperCAmelCase:					Optional[int]		= 0
    for b in range(_lowercase     ):
      for i in buckets[b]:
        __UpperCAmelCase:					str		= i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()