





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case           =			logging.get_logger(__name__)





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      ['pixel_values']







  def __init__(      self  :int   ,							_lowercase  :bool = True   ,							_lowercase  :int = 32   ,							_lowercase  :List[str]=PILImageResampling.BILINEAR   ,							_lowercase  :bool = True   ,							**_lowercase  :Union[str, Any]   ,							):






     '''simple docstring'''





     lowercase__			       =  do_resize
     lowercase__			       =  do_rescale
     lowercase__			       =  size_divisor
     lowercase__			       =  resample
     super().__init__(**_lowercase )







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :np.ndarray   ,							_lowercase  :int   ,							_lowercase  :Dict   ,							_lowercase  :Optional[ChannelDimension] = None   ,							**_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  get_image_size(_lowercase )
     # Rounds the height and width down to the closest multiple of size_divisor
     lowercase__			       =  height // size_divisor * size_divisor
     lowercase__			       =  width // size_divisor * size_divisor
     lowercase__			       =  resize(_lowercase   ,							(new_h, new_w)   ,							resample=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )
     return image







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :np.ndarray   ,							_lowercase  :float   ,							_lowercase  :Optional[ChannelDimension] = None   ,							**_lowercase  :int ):






     '''simple docstring'''





     return rescale(image=_lowercase   ,							scale=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :List[str]=None   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[Union[TensorType, str]] = None   ,							_lowercase  :ChannelDimension = ChannelDimension.FIRST   ,							**_lowercase  :Dict   ,							):






     '''simple docstring'''





     lowercase__			       =  do_resize if do_resize is not None else self.do_resize
     lowercase__			       =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__			       =  size_divisor if size_divisor is not None else self.size_divisor
     lowercase__			       =  resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
        raise ValueError("size_divisor is required for resizing" )

     lowercase__			       =  make_list_of_images(_lowercase )

     if not valid_images(_lowercase ):
        raise ValueError("Invalid image(s)" )

     # All transformations expect numpy arrays.
     lowercase__			       =  [to_numpy_array(_lowercase ) for img in images]

     if do_resize:
        lowercase__			       =  [self.resize(_lowercase   ,							size_divisor=_lowercase   ,							resample=_lowercase ) for image in images]

     if do_rescale:
        lowercase__			       =  [self.rescale(_lowercase   ,							scale=1 / 2_55 ) for image in images]

     lowercase__			       =  [to_channel_dimension_format(_lowercase   ,							_lowercase ) for image in images]

     lowercase__			       =  {"pixel_values": images}
     return BatchFeature(data=_lowercase   ,							tensor_type=_lowercase )













from __future__ import annotations


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  0
   lowercase__			       =  len(__magic_name__	) - 1

   while i < j:
      if nums[i] + nums[j] == target:
         return [i, j]
      elif nums[i] + nums[j] < target:
         lowercase__			       =  i + 1
      else:
         lowercase__			       =  j - 1

   return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")






