





"""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 convert_to_rgb, 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


if is_vision_available():
  import PIL


_snake_case           =			logging.get_logger(__name__)





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







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






     '''simple docstring'''





     super().__init__(**_lowercase )
     lowercase__			       =  size if size is not None else {"height": 3_84, "width": 3_84}
     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )

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







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






     '''simple docstring'''





     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
     lowercase__			       =  (size["height"], size["width"])
     return resize(_lowercase   ,							size=_lowercase   ,							resample=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )







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






     '''simple docstring'''





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







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






     '''simple docstring'''





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







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






     '''simple docstring'''





     lowercase__			       =  do_resize if do_resize is not None else self.do_resize
     lowercase__			       =  resample if resample is not None else self.resample
     lowercase__			       =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__			       =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__			       =  image_mean if image_mean is not None else self.image_mean
     lowercase__			       =  image_std if image_std is not None else self.image_std
     lowercase__			       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     lowercase__			       =  size if size is not None else self.size
     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )

     lowercase__			       =  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 or resample is None:
        raise ValueError("Size and resample 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_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:
        lowercase__			       =  [convert_to_rgb(_lowercase ) for image in images]

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

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

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

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

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

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

     return encoded_outputs













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) = }""")






