
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


lowercase__       =logging.get_logger(__name__)


if is_vision_available():
      import PIL



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      ["pixel_values"]
   def __init__(self :					List[str]					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					bool = True					,				snake_case_ :					Union[int, float] = 1 / 2_5_5					,				snake_case_ :					bool = True					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					bool = True					,				**snake_case_ :					Tuple					,				):
    super().__init__(**snake_case_   )
    __a :  Optional[int]						=  size if size is not None else {'''shortest_edge''': 2_2_4}
    __a :  Any						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )
    __a :  Optional[int]						=  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
    __a :  Union[str, Any]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_					,				param_name='''crop_size'''   )

    __a :  Any						=  do_resize
    __a :  Optional[int]						=  size
    __a :  Optional[Any]						=  resample
    __a :  Union[str, Any]						=  do_center_crop
    __a :  Dict						=  crop_size
    __a :  Optional[int]						=  do_rescale
    __a :  Tuple						=  rescale_factor
    __a :  Union[str, Any]						=  do_normalize
    __a :  Any						=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    __a :  Optional[int]						=  image_std if image_std is not None else OPENAI_CLIP_STD
    __a :  Tuple						=  do_convert_rgb
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					int					,				):
    __a :  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()}"   )
    __a :  Dict						=  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       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Dict					,				):
    __a :  Any						=  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       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					str					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Dict					,				):
    return normalize(snake_case_					,				mean=snake_case_					,				std=snake_case_					,				data_format=snake_case_					,				**snake_case_   )







   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					ImageInput					,				snake_case_ :					bool = None					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					PILImageResampling = None					,				snake_case_ :					bool = None					,				snake_case_ :					int = None					,				snake_case_ :					bool = None					,				snake_case_ :					float = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				snake_case_ :					Optional[ChannelDimension] = ChannelDimension.FIRST					,				**snake_case_ :					Union[str, Any]					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  str						=  size if size is not None else self.size
    __a :  int						=  get_size_dict(snake_case_					,				param_name='''size'''					,				default_to_square=snake_case_   )
    __a :  int						=  resample if resample is not None else self.resample
    __a :  Union[str, Any]						=  do_center_crop if do_center_crop is not None else self.do_center_crop
    __a :  Optional[Any]						=  crop_size if crop_size is not None else self.crop_size
    __a :  List[Any]						=  get_size_dict(snake_case_					,				param_name='''crop_size'''					,				default_to_square=snake_case_   )
    __a :  Dict						=  do_rescale if do_rescale is not None else self.do_rescale
    __a :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  Union[str, Any]						=  do_normalize if do_normalize is not None else self.do_normalize
    __a :  Any						=  image_mean if image_mean is not None else self.image_mean
    __a :  Optional[int]						=  image_std if image_std is not None else self.image_std
    __a :  List[str]						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __a :  Union[str, Any]						=  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:
     __a :  Tuple						=  [convert_to_rgb(snake_case_   ) for image in images]

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

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

    if do_center_crop:
     __a :  Optional[int]						=  [self.center_crop(image=snake_case_					,				size=snake_case_   ) for image in images]

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

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

    __a :  List[Any]						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

    __a :  Optional[int]						=  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_					,				tensor_type=snake_case_   )


from __future__ import annotations




def    __UpperCamelCase     (				lowerCAmelCase__							:				list[float]  ,   lowerCAmelCase__							:				list[float]  ):
 __a :  Dict						=  sorted(numsa + numsa  )
 __a				,       __a :  Optional[Any]						=  divmod(len(lowerCAmelCase__  )  ,   2  )
 if mod == 1:
  return all_numbers[div]
 else:
  return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__       =[float(x) for x in input('Enter the elements of first array: ').split()]
      lowercase__       =[float(x) for x in input('Enter the elements of second array: ').split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
