
def   lowerCamelCase__							(					a		,		a      )      ->   str:
   if a < 0 or b < 0:
      raise ValueError('''the value of both inputs must be positive'''      )

   _A:  Union[str, Any]					=       str(bin(a      )      )[2:]  # remove the leading "0b"
   _A:  Union[str, Any]					=       str(bin(a      )      )[2:]  # remove the leading "0b"

   _A:  Optional[int]					=       max(len(a      )		,		len(a      )      )

   return "0b" + "".join(
       str(int(char_a != char_b      )      )
       for char_a, char_b in zip(a_binary.zfill(a      )		,		b_binary.zfill(a      )      )      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    ['''pixel_values''']


   def __init__(					self     :							Optional[int]			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Union[int, float] = 1 / 2_5_5			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							int = 8			,			**lowerCAmelCase_     :							Tuple			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       do_rescale
      _A:  Any					=       rescale_factor
      _A:  List[Any]					=       do_pad
      _A:  Tuple					=       pad_size


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							float			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      return rescale(lowerCAmelCase_			,			scale=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None     ):

      """simple docstring"""
      _A			,  _A:  Optional[int]					=       get_image_size(lowerCAmelCase_     )
      _A:  Union[str, Any]					=       (old_height // size + 1) * size - old_height
      _A:  Optional[Any]					=       (old_width // size + 1) * size - old_width

      return pad(lowerCAmelCase_			,			((0, pad_height), (0, pad_width))			,			mode='''symmetric'''			,			data_format=lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							ImageInput			,			lowerCAmelCase_     :							Optional[bool] = None			,			lowerCAmelCase_     :							Optional[float] = None			,			lowerCAmelCase_     :							Optional[bool] = None			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							Optional[Union[str, TensorType]] = None			,			lowerCAmelCase_     :							Union[str, ChannelDimension] = ChannelDimension.FIRST			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      _A:  List[str]					=       do_rescale if do_rescale is not None else self.do_rescale
      _A:  int					=       rescale_factor if rescale_factor is not None else self.rescale_factor
      _A:  str					=       do_pad if do_pad is not None else self.do_pad
      _A:  Union[str, Any]					=       pad_size if pad_size is not None else self.pad_size

      _A:  List[Any]					=       make_list_of_images(lowerCAmelCase_     )

      if not valid_images(lowerCAmelCase_     ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

      if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

      # All transformations expect numpy arrays.
      _A:  Union[str, Any]					=       [to_numpy_array(lowerCAmelCase_     ) for image in images]

      if do_rescale:
         _A:  str					=       [self.rescale(image=lowerCAmelCase_			,			scale=lowerCAmelCase_     ) for image in images]

      if do_pad:
         _A:  str					=       [self.pad(lowerCAmelCase_			,			size=lowerCAmelCase_     ) for image in images]

      _A:  Optional[Any]					=       [to_channel_dimension_format(lowerCAmelCase_			,			lowerCAmelCase_     ) for image in images]

      _A:  List[Any]					=       {'''pixel_values''': images}
      return BatchFeature(data=lowerCAmelCase_			,			tensor_type=lowerCAmelCase_     )



