






'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  )    -> str:







   """simple docstring"""
   if a < 0 or b < 0:
      raise ValueError('the value of both inputs must be positive'  )

   __snake_case				:      int     							= str(bin(A  )  )[2:]  # remove the leading "0b"
   __snake_case				:      Optional[int]     							= str(bin(A  )  )[2:]  # remove the leading "0b"

   __snake_case				:      List[Any]     							= max(len(A  )  ,		len(A  )  )

   return "0b" + "".join(
       str(int(char_a == '1' and char_b == '1'  )  )
       for char_a, char_b in zip(a_binary.zfill(A  )  ,		b_binary.zfill(A  )  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()







'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__A    	=   logging.get_logger(__name__)




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""pixel_values"""]







   def __init__(self					,   __a = True					,   __a = None					,   __a = None					,   __a = PILImageResampling.BILINEAR					,   __a = True					,   __a = 1 / 2_5_5					,   __a = True					,   __a = None					,   __a = None					,   **__a					,   )			->      None:
      """simple docstring"""






      super().__init__(**__a)
      __snake_case				:      Tuple     							= size if size is not None else {'shortest_edge': 3_8_4}
      __snake_case				:      List[Any]     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      int     							= do_resize
      __snake_case				:      List[str]     							= size
      # Default value set here for backwards compatibility where the value in config is None
      __snake_case				:      Any     							= crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
      __snake_case				:      Tuple     							= resample
      __snake_case				:      Dict     							= do_rescale
      __snake_case				:      Any     							= rescale_factor
      __snake_case				:      str     							= do_normalize
      __snake_case				:      Union[str, Any]     							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __snake_case				:      Dict     							= image_std if image_std is not None else IMAGENET_STANDARD_STD







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = PILImageResampling.BICUBIC					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Dict     							= get_size_dict(__a					,   default_to_square=__a)
      if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""")
      __snake_case				:      List[str]     							= size['shortest_edge']

      if shortest_edge < 3_8_4:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         __snake_case				:      Any     							= int(shortest_edge / crop_pct)
         __snake_case				:      Any     							= get_resize_output_image_size(__a					,   size=__a					,   default_to_square=__a)
         __snake_case				:      int     							= resize(image=__a					,   size=__a					,   resample=__a					,   data_format=__a					,   **__a)
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=__a					,   size=(shortest_edge, shortest_edge)					,   data_format=__a					,   **__a)
      else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             __a					,   size=(shortest_edge, shortest_edge)					,   resample=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      Any:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = ChannelDimension.FIRST					,   **__a					,   )			->      PIL.Image.Image:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= do_resize if do_resize is not None else self.do_resize
      __snake_case				:      Dict     							= crop_pct if crop_pct is not None else self.crop_pct
      __snake_case				:      Tuple     							= resample if resample is not None else self.resample
      __snake_case				:      Any     							= do_rescale if do_rescale is not None else self.do_rescale
      __snake_case				:      Any     							= rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case				:      Union[str, Any]     							= do_normalize if do_normalize is not None else self.do_normalize
      __snake_case				:      Optional[int]     							= image_mean if image_mean is not None else self.image_mean
      __snake_case				:      Optional[Any]     							= image_std if image_std is not None else self.image_std

      __snake_case				:      List[str]     							= size if size is not None else self.size
      __snake_case				:      Any     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      Dict     							= make_list_of_images(__a)

      if not valid_images(__a):
         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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
         raise ValueError('crop_pct must be specified if size < 384.')

      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.')

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

      if do_resize:
         __snake_case				:      Optional[int]     							= [self.resize(image=__a					,   size=__a					,   crop_pct=__a					,   resample=__a) for image in images]

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

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

      __snake_case				:      Dict     							= [to_channel_dimension_format(__a					,   __a) for image in images]

      __snake_case				:      Union[str, Any]     							= {'pixel_values': images}
      return BatchFeature(data=__a					,   tensor_type=__a)