

"""simple docstring"""




from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  _a					):
      UpperCamelCase_      :		Any										=    ["""pixel_values"""]



      def __init__(      self				:    List[str]      , A__				:    str = True      , A__				:    List[Any] = None      , A__				:    Any = PILImageResampling.BICUBIC      , A__				:    int = True      , A__				:    int = None      , A__				:    Optional[int] = True      , A__				:    List[str] = 1 / 255      , A__				:    Any = True      , A__				:    Any = IMAGENET_DEFAULT_MEAN      , A__				:    Any = IMAGENET_DEFAULT_STD      , **A__				:    str      , )   ->					None:
        super().__init__(**A__		)
        _snake_case						=							size if size is not None else {'shortest_edge': 224}
        _snake_case						=							get_size_dict(A__      , default_to_square=A__		)
        _snake_case						=							crop_size if crop_size is not None else {'height': 224, 'width': 224}
        _snake_case						=							get_size_dict(A__      , param_name='''crop_size'''		)

        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							resample
        _snake_case						=							do_center_crop
        _snake_case						=							crop_size
        _snake_case						=							do_rescale
        _snake_case						=							rescale_factor
        _snake_case						=							do_normalize
        _snake_case						=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _snake_case						=							image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Dict      , A__				:    Union[str, Any]      , A__				:    str = PILImageResampling.BICUBIC      , A__				:    str = None      , **A__				:    Union[str, Any]      , )   ->					np.ndarray:
        _snake_case						=							get_size_dict(A__      , default_to_square=A__		)
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
          _snake_case						=							int((256 / 224) * size['''shortest_edge''']		)
          _snake_case						=							get_resize_output_image_size(A__      , size=A__      , default_to_square=A__		)
          _snake_case						=							{'height': output_size[0], 'width': output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
          raise ValueError(
              f"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""		)
        return resize(
            A__      , size=(size_dict['''height'''], size_dict['''width'''])      , resample=A__      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Union[str, Any]      , A__				:    Optional[int]      , A__				:    Optional[Any] = None      , **A__				:    int      , )   ->					np.ndarray:
        _snake_case						=							get_size_dict(A__		)
        if "height" not in size or "width" not in size:
          raise ValueError(f"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""		)
        return center_crop(A__      , size=(size['''height'''], size['''width'''])      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    str      , A__				:    str      , A__				:    str = None      , **A__				:    Any      , )   ->					np.ndarray:
        return rescale(A__      , scale=A__      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    Tuple      , A__				:    List[Any]      , A__				:    List[str] = None      , **A__				:    Tuple      , )   ->					np.ndarray:
        return normalize(A__      , mean=A__      , std=A__      , data_format=A__      , **A__		)







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    int      , A__				:    Tuple = None      , A__				:    str = None      , A__				:    Optional[Any] = None      , A__				:    int = None      , A__				:    Dict = None      , A__				:    int = None      , A__				:    str = None      , A__				:    Dict = None      , A__				:    Dict = None      , A__				:    List[str] = None      , A__				:    int = None      , A__				:    Tuple = ChannelDimension.FIRST      , **A__				:    Optional[int]      , )   ->					BatchFeature:
        _snake_case						=							do_resize if do_resize is not None else self.do_resize
        _snake_case						=							resample if resample is not None else self.resample
        _snake_case						=							do_center_crop if do_center_crop is not None else self.do_center_crop
        _snake_case						=							do_rescale if do_rescale is not None else self.do_rescale
        _snake_case						=							rescale_factor if rescale_factor is not None else self.rescale_factor
        _snake_case						=							do_normalize if do_normalize is not None else self.do_normalize
        _snake_case						=							image_mean if image_mean is not None else self.image_mean
        _snake_case						=							image_std if image_std is not None else self.image_std

        _snake_case						=							size if size is not None else self.size
        _snake_case						=							get_size_dict(A__      , default_to_square=A__		)
        _snake_case						=							crop_size if crop_size is not None else self.crop_size
        _snake_case						=							get_size_dict(A__      , param_name='''crop_size'''		)

        _snake_case						=							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:
          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.'''		)

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

        if do_resize:
          _snake_case						=							[self.resize(A__      , A__      , A__		) for image in images]

        if do_center_crop:
          _snake_case						=							[self.center_crop(A__      , A__		) for image in images]

        if do_rescale:
          _snake_case						=							[self.rescale(A__      , A__		) for image in images]

        if do_normalize:
          _snake_case						=							[self.normalize(A__      , A__      , A__		) for image in images]

        _snake_case						=							[to_channel_dimension_format(A__      , A__		) for image in images]

        _snake_case						=							{'pixel_values': images}
        return BatchFeature(data=A__      , tensor_type=A__		)




def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''					)
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]  # remove the leading "0b"
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]
  _snake_case						=							max(len(_UpperCamelCase					)			,	len(_UpperCamelCase					)					)
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)					)					)
      for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase					)			,	b_binary.zfill(_UpperCamelCase					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

