

"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A      		=     logging.get_logger(__name__)



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



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

        _snake_case						=							do_resize
        _snake_case						=							do_rescale
        _snake_case						=							do_normalize
        _snake_case						=							do_center_crop
        _snake_case						=							crop_size
        _snake_case						=							size
        _snake_case						=							resample
        _snake_case						=							rescale_factor
        _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				:    Tuple      , A__				:    np.ndarray      , A__				:    Dict[str, int]      , A__				:    PILImageResampling = PILImageResampling.BILINEAR      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    Optional[Any]      , )   ->					Optional[Any]:
        _snake_case						=							get_size_dict(__a		)

        if "shortest_edge" in size:
          _snake_case						=							get_resize_output_image_size(__a      , size=size['''shortest_edge''']      , default_to_square=__a		)
          # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
          _snake_case						=							(size['''height'''], size['''width'''])
        else:
          raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""		)
        return resize(__a      , size=__a      , resample=__a      , data_format=__a      , **__a		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    np.ndarray      , A__				:    Dict[str, int]      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    int      , )   ->					Union[str, Any]:
        _snake_case						=							get_size_dict(__a		)
        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(__a      , size=(size['''height'''], size['''width'''])      , data_format=__a      , **__a		)



      def 						UpperCamelCase_		(      self				:    str      , A__				:    np.ndarray      , A__				:    float      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    List[str]		)   ->					Optional[int]:
        return rescale(__a      , scale=__a      , data_format=__a      , **__a		)



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







      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    ImageInput      , A__				:    Optional[bool] = None      , A__				:    Dict[str, int] = None      , A__				:    PILImageResampling = None      , A__				:    bool = None      , A__				:    int = None      , A__				:    Optional[bool] = None      , A__				:    Optional[float] = None      , A__				:    Optional[bool] = None      , A__				:    Optional[Union[float, List[float]]] = None      , A__				:    Optional[Union[float, List[float]]] = None      , A__				:    Optional[Union[str, TensorType]] = None      , A__				:    Union[str, ChannelDimension] = ChannelDimension.FIRST      , **A__				:    Dict      , )   ->					List[Any]:
        _snake_case						=							do_resize if do_resize is not None else self.do_resize
        _snake_case						=							do_rescale if do_rescale is not None else self.do_rescale
        _snake_case						=							do_normalize if do_normalize is not None else self.do_normalize
        _snake_case						=							do_center_crop if do_center_crop is not None else self.do_center_crop
        _snake_case						=							crop_size if crop_size is not None else self.crop_size
        _snake_case						=							get_size_dict(__a      , param_name='''crop_size'''      , default_to_square=__a		)
        _snake_case						=							resample if resample is not None else self.resample
        _snake_case						=							rescale_factor if rescale_factor is not None else self.rescale_factor
        _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		)

        if not is_batched(__a		):
          _snake_case						=							[images]

        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.'''		)

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

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

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

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

        if do_normalize:
          _snake_case						=							[self.normalize(image=__a      , mean=__a      , std=__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()

