


'''simple docstring'''


def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Tuple          =				int(lowerCamelCase)
      if decimal in (0, 1):  # Exit cases for the recursion
            return str(lowerCamelCase)
      A_   ,					A_						:  List[str]          =				divmod(lowerCamelCase							,						2)
      return binary_recursive(lowerCamelCase) + str(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  Optional[int]          =				str(lowerCamelCase).strip()
      if not number:
            raise ValueError("""No input value was provided""")
      A_						:  int          =				"""-""" if number.startswith("""-""") else """"""
      A_						:  Dict          =				number.lstrip("""-""")
      if not number.isnumeric():
            raise ValueError("""Input value is not an integer""")
      return F'{negative}0b{binary_recursive(int(lowerCamelCase))}'


if __name__ == "__main__":
     from doctest import testmod

     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,
    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


__magic_name__			=      logging.get_logger(__name__)


if is_vision_available():
     import PIL



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Optional[Any]	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       bool = True	,_a :       Dict[str, int] = None	,_a :       bool = True	,_a :       Union[int, float] = 1 / 255	,_a :       bool = True	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = True	,**_a :       Dict	,):

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Tuple          =				size if size is not None else {"""shortest_edge""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Union[str, Any]          =				resample
        A_						:  Dict          =				do_center_crop
        A_						:  List[str]          =				crop_size
        A_						:  Any          =				do_rescale
        A_						:  Union[str, Any]          =				rescale_factor
        A_						:  Any          =				do_normalize
        A_						:  List[str]          =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A_						:  List[Any]          =				image_std if image_std is not None else OPENAI_CLIP_STD
        A_						:  Tuple          =				do_convert_rgb






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BICUBIC	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
        A_						:  Tuple          =				get_resize_output_image_size(_a	,size=size["""shortest_edge"""]	,default_to_square=_a )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Optional[int]	,):

        '''simple docstring'''




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

        '''simple docstring'''




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






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

        '''simple docstring'''




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






  def        _a							(							self :       Optional[Any]	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       int = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       bool = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,**_a :       int	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				size if size is not None else self.size
        A_						:  Optional[int]          =				get_size_dict(_a	,param_name="""size"""	,default_to_square=_a )
        A_						:  List[str]          =				resample if resample is not None else self.resample
        A_						:  int          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Any          =				crop_size if crop_size is not None else self.crop_size
        A_						:  int          =				get_size_dict(_a	,param_name="""crop_size"""	,default_to_square=_a )
        A_						:  List[Any]          =				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_						:  Any          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  int          =				image_mean if image_mean is not None else self.image_mean
        A_						:  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_						:  int          =				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.""" )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              A_						:  Optional[int]          =				[convert_to_rgb(_a ) for image in images]

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

        if do_resize:
              A_						:  int          =				[self.resize(image=_a	,size=_a	,resample=_a ) for image in images]

        if do_center_crop:
              A_						:  Tuple          =				[self.center_crop(image=_a	,size=_a ) for image in images]

        if do_rescale:
              A_						:  List[str]          =				[self.rescale(image=_a	,scale=_a ) for image in images]

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

        A_						:  List[str]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  List[str]          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )
