




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, logging


UpperCamelCase								=	logging.get_logger(__name__)





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Dict[str, int]] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			None:
         super().__init__(**SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					size if size is not None else {"shortest_edge": 256}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size
         lowerCAmelCase__            =					resample
         lowerCAmelCase__            =					do_center_crop
         lowerCAmelCase__            =					crop_size
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__            =					image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BICUBIC  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	List[Any]  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'  )
         lowerCAmelCase__            =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__  ,      size=size["shortest_edge"]  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         return resize(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         return center_crop(SCREAMING_SNAKE_CASE__  ,      size=(size["height"], size["width"])  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	float  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			np.ndarray:
         return rescale(SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


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


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[float] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	Union[str, ChannelDimension] = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			List[Any]:
         lowerCAmelCase__            =					do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__            =					size if size is not None else self.size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					resample if resample is not None else self.resample
         lowerCAmelCase__            =					do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__            =					crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__            =					do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__            =					image_std if image_std is not None else self.image_std

         lowerCAmelCase__            =					make_list_of_images(SCREAMING_SNAKE_CASE__  )

         if not valid_images(SCREAMING_SNAKE_CASE__  ):
              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.
         lowerCAmelCase__            =					[to_numpy_array(SCREAMING_SNAKE_CASE__  ) for image in images]

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

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

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

         if do_normalize:
              lowerCAmelCase__            =					[self.normalize(image=SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ) for image in images]

         lowerCAmelCase__            =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) for image in images]

         lowerCAmelCase__            =					{"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,      tensor_type=SCREAMING_SNAKE_CASE__  )



def 						lowerCamelCase__      ( __A    :int	,__A    :int		):


       """simple docstring"""




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

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

       __snake_case       	=							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()

