



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, 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


SCREAMING_SNAKE_CASE_:int             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["pixel_values"]






    def __init__(       self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PIL.Image.BICUBIC,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)
        A :       List[str]          =   size if size is not None else {"""height""": 256, """width""": 256}
        A :       Tuple          =   get_size_dict(lowerCamelCase__		)
        A :       Tuple          =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A :       Tuple          =   get_size_dict(lowerCamelCase__,		param_name="""crop_size"""		)

        A :       List[Any]          =   do_resize
        A :       Dict          =   size
        A :       Optional[Any]          =   resample
        A :       str          =   do_center_crop
        A :       Dict          =   crop_size
        A :       List[str]          =   do_rescale
        A :       List[Any]          =   rescale_factor
        A :       List[str]          =   do_normalize
        A :       Optional[int]          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A :       Tuple          =   image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PIL.Image.BICUBIC,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Any          =   get_size_dict(lowerCamelCase__		)
        if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
        return resize(
            lowerCamelCase__,		size=(size["""height"""], size["""width"""]),		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Union[str, Any]          =   get_size_dict(lowerCamelCase__		)
        if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
        return center_crop(lowerCamelCase__,		size=(size["""height"""], size["""width"""]),		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return rescale(lowerCamelCase__,		scale=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return normalize(lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__=None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
        A :       Optional[Any]          =   do_resize if do_resize is not None else self.do_resize
        A :       Dict          =   resample if resample is not None else self.resample
        A :       List[Any]          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        A :       int          =   do_rescale if do_rescale is not None else self.do_rescale
        A :       Tuple          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A :       Dict          =   do_normalize if do_normalize is not None else self.do_normalize
        A :       Union[str, Any]          =   image_mean if image_mean is not None else self.image_mean
        A :       Optional[Any]          =   image_std if image_std is not None else self.image_std

        A :       Optional[int]          =   size if size is not None else self.size
        A :       Optional[int]          =   get_size_dict(lowerCamelCase__		)
        A :       int          =   crop_size if crop_size is not None else self.crop_size
        A :       Tuple          =   get_size_dict(lowerCamelCase__,		param_name="""crop_size"""		)

        A :       Tuple          =   make_list_of_images(lowerCamelCase__		)

        if not valid_images(lowerCamelCase__		):
            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_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.
        A :       Optional[Any]          =   [to_numpy_array(lowerCamelCase__		) for image in images]

        if do_resize:
            A :       List[str]          =   [self.resize(image=lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__		) for image in images]

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

        if do_rescale:
            A :       Optional[Any]          =   [self.rescale(image=lowerCamelCase__,		scale=lowerCamelCase__		) for image in images]

        if do_normalize:
            A :       Tuple          =   [self.normalize(image=lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__		) for image in images]

        A :       Any          =   [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

        A :       int          =   {"""pixel_values""": images}
        return BatchFeature(data=lowerCamelCase__,		tensor_type=lowerCamelCase__		)







def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  int:



    """simple docstring"""






    while second != 0:
        A :       int          =   first & second
        first ^= second
        A :       Tuple          =   c << 1
    return first


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_:int             =						int(input("""Enter the first number: """).strip())
     SCREAMING_SNAKE_CASE_:Optional[int]             =						int(input("""Enter the second number: """).strip())
     print(F"""{add(first, second) = }""")


