

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


lowercase_       		=  logging.get_logger(__name__)


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							['pixel_values']


    def __init__(      self :       int,lowercase_ :       Tuple = True,lowercase_ :       Any = None,lowercase_ :       int = PILImageResampling.BILINEAR,lowercase_ :       str = True,lowercase_ :       Optional[Any] = None,lowercase_ :       Union[str, Any] = True,lowercase_ :       int = 1 / 2_5_5,lowercase_ :       Union[str, Any] = True,lowercase_ :       str = None,lowercase_ :       Optional[int] = None,**lowercase_ :       Optional[int],)->    None:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        A__          =   size if size is not None else {'''shortest_edge''': 2_5_6}
        A__          =   get_size_dict(lowercase_,default_to_square=lowercase_		)
        A__          =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
        A__          =   get_size_dict(lowercase_		)
        A__          =   do_resize
        A__          =   size
        A__          =   resample
        A__          =   do_center_crop
        A__          =   crop_size
        A__          =   do_rescale
        A__          =   rescale_factor
        A__          =   do_normalize
        A__          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A__          =   image_std if image_std is not None else IMAGENET_STANDARD_STD


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       str,lowercase_ :       Any,lowercase_ :       Tuple = PILImageResampling.BICUBIC,lowercase_ :       Any = None,**lowercase_ :       Dict,)->    np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Any,lowercase_ :       str,lowercase_ :       Tuple,lowercase_ :       List[Any] = None,**lowercase_ :       str,)->    np.ndarray:





        '''simple docstring'''







        A__          =   get_size_dict(lowercase_		)
        return center_crop(lowercase_,size=(size['height'], size['width']),data_format=lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       str,lowercase_ :       List[Any],lowercase_ :       int,lowercase_ :       int = None,**lowercase_ :       str		)->    np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       Optional[Any],lowercase_ :       Optional[int],lowercase_ :       str = None,**lowercase_ :       List[Any],)->    np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Any,lowercase_ :       Tuple,lowercase_ :       List[str] = None,lowercase_ :       Dict = None,lowercase_ :       Any = None,lowercase_ :       Tuple = None,lowercase_ :       str = None,lowercase_ :       Union[str, Any] = None,lowercase_ :       List[str] = None,lowercase_ :       Optional[Any] = None,lowercase_ :       Dict = None,lowercase_ :       Dict = None,lowercase_ :       int = None,lowercase_ :       int = ChannelDimension.FIRST,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        A__          =   do_resize if do_resize is not None else self.do_resize
        A__          =   size if size is not None else self.size
        A__          =   get_size_dict(lowercase_,default_to_square=lowercase_		)
        A__          =   resample if resample is not None else self.resample
        A__          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        A__          =   crop_size if crop_size is not None else self.crop_size
        A__          =   get_size_dict(lowercase_		)
        A__          =   do_rescale if do_rescale is not None else self.do_rescale
        A__          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A__          =   do_normalize if do_normalize is not None else self.do_normalize
        A__          =   image_mean if image_mean is not None else self.image_mean
        A__          =   image_std if image_std is not None else self.image_std

        A__          =   make_list_of_images(lowercase_		)

        if not valid_images(lowercase_		):
            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.
        A__          =   [to_numpy_array(lowercase_		) for image in images]

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

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

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

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

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

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






"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    if a < 0 or b < 0:
        raise ValueError('''the value of both inputs must be positive'''							)
    _UpperCAmelCase							:  List[str]											=    str(bin(UpperCamelCase__							)							)[2:]  # remove the leading "0b"
    _UpperCAmelCase							:  str											=    str(bin(UpperCamelCase__							)							)[2:]
    _UpperCAmelCase							:  List[str]											=    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()


