
"""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 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


a		:					List[str]    	=					logging.get_logger(__name__)







class 						lowercase(_lowercase					):
     __snake_case:  Optional[Any]  				=    ['pixel_values']



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 1 / 2_5_5    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    None:







         """simple docstring"""



         super().__init__(**__SCREAMING_SNAKE_CASE      )
         a__    = size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         a__    = get_size_dict(__SCREAMING_SNAKE_CASE      )
         a__    = do_resize
         a__    = do_rescale
         a__    = do_normalize
         a__    = size
         a__    = resample
         a__    = rescale_factor
         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    np.ndarray:







         """simple docstring"""



         a__    = get_size_dict(__SCREAMING_SNAKE_CASE      )
         if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'      )
         return resize(
             __SCREAMING_SNAKE_CASE    ,							size=(size['height'], size['width'])    ,							resample=__SCREAMING_SNAKE_CASE    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE      )		->    np.ndarray:







         """simple docstring"""



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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    np.ndarray:







         """simple docstring"""



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




     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    int:







         """simple docstring"""



         a__    = do_resize if do_resize is not None else self.do_resize
         a__    = do_rescale if do_rescale is not None else self.do_rescale
         a__    = do_normalize if do_normalize is not None else self.do_normalize
         a__    = resample if resample is not None else self.resample
         a__    = rescale_factor if rescale_factor is not None else self.rescale_factor
         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__    = size if size is not None else self.size
         a__    = get_size_dict(__SCREAMING_SNAKE_CASE      )

         a__    = 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_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

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

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

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

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

         a__    = [to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      ) for image in images]

         a__    = {'pixel_values': images}
         return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,							tensor_type=__SCREAMING_SNAKE_CASE      )




"""simple docstring"""




a		:					str    	=					(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
a		:					tuple    	=					(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    a__    = 0
    a__    = 0
    a__    = n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    a__    = n
    while temp > 0:
        a__    = temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Init a "histogram" of the digits
    a__    = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    a__    = 0
    a__    = 0
    a__    = n
    while temp > 0:
        a__  ,    a__    = divmod(UpperCamelCase       ,		10    )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(UpperCamelCase       ,		range(len(UpperCamelCase    )    )    ):
        total += cnt * i**digit_total

    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False
    a__    = len(str(UpperCamelCase    )    )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(UpperCamelCase    ) ** expo for i in str(UpperCamelCase    )    )



def 						__magic_name__		(				)    ->	List[Any]:
    a__    = int(input('Enter an integer to see if it is an Armstrong number: '    ).strip()    )
    print(f'{num} is {"" if armstrong_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if narcissistic_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if pluperfect_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


