from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					:  int             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	__snake_case	):






  """simple docstring"""


  a									= ["pixel_values"]






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Union[int, float] = 1 / 255     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		int = 8     ,       **__lowerCamelCase						:		List[str]     ,       )		->							None:
       super().__init__(**lowerCamelCase_ )

       SCREAMING_SNAKE_CASE__   =   do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_pad
       SCREAMING_SNAKE_CASE__   =   pad_size






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






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		np.ndarray     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Optional[Union[str, ChannelDimension]] = None )		->							int:
       SCREAMING_SNAKE_CASE__   =   get_image_size(lowerCamelCase_ )
       SCREAMING_SNAKE_CASE__   =   (old_height // size + 1) * size - old_height
       SCREAMING_SNAKE_CASE__   =   (old_width // size + 1) * size - old_width

       return pad(lowerCamelCase_     ,       ((0, pad_height), (0, pad_width))     ,       mode='''symmetric'''     ,       data_format=lowerCamelCase_ )







  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		ImageInput     ,       __lowerCamelCase						:		Optional[bool] = None     ,       __lowerCamelCase						:		Optional[float] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		Union[str, ChannelDimension] = ChannelDimension.FIRST     ,       **__lowerCamelCase						:		str     ,       )		->							str:
       SCREAMING_SNAKE_CASE__   =   do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE__   =   pad_size if pad_size is not None else self.pad_size

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

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

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

       if do_pad:
            SCREAMING_SNAKE_CASE__   =   [self.pad(lowerCamelCase_     ,       size=lowerCamelCase_ ) for image in images]

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

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





from functools import reduce

_SCREAMING_SNAKE_CASE					:  Any             =      (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def     UpperCAmelCase_       (						_A = N    ):





     '''simple docstring'''




     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda _A				,		_A    : str(int(_A    ) * int(_A    )    )				,		n[i : i + 13]    )    )
         for i in range(len(_A    ) - 12    )    )


if __name__ == "__main__":
       print(F"{solution() = }")


