"""simple docstring"""



import sys

__lowercase		:		Any														=							(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)





def    SCREAMING_SNAKE_CASE       (     snake_case = N):
     __snake_case				=							-sys.maxsize - 1
     for i in range(len(snake_case) - 12):
          __snake_case				=							1
          for j in range(13):
               product *= int(n[i + j])
          if product > largest_product:
               __snake_case				=							product
     return largest_product


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



from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

         __snake_case				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							make_list_of_images(A_    )

         if not valid_images(A_    ):
              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.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )