






'''simple docstring'''
import sys

UpperCamelCase__ =			(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase = N							):


   """simple docstring"""




   lowercase_	:    str           =     -sys.maxsize - 1
   for i in range(len(_UpperCamelCase							) - 12							):
      lowercase_	:    List[str]           =     1
      for j in range(13							):
         product *= int(n[i + j]							)
      if product > largest_product:
         lowercase_	:    int           =     product
   return largest_product


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












'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Tuple  ,     a  :       NestedDataStructureLike[PathLike]  ,     a  :       Optional[NamedSplit] = None  ,     a  :       Optional[Features] = None  ,     a  :       str = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       Optional[str] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






       super().__init__(
           a  ,     split=a  ,     features=a  ,     cache_dir=a  ,     keep_in_memory=a  ,     streaming=a  ,     num_proc=a  ,     **a  ,     )
       lowercase_	:    str           =     field
       lowercase_	:    Optional[Any]           =     path_or_paths if isinstance(a  ,     a			) else {self.split: path_or_paths}
       lowercase_	:    Any           =     Json(
           cache_dir=a  ,     data_files=a  ,     features=a  ,     field=a  ,     **a  ,     )





    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       if self.streaming:
          lowercase_	:    Any           =     self.builder.as_streaming_dataset(split=self.split			)
       # Build regular (map-style) dataset
       else:
          lowercase_	:    Dict           =     None
          lowercase_	:    Optional[int]           =     None
          lowercase_	:    str           =     None
          lowercase_	:    str           =     None

          self.builder.download_and_prepare(
              download_config=a  ,     download_mode=a  ,     verification_mode=a  ,     base_path=a  ,     num_proc=self.num_proc  ,     )
          lowercase_	:    int           =     self.builder.as_dataset(
              split=self.split  ,     verification_mode=a  ,     in_memory=self.keep_in_memory			)
       return dataset


class        _UpperCAmelCase						:




    def __init__(			self  :       str  ,     a  :       Dataset  ,     a  :       Union[PathLike, BinaryIO]  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






       if num_proc is not None and num_proc <= 0:
          raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""			)

       lowercase_	:    Dict           =     dataset
       lowercase_	:    Optional[int]           =     path_or_buf
       lowercase_	:    List[Any]           =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowercase_	:    Optional[Any]           =     num_proc
       lowercase_	:    List[Any]           =     "utf-8"
       lowercase_	:    List[str]           =     to_json_kwargs




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    str           =     self.to_json_kwargs.pop("path_or_buf"  ,     a			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("orient"  ,     "records"			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("lines"  ,     True if orient == "records" else False			)
       lowercase_	:    List[str]           =     self.to_json_kwargs.pop("index"  ,     False if orient in ["split", "table"] else True			)
       lowercase_	:    int           =     self.to_json_kwargs.pop("compression"  ,     a			)

       if compression not in [None, "infer", "gzip", "bz2", "xz"]:
          raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""			)

       if isinstance(self.path_or_buf  ,     (str, bytes, os.PathLike)			):
          with fsspec.open(self.path_or_buf  ,     "wb"  ,     compression=a			) as buffer:
             lowercase_	:    Dict           =     self._write(file_obj=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **self.to_json_kwargs			)
       else:
          if compression:
             raise NotImplementedError(
                 f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                 " was passed. Please provide a local path instead."			)
          lowercase_	:    Dict           =     self._write(
              file_obj=self.path_or_buf  ,     orient=a  ,     lines=a  ,     index=a  ,     **self.to_json_kwargs			)
       return written




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     args

       lowercase_	:    Optional[int]           =     query_table(
           table=self.dataset.data  ,     key=slice(a  ,     offset + self.batch_size			)  ,     indices=self.dataset._indices  ,     )
       lowercase_	:    Dict           =     batch.to_pandas().to_json(
           path_or_buf=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **a			)
       if not json_str.endswith("\n"			):
          json_str += "\n"
       return json_str.encode(self.encoding			)





    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       BinaryIO  ,     a  :       int  ,     a  :       str  ,     a  :       Union[str, Any]  ,     **a  :       str  ,     ):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     0

       if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0  ,     len(self.dataset			)  ,     self.batch_size			)  ,     unit="ba"  ,     disable=not logging.is_progress_bar_enabled()  ,     desc="Creating json from Arrow format"  ,     ):
             lowercase_	:    Dict           =     self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
             written += file_obj.write(a			)
       else:
          lowercase_    ,		lowercase_	:    Any           =     len(self.dataset			), self.batch_size
          with multiprocessing.Pool(self.num_proc			) as pool:
             for json_str in logging.tqdm(
                 pool.imap(
                     self._batch_json  ,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0  ,     a  ,     a			)]  ,     )  ,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,     unit="ba"  ,     disable=not logging.is_progress_bar_enabled()  ,     desc="Creating json from Arrow format"  ,     ):
                written += file_obj.write(a			)

       return written




