

import sys

lowercase_   							=  (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)
def 	a (     A__      :  str = N	)       -> int:





      """simple docstring"""



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


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





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


