






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--mobilebert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained MobileBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)










'''simple docstring'''





import sys

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





def 		__snake_case(							_lowerCAmelCase = N						)						->					int:
      snake_case__    :       Dict       		= -sys.maxsize - 1
      for i in range(len(_lowerCAmelCase						) - 12						):
            snake_case__    :       Any       		= 1
            for j in range(13						):
                  product *= int(n[i + j]						)
            if product > largest_product:
                  snake_case__    :       List[str]       		= product
      return largest_product


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