import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     if openai_config_file == "":
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTConfig()
     else:
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTConfig.from_json_file(_A    )
     SCREAMING_SNAKE_CASE__   =   OpenAIGPTModel(_A    )

     # Load weights from numpy
     load_tf_weights_in_openai_gpt(_A				,		_A				,		_A    )

     # Save pytorch-model
     SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''    )
     torch.save(model.state_dict()				,		_A    )
     print(F'''Save configuration file to {pytorch_config_dump_path}'''    )
     with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
          f.write(config.to_json_string()    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--openai_checkpoint_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the TensorFlow checkpoint path.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--openai_config_file''',
           default='''''',
           type=str,
           help=(
               '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       _SCREAMING_SNAKE_CASE					:  List[str]             =      parser.parse_args()
       convert_openai_checkpoint_to_pytorch(
           args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
       )





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() = }")


