






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_									=     AlbertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_									=     AlbertForPreTraining(__lowercase)

   # Load weights from tf checkpoint
   load_tf_weights_in_albert(__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__      :	Tuple       	=      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(
     """--albert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained ALBERT 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__      :	Any       	=      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)













import os
from math import logaa






def     _snake_case    (__lowercase = "base_exp.txt"):
   UpperCamelCase_									=     0
   UpperCamelCase_									=     0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__lowercase)    ,      __lowercase))):
      UpperCamelCase_		,      UpperCamelCase_									=     list(map(__lowercase    ,      line.split(',')))
      if x * logaa(__lowercase) > largest:
         UpperCamelCase_									=     x * logaa(__lowercase)
         UpperCamelCase_									=     i + 1
   return result


if __name__ == "__main__":
 print(solution())





