'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : str					,    snake_case_       : Tuple			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   AlbertConfig.from_json_file(snake_case_			)
     print(f"""Building PyTorch model from configuration: {config}"""			)
     UpperCAmelCase_           =   AlbertForPreTraining(snake_case_			)

     # Load weights from tf checkpoint
     load_tf_weights_in_albert(snake_case_					,    snake_case_					,    snake_case_			)

     # Save pytorch-model
     print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
     torch.save(model.state_dict()					,    snake_case_			)


if __name__ == "__main__":
    SCREAMING_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.'
    )
    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





'''simple docstring'''





import os
from math import logaa





def 	lowerCAmelCase_				(					snake_case_       : str = "base_exp.txt"			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_			)					,    snake_case_			)			)			):
          UpperCAmelCase_    , UpperCAmelCase_           =   list(map(snake_case_					,    line.split(","			)			)			)
          if x * logaa(snake_case_			) > largest:
               UpperCAmelCase_           =   x * logaa(snake_case_			)
               UpperCAmelCase_           =   i + 1
     return result


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




