

'''simple docstring'''

import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					int      , __lowerCamelCase				:					str					)		->int:
    _SCREAMING_SNAKE_CASE             =    RoFormerConfig.from_json_file(_A					)
    print(F'Building PyTorch model from configuration: {config}'					)
    _SCREAMING_SNAKE_CASE             =    RoFormerForMaskedLM(_A					)

    # Load weights from tf checkpoint
    load_tf_weights_in_roformer(_A      , _A      , _A					)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'					)
    torch.save(model.state_dict()      , _A      , _use_new_zipfile_serialization=_A					)


if __name__ == "__main__":
      lowercase_			       =				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(
          """--bert_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT 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."""
      )
      lowercase_			       =				parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''

import math
from timeit import timeit





def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
    _SCREAMING_SNAKE_CASE             =    0
    _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
    while True:
        _SCREAMING_SNAKE_CASE             =    n // 10
        digits += 1
        if n == 0:
            break
    return digits





def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
    return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)





def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
    return len(str(abs(__lowerCamelCase					)					)					)





def lowerCamelCase		(  )		->None:
    from collections.abc import Callable

    def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
        _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
        _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
        print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)

    for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
        print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



