






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			Any    )						->      str:





    """simple docstring"""

    __A         =	BertConfig.from_json_file(_lowerCamelCase    )
    print(f"Building PyTorch model from configuration: {config}"    )
    __A         =	BertForPreTraining(_lowerCamelCase    )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(_lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase    )

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


if __name__ == "__main__":
  __a				  =   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."
  )
  __a				  =   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __A         =	set()

    # Replace all the whitespace in our sentence
    __A         =	input_str.replace(""" """			,				""""""    )
    for alpha in input_str:
        if "a" <= alpha.lower() <= "z":
            frequency.add(alpha.lower()    )
    return len(__lowercase    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __A         =	[False] * 2_6
    for char in input_str:
        if char.islower():
            __A         =	True
        elif char.isupper():
            __A         =	True
    return all(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    return len({char for char in input_str.lower() if char.isalpha()}    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( )						->      None:





    """simple docstring"""

    from timeit import timeit

    __A         =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
    print(timeit("""is_pangram()"""			,				setup=__lowercase    )    )
    print(timeit("""is_pangram_faster()"""			,				setup=__lowercase    )    )
    print(timeit("""is_pangram_fastest()"""			,				setup=__lowercase    )    )
    # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
    # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()




