


"""simple docstring"""



import torch







def     __lowerCAmelCase		(		)      ->		Any:


    """simple docstring"""





    if torch.cuda.is_available():
        snake_case       : Tuple    =       torch.cuda.device_count()
    else:
        snake_case       : List[Any]    =       0
    print(F'Successfully ran on {num_gpus} GPUs'					)


if __name__ == "__main__":
  main()









"""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		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       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       =       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       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





