



'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



       """simple docstring"""



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

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

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


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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase	 =       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_bertweet import BertweetTokenizer

else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



