import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      # Initialise PyTorch model
      SCREAMING_SNAKE_CASE_          =  MobileBertConfig.from_json_file(__UpperCamelCase				)
      print(F'''Building PyTorch model from configuration: {config}'''				)
      SCREAMING_SNAKE_CASE_          =  MobileBertForPreTraining(__UpperCamelCase				)
      # Load weights from tf checkpoint
      SCREAMING_SNAKE_CASE_          =  load_tf_weights_in_mobilebert(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)
      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()     ,      __UpperCamelCase				)


if __name__ == "__main__":
      A     :  Union[str, Any]					     =       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(
          "--mobilebert_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained MobileBERT 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     :  Union[str, Any]					     =       parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A     :  Dict					     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Optional[Any]					     =       ["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      A     :  Optional[int]					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


