
"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__							: int 							=						BertConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__							: int 							=						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__      :	Optional[int]          =    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__      :	Any          =    parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





"""simple docstring"""




from typing import TYPE_CHECKING

from ....utils import _LazyModule


A__      :	List[str]          =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    A__      :	Any          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



