



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						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    BertConfig.from_json_file(a__					)
			print(f'''Building PyTorch model from configuration: {config}'''					)
			A__       =    BertForPreTraining(a__					)

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

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


if __name__ == "__main__":
		SCREAMING_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(
		    '''--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.'''
		)
		SCREAMING_SNAKE_CASE__       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









from typing import TYPE_CHECKING

from ..utils import _LazyModule


SCREAMING_SNAKE_CASE__       =      {
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
							from .config import (
							    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
							    OnnxConfig,
							    OnnxConfigWithPast,
							    OnnxSeqaSeqConfigWithPast,
							    PatchingSpec,
							)
							from .convert import export, validate_model_outputs
							from .features import FeaturesManager
							from .utils import ParameterFormat, compute_serialized_parameters_size

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




