






import logging

from transformers import PretrainedConfig


A_						:						int						      =					logging.getLogger(__name__)


A_						:						Union[str, Any]						      =					{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Optional[Any]										='''bertabs'''





       def __init__(   self    ,     _lowerCamelCase=3_0_5_2_2    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=6    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=8    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=0.2    ,     _lowerCamelCase=6    ,     _lowerCamelCase=7_6_8    ,     _lowerCamelCase=8    ,     _lowerCamelCase=2_0_4_8    ,     _lowerCamelCase=0.2    ,     **_lowerCamelCase    ,     ):
         super().__init__(**_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							vocab_size
         UpperCamelCase_:   Union[str, Any]        =							max_pos

         UpperCamelCase_:   str        =							enc_layers
         UpperCamelCase_:   int        =							enc_hidden_size
         UpperCamelCase_:   str        =							enc_heads
         UpperCamelCase_:   Any        =							enc_ff_size
         UpperCamelCase_:   Optional[int]        =							enc_dropout

         UpperCamelCase_:   List[Any]        =							dec_layers
         UpperCamelCase_:   Union[str, Any]        =							dec_hidden_size
         UpperCamelCase_:   Optional[Any]        =							dec_heads
         UpperCamelCase_:   Union[str, Any]        =							dec_ff_size
         UpperCamelCase_:   str        =							dec_dropout
'''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    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	# Initialise PyTorch model
	__UpperCAmelCase      					=      BertConfig.from_json_file(UpperCamelCase__							)
	print(f"""Building PyTorch model from configuration: {config}"""							)
	__UpperCAmelCase      					=      BertForPreTraining(UpperCamelCase__							)

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

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


if __name__ == "__main__":
					__lowerCAmelCase						:							Tuple    							=						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."
					)
					__lowerCAmelCase						:							Optional[Any]    							=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
