






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:
  # Initialise PyTorch model
  UpperCamelCase_:   Union[str, Any]        =							RemBertConfig.from_json_file(UpperCAmelCase__    )
  print('Building PyTorch model from configuration: {}'.format(str(UpperCAmelCase__    )    )    )
  UpperCamelCase_:   Any        =							RemBertModel(UpperCAmelCase__    )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

  # Save pytorch-model
  print('Save PyTorch model to {}'.format(UpperCAmelCase__    )    )
  torch.save(model.state_dict()      ,       UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						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(
       '--rembert_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained RemBERT 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_						:						List[Any]						      =					parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
