



'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # Initialise PyTorch model
  a						: str					       =					RemBertConfig.from_json_file(_A    )
  print('Building PyTorch model from configuration: {}'.format(str(_A    )    )    )
  a						: Dict					       =					RemBertModel(_A    )

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

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


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						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.'
     )
     lowerCAmelCase: Union[str, Any]							=						parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output