






import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  RobertaPreLayerNormConfig.from_pretrained(
          _UpperCamelCase       ,							architectures=['RobertaPreLayerNormForMaskedLM'])

      # convert state_dict
      UpperCamelCase					       =  torch.load(hf_hub_download(repo_id=_UpperCamelCase       ,							filename='pytorch_model.bin'))
      UpperCamelCase					       =  {}
      for tensor_key, tensor_value in original_state_dict.items():
            # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
            if tensor_key.startswith('roberta.'):
                  UpperCamelCase					       =  'roberta_prelayernorm.' + tensor_key[len('roberta.') :]

            # The original implementation contains weights which are not used, remove them from the state_dict
            if tensor_key.endswith('.self.LayerNorm.weight') or tensor_key.endswith('.self.LayerNorm.bias'):
                  continue

            UpperCamelCase					       =  tensor_value

      UpperCamelCase					       =  RobertaPreLayerNormForMaskedLM.from_pretrained(
          pretrained_model_name_or_path=_UpperCamelCase       ,							config=_UpperCamelCase       ,							state_dict=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)

      # convert tokenizer
      UpperCamelCase					       =  AutoTokenizer.from_pretrained(_UpperCamelCase)
      tokenizer.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  str        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint-repo''',
           default=None,
           type=str,
           required=True,
           help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       __magic_name__   :  Optional[Any]        =							parser.parse_args()
       convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)











from pathlib import Path

import fire
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  Path(_UpperCamelCase)
      UpperCamelCase					       =  Path(_UpperCamelCase)
      dest_dir.mkdir(exist_ok=_UpperCamelCase)
      for path in src_dir.iterdir():
            UpperCamelCase					       =  [x.rstrip() for x in list(path.open().readlines())][:n]
            UpperCamelCase					       =  dest_dir.joinpath(path.name)
            print(_UpperCamelCase)
            dest_path.open('w').write('\n'.join(_UpperCamelCase))


if __name__ == "__main__":
       fire.Fire(minify)



