






"""simple docstring"""


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()
_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:str     ,      A_				:str				):







  '''simple docstring'''

  a					:      Any    =       RobertaPreLayerNormConfig.from_pretrained(
      A_     ,      architectures=['RobertaPreLayerNormForMaskedLM']				)

  # convert state_dict
  a					:      Optional[int]    =       torch.load(hf_hub_download(repo_id=A_     ,      filename='pytorch_model.bin'				)				)
  a					:      Any    =       {}
  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.'				):
      a					:      List[str]    =       '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

    a					:      str    =       tensor_value

  a					:      Union[str, Any]    =       RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=A_     ,      config=A_     ,      state_dict=A_				)
  model.save_pretrained(A_				)

  # convert tokenizer
  a					:      str    =       AutoTokenizer.from_pretrained(A_				)
  tokenizer.save_pretrained(A_				)


if __name__ == "__main__":
   _UpperCamelCase    :   Tuple   			=						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.'
   )
   _UpperCamelCase    :   Optional[int]   			=						parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

  return AutoConfig.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoTokenizer.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

  return AutoModel.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

  return AutoModelForCausalLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

  return AutoModelForMaskedLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

  return AutoModelForSequenceClassification.from_pretrained(*A_     ,      **A_				)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoModelForQuestionAnswering.from_pretrained(*A_     ,      **A_				)




