


"""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()
__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				RobertaPreLayerNormConfig.from_pretrained(
         _lowerCAmelCase,     architectures=['''RobertaPreLayerNormForMaskedLM''']       )

     # convert state_dict
     _a    	=				torch.load(hf_hub_download(repo_id=_lowerCAmelCase,     filename='''pytorch_model.bin'''       )       )
     _a    	=				{}
     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    	=				'''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    	=				tensor_value

     _a    	=				RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=_lowerCAmelCase,     config=_lowerCAmelCase,     state_dict=_lowerCAmelCase       )
     model.save_pretrained(_lowerCAmelCase       )

     # convert tokenizer
     _a    	=				AutoTokenizer.from_pretrained(_lowerCAmelCase       )
     tokenizer.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       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.'''
   )
   __snake_case		=       parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)



"""simple docstring"""






from typing import Any
def       A_   (   _lowerCAmelCase     :					list       ):






     """simple docstring"""

     if not input_list:
          return []
     _a    	=				[input_list.count(_lowerCAmelCase       ) for value in input_list]
     _a    	=				max(_lowerCAmelCase       )  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase       ) if value == y}       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()