

'''simple docstring'''






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_A	(       ):






 """simple docstring"""



 __lowercase				    =					ArgumentParser(
     description=(
         '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
     ) )

 # Optional arguments for the launch helper
 parser.add_argument('''--num_cores'''	,							type=A__	,							default=1	,							help='''Number of TPU cores to use (1 or 8).''' )

 # positional
 parser.add_argument(
     '''training_script'''	,							type=A__	,							help=(
         '''The full path to the single TPU training '''
         '''program/script to be launched in parallel, '''
         '''followed by all the arguments for the '''
         '''training script'''
     )	,							)

 # rest from the training program
 parser.add_argument('''training_script_args'''	,							nargs=A__ )

 return parser.parse_args()


def 						_A	(       ):






 """simple docstring"""



 __lowercase				    =					parse_args()

 # Import training_script as a module.
 __lowercase				    =					Path(args.training_script )
 sys.path.append(str(script_fpath.parent.resolve() ) )
 __lowercase				    =					script_fpath.stem
 __lowercase				    =					importlib.import_module(A__ )

 # Patch sys.argv
 __lowercase				    =					[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]

 xmp.spawn(mod._mp_fn	,							args=()	,							nprocs=args.num_cores )


if __name__ == "__main__":
 main()








'''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()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     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		:					Dict						=			'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=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       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.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)