

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

import torch_xla.distributed.xla_multiprocessing as xmp




def     UpperCAmelCase__       (    ):





  '''simple docstring'''







  a__						=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     UpperCAmelCase__       (    ):





  '''simple docstring'''







  a__						=parse_args()

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

  # Patch sys.argv
  a__						=[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()



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

     if token_ids_a is None:
       return len(sep + token_ids_a ) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.''' )

     if not os.path.isdir(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)
