


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:
   _snake_case					=					None


_snake_case					=					logging.get_logger(__name__)

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

_snake_case					=					{
    '''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''',
    },
}

_snake_case					=					{
    '''facebook/xglm-564M''': 20_48,
}
class      UpperCAmelCase_ ( UpperCamelCase__   ):

   '''simple docstring'''




   __A			:				Dict							=		VOCAB_FILES_NAMES
   __A			:				Dict							=		PRETRAINED_VOCAB_FILES_MAP
   __A			:				Dict							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:				str							=		["input_ids", "attention_mask"]
   __A			:				Optional[Any]							=		XGLMTokenizer






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






     """simple docstring"""





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

     lowerCamelCase			: Union[str, Any]   =    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__(
         __lowerCamelCase						,    tokenizer_file=__lowerCamelCase						,    bos_token=__lowerCamelCase						,    eos_token=__lowerCamelCase						,    sep_token=__lowerCamelCase						,    cls_token=__lowerCamelCase						,    unk_token=__lowerCamelCase						,    pad_token=__lowerCamelCase						,    **__lowerCamelCase						,    )

     lowerCamelCase			: Dict   =    vocab_file
     lowerCamelCase			: Union[str, Any]   =    False if not self.vocab_file else True






   def _snake_case   (		self						,    __A						,    __A = None			):






     """simple docstring"""





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






   def _snake_case   (		self						,    __A						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: Any   =    [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 _snake_case   (		self						,    __A						,    __A = None			):






     """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(__lowerCamelCase			):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""			)
       return
     lowerCamelCase			: List[Any]   =    os.path.join(
         __lowerCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

     return (out_vocab_file,)








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

import torch_xla.distributed.xla_multiprocessing as xmp






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: str   =    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=SCREAMING_SNAKE_CASE_				,						default=1				,						help="Number of TPU cores to use (1 or 8)."     )

  # positional
  parser.add_argument(
      "training_script"				,						type=SCREAMING_SNAKE_CASE_				,						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=SCREAMING_SNAKE_CASE_     )

  return parser.parse_args()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[Any]   =    parse_args()

  # Import training_script as a module.
  lowerCamelCase			: Tuple   =    Path(args.training_script     )
  sys.path.append(str(script_fpath.parent.resolve()     )     )
  lowerCamelCase			: int   =    script_fpath.stem
  lowerCamelCase			: Tuple   =    importlib.import_module(SCREAMING_SNAKE_CASE_     )

  # Patch sys.argv
  lowerCamelCase			: Union[str, Any]   =    [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()



