





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

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


if is_sentencepiece_available():
    from .tokenization_rembert import RemBertTokenizer
else:
    __lowerCamelCase :  Optional[int]										=  None

__lowerCamelCase :  Optional[int]										=  logging.get_logger(__name__)
__lowerCamelCase :  Union[str, Any]										=  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :  Tuple										=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :  Any										=  {
    """google/rembert""": 256,
}

__lowerCamelCase :  Union[str, Any]										=  """▁"""



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:List[Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:List[str]							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:Any							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Tuple							=     RemBertTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_=True ,							A_=True ,							A_=False ,							A_="[CLS]" ,							A_="[SEP]" ,							A_="<unk>" ,							A_="[SEP]" ,							A_="<pad>" ,							A_="[CLS]" ,							A_="[MASK]" ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else mask_token

       super().__init__(
           A_ ,							tokenizer_file=A_ ,							do_lower_case=A_ ,							remove_space=A_ ,							keep_accents=A_ ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							sep_token=A_ ,							pad_token=A_ ,							cls_token=A_ ,							mask_token=A_ ,							**A_ ,							)

       UpperCamelCase			:      str 			=     do_lower_case
       UpperCamelCase			:      List[str] 			=     remove_space
       UpperCamelCase			:      int 			=     keep_accents
       UpperCamelCase			:      List[Any] 			=     vocab_file
       UpperCamelCase			:      List[Any] 			=     False if not self.vocab_file else True





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     [self.sep_token_id]
       UpperCamelCase			:      Optional[Any] 			=     [self.cls_token_id]
       if token_ids_a is None:
        return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = False     ):
       '''simple docstring'''




       if already_has_special_tokens:
        if token_ids_a is not None:
         raise ValueError(
             "You should not supply a second sequence if the provided sequence of "
             "ids is already formatted with special tokens for the model."     )
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
        return [1] + ([0] * len(A_     )) + [1] + ([0] * len(A_     )) + [1]
       return [1] + ([0] * len(A_     )) + [1]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
       return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if not os.path.isdir(A_     ):
        logger.error("Vocabulary path ({}) should be a directory".format(A_     )     )
        return
       UpperCamelCase			:      Dict 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

       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 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     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=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

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

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[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()
