






'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A      :       Optional[int]      =	logging.get_logger(__name__)

__A      :       List[str]      =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A      :       Optional[int]      =	{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

__A      :       List[str]      =	{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  VOCAB_FILES_NAMES
     _snake_case             =  PRETRAINED_VOCAB_FILES_MAP
     _snake_case             =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case             =  ['input_ids', 'attention_mask']
     _snake_case             =  LongformerTokenizer






     def __init__(self    :	List[Any]				,				lowercase__    :	Tuple=None				,				lowercase__    :	Optional[Any]=None				,				lowercase__    :	Tuple=None				,				lowercase__    :	str="replace"				,				lowercase__    :	str="<s>"				,				lowercase__    :	List[str]="</s>"				,				lowercase__    :	str="</s>"				,				lowercase__    :	Union[str, Any]="<s>"				,				lowercase__    :	int="<unk>"				,				lowercase__    :	Dict="<pad>"				,				lowercase__    :	int="<mask>"				,				lowercase__    :	Any=False				,				lowercase__    :	Tuple=True				,				**lowercase__    :	Tuple				,				)  ->       List[Any]:
           super().__init__(
               lowercase__				,				lowercase__				,				tokenizer_file=lowercase__				,				errors=lowercase__				,				bos_token=lowercase__				,				eos_token=lowercase__				,				sep_token=lowercase__				,				cls_token=lowercase__				,				unk_token=lowercase__				,				pad_token=lowercase__				,				mask_token=lowercase__				,				add_prefix_space=lowercase__				,				trim_offsets=lowercase__				,				**lowercase__				,				)

           __A		   =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
           if pre_tok_state.get("add_prefix_space"				,				lowercase__					) != add_prefix_space:
                 __A		   =      getattr(lowercase__				,				pre_tok_state.pop("type"					)					)
                 __A		   =      add_prefix_space
                 __A		   =      pre_tok_class(**lowercase__					)

           __A		   =      add_prefix_space

           __A		   =      "post_processor"
           __A		   =      getattr(self.backend_tokenizer				,				lowercase__				,				lowercase__					)
           if tokenizer_component_instance:
                 __A		   =      json.loads(tokenizer_component_instance.__getstate__()					)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       __A		   =      tuple(state["sep"]					)
                 if "cls" in state:
                       __A		   =      tuple(state["cls"]					)

                 __A		   =      False

                 if state.get("add_prefix_space"				,				lowercase__					) != add_prefix_space:
                       __A		   =      add_prefix_space
                       __A		   =      True

                 if state.get("trim_offsets"				,				lowercase__					) != trim_offsets:
                       __A		   =      trim_offsets
                       __A		   =      True

                 if changes_to_apply:
                       __A		   =      getattr(lowercase__				,				state.pop("type"					)					)
                       __A		   =      component_class(**lowercase__					)
                       setattr(self.backend_tokenizer				,				lowercase__				,				lowercase__					)






     @property
     def     _UpperCamelCase   (self    :	Tuple					)  ->       str:
           if self._mask_token is None:
                 if self.verbose:
                       logger.error("Using mask_token, but it is not set yet."					)
                 return None
           return str(self._mask_token					)






     @mask_token.setter
     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Union[str, Any]					)  ->       Any:
           __A		   =      AddedToken(lowercase__				,				lstrip=lowercase__				,				rstrip=lowercase__					) if isinstance(lowercase__				,				lowercase__					) else value
           __A		   =      value






     def     _UpperCamelCase   (self    :	Tuple				,				*lowercase__    :	Union[str, Any]				,				**lowercase__    :	Dict					)  ->       BatchEncoding:
           __A		   =      kwargs.get("is_split_into_words"				,				lowercase__					)
           assert self.add_prefix_space or not is_split_into_words, (
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Any				,				*lowercase__    :	Any				,				**lowercase__    :	List[Any]					)  ->       BatchEncoding:
           __A		   =      kwargs.get("is_split_into_words"				,				lowercase__					)

           assert self.add_prefix_space or not is_split_into_words, (
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	str				,				lowercase__    :	Optional[str] = None					)  ->       Tuple[str]:
           __A		   =      self._tokenizer.model.save(lowercase__				,				name=lowercase__					)
           return tuple(lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	List[Any]				,				lowercase__    :	Dict=None					)  ->       Tuple:
           __A		   =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def     _UpperCamelCase   (self    :	List[str]				,				lowercase__    :	List[int]				,				lowercase__    :	Optional[List[int]] = None					)  ->       List[int]:
           __A		   =      [self.sep_token_id]
           __A		   =      [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 + sep + token_ids_a + sep					) * [0]










'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __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=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

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

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __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(snake_case__							)

      # 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()


