
'''simple docstring'''


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_fnet import FNetTokenizer
else:
   snake_case_ :  List[Any]     =					None

snake_case_ :  Optional[int]     =					logging.get_logger(__name__)
snake_case_ :  List[str]     =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_ :  List[Any]     =					{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

snake_case_ :  Dict     =					{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

snake_case_ :  Any     =					'▁'


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''token_type_ids''']
   _snake_case		   =							FNetTokenizer







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

      '''simple docstring'''







      UpperCamelCase       =  (
          AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__ ,   normalized=lowerCamelCase__   )
          if isinstance(lowerCamelCase__ ,   lowerCamelCase__   )
          else mask_token
      )

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

      UpperCamelCase       =  do_lower_case
      UpperCamelCase       =  remove_space
      UpperCamelCase       =  keep_accents
      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  False if not self.vocab_file else True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [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 ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [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 ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  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,)







'''simple docstring'''


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

import torch_xla.distributed.xla_multiprocessing as xmp



def  __snake_case     (    ):
   UpperCamelCase       =  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=_UpperCAmelCase,      default=1,      help='''Number of TPU cores to use (1 or 8).''')

   # positional
   parser.add_argument(
       '''training_script''',      type=_UpperCAmelCase,      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=_UpperCAmelCase)

   return parser.parse_args()



def  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

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

   # Patch sys.argv
   UpperCamelCase       =  [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()





