code: 
  
  
  
  
  
  
  import os
  from shutil import copyfile
  from typing import Optional, Tuple
  
  from ...file_utils import is_sentencepiece_available
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  
  
  if is_sentencepiece_available():
   from .tokenization_deberta_va import DebertaVaTokenizer
  else:
   lowerCamelCase			:							Optional[int]       =None
  
  lowerCamelCase			:							Any       =logging.get_logger(__name__)
  
  lowerCamelCase			:							List[str]       ={'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase			:							str       ={
      '''vocab_file''': {
          '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
          '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
          '''microsoft/deberta-v2-xlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
          ),
          '''microsoft/deberta-v2-xxlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
          ),
      }
  }
  
  lowerCamelCase			:							List[str]       ={
      '''microsoft/deberta-v2-xlarge''': 512,
      '''microsoft/deberta-v2-xxlarge''': 512,
      '''microsoft/deberta-v2-xlarge-mnli''': 512,
      '''microsoft/deberta-v2-xxlarge-mnli''': 512,
  }
  
  lowerCamelCase			:							Optional[int]       ={
      '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
      '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
      '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
      '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
  }
  
  
  
  
  
  class     __a						(					A__ ):
   _lowerCAmelCase : str 				=				VOCAB_FILES_NAMES
   _lowerCAmelCase : Optional[Any] 				=				PRETRAINED_VOCAB_FILES_MAP
   _lowerCAmelCase : List[Any] 				=				PRETRAINED_INIT_CONFIGURATION
   _lowerCAmelCase : Optional[Any] 				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCAmelCase : Tuple 				=				DebertaVaTokenizer
  
  
  
  
  
  
  
   def __init__(	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     List[str]=None							,     SCREAMING_SNAKE_CASE      :     Any=None							,     SCREAMING_SNAKE_CASE      :     Tuple=False							,     SCREAMING_SNAKE_CASE      :     Any=False							,     SCREAMING_SNAKE_CASE      :     List[str]="[CLS]"							,     SCREAMING_SNAKE_CASE      :     List[str]="[SEP]"							,     SCREAMING_SNAKE_CASE      :     Tuple="[UNK]"							,     SCREAMING_SNAKE_CASE      :     int="[SEP]"							,     SCREAMING_SNAKE_CASE      :     Tuple="[PAD]"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="[CLS]"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="[MASK]"							,     **SCREAMING_SNAKE_CASE      :     Tuple							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().__init__(
            SCREAMING_SNAKE_CASE							,     tokenizer_file=SCREAMING_SNAKE_CASE							,     do_lower_case=SCREAMING_SNAKE_CASE							,     bos_token=SCREAMING_SNAKE_CASE							,     eos_token=SCREAMING_SNAKE_CASE							,     unk_token=SCREAMING_SNAKE_CASE							,     sep_token=SCREAMING_SNAKE_CASE							,     pad_token=SCREAMING_SNAKE_CASE							,     cls_token=SCREAMING_SNAKE_CASE							,     mask_token=SCREAMING_SNAKE_CASE							,     split_by_punct=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE							,     )
  
        UpperCamelCase__    :     Union[str, Any]        =						do_lower_case
        UpperCamelCase__    :     Optional[int]        =						split_by_punct
        UpperCamelCase__    :     Optional[Any]        =						vocab_file
        UpperCamelCase__    :     List[Any]        =						False if not self.vocab_file else True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Tuple=None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        UpperCamelCase__    :     Tuple        =						[self.cls_token_id]
        UpperCamelCase__    :     Any        =						[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     str=None							,     SCREAMING_SNAKE_CASE      :     Tuple=False		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE							,     token_ids_a=SCREAMING_SNAKE_CASE							,     already_has_special_tokens=SCREAMING_SNAKE_CASE		)
  
        if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     Dict=None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						[self.sep_token_id]
        UpperCamelCase__    :     Optional[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       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Optional[str] = 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(SCREAMING_SNAKE_CASE		):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
             return
        UpperCamelCase__    :     Union[str, Any]        =						os.path.join(
            SCREAMING_SNAKE_CASE							,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
  
        if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE		):
             copyfile(self.vocab_file							,     SCREAMING_SNAKE_CASE		)
  
        return (out_vocab_file,)
style_context: 
  
  
  
  
  
  
  import os
  from dataclasses import replace
  
  import jax
  import wandb
  from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
  from datasets import load_dataset
  from flax import jax_utils
  
  from transformers import BigBirdTokenizerFast
  
  
  if __name__ == "__main__":
   print('''#################### AVAILABLE DEVICES ####################''')
   print(jax.devices())
   print('''###########################################################''')
  
   # setup for wandb sweep
   lowerCamelCase			:							str       =Args()
   lowerCamelCase			:							str       =wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
   lowerCamelCase			:							Any       =dict(logger.config)
   del wandb_args["batch_size"]
   lowerCamelCase			:							str       =replace(args, **wandb_args)
   lowerCamelCase			:							List[str]       =args.base_dir + '''-''' + wandb.run.id
   lowerCamelCase			:							str       =replace(args, base_dir=base_dir)
   print(args)
  
   lowerCamelCase			:							List[Any]       =load_dataset('''json''', data_files=args.tr_data_path)['''train''']
   lowerCamelCase			:							str       =load_dataset('''json''', data_files=args.val_data_path)['''train''']
  
   # drop extra batch for now
   lowerCamelCase			:							List[Any]       =range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   lowerCamelCase			:							Union[str, Any]       =tr_dataset.shuffle().select(indices)
   lowerCamelCase			:							List[str]       =range(len(val_dataset) - len(val_dataset) % args.batch_size)
   lowerCamelCase			:							Dict       =val_dataset.shuffle().select(indices)
  
   if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
    lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
    lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))
  
   print(tr_dataset)
   print(val_dataset)
  
   lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)
  
   lowerCamelCase			:							List[str]       ={
       '''lr''': args.lr,
       '''init_lr''': args.init_lr,
       '''warmup_steps''': args.warmup_steps,
       '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
       '''weight_decay''': args.weight_decay,
   }
   lowerCamelCase       ,    lowerCamelCase			:							Tuple       =build_tx(**tx_args)
  
   lowerCamelCase			:							Optional[Any]       =Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )
  
   lowerCamelCase			:							Tuple       =None
   lowerCamelCase			:							str       =trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
   try:
    trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
    print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')
  
   print('''SAVING WEIGHTS IN `final-weights`''')
   lowerCamelCase			:							str       =jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)