code: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCamelCase			:							List[str]       =    {
      '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase			:							List[str]       =    [
       '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''VideoMAEForPreTraining''',
       '''VideoMAEModel''',
       '''VideoMAEPreTrainedModel''',
       '''VideoMAEForVideoClassification''',
   ]
  
  try:
   if not is_vision_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase			:							Optional[Any]       =    ['''VideoMAEFeatureExtractor''']
   lowerCamelCase			:							int       =    ['''VideoMAEImageProcessor''']
  
  if TYPE_CHECKING:
   from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_videomae import (
        VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
        VideoMAEForPreTraining,
        VideoMAEForVideoClassification,
        VideoMAEModel,
        VideoMAEPreTrainedModel,
    )
  
   try:
    if not is_vision_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .feature_extraction_videomae import VideoMAEFeatureExtractor
    from .image_processing_videomae import VideoMAEImageProcessor
  
  else:
   import sys
  
   lowerCamelCase			:							List[str]       =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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)