code: 
  
  
  
  
  
  
  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
  						SCREAMING_SNAKE_CASE__		:       List[Any]   							=							Args()
  						SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
  						SCREAMING_SNAKE_CASE__		:       List[str]   							=							dict(logger.config)
  						del wandb_args["batch_size"]
  						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							replace(args, **wandb_args)
  						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							args.base_dir + """-""" + wandb.run.id
  						SCREAMING_SNAKE_CASE__		:       Tuple   							=							replace(args, base_dir=base_dir)
  						print(args)
  
  						SCREAMING_SNAKE_CASE__		:       Tuple   							=							load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  						SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							load_dataset("""json""", data_files=args.val_data_path)["""train"""]
  
  						# drop extra batch for now
  						SCREAMING_SNAKE_CASE__		:       Dict   							=							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  						SCREAMING_SNAKE_CASE__		:       int   							=							tr_dataset.shuffle().select(indices)
  						SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							range(len(val_dataset) - len(val_dataset) % args.batch_size)
  						SCREAMING_SNAKE_CASE__		:       List[Any]   							=							val_dataset.shuffle().select(indices)
  
  						if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
  												SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							tr_dataset.shuffle().select(range(8_00_00))
  												SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							val_dataset.shuffle().select(range(80_00))
  
  						print(tr_dataset)
  						print(val_dataset)
  
  						SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							FlaxBigBirdForNaturalQuestions.from_pretrained(
  						    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  						)
  						SCREAMING_SNAKE_CASE__		:       List[Any]   							=							BigBirdTokenizerFast.from_pretrained(args.model_id)
  						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)
  
  						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							{
  						    """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,
  						}
  						SCREAMING_SNAKE_CASE__		:       Tuple   							=							build_tx(**tx_args)
  
  						SCREAMING_SNAKE_CASE__		:       Union[str, 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,
  						)
  
  						SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							None
  						SCREAMING_SNAKE_CASE__		:       Any   							=							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`""")
  						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							jax_utils.unreplicate(state.params)
  						model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  