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__							, 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 __future__ import annotations
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[Any]										= value
            UpperCAmelCase__			:     Node | None										= None
            UpperCAmelCase__			:     Node | None										= None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     42
  
  
  
  
       def __init__(							self		):
            UpperCAmelCase__			:     Any										= 0
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if node is None:
                 return
  
            if path_sum == self.target:
                 self.paths += 1
  
            if node.left:
                 self.depth_first_search(node.left  ,	path_sum + node.left.value		)
            if node.right:
                 self.depth_first_search(node.right  ,	path_sum + node.right.value		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
            if node is None:
                 return 0
            if target is not None:
                 UpperCAmelCase__			:     List[Any]										= target
  
            self.depth_first_search(_lowerCAmelCase  ,	node.value		)
            self.path_sum(node.left		)
            self.path_sum(node.right		)
  
            return self.paths
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  
  