




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
   UpperCAmelCase__    :							List[str]       		=     Args()
   UpperCAmelCase__    :							Any       		=     wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   UpperCAmelCase__    :							Optional[int]       		=     dict(logger.config)
   del wandb_args["batch_size"]
   UpperCAmelCase__    :							Optional[Any]       		=     replace(args, **wandb_args)
   UpperCAmelCase__    :							Optional[Any]       		=     args.base_dir + '-' + wandb.run.id
   UpperCAmelCase__    :							int       		=     replace(args, base_dir=base_dir)
   print(args)

   UpperCAmelCase__    :							Optional[Any]       		=     load_dataset('json', data_files=args.tr_data_path)['train']
   UpperCAmelCase__    :							Union[str, Any]       		=     load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   UpperCAmelCase__    :							Dict       		=     range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   UpperCAmelCase__    :							str       		=     tr_dataset.shuffle().select(indices)
   UpperCAmelCase__    :							int       		=     range(len(val_dataset) - len(val_dataset) % args.batch_size)
   UpperCAmelCase__    :							Any       		=     val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      UpperCAmelCase__    :							Optional[Any]       		=     tr_dataset.shuffle().select(range(80000))
      UpperCAmelCase__    :							Union[str, Any]       		=     val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   UpperCAmelCase__    :							List[str]       		=     FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   UpperCAmelCase__    :							Optional[Any]       		=     BigBirdTokenizerFast.from_pretrained(args.model_id)
   UpperCAmelCase__    :							int       		=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

   UpperCAmelCase__    :							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,
   }
   UpperCAmelCase__		,  UpperCAmelCase__    :							Optional[Any]       		=     build_tx(**tx_args)

   UpperCAmelCase__    :							Optional[int]       		=     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,
   )

   UpperCAmelCase__    :							Optional[int]       		=     None
   UpperCAmelCase__    :							int       		=     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`')
   UpperCAmelCase__    :							str       		=     jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)






from __future__ import annotations

class   UpperCAmelCase    :






   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      None:
     _lowercase:			Optional[Any]      =     data
     _lowercase:			Optional[int]      =     None

class   UpperCAmelCase    :






   def __init__(						self :							Optional[Any]  )->      Union[str, Any]:
     _lowercase:			List[str]      =     None






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      int:
     _lowercase:			Any      =     Node(UpperCamelCase_  )
     _lowercase:			List[Any]      =     self.head
     _lowercase:			List[str]      =     new_node
     return self.head.data






   def 			_a    (						self :							Tuple  )->      int | None:
     _lowercase:			List[str]      =     self.head
     _lowercase:			str      =     self.head
     if self.head:
       while fast_pointer and fast_pointer.next:
         _lowercase:			Optional[int]      =     fast_pointer.next.next
         _lowercase:			Tuple      =     slow_pointer.next
       return slow_pointer.data
     else:
       print('No element found.'  )
       return None


if __name__ == "__main__":
   UpperCAmelCase__    :							Union[str, Any]       		=     LinkedList()
   for _ in range(int(input().strip())):
      UpperCAmelCase__    :							List[Any]       		=     int(input().strip())
      link.push(data)
   print(link.middle_element())
