

"""simple docstring"""





from __future__ import annotations




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    List[Any],      UpperCAmelCase__							:    int = 0			):
    __lowercase			=		value
    __lowercase			=		None
    __lowercase			=		None







def 						_A						(						UpperCamelCase_  : Node | None,    UpperCamelCase_  : Node | None)			->		Node | None:
   '''simple docstring'''


   if treea is None:
      return treea
   if treea is None:
      return treea

   __lowercase			=		treea.value + treea.value
   __lowercase			=		merge_two_binary_trees(treea.left,    treea.left)
   __lowercase			=		merge_two_binary_trees(treea.right,    treea.right)
   return treea




def 						_A						(						UpperCamelCase_  : Node | None)			->		None:
   '''simple docstring'''


   if root:
      print(root.value)
      print_preorder(root.left)
      print_preorder(root.right)


if __name__ == "__main__":
   _a				   =							Node(1)
   _a				   =							Node(2)
   _a				   =							Node(3)
   _a				   =							Node(4)

   _a				   =							Node(2)
   _a				   =							Node(4)
   _a				   =							Node(6)
   _a				   =							Node(9)
   _a				   =							Node(5)

   print('Tree1 is: ')
   print_preorder(treea)
   print('Tree2 is: ')
   print_preorder(treea)
   _a				   =							merge_two_binary_trees(treea, treea)
   print('Merged Tree is: ')
   print_preorder(merged_tree)







"""simple docstring"""





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

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       '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,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							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,
   )

   _a				   =							None
   _a				   =							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`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




