



"""simple docstring"""







from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass







@dataclass
class  UpperCamelCase__  :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None
      _SCREAMING_SNAKE_CASE    							=	None




def 							UpperCamelCase_       (       )		->  TreeNode:

  """simple docstring"""




  return TreeNode(3       ,  TreeNode(9		)       ,  TreeNode(20       ,  TreeNode(15		)       ,  TreeNode(7		)		)		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							TreeNode		)		->  list[int]:

  """simple docstring"""




  def depth_first_search(
      lowerCAmelCase__ :							TreeNode | None       ,  lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							list[int]		) -> None:
    if not root:
      return

    if depth == len(lowerCAmelCase__		):
      right_view.append(root.val		)

    depth_first_search(root.right       ,  depth + 1       ,  lowerCAmelCase__		)
    depth_first_search(root.left       ,  depth + 1       ,  lowerCAmelCase__		)

  lowerCAmelCase_  :							list								=					[]
  if not root:
    return right_view

  depth_first_search(lowerCAmelCase__       ,  0       ,  lowerCAmelCase__		)
  return right_view

def 							UpperCamelCase_       (       lowerCAmelCase__ :							TreeNode		)		->  list[int]:

  """simple docstring"""




  def depth_first_search(
      lowerCAmelCase__ :							TreeNode | None       ,  lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							list[int]		) -> None:
    if not root:
      return

    if depth == len(lowerCAmelCase__		):
      left_view.append(root.val		)

    depth_first_search(root.left       ,  depth + 1       ,  lowerCAmelCase__		)
    depth_first_search(root.right       ,  depth + 1       ,  lowerCAmelCase__		)

  lowerCAmelCase_  :							list								=					[]
  if not root:
    return left_view

  depth_first_search(lowerCAmelCase__       ,  0       ,  lowerCAmelCase__		)
  return left_view

def 							UpperCamelCase_       (       lowerCAmelCase__ :							TreeNode		)		->  list[int]:

  """simple docstring"""




  def breadth_first_search(lowerCAmelCase__ :							TreeNode       ,  lowerCAmelCase__ :							list[int]		) -> None:
    lowerCAmelCase_  :							Dict								=					[(root, 0)]
    lowerCAmelCase_  :							Optional[int]								=					defaultdict(lowerCAmelCase__		)

    while queue:
      lowerCAmelCase_  :							List[Any]								=					queue.pop(0		)
      lowerCAmelCase_		,lowerCAmelCase_  :							int								=					first

      lookup[hd].append(node.val		)

      if node.left:
        queue.append((node.left, hd - 1)		)
      if node.right:
        queue.append((node.right, hd + 1)		)

    for pair in sorted(lookup.items()       ,  key=lambda lowerCAmelCase__		: each[0]		):
      top_view.append(pair[1][0]		)

  lowerCAmelCase_  :							list								=					[]
  if not root:
    return top_view

  breadth_first_search(lowerCAmelCase__       ,  lowerCAmelCase__		)
  return top_view

def 							UpperCamelCase_       (       lowerCAmelCase__ :							TreeNode		)		->  list[int]:

  """simple docstring"""




  from collections import defaultdict

  def breadth_first_search(lowerCAmelCase__ :							TreeNode       ,  lowerCAmelCase__ :							list[int]		) -> None:
    lowerCAmelCase_  :							int								=					[(root, 0)]
    lowerCAmelCase_  :							List[str]								=					defaultdict(lowerCAmelCase__		)

    while queue:
      lowerCAmelCase_  :							Tuple								=					queue.pop(0		)
      lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					first
      lookup[hd].append(node.val		)

      if node.left:
        queue.append((node.left, hd - 1)		)
      if node.right:
        queue.append((node.right, hd + 1)		)

    for pair in sorted(lookup.items()       ,  key=lambda lowerCAmelCase__		: each[0]		):
      bottom_view.append(pair[1][-1]		)

  lowerCAmelCase_  :							list								=					[]
  if not root:
    return bottom_view

  breadth_first_search(lowerCAmelCase__       ,  lowerCAmelCase__		)
  return bottom_view


if __name__ == "__main__":
     import doctest

     doctest.testmod()










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

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

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





