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

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

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

						if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
												__lowercase				   =  tr_dataset.shuffle().select(range(8_0_0_0_0))
												__lowercase				   =  val_dataset.shuffle().select(range(8_0_0_0))

						print(tr_dataset)
						print(val_dataset)

						__lowercase				   =  FlaxBigBirdForNaturalQuestions.from_pretrained(
						    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
						)
						__lowercase				   =  BigBirdTokenizerFast.from_pretrained(args.model_id)
						__lowercase				   =  DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

						__lowercase				   =  {
						    '''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				   =  build_tx(**tx_args)

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





'''simple docstring'''




from __future__ import annotations

__lowercase				   =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  -1
					lowerCAmelCase        =  -1
					lowerCAmelCase        =  [-1] * length
					lowerCAmelCase        =  None
					lowerCAmelCase        =  None







			def __repr__(       self):




					"""simple docstring"""


					return f"Node(min_value={self.minn} max_value={self.maxx})"







def     snake_case__							(      _A: list[int]			)   ->     Node | None:

		'''simple docstring'''


		lowerCAmelCase        =  Node(len(_A			)			)
		lowerCAmelCase ,					lowerCAmelCase        =  min(_A			), max(_A			)
		# Leaf node case where the node contains only one unique value
		if root.minn == root.maxx:
				return root
		lowerCAmelCase        =  (root.minn + root.maxx) // 2

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		for index, num in enumerate(_A			):
				if num <= pivot:
						left_arr.append(_A			)
				else:
						right_arr.append(_A			)
				lowerCAmelCase        =  len(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		return root






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index < 0 or node is None:
				return 0
		# Leaf node cases
		if node.minn == node.maxx:
				return index + 1 if node.minn == num else 0
		lowerCAmelCase        =  (node.minn + node.maxx) // 2
		if num <= pivot:
				# go the left subtree and map index to the left subtree
				return rank_till_index(node.left				,       _A				,       node.map_left[index] - 1			)
		else:
				# go to the right subtree and map index to the right subtree
				return rank_till_index(node.right				,       _A				,       index - node.map_left[index]			)






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if start > end:
				return 0
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       _A			)
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       start - 1			)
		return rank_till_end - rank_before_start






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index > (end - start) or start > end or node is None:
				return -1
		# Leaf node case
		if node.minn == node.maxx:
				return node.minn
		# Number of elements in the left subtree in interval [start, end]
		lowerCAmelCase        =  node.map_left[end] - (
		    node.map_left[start - 1] if start else 0
		)
		if num_elements_in_left_tree > index:
				return quantile(
				    node.left				,       _A				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       )
		else:
				return quantile(
				    node.right				,       index - num_elements_in_left_tree				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       )






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if (
		    start > end
		    or node is None
		    or start_num > end_num
		    or node.minn > end_num
		    or node.maxx < start_num
		):
				return 0
		if start_num <= node.minn and node.maxx <= end_num:
				return end - start + 1
		lowerCAmelCase        =  range_counting(
		    node.left				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       _A				,       _A				,       )
		lowerCAmelCase        =  range_counting(
		    node.right				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       _A				,       _A				,       )
		return left + right


if __name__ == "__main__":
						import doctest

						doctest.testmod()




