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

					SCREAMING_SNAKE_CASE__       =	load_dataset("json", data_files=args.tr_data_path)["train"]
					SCREAMING_SNAKE_CASE__       =	load_dataset("json", data_files=args.val_data_path)["train"]

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

					if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
										SCREAMING_SNAKE_CASE__       =	tr_dataset.shuffle().select(range(80_000))
										SCREAMING_SNAKE_CASE__       =	val_dataset.shuffle().select(range(8_000))

					print(tr_dataset)
					print(val_dataset)

					SCREAMING_SNAKE_CASE__       =	FlaxBigBirdForNaturalQuestions.from_pretrained(
					    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
					)
					SCREAMING_SNAKE_CASE__       =	BigBirdTokenizerFast.from_pretrained(args.model_id)
					SCREAMING_SNAKE_CASE__       =	DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

					SCREAMING_SNAKE_CASE__       =	{
					    "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__       =	build_tx(**tx_args)

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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__       =	{"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}


if TYPE_CHECKING:
					from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


