



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

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

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

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

							print(tr_dataset)
							print(val_dataset)

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

							UpperCamelCase_	       =   {
							    "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_	       =   build_tx(**tx_args)

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










from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 							a_  (		datasets.BuilderConfig				):
						UpperCamelCase__      :						Optional[Callable]				       =None
						UpperCamelCase__      :						Optional[dict]				       =None
						UpperCamelCase__      :						Optional[datasets.Features]				       =None

						def 	__a       (    self	:List[str])		->					List[str]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase_     							=     {}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						List[str]				       =GeneratorConfig

						def 	__a       (    self	:List[str])		->					List[Any]:
												return datasets.DatasetInfo(features=self.config.features)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:List[str])		->					Optional[Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs=self.config.gen_kwargs)]




						def 	__a       (    self	:Optional[int]   ,						**_lowercase	:str)		->					Union[str, Any]:
												for idx, ex in enumerate(self.config.generator(**_lowercase)):
																		yield idx, ex





