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

						_lowercase		:					Dict													=		load_dataset("json", data_files=args.tr_data_path)["train"]
						_lowercase		:					Any													=		load_dataset("json", data_files=args.val_data_path)["train"]

						# drop extra batch for now
						_lowercase		:					Union[str, Any]													=		range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
						_lowercase		:					int													=		tr_dataset.shuffle().select(indices)
						_lowercase		:					List[Any]													=		range(len(val_dataset) - len(val_dataset) % args.batch_size)
						_lowercase		:					Tuple													=		val_dataset.shuffle().select(indices)

						if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
												_lowercase		:					Optional[Any]													=		tr_dataset.shuffle().select(range(80000))
												_lowercase		:					Optional[Any]													=		val_dataset.shuffle().select(range(8000))

						print(tr_dataset)
						print(val_dataset)

						_lowercase		:					Union[str, Any]													=		FlaxBigBirdForNaturalQuestions.from_pretrained(
						    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
						)
						_lowercase		:					Any													=		BigBirdTokenizerFast.from_pretrained(args.model_id)
						_lowercase		:					List[Any]													=		DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

						_lowercase		:					str													=		{
						    "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		:					Any													=		build_tx(**tx_args)

						_lowercase		:					Union[str, 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		:					Optional[Any]													=		None
						_lowercase		:					List[Any]													=		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		:					List[str]													=		jax_utils.unreplicate(state.params)
						model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)






'''simple docstring'''




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments




def 				lowerCamelCase      ( )     -> List[Any]:
							lowercase_   :     Tuple									=			HfArgumentParser(UpperCAmelCase__					)
							try:
														lowercase_   :     List[Any]									=			parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowercase_   :     int									=			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
														lowercase_   :     Any									=			""" """.join(str(UpperCAmelCase__					).split(""" """					)[:-1]					)
														lowercase_   :     Optional[Any]									=			""""""
														lowercase_   :     List[str]									=			eval(str(UpperCAmelCase__					).split(""" """					)[-1]					)
														lowercase_   :     Dict									=			[]
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]					)
																					else:
																												wrong_args.append(UpperCAmelCase__					)
														if len(UpperCAmelCase__					) > 0:
																					lowercase_   :     Dict									=			full_error_msg + begin_error_msg + str(UpperCAmelCase__					)
														raise ValueError(UpperCAmelCase__					)

							lowercase_   :     Optional[int]									=			PyTorchBenchmark(args=UpperCAmelCase__					)
							benchmark.run()


if __name__ == "__main__":
						main()





