



"""simple docstring"""


import torch

from accelerate import Accelerator, DistributedType







class  A_     :



						"""simple docstring"""






						def __enter__(   self					:str   )							->     int:
												if self.enabled:
																		UpperCAmelCase			 =					self.model.no_sync()
																		self.model_sync_obj.__enter__()

												return self






						def __exit__(   self					:Optional[int]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]   )							->     int:
												if self.enabled:
																		# Average all models on exit
																		self._sync_and_avg_model_params()
																		self.model_sync_obj.__exit__(lowercase_  ,    lowercase_  ,    lowercase_   )






						def __init__(   self					:Optional[int]  ,    lowercase_					:Accelerator  ,    lowercase_					:torch.nn.Module  ,    lowercase_					:int  ,    lowercase_					:bool = True   )							->     Optional[int]:
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																		raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'   )
												UpperCAmelCase			 =					enabled and accelerator.distributed_type != DistributedType.NO
												UpperCAmelCase			 =					0
												if self.enabled:
																		UpperCAmelCase			 =					accelerator
																		UpperCAmelCase			 =					model
																		UpperCAmelCase			 =					local_sgd_steps






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[str]:
												self.num_steps += 1
												if not self.enabled:
																		return

												if self.num_steps % self.local_sgd_steps == 0:
																		self._sync_and_avg_model_params()






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Union[str, Any]:
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																		for param in self.model.parameters():
																								UpperCAmelCase			 =					self.accelerator.reduce(param.data  ,    reduction='mean'   )










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

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

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

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				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,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				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,
				)

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





