






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

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

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

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

							print(tr_dataset)
							print(val_dataset)

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

							a_  = {
							    '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,
							}
							a_ ,    a_  = build_tx(**tx_args)

							a_  = 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,
							)

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











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



