






'''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 functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						if not inplace:
												a_				     =   ma + ma
						else:
												ma += ma

						return ma



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   -1 * len(lowercase__				)
						a_				     =   list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						return t.reshape(t.shape[:-no_dims] + (-1,)				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__ = 1e-4				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   mask.expand(*value.shape				)
						return torch.sum(mask * value	,dim=lowercase__				) / (eps + torch.sum(lowercase__	,dim=lowercase__				))



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = 2.3125	,lowercase__ = 21.6875	,lowercase__ = 64				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   torch.linspace(lowercase__	,lowercase__	,no_bins - 1	,device=pts.device				)
						a_				     =   torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2	,dim=-1				)				)
						return torch.bucketize(lowercase__	,lowercase__				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						a_				     =   dicts[0]
						a_				     =   {}
						for k, v in first.items():
												a_				     =   [d[k] for d in dicts]
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_multimap(lowercase__	,lowercase__				)
												else:
																		a_				     =   fn(lowercase__				)

						return new_dict



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						a_				     =   x[..., None] - reshaped_bins
						a_				     =   torch.argmin(torch.abs(lowercase__				)	,dim=-1				)
						return nn.functional.one_hot(lowercase__	,num_classes=len(lowercase__				)				).float()



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__ = 0	,lowercase__ = 0				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												a_				     =   torch.arange(lowercase__				)
												a_				     =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						a_				     =   [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						a_				     =   inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


a_      = TypeVar('T')



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Dict[Any, Union[dict, list, tuple, Any]]:

						'''simple docstring'''


						a_				     =   {}
						for k, v in dic.items():
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_map(lowercase__	,lowercase__	,lowercase__				)
												else:
																		a_				     =   tree_map(lowercase__	,lowercase__	,lowercase__				)

						return new_dict



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				list:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				tuple:

						'''simple docstring'''


						...



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						if isinstance(lowercase__	,lowercase__				):
												return dict_map(lowercase__	,lowercase__	,lowercase__				)
						elif isinstance(lowercase__	,lowercase__				):
												return [tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree]
						elif isinstance(lowercase__	,lowercase__				):
												return tuple(tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree				)
						elif isinstance(lowercase__	,lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError("Not supported"				)
a_      = partial(tree_map, leaf_type=torch.Tensor)



