


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

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

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

		if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
				lowercase_								=    tr_dataset.shuffle().select(range(8_00_00))
				lowercase_								=    val_dataset.shuffle().select(range(80_00))

		print(tr_dataset)
		print(val_dataset)

		lowercase_								=    FlaxBigBirdForNaturalQuestions.from_pretrained(
		    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
		)
		lowercase_								=    BigBirdTokenizerFast.from_pretrained(args.model_id)
		lowercase_								=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

		lowercase_								=    {
		    '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_								=    build_tx(**tx_args)

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






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
						_a     =			min(img.size				)

						if s < target_image_size:
												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)

						_a     =			target_image_size / s
						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
						return img


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
						_a     =			2.0 * x - 1.0
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
						_a     =			x.detach().cpu()
						if process:
												_a     =			post_process_tensor(_UpperCAmelCase				)
						_a     =			x.numpy()
						if process:
												_a     =			(255 * x).astype(np.uinta				)
						_a     =			Image.fromarray(_UpperCAmelCase				)
						if not x.mode == mode:
												_a     =			x.convert(_UpperCAmelCase				)
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
						_a     =			(x + 1.0) / 2.0
						_a     =			x.permute(1							,     2							,     0				)
						return x






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
						_a     =			post_process_tensor(x.squeeze()				)
						return x.permute(2							,     0							,     1				).unsqueeze(0				)


