




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

		__lowerCAmelCase		:    Tuple	      =		load_dataset('json', data_files=args.tr_data_path)['train']
		__lowerCAmelCase		:    Tuple	      =		load_dataset('json', data_files=args.val_data_path)['train']

		# drop extra batch for now
		__lowerCAmelCase		:    List[str]	      =		range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
		__lowerCAmelCase		:    List[str]	      =		tr_dataset.shuffle().select(indices)
		__lowerCAmelCase		:    List[Any]	      =		range(len(val_dataset) - len(val_dataset) % args.batch_size)
		__lowerCAmelCase		:    Optional[Any]	      =		val_dataset.shuffle().select(indices)

		if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
				__lowerCAmelCase		:    List[str]	      =		tr_dataset.shuffle().select(range(8_0000))
				__lowerCAmelCase		:    List[str]	      =		val_dataset.shuffle().select(range(8000))

		print(tr_dataset)
		print(val_dataset)

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

		__lowerCAmelCase		:    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,
		}
		__lowerCAmelCase		:    Tuple	      =		build_tx(**tx_args)

		__lowerCAmelCase		:    Dict	      =		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,
		)

		__lowerCAmelCase		:    int	      =		None
		__lowerCAmelCase		:    Tuple	      =		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`')
		__lowerCAmelCase		:    List[str]	      =		jax_utils.unreplicate(state.params)
		model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






