





'''simple docstring'''






import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
	__UpperCamelCase      :   Tuple								=     []

	__UpperCamelCase      :   str								=     os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
	__UpperCamelCase      :   Union[str, Any]								=     glob.glob(pattern)
	# TODO: deal with TF/Flax too
	__UpperCamelCase      :   List[str]								=     [
	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
	]

	for test_file in test_files:
		__UpperCamelCase      :   List[str]								=     get_tester_classes(test_file)
		for tester_class in tester_classes:
			# A few tester classes don't have `parent` parameter in `__init__`.
			# TODO: deal this better
			try:
				__UpperCamelCase      :   List[Any]								=     tester_class(parent=None)
			except Exception:
				continue
			if hasattr(tester, '''get_config'''):
				__UpperCamelCase      :   Optional[Any]								=     tester.get_config()
				for k, v in config.to_dict().items():
					if isinstance(v, int):
						__UpperCamelCase      :   str								=     None
						if k in ["vocab_size"]:
							__UpperCamelCase      :   Dict								=     100
						elif k in ["max_position_embeddings"]:
							__UpperCamelCase      :   Optional[int]								=     128
						elif k in ["hidden_size", "d_model"]:
							__UpperCamelCase      :   Optional[int]								=     40
						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
							__UpperCamelCase      :   Union[str, Any]								=     5
						if target is not None and v > target:
							failures.append(
							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
							    f' than {target}.'
							)

	if len(failures) > 0:
		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))







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

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

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

	if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
		__UpperCamelCase      :   str								=     tr_dataset.shuffle().select(range(80000))
		__UpperCamelCase      :   List[Any]								=     val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

	__UpperCamelCase      :   Tuple								=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	__UpperCamelCase      :   Optional[Any]								=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	__UpperCamelCase      :   int								=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

	__UpperCamelCase      :   Dict								=     {
	    '''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,
	}
	__UpperCamelCase							,  __UpperCamelCase      :   str								=     build_tx(**tx_args)

	__UpperCamelCase      :   List[str]								=     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,
	)

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