


"""simple docstring"""
import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


SCREAMING_SNAKE_CASE					:       Any							   =							1000_0000_0000
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_0000

SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       Dict							   =							os.path.split(__file__)
SCREAMING_SNAKE_CASE					:       str							   =							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






def 						lowercase				(	_snake_case		: int	,  _snake_case		: int	)	->datasets.Dataset:
	"""simple docstring"""





	__snake_case      :		Optional[Any]         =							pa.Table.from_pydict({'''col''': [0] * chunk_size}	)
	__snake_case      :		str         =							pa.concat_tables([table] * (num_examples // chunk_size)	)
	return datasets.Dataset(_snake_case	,  fingerprint='''table_100B'''	)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =42
		lowerCamelCase__           =42
		lowerCamelCase__           =42
		lowerCamelCase__           =42



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							np.random.default_rng(self.seed   )
			__snake_case      :		Dict         =							rng.integers(low=self.low							,			high=self.high							,			size=self.size   ).tolist()



		def __iter__(self   ):





			'''simple docstring'''

			return iter(self._sampled_values   )



		def __len__(self   ):





			'''simple docstring'''

			return self.size






@get_duration
def 						lowercase				(	_snake_case		: datasets.Dataset	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Optional[Any]         =							dataset[0]






@get_duration
def 						lowercase				(	_snake_case		: datasets.Dataset	)	->Dict:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							dataset[-1]






@get_duration
def 						lowercase				(	_snake_case		: datasets.Dataset	)	->Any:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							dataset[range(len(_snake_case	) // 2	,  len(_snake_case	) // 2 + 1_024	)]






@get_duration
def 						lowercase				(	_snake_case		: datasets.Dataset	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Any         =							dataset[RandIter(0	,  len(_snake_case	)	,  1_024	,  seed=42	)]






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Dict         =							{'''num examples''': SPEED_TEST_N_EXAMPLES}
	__snake_case      :		Tuple         =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('''generating dataset'''	)
	__snake_case      :		Tuple         =							generate_100B_dataset(num_examples=_snake_case	,  chunk_size=_snake_case	)
	print('''Functions'''	)
	for func in functions:
		print(func.__name__	)
		__snake_case      :		Optional[Any]         =							func(_snake_case	)

	with open(_snake_case	,  '''wb'''	) as f:
		f.write(json.dumps(_snake_case	).encode('''utf-8'''	)	)


if __name__ == "__main__":  # useful to run the profiler
			benchmark_table_100B()












'''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)
