



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

							__SCREAMING_SNAKE_CASE     =	load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
							__SCREAMING_SNAKE_CASE     =	load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

							if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
														__SCREAMING_SNAKE_CASE     =	tr_dataset.shuffle().select(range(80000))
														__SCREAMING_SNAKE_CASE     =	val_dataset.shuffle().select(range(8000))

							print(tr_dataset)
							print(val_dataset)

							__SCREAMING_SNAKE_CASE     =	FlaxBigBirdForNaturalQuestions.from_pretrained(
							    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
							)
							__SCREAMING_SNAKE_CASE     =	BigBirdTokenizerFast.from_pretrained(args.model_id)
							__SCREAMING_SNAKE_CASE     =	DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

							__SCREAMING_SNAKE_CASE     =	{
							    """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,
							}
							__SCREAMING_SNAKE_CASE   ,     __SCREAMING_SNAKE_CASE     =	build_tx(**tx_args)

							__SCREAMING_SNAKE_CASE     =	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,
							)

							__SCREAMING_SNAKE_CASE     =	None
							__SCREAMING_SNAKE_CASE     =	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`""")
							__SCREAMING_SNAKE_CASE     =	jax_utils.unreplicate(state.params)
							model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)