



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

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

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

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

	print(tr_dataset)
	print(val_dataset)

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

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

	A__       =			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,
	)

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











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




