

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_0000))
            A__        =			val_dataset.shuffle().select(range(8000))

      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=4096)

      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__        =			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)





from collections import namedtuple

A__        =			namedtuple("from_to", "from_ to")

A__        =			{
    "atm": from_to(1, 1),
    "pascal": from_to(0.0_000_098, 10_1325),
    "bar": from_to(0.986_923, 1.01_325),
    "kilopascal": from_to(0.00_986_923, 101.325),
    "megapascal": from_to(9.86_923, 0.101_325),
    "psi": from_to(0.068_046, 14.6_959),
    "inHg": from_to(0.0_334_211, 29.9_213),
    "torr": from_to(0.00_131_579, 760),
}





def 							__a       (					__A ,       __A ,       __A	):
    if from_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
            + ", ".join(__A	)	)
    if to_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
            + ", ".join(__A	)	)
    return (
        value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


