



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

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

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

    if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
        UpperCAmelCase_          =				tr_dataset.shuffle().select(range(8_0_0_0_0))
        UpperCAmelCase_          =				val_dataset.shuffle().select(range(8_0_0_0))

    print(tr_dataset)
    print(val_dataset)

    UpperCAmelCase_          =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    UpperCAmelCase_          =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    UpperCAmelCase_          =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

    UpperCAmelCase_          =				{
        "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_          =				build_tx(**tx_args)

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




import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()