

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

  lowercase_     =       load_dataset('json', data_files=args.tr_data_path)['train']
  lowercase_     =       load_dataset('json', data_files=args.val_data_path)['train']

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

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

  print(tr_dataset)
  print(val_dataset)

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

  lowercase_     =       {
      '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,
  }
  lowercase_    ,						lowercase_     =       build_tx(**tx_args)

  lowercase_     =       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,
  )

  lowercase_     =       None
  lowercase_     =       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`')
  lowercase_     =       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_torch_available, is_vision_available


lowercase_     =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       ['OneFormerImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_     =       [
      'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OneFormerForUniversalSegmentation',
      'OneFormerModel',
      'OneFormerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
  from .processing_oneformer import OneFormerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_oneformer import OneFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_oneformer import (
        ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        OneFormerForUniversalSegmentation,
        OneFormerModel,
        OneFormerPreTrainedModel,
    )


else:
  import sys

  lowercase_     =       _LazyModule(__name__, globals()['__file__'], _import_structure)


