
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(80000))
    _UpperCAmelCase    			=					val_dataset.shuffle().select(range(8000))

  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=4096)

  _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 warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




