






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

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

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

   if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
      snake_case			 =						tr_dataset.shuffle().select(range(80_000))
      snake_case			 =						val_dataset.shuffle().select(range(8_000))

   print(tr_dataset)
   print(val_dataset)

   snake_case			 =						FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   snake_case			 =						BigBirdTokenizerFast.from_pretrained(args.model_id)
   snake_case			 =						DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

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

   snake_case			 =						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,
   )

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














import re


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  def docstring_decorator(lowercase ):
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			fn.__doc__
    SCREAMING_SNAKE_CASE				:					Tuple		  =			func_doc.split("\n" )
    SCREAMING_SNAKE_CASE				:					Tuple		  =			0
    while i < len(lowercase ) and re.search(R"^\s*Examples?:\s*$"  ,   lines[i] ) is None:
      i += 1
    if i < len(lowercase ):
      SCREAMING_SNAKE_CASE				:					int		  =			example_docstring
      SCREAMING_SNAKE_CASE				:					Tuple		  =			"\n".join(lowercase )
    else:
      raise ValueError(
          F'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
          F'''current docstring is:\n{func_doc}''' )
    SCREAMING_SNAKE_CASE				:					int		  =			func_doc
    return fn

  return docstring_decorator






