

"""simple docstring"""




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_0000))
            __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	=			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)








"""simple docstring"""




from math import factorial
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 return factorial(__UpperCamelCase ) / (factorial(__UpperCamelCase ) * factorial(n - r ))
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Any   				=   0

 for i in range(1							,      101 ):
  for j in range(1							,      i + 1 ):
   if combinations(__UpperCamelCase							,      __UpperCamelCase ) > 1e6:
    total += 1
 return total


if __name__ == "__main__":
      print(solution())





