


'''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
       lowerCAmelCase			:   str 		=  Args()
       lowerCAmelCase			:   List[str] 		=  wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
       lowerCAmelCase			:   Optional[int] 		=  dict(logger.config)
       del wandb_args["batch_size"]
       lowerCAmelCase			:   Union[str, Any] 		=  replace(args, **wandb_args)
       lowerCAmelCase			:   Any 		=  args.base_dir + '''-''' + wandb.run.id
       lowerCAmelCase			:   Optional[int] 		=  replace(args, base_dir=base_dir)
       print(args)

       lowerCAmelCase			:   str 		=  load_dataset("""json""", data_files=args.tr_data_path)['''train''']
       lowerCAmelCase			:   Optional[int] 		=  load_dataset("""json""", data_files=args.val_data_path)['''train''']

       # drop extra batch for now
       lowerCAmelCase			:   Optional[int] 		=  range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
       lowerCAmelCase			:   Any 		=  tr_dataset.shuffle().select(indices)
       lowerCAmelCase			:   Union[str, Any] 		=  range(len(val_dataset) - len(val_dataset) % args.batch_size)
       lowerCAmelCase			:   List[Any] 		=  val_dataset.shuffle().select(indices)

       if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
              lowerCAmelCase			:   Optional[Any] 		=  tr_dataset.shuffle().select(range(8_00_00))
              lowerCAmelCase			:   Dict 		=  val_dataset.shuffle().select(range(80_00))

       print(tr_dataset)
       print(val_dataset)

       lowerCAmelCase			:   Optional[Any] 		=  FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       lowerCAmelCase			:   Optional[Any] 		=  BigBirdTokenizerFast.from_pretrained(args.model_id)
       lowerCAmelCase			:   Tuple 		=  DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

       lowerCAmelCase			:   Any 		=  {
           '''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,
       }
       lowerCAmelCase			:   Optional[Any] 		=  build_tx(**tx_args)

       lowerCAmelCase			:   Any 		=  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,
       )

       lowerCAmelCase			:   Optional[Any] 		=  None
       lowerCAmelCase			:   Union[str, Any] 		=  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`""")
       lowerCAmelCase			:   List[str] 		=  jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)









def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	int = 13			):
		snake_case_ :							List[Any]         =				""
		for c in s:
				if "A" <= c <= "Z":
						out += chr(ord("A"			) + (ord(_a			) - ord("A"			) + n) % 26			)
				elif "a" <= c <= "z":
						out += chr(ord("a"			) + (ord(_a			) - ord("a"			) + n) % 26			)
				else:
						out += c
		return out

def    lowerCAmelCase__		(			):
		snake_case_ :							int         =				input("Enter message: "			)

		snake_case_ :							List[Any]         =				dencrypt(_a     ,					13			)
		print("Encryption:"     ,					_a			)

		snake_case_ :							int         =				dencrypt(_a     ,					13			)
		print("Decryption: "     ,					_a			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()
