code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import random
  import string
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     int							,     SCREAMING_SNAKE_CASE      :     str | None = None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						passcode or self.__passcode_creator()
        UpperCamelCase__    :     Dict        =						self.__make_key_list()
        UpperCamelCase__    :     Dict        =						self.__make_shift_key()
  
  
  
  
  
  
  
   def __str__(	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return "".join(self.__passcode		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     list[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        for i in range(1							,     len(SCREAMING_SNAKE_CASE		)							,     2		):
             iterlist[i] *= -1
        return iterlist
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						string.ascii_letters + string.digits
        UpperCamelCase__    :     Optional[Any]        =						[random.choice(SCREAMING_SNAKE_CASE		) for _ in range(random.randint(10							,     20		)		)]
        return password
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						(
            string.ascii_letters + string.digits + string.punctuation + " \t\n"
        )
  
        UpperCamelCase__    :     Optional[Any]        =						[]
  
        # creates points known as breakpoints to break the key_list_options at those
        # points and pivot each substring
        UpperCamelCase__    :     List[Any]        =						sorted(set(self.__passcode		)		)
        UpperCamelCase__    :     list[str]        =						[]
  
        # algorithm for creating a new shuffled list, keys_l, out of key_list_options
        for i in key_list_options:
             temp_list.extend(SCREAMING_SNAKE_CASE		)
  
             # checking breakpoints at which to pivot temporary sublist and add it into
             # keys_l
             if i in breakpoints or i == key_list_options[-1]:
                  keys_l.extend(temp_list[::-1]		)
                  temp_list.clear()
  
          # returning a shuffled keys_l to prevent brute force guessing of shift key
        return keys_l
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						sum(self.__neg_pos([ord(SCREAMING_SNAKE_CASE		) for x in self.__passcode]		)		)
        return num if num > 0 else len(self.__passcode		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						""
  
        # decoding shift like Caesar cipher algorithm implementing negative shift or
        # reverse shift or left shift
        for i in encoded_message:
             UpperCamelCase__    :     Optional[int]        =						self.__key_list.index(SCREAMING_SNAKE_CASE		)
             decoded_message += self.__key_list[
                 (position - self.__shift_key) % -len(self.__key_list		)
             ]
  
        return decoded_message
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						""
  
        # encoding shift like Caesar cipher algorithm implementing positive shift or
        # forward shift or right shift
        for i in plaintext:
             UpperCamelCase__    :     Optional[int]        =						self.__key_list.index(SCREAMING_SNAKE_CASE		)
             encoded_message += self.__key_list[
                 (position + self.__shift_key) % len(self.__key_list		)
             ]
  
        return encoded_message
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase = "Hello, this is a modified Caesar cipher"    )				->			str:
       UpperCamelCase__    :     List[Any]        =						ShuffledShiftCipher()
       return cipa.decrypt(cipa.encrypt(__lowerCAmelCase    )    )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
style_context: 
  
  
  
  
  
  
  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			:							str       =wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
   lowerCamelCase			:							Any       =dict(logger.config)
   del wandb_args["batch_size"]
   lowerCamelCase			:							str       =replace(args, **wandb_args)
   lowerCamelCase			:							List[str]       =args.base_dir + '''-''' + wandb.run.id
   lowerCamelCase			:							str       =replace(args, base_dir=base_dir)
   print(args)
  
   lowerCamelCase			:							List[Any]       =load_dataset('''json''', data_files=args.tr_data_path)['''train''']
   lowerCamelCase			:							str       =load_dataset('''json''', data_files=args.val_data_path)['''train''']
  
   # drop extra batch for now
   lowerCamelCase			:							List[Any]       =range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   lowerCamelCase			:							Union[str, Any]       =tr_dataset.shuffle().select(indices)
   lowerCamelCase			:							List[str]       =range(len(val_dataset) - len(val_dataset) % args.batch_size)
   lowerCamelCase			:							Dict       =val_dataset.shuffle().select(indices)
  
   if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
    lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
    lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))
  
   print(tr_dataset)
   print(val_dataset)
  
   lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)
  
   lowerCamelCase			:							List[str]       ={
       '''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       ,    lowerCamelCase			:							Tuple       =build_tx(**tx_args)
  
   lowerCamelCase			:							Optional[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			:							Tuple       =None
   lowerCamelCase			:							str       =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			:							str       =jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)