






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()







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)