
"""simple docstring"""







from typing import Literal

lowerCamelCase__     =						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		str		     = LETTERS if mode == '''decrypt''' else key
	__lowerCAmelCase     :		int		     = key if mode == '''decrypt''' else LETTERS
	__lowerCAmelCase     :		Dict		     = ''''''
	# loop through each symbol in the message
	for symbol in message:
		if symbol.upper() in chars_a:
			# encrypt/decrypt the symbol
			__lowerCAmelCase     :		Any		     = chars_a.find(symbol.upper()						)
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			# symbol is not in LETTERS, just add it
			translated += symbol
	return translated
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	return translate_message(_UpperCamelCase			,    _UpperCamelCase			,    'encrypt'						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	return translate_message(_UpperCamelCase			,    _UpperCamelCase			,    'decrypt'						)
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		str		     = '''Hello World'''
	__lowerCAmelCase     :		Optional[Any]		     = '''QWERTYUIOPASDFGHJKLZXCVBNM'''
	__lowerCAmelCase     :		Dict		     = '''decrypt'''  # set to 'encrypt' or 'decrypt'

	if mode == "encrypt":
		__lowerCAmelCase     :		int		     = encrypt_message(_UpperCamelCase			,    _UpperCamelCase						)
	elif mode == "decrypt":
		__lowerCAmelCase     :		str		     = decrypt_message(_UpperCamelCase			,    _UpperCamelCase						)
	print(F"Using the key {key}, the {mode}ed message is: {translated}"						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()






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

	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.tr_data_path)['''train''']
	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.val_data_path)['''train''']

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

	if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
		__UpperCamelCase      :   str								=     tr_dataset.shuffle().select(range(80000))
		__UpperCamelCase      :   List[Any]								=     val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

	__UpperCamelCase      :   Tuple								=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	__UpperCamelCase      :   Optional[Any]								=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	__UpperCamelCase      :   int								=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

	__UpperCamelCase      :   Dict								=     {
	    '''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							,  __UpperCamelCase      :   str								=     build_tx(**tx_args)

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