


import string






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			UpperCamelCase								=       ''
			for i in sequence:
						UpperCamelCase								=       ord(UpperCamelCase__						)
						if 65 <= extract <= 90:
									output += chr(155 - extract						)
						elif 97 <= extract <= 122:
									output += chr(219 - extract						)
						else:
									output += i
			return output






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			UpperCamelCase								=       string.ascii_letters
			UpperCamelCase								=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
			return "".join(
			    letters_reversed[letters.index(UpperCamelCase__						)] if c in letters else c for c in sequence						)






def 				_lowercase     (		):





			"""simple docstring"""




			from timeit import timeit

			print("""Running performance benchmarks..."""						)
			UpperCamelCase								=       'from string import printable ; from __main__ import atbash, atbash_slow'
			print(f'> atbash_slow(): {timeit("atbash_slow(printable)"							,	setup=UpperCamelCase__						)} seconds'						)
			print(f'>      atbash(): {timeit("atbash(printable)"							,	setup=UpperCamelCase__						)} seconds'						)


if __name__ == "__main__":
			for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
						print(F'''{example} encrypted in atbash: {atbash(example)}''')
			benchmark()








'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






