





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

UpperCamelCase__           =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase__           =			[ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase__           =			{ord(char) for char in VALID_CHARS}

UpperCamelCase__           =			["the", "be", "to", "of", "and", "in", "that", "have"]





def      _UpperCamelCase  (a__ :list[int] ,      a__ :tuple[int, ...]							):






							"""simple docstring"""





							UpperCamelCase__					  =      ""
							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      42

							for keychar, cipherchar in zip(cycle(a__							) ,      a__							):
														UpperCamelCase__					  =      cipherchar ^ keychar
														if decodedchar not in VALID_INTS:
																					return None
														decoded += chr(a__							)

							return decoded





def      _UpperCamelCase  (a__ :list[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      []
							for key in product(a__ ,      repeat=3							):
														UpperCamelCase__					  =      try_key(a__ ,      a__							)
														if encoded is not None:
																					possibles.append(a__							)
							return possibles





def      _UpperCamelCase  (a__ :list[str] ,      a__ :str							):






							"""simple docstring"""





							return [possible for possible in possibles if common_word in possible.lower()]





def      _UpperCamelCase  (a__ :str = "p059_cipher.txt"							):






							"""simple docstring"""





							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      42
							UpperCamelCase__					  =      Path(a__							).parent.joinpath(a__							).read_text(encoding="""utf-8"""							)

							UpperCamelCase__					  =      [int(a__							) for number in data.strip().split(""","""							)]

							UpperCamelCase__					  =      filter_valid_chars(a__							)
							for common_word in COMMON_WORDS:
														UpperCamelCase__					  =      filter_common_word(a__ ,      a__							)
														if len(a__							) == 1:
																					break

							UpperCamelCase__					  =      possibles[0]
							return sum(ord(a__							) for char in decoded_text							)


if __name__ == "__main__":
					print(f"""{solution() = }""")











# 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 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      scheduler
														UpperCamelCase__					  =      optimizers if isinstance(__lowerCAmelCase	,			(list, tuple)	) else [optimizers]
														UpperCamelCase__					  =      split_batches
														UpperCamelCase__					  =      step_with_optimizer
														UpperCamelCase__					  =      GradientState()






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					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(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														else:
																					# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																					# num_processes steps per training step
																					UpperCamelCase__					  =      AcceleratorState().num_processes
																					for _ in range(__lowerCAmelCase	):
																												# 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(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												else:
																																			self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_last_lr()






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.state_dict()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														self.scheduler.load_state_dict(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_lr()







							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														return self.scheduler.print_lr(*__lowerCAmelCase	,			**__lowerCAmelCase	)




