






from __future__ import annotations

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

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

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str | None:





						"""simple docstring"""



						_UpperCAmelCase									=			""
						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			42

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

						return decoded


def __A    (      __lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase = "p059_cipher.txt"	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			42
						_UpperCAmelCase									=			Path(__lowerCAmelCase	).parent.joinpath(__lowerCAmelCase	).read_text(encoding='utf-8'	)

						_UpperCAmelCase									=			[int(__lowerCAmelCase	) for number in data.strip().split(','	)]

						_UpperCAmelCase									=			filter_valid_chars(__lowerCAmelCase	)
						for common_word in COMMON_WORDS:
												_UpperCAmelCase									=			filter_common_word(__lowerCAmelCase ,		__lowerCAmelCase	)
												if len(__lowerCAmelCase	) == 1:
																		break

						_UpperCAmelCase									=			possibles[0]
						return sum(ord(__lowerCAmelCase	) for char in decoded_text	)


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





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




