"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase__						:						int		  =			logging.get_logger(__name__)  # pylint: disable=invalid-name
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __init__(       self     :			List[str]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Any    ):
							super().__init__()
							self.register_modules(unet=lowerCamelCase__					,scheduler=lowerCamelCase__    )







					@torch.no_grad()
					def __call__(       self     :			List[str]					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			int = 100					,lowerCamelCase__     :			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,lowerCamelCase__     :			Optional[float] = None					,lowerCamelCase__     :			bool = True					,):

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

							UpperCAmelCase__    =							audio_length_in_s * self.unet.config.sample_rate

							UpperCAmelCase__    =							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}.'''    )

							UpperCAmelCase__    =							int(lowerCamelCase__    )
							if sample_size % down_scale_factor != 0:
									UpperCAmelCase__    =							(
									    (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.'    )
							UpperCAmelCase__    =							int(lowerCamelCase__    )

							UpperCAmelCase__    =							next(iter(self.unet.parameters()    )    ).dtype
							UpperCAmelCase__    =							(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.'''    )

							UpperCAmelCase__    =							randn_tensor(lowerCamelCase__					,generator=lowerCamelCase__					,device=self.device					,dtype=lowerCamelCase__    )

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

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

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

							UpperCAmelCase__    =							audio.clamp(-1					,1    ).float().cpu().numpy()

							UpperCAmelCase__    =							audio[:, :, :original_sample_size]

							if not return_dict:
									return (audio,)

							return AudioPipelineOutput(audios=lowerCamelCase__    )




"""simple docstring"""






from __future__ import annotations

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

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				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 ):
				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 ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				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() = }""")



