from typing import List, Optional, Tuple, Union

import torch

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


A__           =							logging.get_logger(__name__)  # pylint: disable=invalid-name
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							def __init__(				self     :    Tuple	,					__snake_case     :    Dict	,					__snake_case     :    Tuple		):
										super().__init__()
										self.register_modules(unet=__snake_case	,					scheduler=__snake_case		)







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

										if audio_length_in_s is None:
													lowerCamelCase						:Union[str, Any]						  =     self.unet.config.sample_size / self.unet.config.sample_rate

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

										lowerCamelCase						:int						  =     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						:Union[str, Any]						  =     int(__snake_case		)
										if sample_size % down_scale_factor != 0:
													lowerCamelCase						:Dict						  =     (
													    (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						:str						  =     int(__snake_case		)

										lowerCamelCase						:List[Any]						  =     next(iter(self.unet.parameters()		)		).dtype
										lowerCamelCase						:Tuple						  =     (batch_size, self.unet.config.in_channels, sample_size)
										if isinstance(__snake_case	,					__snake_case		) and len(__snake_case		) != batch_size:
													raise ValueError(
													    F"You have passed a list of generators of length {len(__snake_case		)}, 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(__snake_case	,					generator=__snake_case	,					device=self.device	,					dtype=__snake_case		)

										# set step values
										self.scheduler.set_timesteps(__snake_case	,					device=audio.device		)
										lowerCamelCase						:Optional[Any]						  =     self.scheduler.timesteps.to(__snake_case		)

										for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													lowerCamelCase						:List[Any]						  =     self.unet(__snake_case	,					__snake_case		).sample

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

										lowerCamelCase						:List[str]						  =     audio.clamp(-1	,					1		).float().cpu().numpy()

										lowerCamelCase						:Optional[Any]						  =     audio[:, :, :original_sample_size]

										if not return_dict:
													return (audio,)

										return AudioPipelineOutput(audios=__snake_case		)




import os
from math import logaa





def  _lowerCamelCase					(   a_				: str = "base_exp.txt"):
			lowerCamelCase						:float						  =     0
			lowerCamelCase						:Optional[int]						  =     0
			for i, line in enumerate(open(os.path.join(os.path.dirname(a_)						,			a_))):
						lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     list(map(a_						,			line.split(''',''')))
						if x * logaa(a_) > largest:
									lowerCamelCase						:List[Any]						  =     x * logaa(a_)
									lowerCamelCase						:Any						  =     i + 1
			return result


if __name__ == "__main__":
			print(solution())



