






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 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Optional[int]		,     __a				:	Any		,     __a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												super().__init__()
												self.register_modules(unet=__a		,     scheduler=__a							)



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


												"""simple docstring"""

												if audio_length_in_s is None:
																			__lowercase      :							List[str]				=     self.unet.config.sample_size / self.unet.config.sample_rate

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

												__lowercase      :							str				=     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}."							)

												__lowercase      :							int				=     int(__a							)
												if sample_size % down_scale_factor != 0:
																			__lowercase      :							Optional[Any]				=     (
																			    (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."""							)
												__lowercase      :							str				=     int(__a							)

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

												__lowercase      :							Dict				=     randn_tensor(__a		,     generator=__a		,     device=self.device		,     dtype=__a							)

												# set step values
												self.scheduler.set_timesteps(__a		,     device=audio.device							)
												__lowercase      :							Tuple				=     self.scheduler.timesteps.to(__a							)

												for t in self.progress_bar(self.scheduler.timesteps							):
																			# 1. predict noise model_output
																			__lowercase      :							Optional[Any]				=     self.unet(__a		,     __a							).sample

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

												__lowercase      :							Dict				=     audio.clamp(-1		,     1							).float().cpu().numpy()

												__lowercase      :							List[str]				=     audio[:, :, :original_sample_size]

												if not return_dict:
																			return (audio,)

												return AudioPipelineOutput(audios=__a							)







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(1      ) != 0      )
def       snake_case_		(     ):
							assert or_gate(0       ,       0      ) == 0
							assert or_gate(0       ,       1      ) == 1
							assert or_gate(1       ,       0      ) == 1
							assert or_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	print(or_gate(0, 1))
	print(or_gate(1, 0))
	print(or_gate(0, 0))
	print(or_gate(1, 1))