




"""simple docstring"""


from typing import Any






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		Dict,				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  data
   _snake_case							:		Optional[Any]								=  None







class  lowercase:







 '''simple docstring'''





 def __init__(    self:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  None


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.head
   while temp is not None:
     print(temp.data,				end=""" """		)
     _snake_case							:		int								=  temp.next
   print()


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  Node(a_		)
   _snake_case							:		Union[str, Any]								=  self.head
   _snake_case							:		List[Any]								=  new_node


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		List[str],				a_:		Union[str, Any]		):




   '''simple docstring'''



   if node_data_a == node_data_a:
     return
   else:
     _snake_case							:		int								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     _snake_case							:		List[Any]								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     if node_a is None or node_a is None:
       return

     _snake_case		, _snake_case							:		int								=  node_a.data, node_a.data


if __name__ == "__main__":
     A_      =    LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()








"""simple docstring"""


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  lowercase(					__a			):







 '''simple docstring'''





 def __init__(    self:		Optional[int],				a_:		str,				a_:		Optional[Any]		):




   '''simple docstring'''



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


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




   '''simple docstring'''



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

   _snake_case							:		Optional[int]								=  audio_length_in_s * self.unet.config.sample_rate

   _snake_case							:		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}."		)

   _snake_case							:		Union[str, Any]								=  int(a_		)
   if sample_size % down_scale_factor != 0:
     _snake_case							:		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."""		)
   _snake_case							:		str								=  int(a_		)

   _snake_case							:		int								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Optional[Any]								=  (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."		)

   _snake_case							:		Optional[Any]								=  randn_tensor(a_,				generator=a_,				device=self.device,				dtype=a_		)

   # set step values
   self.scheduler.set_timesteps(a_,				device=audio.device		)
   _snake_case							:		Optional[int]								=  self.scheduler.timesteps.to(a_		)

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

     # 2. compute previous image: x_t -> t_t-1
     _snake_case							:		Optional[Any]								=  self.scheduler.step(a_,				a_,				a_		).prev_sample

   _snake_case							:		Tuple								=  audio.clamp(-1,				1		).float().cpu().numpy()

   _snake_case							:		Dict								=  audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)


