





from __future__ import annotations





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[Any] =				data
          a__						:	Node | None =				None
          a__						:	Node | None =				None



def A_	(		A__				)  ->		None:  # In Order traversal of the tree
       if tree:
              display(tree.left				)
              print(tree.data				)
              display(tree.right				)



def A_	(		A__				)  ->		int:
       return 1 + max(depth_of_tree(tree.left				)		,      depth_of_tree(tree.right				)				) if tree else 0



def A_	(		A__				)  ->		bool:
       if not tree:
              return True
       if tree.left and tree.right:
              return is_full_binary_tree(tree.left				) and is_full_binary_tree(tree.right				)
       else:
              return not tree.left and not tree.right



def A_	(		)  ->		None:  # Main function for testing.
       a__						:	Optional[int] =				Node(1				)
       a__						:	Optional[int] =				Node(2				)
       a__						:	Tuple =				Node(3				)
       a__						:	Tuple =				Node(4				)
       a__						:	Tuple =				Node(5				)
       a__						:	Optional[Any] =				Node(6				)
       a__						:	Optional[int] =				Node(7				)
       a__						:	str =				Node(8				)
       a__						:	Optional[int] =				Node(9				)

       print(is_full_binary_tree(A__				)				)
       print(depth_of_tree(A__				)				)
       print('Tree is: '				)
       display(A__				)


if __name__ == "__main__":
    main()









from typing import List, Optional, Tuple, Union

import torch

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


lowercase      :       Any									=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

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

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, 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}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (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.')
          a__						:	List[Any] =				int(lowercase)

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

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

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

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

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

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

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


