

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class  _A		:
  SCREAMING_SNAKE_CASE    :      int
  SCREAMING_SNAKE_CASE    :      Node | None       =  None
  SCREAMING_SNAKE_CASE    :      Node | None       =  None

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  str           =     Node(1  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     Node(2  )
      SCREAMING_SNAKE_CASE_				:  str           =     Node(3  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     Node(4  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     Node(5  )
      return tree

def    A_  (							a  ):

      """simple docstring"""



      return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []

def    A_  (							a  ):

      """simple docstring"""



      return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []

def    A_  (							a  ):

      """simple docstring"""



      return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []

def    A_  (							a  ):

      """simple docstring"""



      return (max(height(root.left  )					,   height(root.right  )  ) + 1) if root else 0

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  list[Any]           =     []

      if root is None:
            return output

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     deque([root]  )

      while process_queue:
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     process_queue.popleft()
            output.append(node.data  )

            if node.left:
                  process_queue.append(node.left  )
            if node.right:
                  process_queue.append(node.right  )
      return output

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  list[Any]           =     []

      def populate_output(a					,   a  ) -> None:
            if not root:
                  return
            if level == 1:
                  output.append(root.data  )
            elif level > 1:
                  populate_output(root.left					,   level - 1  )
                  populate_output(root.right					,   level - 1  )

      populate_output(a					,   a  )
      return output

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  list[Any]           =     []

      def populate_output(a					,   a  ) -> None:
            if root is None:
                  return
            if level == 1:
                  output.append(root.data  )
            elif level > 1:
                  populate_output(root.right					,   level - 1  )
                  populate_output(root.left					,   level - 1  )

      populate_output(a					,   a  )
      return output

def    A_  (							a  ):

      """simple docstring"""



      if root is None:
            return []

      SCREAMING_SNAKE_CASE_				:  list[Sequence[Node | None]]           =     []

      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     0
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     height(a  )

      for h in range(1					,   height_tree + 1  ):
            if not flag:
                  output.append(get_nodes_from_left_to_right(a					,   a  )  )
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     1
            else:
                  output.append(get_nodes_from_right_to_left(a					,   a  )  )
                  SCREAMING_SNAKE_CASE_				:  Optional[int]           =     0

      return output

def    A_  (							):  # Main function for testing.

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     make_tree()
      print(f"In-order Traversal: {inorder(a  )}"  )
      print(f"Pre-order Traversal: {preorder(a  )}"  )
      print(f"Post-order Traversal: {postorder(a  )}"					,   '\n'  )

      print(f"Height of Tree: {height(a  )}"					,   '\n'  )

      print('Complete Level Order Traversal: '  )
      print(level_order(a  )					,   '\n'  )

      print('Level-wise order Traversal: '  )

      for level in range(1					,   height(a  ) + 1  ):
            print(f"Level {level}:"					,   get_nodes_from_left_to_right(a					,   level=a  )  )

      print('\nZigZag order Traversal: '  )
      print(zigzag(a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()









import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     UNetaDModel(
            sample_size=(32, 64) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,					cross_attention_dim=10 ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            sample_size=(128, 64) ,					in_channels=1 ,					out_channels=1 ,					latent_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     UNetaDModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return vqvae, unet



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     Mel(
            x_res=self.dummy_unet.config.sample_size[1] ,					y_res=self.dummy_unet.config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_				:  str           =     AudioDiffusionPipeline(vqvae=_SCREAMING_SNAKE_CASE ,					unet=self.dummy_unet ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4 ,					return_dict=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output[0][0]

        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
        assert (
            image.height == self.dummy_unet.config.sample_size[0]
            and image.width == self.dummy_unet.config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image_from_tuple.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     Mel(
            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_vqvae_and_unet
        SCREAMING_SNAKE_CASE_				:  int           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=dummy_vqvae_and_unet[1] ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.random.uniform(-1 ,					1 ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe(raw_audio=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					start_step=5 ,					steps=10					)
        SCREAMING_SNAKE_CASE_				:  int           =     output.images[0]

        assert (
            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  int           =     np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_unet_condition
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=_SCREAMING_SNAKE_CASE ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.rand((1, 1, 10)					)
        SCREAMING_SNAKE_CASE_				:  Any           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					encoding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

@slow
@require_torch_gpu
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch_device

        SCREAMING_SNAKE_CASE_				:  str           =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     output.images[0]

        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0






