



'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 	UpperCAmelCase	:


 '''simple docstring'''




 __magic_name__					: int
 __magic_name__					: Node | None  =      None
 __magic_name__					: Node | None  =      None






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=Node(1       )
     a_       		=Node(2       )
     a_       		=Node(3       )
     a_       		=Node(4       )
     a_       		=Node(5       )
     return tree






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=[]

     if root is None:
          return output

     a_       		=deque([root]       )

     while process_queue:
          a_       		=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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[]

     def populate_output(lowercase__       ,	lowercase__       ) -> 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(lowercase__       ,	lowercase__       )
     return output






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[]

     def populate_output(lowercase__       ,	lowercase__       ) -> 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(lowercase__       ,	lowercase__       )
     return output






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     if root is None:
          return []

     a_       		=[]

     a_       		=0
     a_       		=height(lowercase__       )

     for h in range(1       ,	height_tree + 1       ):
          if not flag:
               output.append(get_nodes_from_left_to_right(lowercase__       ,	lowercase__       )       )
               a_       		=1
          else:
               output.append(get_nodes_from_right_to_left(lowercase__       ,	lowercase__       )       )
               a_       		=0

     return output






def  UpperCAmelCase_       (		):  # Main function for testing.


     '''simple docstring'''







     a_       		=make_tree()
     print(F"""In-order Traversal: {inorder(lowercase__       )}"""       )
     print(F"""Pre-order Traversal: {preorder(lowercase__       )}"""       )
     print(F"""Post-order Traversal: {postorder(lowercase__       )}"""       ,	"\n"       )

     print(F"""Height of Tree: {height(lowercase__       )}"""       ,	"\n"       )

     print("Complete Level Order Traversal: "       )
     print(level_order(lowercase__       )       ,	"\n"       )

     print("Level-wise order Traversal: "       )

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

     print("\nZigZag order Traversal: "       )
     print(zigzag(lowercase__       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()







'''simple docstring'''


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class 	UpperCAmelCase	:


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      return self.get_dummy_input()




 @property
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      if self.block_type == "down":
           return (4, 3_2, 1_6, 1_6)
      elif self.block_type == "mid":
           return (4, 3_2, 3_2, 3_2)
      elif self.block_type == "up":
           return (4, 3_2, 6_4, 6_4)

      raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")




 def       lowercase_  (   self							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  ) ->      Dict:



      """simple docstring"""







      a_       		=4
      a_       		=3_2
      a_       		=(3_2, 3_2)

      a_       		=torch.manual_seed(0)
      a_       		=torch.device(lowerCAmelCase_)
      a_       		=(batch_size, num_channels) + sizes
      a_       		=randn_tensor(lowerCAmelCase_							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)
      a_       		={"hidden_states": hidden_states}

      if include_temb:
           a_       		=1_2_8
           a_       		=randn_tensor((batch_size, temb_channels)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      if include_res_hidden_states_tuple:
           a_       		=torch.manual_seed(1)
           a_       		=(randn_tensor(lowerCAmelCase_							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_),)

      if include_encoder_hidden_states:
           a_       		=floats_tensor((batch_size, 3_2, 3_2)).to(lowerCAmelCase_)

      if include_skip_sample:
           a_       		=randn_tensor(((batch_size, 3) + sizes)							,  generator=lowerCAmelCase_							,  device=lowerCAmelCase_)

      return dummy_input




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		={
          "in_channels": 3_2,
          "out_channels": 3_2,
          "temb_channels": 1_2_8,
      }
      if self.block_type == "up":
           a_       		=3_2

      if self.block_type == "mid":
           init_dict.pop("out_channels")

      a_       		=self.dummy_input
      return init_dict, inputs_dict




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      unet_block.to(lowerCAmelCase_)
      unet_block.eval()

      with torch.no_grad():
           a_       		=unet_block(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

      self.assertEqual(output.shape							,  self.output_shape)

      a_       		=output[0, -1, -3:, -3:]
      a_       		=torch.tensor(lowerCAmelCase_).to(lowerCAmelCase_)
      assert torch_all_close(output_slice.flatten()							,  lowerCAmelCase_							,  atol=5e-3)




 @unittest.skipIf(torch_device == "mps"							,  "Training is not supported in mps")
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_		,						a_       		=self.prepare_init_args_and_inputs_for_common()
      a_       		=self.block_class(**lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.train()
      a_       		=model(**lowerCAmelCase_)

      if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
           a_       		=output[0]

      a_       		=torch.device(lowerCAmelCase_)
      a_       		=randn_tensor(output.shape							,  device=lowerCAmelCase_)
      a_       		=torch.nn.functional.mse_loss(lowerCAmelCase_							,  lowerCAmelCase_)
      loss.backward()


