






'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      str     							= StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4')
      __snake_case				:      Optional[Any]     							= sd_pipe.to(__a)
      sd_pipe.set_progress_bar_config(disable=__a)

      sd_pipe.set_scheduler('sample_euler')

      __snake_case				:      List[str]     							= 'A painting of a squirrel eating a burger'
      __snake_case				:      List[str]     							= torch.manual_seed(0)
      __snake_case				:      Union[str, Any]     							= sd_pipe([prompt]					,   generator=__a					,   guidance_scale=9.0					,   num_inference_steps=2_0					,   output_type='np')

      __snake_case				:      int     							= output.images

      __snake_case				:      Any     							= image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      __snake_case				:      Optional[Any]     							= np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
      __snake_case				:      List[Any]     							= sd_pipe.to(__a)
      sd_pipe.set_progress_bar_config(disable=__a)

      sd_pipe.set_scheduler('sample_euler')

      __snake_case				:      Tuple     							= 'A painting of a squirrel eating a burger'
      __snake_case				:      List[str]     							= torch.manual_seed(0)
      __snake_case				:      Union[str, Any]     							= sd_pipe([prompt]					,   generator=__a					,   guidance_scale=9.0					,   num_inference_steps=2_0					,   output_type='np')

      __snake_case				:      Optional[Any]     							= output.images

      __snake_case				:      Any     							= image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      __snake_case				:      str     							= np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-1







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
      __snake_case				:      Optional[Any]     							= sd_pipe.to(__a)
      sd_pipe.set_progress_bar_config(disable=__a)

      sd_pipe.set_scheduler('sample_dpmpp_2m')

      __snake_case				:      Dict     							= 'A painting of a squirrel eating a burger'
      __snake_case				:      Optional[Any]     							= torch.manual_seed(0)
      __snake_case				:      Union[str, Any]     							= sd_pipe(
          [prompt]					,   generator=__a					,   guidance_scale=7.5					,   num_inference_steps=1_5					,   output_type='np'					,   use_karras_sigmas=__a					,   )

      __snake_case				:      Optional[Any]     							= output.images

      __snake_case				:      str     							= image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      __snake_case				:      Dict     							= np.array(
          [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







'''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 a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> 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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> 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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

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

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __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()