




'''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      lowerCAmelCase :
       __lowercase	:   int
       __lowercase	:   Node | None										=      None
       __lowercase	:   Node | None										=      None







def       _UpperCamelCase			()-> Node | None:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> list[int]:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> list[int]:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> list[int]:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> int:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> Sequence[Node | None]:




     '''simple docstring'''





     __snake_case            =	[]

     if root is None:
          return output

     __snake_case            =	deque([root]						)

     while process_queue:
          __snake_case            =	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			(_lowerCamelCase				:  Node | None ,						_lowerCamelCase				:  int						)-> Sequence[Node | None]:




     '''simple docstring'''





     __snake_case            =	[]

     def populate_output(_lowerCamelCase				:  Node | None ,						_lowerCamelCase				:  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(_lowerCamelCase ,						_lowerCamelCase						)
     return output







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None ,						_lowerCamelCase				:  int						)-> Sequence[Node | None]:




     '''simple docstring'''





     __snake_case            =	[]

     def populate_output(_lowerCamelCase				:  Node | None ,						_lowerCamelCase				:  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(_lowerCamelCase ,						_lowerCamelCase						)
     return output







def       _UpperCamelCase			(_lowerCamelCase				:  Node | None						)-> Sequence[Node | None] | list[Any]:




     '''simple docstring'''





     if root is None:
          return []

     __snake_case            =	[]

     __snake_case            =	0
     __snake_case            =	height(_lowerCamelCase						)

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

     return output







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




     '''simple docstring'''





     __snake_case            =	make_tree()
     print(f'''In-order Traversal: {inorder(_lowerCamelCase						)}'''						)
     print(f'''Pre-order Traversal: {preorder(_lowerCamelCase						)}'''						)
     print(f'''Post-order Traversal: {postorder(_lowerCamelCase						)}''' ,						'''\n'''						)

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

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

     print('''Level-wise order Traversal: '''						)

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

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()








import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Dict			=	StableDiffusionDiffEditPipeline
   A__   :						str			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
   A__   :						List[Any]			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
   A__   :						Optional[Any]			=	frozenset(
       []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   A__   :						int			=	frozenset([]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          torch.manual_seed(0					)
          snake_case			:  Dict     							= UNetaDConditionModel(
              block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=4       ,		out_channels=4       ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,		cross_attention_dim=32       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=snake_case__       ,		)
          snake_case			:  str     							= DDIMScheduler(
              beta_start=0.00085       ,		beta_end=0.012       ,		beta_schedule="scaled_linear"       ,		clip_sample=snake_case__       ,		set_alpha_to_one=snake_case__       ,		)
          snake_case			:  List[Any]     							= DDIMInverseScheduler(
              beta_start=0.00085       ,		beta_end=0.012       ,		beta_schedule="scaled_linear"       ,		clip_sample=snake_case__       ,		set_alpha_to_zero=snake_case__       ,		)
          torch.manual_seed(0					)
          snake_case			:  Any     							= AutoencoderKL(
              block_out_channels=[32, 64]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,		latent_channels=4       ,		sample_size=1_28       ,		)
          torch.manual_seed(0					)
          snake_case			:  Union[str, Any]     							= CLIPTextConfig(
              bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1e-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=10_00       ,		hidden_act="gelu"       ,		projection_dim=5_12       ,		)
          snake_case			:  str     							= CLIPTextModel(snake_case__					)
          snake_case			:  Optional[Any]     							= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

          snake_case			:  List[str]     							= {
              "unet": unet,
              "scheduler": scheduler,
              "inverse_scheduler": inverse_scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }

          return components



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Dict       ,		snake_case__						:   Union[str, Any]=0					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= floats_tensor((1, 16, 16)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  List[Any]     							= floats_tensor((1, 2, 4, 16, 16)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  Any     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  Optional[Any]     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Union[str, Any]     							= {
              "prompt": "a dog and a newt",
              "mask_image": mask,
              "image_latents": latents,
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[int]=0					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Tuple     							= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  Optional[Any]     							= image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
          snake_case			:  Any     							= Image.fromarray(np.uinta(snake_case__					)					).convert("RGB"					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  int     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  Optional[int]     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Any     							= {
              "image": image,
              "source_prompt": "a cat and a frog",
              "target_prompt": "a dog and a newt",
              "generator": generator,
              "num_inference_steps": 2,
              "num_maps_per_mask": 2,
              "mask_encode_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Tuple=0					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  Tuple     							= image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
          snake_case			:  List[Any]     							= Image.fromarray(np.uinta(snake_case__					)					).convert("RGB"					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  str     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  int     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  List[str]     							= {
              "image": image,
              "prompt": "a cat and a frog",
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "decode_latents": True,
              "output_type": "numpy",
          }
          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          if not hasattr(self.pipeline_class       ,		"_optional_components"					):
                 return

          snake_case			:  int     							= self.get_dummy_components()
          snake_case			:  Optional[int]     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          # set all optional components to None and update pipeline config accordingly
          for optional_component in pipe._optional_components:
                 setattr(snake_case__       ,		snake_case__       ,		snake_case__					)
          pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}					)

          snake_case			:  Dict     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  int     							= pipe(**snake_case__					)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(snake_case__					)
                 snake_case			:  Any     							= self.pipeline_class.from_pretrained(snake_case__					)
                 pipe_loaded.to(snake_case__					)
                 pipe_loaded.set_progress_bar_config(disable=snake_case__					)

          for optional_component in pipe._optional_components:
                 self.assertTrue(
                     getattr(snake_case__       ,		snake_case__					) is None       ,		f"""`{optional_component}` did not stay set to None after loading."""       ,		)

          snake_case			:  Optional[int]     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  int     							= pipe_loaded(**snake_case__					)[0]

          snake_case			:  int     							= np.abs(output - output_loaded					).max()
          self.assertLess(snake_case__       ,		1e-4					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      int:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= "cpu"

          snake_case			:  int     							= self.get_dummy_components()
          snake_case			:  Any     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Any     							= self.get_dummy_mask_inputs(snake_case__					)
          snake_case			:  Union[str, Any]     							= pipe.generate_mask(**snake_case__					)
          snake_case			:  List[Any]     							= mask[0, -3:, -3:]

          self.assertEqual(mask.shape       ,		(1, 16, 16)					)
          snake_case			:  int     							= np.array([0] * 9					)
          snake_case			:  str     							= np.abs(mask_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)
          self.assertEqual(mask[0, -3, -4]       ,		0					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      str:


          '''simple docstring'''


          snake_case			:  Any     							= "cpu"

          snake_case			:  List[Any]     							= self.get_dummy_components()
          snake_case			:  Optional[int]     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  List[str]     							= self.get_dummy_inversion_inputs(snake_case__					)
          snake_case			:  List[str]     							= pipe.invert(**snake_case__					).images
          snake_case			:  Optional[Any]     							= image[0, -1, -3:, -3:]

          self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
          snake_case			:  int     							= np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]       ,		)
          snake_case			:  Optional[Any]     							= np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


          super().test_inference_batch_single_identical(expected_max_diff=5e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''simple docstring'''


          snake_case			:  Any     							= "cpu"

          snake_case			:  int     							= self.get_dummy_components()

          snake_case			:  Tuple     							= {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
          snake_case			:  int     							= DPMSolverMultistepScheduler(**snake_case__					)
          snake_case			:  Optional[Any]     							= DPMSolverMultistepInverseScheduler(**snake_case__					)

          snake_case			:  Any     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Tuple     							= self.get_dummy_inversion_inputs(snake_case__					)
          snake_case			:  Optional[int]     							= pipe.invert(**snake_case__					).images
          snake_case			:  Optional[Any]     							= image[0, -1, -3:, -3:]

          self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
          snake_case			:  int     							= np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]       ,		)
          snake_case			:  Any     							= np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)






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



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      int:


          '''simple docstring'''


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



   @classmethod
   def 							_SCREAMING_SNAKE_CASE  (cls						:   Optional[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Tuple     							= load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"					)

          snake_case			:  Tuple     							= raw_image.convert("RGB"					).resize((7_68, 7_68)					)

          snake_case			:  Tuple     							= raw_image



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= torch.manual_seed(0					)

          snake_case			:  List[str]     							= StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1"       ,		safety_checker=snake_case__       ,		torch_dtype=torch.floataa					)
          snake_case			:  int     							= DDIMScheduler.from_config(pipe.scheduler.config					)
          snake_case			:  Dict     							= DDIMInverseScheduler.from_config(pipe.scheduler.config					)
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Union[str, Any]     							= "a bowl of fruit"
          snake_case			:  int     							= "a bowl of pears"

          snake_case			:  List[Any]     							= pipe.generate_mask(
              image=self.raw_image       ,		source_prompt=snake_case__       ,		target_prompt=snake_case__       ,		generator=snake_case__       ,		)

          snake_case			:  Optional[int]     							= pipe.invert(
              prompt=snake_case__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=snake_case__					).latents

          snake_case			:  List[str]     							= pipe(
              prompt=snake_case__       ,		mask_image=snake_case__       ,		image_latents=snake_case__       ,		generator=snake_case__       ,		negative_prompt=snake_case__       ,		inpaint_strength=0.7       ,		output_type="numpy"       ,		).images[0]

          snake_case			:  Dict     							= (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"					).resize((7_68, 7_68)					)					)
              / 2_55
          )
          assert np.abs((expected_image - image).max()					) < 5e-1



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  int     							= torch.manual_seed(0					)

          snake_case			:  Tuple     							= StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1"       ,		safety_checker=snake_case__       ,		torch_dtype=torch.floataa					)
          snake_case			:  Union[str, Any]     							= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
          snake_case			:  Optional[int]     							= DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  str     							= "a bowl of fruit"
          snake_case			:  Optional[int]     							= "a bowl of pears"

          snake_case			:  Optional[Any]     							= pipe.generate_mask(
              image=self.raw_image       ,		source_prompt=snake_case__       ,		target_prompt=snake_case__       ,		generator=snake_case__       ,		)

          snake_case			:  List[str]     							= pipe.invert(
              prompt=snake_case__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=snake_case__       ,		num_inference_steps=25       ,		).latents

          snake_case			:  Dict     							= pipe(
              prompt=snake_case__       ,		mask_image=snake_case__       ,		image_latents=snake_case__       ,		generator=snake_case__       ,		negative_prompt=snake_case__       ,		inpaint_strength=0.7       ,		num_inference_steps=25       ,		output_type="numpy"       ,		).images[0]

          snake_case			:  int     							= (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"					).resize((7_68, 7_68)					)					)
              / 2_55
          )
          assert np.abs((expected_image - image).max()					) < 5e-1





