





'''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					:






       """simple docstring"""
       _snake_case     :					int
       _snake_case     :					Node | None						    =   None
       _snake_case     :					Node | None						    =   None







def  a__    (    )		->		Node | None:





     """simple docstring"""

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







def  a__    (    lowercase :					Node | None				)		->		list[int]:





     """simple docstring"""

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







def  a__    (    lowercase :					Node | None				)		->		list[int]:





     """simple docstring"""

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







def  a__    (    lowercase :					Node | None				)		->		list[int]:





     """simple docstring"""

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







def  a__    (    lowercase :					Node | None				)		->		int:





     """simple docstring"""

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







def  a__    (    lowercase :					Node | None				)		->		Sequence[Node | None]:





     """simple docstring"""

     _UpperCamelCase       =	[]

     if root is None:
          return output

     _UpperCamelCase       =	deque([root]				)

     while process_queue:
          _UpperCamelCase       =	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__    (    lowercase :					Node | None, lowercase :					int				)		->		Sequence[Node | None]:





     """simple docstring"""

     _UpperCamelCase       =	[]

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







def  a__    (    lowercase :					Node | None, lowercase :					int				)		->		Sequence[Node | None]:





     """simple docstring"""

     _UpperCamelCase       =	[]

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







def  a__    (    lowercase :					Node | None				)		->		Sequence[Node | None] | list[Any]:





     """simple docstring"""

     if root is None:
          return []

     _UpperCamelCase       =	[]

     _UpperCamelCase       =	0
     _UpperCamelCase       =	height(lowercase				)

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

     return output







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





     """simple docstring"""

     _UpperCamelCase       =	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 gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard
@slow
@require_flax
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            super().tearDown()
            gc.collect()





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-canny'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''bird'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

            _UpperCamelCase       =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					)
            _UpperCamelCase       =	pipe.prepare_image_inputs([canny_image] * num_samples					)

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

            _UpperCamelCase       =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
            _UpperCamelCase       =	images[0, 253:256, 253:256, -1]

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







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

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-openpose'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''Chef in the kitchen'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

            _UpperCamelCase       =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''					)
            _UpperCamelCase       =	pipe.prepare_image_inputs([pose_image] * num_samples					)

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

            _UpperCamelCase       =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
            _UpperCamelCase       =	images[0, 253:256, 253:256, -1]

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



