




"""simple docstring"""







import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

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


enable_full_determinism()

class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       StableDiffusionInpaintPipeline
   A_				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   A_				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   A_				      =       frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   A_				      =       frozenset([]			)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       torch.manual_seed(0)
       UpperCamelCase              =	UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)       ,					layers_per_block=2       ,					sample_size=3_2       ,					in_channels=9       ,					out_channels=4       ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,					cross_attention_dim=3_2       ,					attention_head_dim=(2, 4)       ,					use_linear_projection=lowerCamelCase_       ,					)
       UpperCamelCase              =	PNDMScheduler(skip_prk_steps=lowerCamelCase_)
       torch.manual_seed(0)
       UpperCamelCase              =	AutoencoderKL(
           block_out_channels=[3_2, 6_4]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,					latent_channels=4       ,					sample_size=1_2_8       ,					)
       torch.manual_seed(0)
       UpperCamelCase              =	CLIPTextConfig(
           bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1e-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					hidden_act='''gelu'''       ,					projection_dim=5_1_2       ,					)
       UpperCamelCase              =	CLIPTextModel(lowerCamelCase_)
       UpperCamelCase              =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

       UpperCamelCase              =	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=0)				->   Dict:
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       UpperCamelCase              =	floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)
       UpperCamelCase              =	image.cpu().permute(0       ,					2       ,					3       ,					1)[0]
       UpperCamelCase              =	Image.fromarray(np.uinta(lowerCamelCase_)).convert('''RGB''').resize((6_4, 6_4))
       UpperCamelCase              =	Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((6_4, 6_4))
       if str(lowerCamelCase_).startswith('''mps'''):
           UpperCamelCase              =	torch.manual_seed(lowerCamelCase_)
       else:
           UpperCamelCase              =	torch.Generator(device=lowerCamelCase_).manual_seed(lowerCamelCase_)
       UpperCamelCase              =	{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase              =	self.get_dummy_components()
       UpperCamelCase              =	StableDiffusionInpaintPipeline(**lowerCamelCase_)
       UpperCamelCase              =	sd_pipe.to(lowerCamelCase_)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

       UpperCamelCase              =	self.get_dummy_inputs(lowerCamelCase_)
       UpperCamelCase              =	sd_pipe(**lowerCamelCase_).images
       UpperCamelCase              =	image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       UpperCamelCase              =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3)

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







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''')
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
       UpperCamelCase              =	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''')

       UpperCamelCase              =	'''stabilityai/stable-diffusion-2-inpainting'''
       UpperCamelCase              =	StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase_       ,					safety_checker=lowerCamelCase_)
       pipe.to(lowerCamelCase_)
       pipe.set_progress_bar_config(disable=lowerCamelCase_)
       pipe.enable_attention_slicing()

       UpperCamelCase              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	pipe(
           prompt=lowerCamelCase_       ,					image=lowerCamelCase_       ,					mask_image=lowerCamelCase_       ,					generator=lowerCamelCase_       ,					output_type='''np'''       ,					)
       UpperCamelCase              =	output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image).max() < 9e-3







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''')
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
       UpperCamelCase              =	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''')

       UpperCamelCase              =	'''stabilityai/stable-diffusion-2-inpainting'''
       UpperCamelCase              =	StableDiffusionInpaintPipeline.from_pretrained(
           lowerCamelCase_       ,					torch_dtype=torch.floataa       ,					safety_checker=lowerCamelCase_       ,					)
       pipe.to(lowerCamelCase_)
       pipe.set_progress_bar_config(disable=lowerCamelCase_)
       pipe.enable_attention_slicing()

       UpperCamelCase              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	pipe(
           prompt=lowerCamelCase_       ,					image=lowerCamelCase_       ,					mask_image=lowerCamelCase_       ,					generator=lowerCamelCase_       ,					output_type='''np'''       ,					)
       UpperCamelCase              =	output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image).max() < 5e-1







   def   UpperCAmelCase__			(		self)				->   List[str]:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''')
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

       UpperCamelCase              =	'''stabilityai/stable-diffusion-2-inpainting'''
       UpperCamelCase              =	PNDMScheduler.from_pretrained(lowerCamelCase_       ,					subfolder='''scheduler''')
       UpperCamelCase              =	StableDiffusionInpaintPipeline.from_pretrained(
           lowerCamelCase_       ,					safety_checker=lowerCamelCase_       ,					scheduler=lowerCamelCase_       ,					torch_dtype=torch.floataa       ,					)
       pipe.to(lowerCamelCase_)
       pipe.set_progress_bar_config(disable=lowerCamelCase_)
       pipe.enable_attention_slicing(1)
       pipe.enable_sequential_cpu_offload()

       UpperCamelCase              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	pipe(
           prompt=lowerCamelCase_       ,					image=lowerCamelCase_       ,					mask_image=lowerCamelCase_       ,					generator=lowerCamelCase_       ,					num_inference_steps=2       ,					output_type='''np'''       ,					)

       UpperCamelCase              =	torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 1_0**9






'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



