




"""simple docstring"""




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_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				StableDiffusionDiffEditPipeline
    UpperCAmelCase__							  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
    UpperCAmelCase__							  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
    UpperCAmelCase__							  =				frozenset(
        []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCAmelCase__							  =				frozenset([]					)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Tuple:





           '''simple docstring'''

           torch.manual_seed(0)
           A__							=   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=UpperCAmelCase__ ,			)
           A__							=   DDIMScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''' ,			clip_sample=UpperCAmelCase__ ,			set_alpha_to_one=UpperCAmelCase__ ,			)
           A__							=   DDIMInverseScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''' ,			clip_sample=UpperCAmelCase__ ,			set_alpha_to_zero=UpperCAmelCase__ ,			)
           torch.manual_seed(0)
           A__							=   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=128 ,			)
           torch.manual_seed(0)
           A__							=   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=1_000 ,			hidden_act='''gelu''' ,			projection_dim=512 ,			)
           A__							=   CLIPTextModel(UpperCAmelCase__)
           A__							=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__							=   {
               '''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				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Any=0)						->str:





           '''simple docstring'''

           A__							=   floats_tensor((1, 16, 16) ,			rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__							=   floats_tensor((1, 2, 4, 16, 16) ,			rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__							=   torch.manual_seed(UpperCAmelCase__)
           else:
                  A__							=   torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__							=   {
               '''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 ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Any=0)						->int:





           '''simple docstring'''

           A__							=   floats_tensor((1, 3, 32, 32) ,			rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__							=   image.cpu().permute(0 ,			2 ,			3 ,			1)[0]
           A__							=   Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''')
           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__							=   torch.manual_seed(UpperCAmelCase__)
           else:
                  A__							=   torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__							=   {
               '''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				:     List[Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Optional[int]=0)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   floats_tensor((1, 3, 32, 32) ,			rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__							=   image.cpu().permute(0 ,			2 ,			3 ,			1)[0]
           A__							=   Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''')
           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__							=   torch.manual_seed(UpperCAmelCase__)
           else:
                  A__							=   torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__							=   {
               '''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[Any])						->int:





           '''simple docstring'''

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

           A__							=   self.get_dummy_components()
           A__							=   self.pipeline_class(**UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

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

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   pipe(**UpperCAmelCase__)[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(UpperCAmelCase__)
                  A__							=   self.pipeline_class.from_pretrained(UpperCAmelCase__)
                  pipe_loaded.to(UpperCAmelCase__)
                  pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase__)

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

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   pipe_loaded(**UpperCAmelCase__)[0]

           A__							=   np.abs(output - output_loaded).max()
           self.assertLess(UpperCAmelCase__ ,			1e-4)


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





           '''simple docstring'''

           A__							=   '''cpu'''

           A__							=   self.get_dummy_components()
           A__							=   self.pipeline_class(**UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_mask_inputs(UpperCAmelCase__)
           A__							=   pipe.generate_mask(**UpperCAmelCase__)
           A__							=   mask[0, -3:, -3:]

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           A__							=   '''cpu'''

           A__							=   self.get_dummy_components()
           A__							=   self.pipeline_class(**UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inversion_inputs(UpperCAmelCase__)
           A__							=   pipe.invert(**UpperCAmelCase__).images
           A__							=   image[0, -1, -3:, -3:]

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[Any]:





           '''simple docstring'''

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





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





           '''simple docstring'''

           A__							=   '''cpu'''

           A__							=   self.get_dummy_components()

           A__							=   {'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
           A__							=   DPMSolverMultistepScheduler(**UpperCAmelCase__)
           A__							=   DPMSolverMultistepInverseScheduler(**UpperCAmelCase__)

           A__							=   self.pipeline_class(**UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inversion_inputs(UpperCAmelCase__)
           A__							=   pipe.invert(**UpperCAmelCase__).images
           A__							=   image[0, -1, -3:, -3:]

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


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

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Dict:





           '''simple docstring'''

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


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     List[str])						->str:





           '''simple docstring'''

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

           A__							=   raw_image.convert('''RGB''').resize((768, 768))

           A__							=   raw_image


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





           '''simple docstring'''

           A__							=   torch.manual_seed(0)

           A__							=   StableDiffusionDiffEditPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-1''' ,			safety_checker=UpperCAmelCase__ ,			torch_dtype=torch.floataa)
           A__							=   DDIMScheduler.from_config(pipe.scheduler.config)
           A__							=   DDIMInverseScheduler.from_config(pipe.scheduler.config)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   '''a bowl of fruit'''
           A__							=   '''a bowl of pears'''

           A__							=   pipe.generate_mask(
               image=self.raw_image ,			source_prompt=UpperCAmelCase__ ,			target_prompt=UpperCAmelCase__ ,			generator=UpperCAmelCase__ ,			)

           A__							=   pipe.invert(
               prompt=UpperCAmelCase__ ,			image=self.raw_image ,			inpaint_strength=0.7 ,			generator=UpperCAmelCase__).latents

           A__							=   pipe(
               prompt=UpperCAmelCase__ ,			mask_image=UpperCAmelCase__ ,			image_latents=UpperCAmelCase__ ,			generator=UpperCAmelCase__ ,			negative_prompt=UpperCAmelCase__ ,			inpaint_strength=0.7 ,			output_type='''numpy''' ,			).images[0]

           A__							=   (
               np.array(
                   load_image(
                       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                       '''/diffedit/pears.png''').resize((768, 768)))
               / 255
           )
           assert np.abs((expected_image - image).max()) < 5e-1





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





           '''simple docstring'''

           A__							=   torch.manual_seed(0)

           A__							=   StableDiffusionDiffEditPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-1''' ,			safety_checker=UpperCAmelCase__ ,			torch_dtype=torch.floataa)
           A__							=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           A__							=   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   '''a bowl of fruit'''
           A__							=   '''a bowl of pears'''

           A__							=   pipe.generate_mask(
               image=self.raw_image ,			source_prompt=UpperCAmelCase__ ,			target_prompt=UpperCAmelCase__ ,			generator=UpperCAmelCase__ ,			)

           A__							=   pipe.invert(
               prompt=UpperCAmelCase__ ,			image=self.raw_image ,			inpaint_strength=0.7 ,			generator=UpperCAmelCase__ ,			num_inference_steps=25 ,			).latents

           A__							=   pipe(
               prompt=UpperCAmelCase__ ,			mask_image=UpperCAmelCase__ ,			image_latents=UpperCAmelCase__ ,			generator=UpperCAmelCase__ ,			negative_prompt=UpperCAmelCase__ ,			inpaint_strength=0.7 ,			num_inference_steps=25 ,			output_type='''numpy''' ,			).images[0]

           A__							=   (
               np.array(
                   load_image(
                       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                       '''/diffedit/pears.png''').resize((768, 768)))
               / 255
           )
           assert np.abs((expected_image - image).max()) < 5e-1








from __future__ import annotations

import queue


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   data
           A__							=   None
           A__							=   None







def SCREAMING_SNAKE_CASE    (	)       ->	TreeNode:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   []
              while not q.empty():
                     A__							=   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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__			,							A__							=   [], []
       A__							=   node
       stacka.append(lowercase_						)
       while stacka:  # to find the reversed order of post order, store it in stack2
              A__							=   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 SCREAMING_SNAKE_CASE    (	lowercase_ = ""			,    lowercase_=50			,    lowercase_="*"						)       ->	str:



       """simple docstring"""

       if not s:
              return "\n" + width * char
       A__			,							A__							=   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"""))

  _lowerCamelCase     :		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())


