



from __future__ import annotations

import unittest
from collections.abc import Iterator







class 							_a							:

   def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Optional[int]:
      UpperCAmelCase_:		int       			=    label
      UpperCAmelCase_:		Optional[Any]       			=    parent
      UpperCAmelCase_:		Any       			=    None
      UpperCAmelCase_:		Tuple       			=    None







class 							_a							:

   def __init__(self				)					->      int:
      UpperCAmelCase_:		Optional[int]       			=    None

   def     __snake_case					(self				)					->      Any:
      UpperCAmelCase_:		List[str]       			=    None

   def     __snake_case					(self				)					->      Dict:
      return self.root is None

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Dict:
      UpperCAmelCase_:		Optional[Any]       			=    self._put(self.root,      SCREAMING_SNAKE_CASE_				)

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None				)					->      Union[str, Any]:







      if node is None:
         UpperCAmelCase_:		Any       			=    Node(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
      else:
         if label < node.label:
            UpperCAmelCase_:		Any       			=    self._put(node.left,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
         elif label > node.label:
            UpperCAmelCase_:		Tuple       			=    self._put(node.right,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
         else:
            UpperCAmelCase_:		str       			=    f'Node with label {label} already exists'
            raise Exception(SCREAMING_SNAKE_CASE_				)

      return node

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
      return self._search(self.root,      SCREAMING_SNAKE_CASE_				)

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:







      if node is None:
         UpperCAmelCase_:		Optional[Any]       			=    f'Node with label {label} does not exist'
         raise Exception(SCREAMING_SNAKE_CASE_				)
      else:
         if label < node.label:
            UpperCAmelCase_:		List[Any]       			=    self._search(node.left,      SCREAMING_SNAKE_CASE_				)
         elif label > node.label:
            UpperCAmelCase_:		List[str]       			=    self._search(node.right,      SCREAMING_SNAKE_CASE_				)

      return node

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      str:
      UpperCAmelCase_:		Dict       			=    self.search(SCREAMING_SNAKE_CASE_				)
      if node.right and node.left:
         UpperCAmelCase_:		Tuple       			=    self._get_lowest_node(node.right				)
         UpperCAmelCase_:		Optional[int]       			=    node.left
         UpperCAmelCase_:		int       			=    node.right
         UpperCAmelCase_:		Optional[int]       			=    lowest_node
         if node.right:
            UpperCAmelCase_:		Tuple       			=    lowest_node
         self._reassign_nodes(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
      elif not node.right and node.left:
         self._reassign_nodes(SCREAMING_SNAKE_CASE_,      node.left				)
      elif node.right and not node.left:
         self._reassign_nodes(SCREAMING_SNAKE_CASE_,      node.right				)
      else:
         self._reassign_nodes(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

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







      if new_children:
         UpperCAmelCase_:		Union[str, Any]       			=    node.parent

      if node.parent:
         if node.parent.right == node:
            UpperCAmelCase_:		Optional[Any]       			=    new_children
         else:
            UpperCAmelCase_:		Union[str, Any]       			=    new_children
      else:
         UpperCAmelCase_:		Dict       			=    new_children

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      List[str]:







      if node.left:
         UpperCAmelCase_:		List[str]       			=    self._get_lowest_node(node.left				)
      else:
         UpperCAmelCase_:		Tuple       			=    node
         self._reassign_nodes(SCREAMING_SNAKE_CASE_,      node.right				)

      return lowest_node

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:







      try:
         self.search(SCREAMING_SNAKE_CASE_				)
         return True
      except Exception:
         return False

   def     __snake_case					(self				)					->      Any:







      if self.root is None:
         raise Exception("""Binary search tree is empty"""				)

      UpperCAmelCase_:		Any       			=    self.root
      while node.right is not None:
         UpperCAmelCase_:		Optional[int]       			=    node.right

      return node.label

   def     __snake_case					(self				)					->      Any:







      if self.root is None:
         raise Exception("""Binary search tree is empty"""				)

      UpperCAmelCase_:		Tuple       			=    self.root
      while node.left is not None:
         UpperCAmelCase_:		Dict       			=    node.left

      return node.label

   def     __snake_case					(self				)					->      int:
      return self._inorder_traversal(self.root				)

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Tuple:







      if node is not None:
         yield from self._inorder_traversal(node.left				)
         yield node
         yield from self._inorder_traversal(node.right				)

   def     __snake_case					(self				)					->      List[Any]:
      return self._preorder_traversal(self.root				)

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Optional[Any]:







      if node is not None:
         yield node
         yield from self._preorder_traversal(node.left				)
         yield from self._preorder_traversal(node.right				)







class 							_a							(      unittest.TestCase				):

   @staticmethod
   def     __snake_case					()					->      List[str]:
      UpperCAmelCase_:		Any       			=    BinarySearchTree()
      t.put(8				)
      t.put(3				)
      t.put(6				)
      t.put(1				)
      t.put(10				)
      t.put(14				)
      t.put(13				)
      t.put(4				)
      t.put(7				)
      t.put(5				)

      return t

   def     __snake_case					(self				)					->      List[str]:
      UpperCAmelCase_:		List[Any]       			=    BinarySearchTree()
      assert t.is_empty()

      t.put(8				)
      assert t.root is not None
      assert t.root.parent is None
      assert t.root.label == 8

      t.put(10				)
      assert t.root.right is not None
      assert t.root.right.parent == t.root
      assert t.root.right.label == 10

      t.put(3				)
      assert t.root.left is not None
      assert t.root.left.parent == t.root
      assert t.root.left.label == 3

      t.put(6				)
      assert t.root.left.right is not None
      assert t.root.left.right.parent == t.root.left
      assert t.root.left.right.label == 6

      t.put(1				)
      assert t.root.left.left is not None
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.left.label == 1

      with self.assertRaises(SCREAMING_SNAKE_CASE_				):  # noqa: B017
         t.put(1				)

   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		Tuple       			=    self._get_binary_search_tree()

      UpperCAmelCase_:		str       			=    t.search(6				)
      assert node.label == 6

      UpperCAmelCase_:		Union[str, Any]       			=    t.search(13				)
      assert node.label == 13

      with self.assertRaises(SCREAMING_SNAKE_CASE_				):  # noqa: B017
         t.search(2				)

   def     __snake_case					(self				)					->      Tuple:
      UpperCAmelCase_:		List[Any]       			=    self._get_binary_search_tree()

      t.remove(13				)
      assert t.root is not None
      assert t.root.right is not None
      assert t.root.right.right is not None
      assert t.root.right.right.right is None
      assert t.root.right.right.left is None

      t.remove(7				)
      assert t.root.left is not None
      assert t.root.left.right is not None
      assert t.root.left.right.left is not None
      assert t.root.left.right.right is None
      assert t.root.left.right.left.label == 4

      t.remove(6				)
      assert t.root.left.left is not None
      assert t.root.left.right.right is not None
      assert t.root.left.left.label == 1
      assert t.root.left.right.label == 4
      assert t.root.left.right.right.label == 5
      assert t.root.left.right.left is None
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.parent == t.root.left

      t.remove(3				)
      assert t.root is not None
      assert t.root.left.label == 4
      assert t.root.left.right.label == 5
      assert t.root.left.left.label == 1
      assert t.root.left.parent == t.root
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.parent == t.root.left

      t.remove(4				)
      assert t.root.left is not None
      assert t.root.left.left is not None
      assert t.root.left.label == 5
      assert t.root.left.right is None
      assert t.root.left.left.label == 1
      assert t.root.left.parent == t.root
      assert t.root.left.left.parent == t.root.left

   def     __snake_case					(self				)					->      Union[str, Any]:
      UpperCAmelCase_:		List[Any]       			=    self._get_binary_search_tree()

      t.remove(3				)
      assert t.root is not None
      assert t.root.left is not None
      assert t.root.left.left is not None
      assert t.root.left.right is not None
      assert t.root.left.right.left is not None
      assert t.root.left.right.right is not None
      assert t.root.left.label == 4
      assert t.root.left.right.label == 6
      assert t.root.left.left.label == 1
      assert t.root.left.right.right.label == 7
      assert t.root.left.right.left.label == 5
      assert t.root.left.parent == t.root
      assert t.root.left.right.parent == t.root.left
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.left.parent == t.root.left.right

   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		Tuple       			=    self._get_binary_search_tree()
      t.empty()
      assert t.root is None

   def     __snake_case					(self				)					->      Union[str, Any]:
      UpperCAmelCase_:		int       			=    self._get_binary_search_tree()
      assert not t.is_empty()

      t.empty()
      assert t.is_empty()

   def     __snake_case					(self				)					->      Tuple:
      UpperCAmelCase_:		List[Any]       			=    self._get_binary_search_tree()

      assert t.exists(6				)
      assert not t.exists(-1				)

   def     __snake_case					(self				)					->      Any:
      UpperCAmelCase_:		Union[str, Any]       			=    self._get_binary_search_tree()

      assert t.get_max_label() == 14

      t.empty()
      with self.assertRaises(SCREAMING_SNAKE_CASE_				):  # noqa: B017
         t.get_max_label()

   def     __snake_case					(self				)					->      int:
      UpperCAmelCase_:		Dict       			=    self._get_binary_search_tree()

      assert t.get_min_label() == 1

      t.empty()
      with self.assertRaises(SCREAMING_SNAKE_CASE_				):  # noqa: B017
         t.get_min_label()

   def     __snake_case					(self				)					->      str:
      UpperCAmelCase_:		Tuple       			=    self._get_binary_search_tree()

      UpperCAmelCase_:		Optional[int]       			=    [i.label for i in t.inorder_traversal()]
      assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]

   def     __snake_case					(self				)					->      Optional[int]:
      UpperCAmelCase_:		str       			=    self._get_binary_search_tree()

      UpperCAmelCase_:		Union[str, Any]       			=    [i.label for i in t.preorder_traversal()]
      assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]







def    lowerCAmelCase_  ():





   """simple docstring"""


   UpperCAmelCase_:		List[Any]       			=    BinarySearchTree()
   t.put(8     )
   t.put(3     )
   t.put(6     )
   t.put(1     )
   t.put(1_0     )
   t.put(1_4     )
   t.put(1_3     )
   t.put(4     )
   t.put(7     )
   t.put(5     )

   print(
       """
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        """     )

   print("""Label 6 exists:"""		,	t.exists(6     )     )
   print("""Label 13 exists:"""		,	t.exists(1_3     )     )
   print("""Label -1 exists:"""		,	t.exists(-1     )     )
   print("""Label 12 exists:"""		,	t.exists(1_2     )     )

   # Prints all the elements of the list in inorder traversal
   UpperCAmelCase_:		Optional[int]       			=    [i.label for i in t.inorder_traversal()]
   print("""Inorder traversal:"""		,	lowerCAmelCase__     )

   # Prints all the elements of the list in preorder traversal
   UpperCAmelCase_:		Union[str, Any]       			=    [i.label for i in t.preorder_traversal()]
   print("""Preorder traversal:"""		,	lowerCAmelCase__     )

   print("""Max. label:"""		,	t.get_max_label()     )
   print("""Min. label:"""		,	t.get_min_label()     )

   # Delete elements
   print("""\nDeleting elements 13, 10, 8, 3, 6, 14"""     )
   print(
       """
          4
         / \\
        1   7
             \\
              5
        """     )
   t.remove(1_3     )
   t.remove(1_0     )
   t.remove(8     )
   t.remove(3     )
   t.remove(6     )
   t.remove(1_4     )

   # Prints all the elements of the list in inorder traversal after delete
   UpperCAmelCase_:		Optional[Any]       			=    [i.label for i in t.inorder_traversal()]
   print("""Inorder traversal after delete:"""		,	lowerCAmelCase__     )

   # Prints all the elements of the list in preorder traversal after delete
   UpperCAmelCase_:		Optional[int]       			=    [i.label for i in t.preorder_traversal()]
   print("""Preorder traversal after delete:"""		,	lowerCAmelCase__     )

   print("""Max. label:"""		,	t.get_max_label()     )
   print("""Min. label:"""		,	t.get_min_label()     )


if __name__ == "__main__":
 binary_search_tree_example()











import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 							_a							(      unittest.TestCase				):

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

			@property
			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Tuple       			=    1
						UpperCAmelCase_:		Dict       			=    3
						UpperCAmelCase_:		Optional[int]       			=    (32, 32)

						UpperCAmelCase_:		int       			=    floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE_				)
						return image

			@property
			def     __snake_case					(self				)					->      Optional[Any]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    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,      )
						return model

			@property
			def     __snake_case					(self				)					->      str:
						torch.manual_seed(0				)
						UpperCAmelCase_:		int       			=    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,      )
						return model

			@property
			def     __snake_case					(self				)					->      List[str]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    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=1000,      )
						return CLIPTextModel(SCREAMING_SNAKE_CASE_				)

			@property
			def     __snake_case					(self				)					->      str:
						def extract(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				):







									class 							_a							:

												def __init__(self				)					->      Any:
															UpperCAmelCase_:		Optional[Any]       			=    torch.ones([0]				)

												def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_				)
															return self






									return Out()

						return extract

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Any       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Union[str, Any]       			=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      beta_schedule="""scaled_linear""",      clip_sample=SCREAMING_SNAKE_CASE_,      set_alpha_to_one=SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		str       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""

						UpperCAmelCase_:		Tuple       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Dict       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)
						UpperCAmelCase_:		Tuple       			=    output.images

						UpperCAmelCase_:		List[Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[str]       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		int       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[str]       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Dict       			=    np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]				)

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

			def     __snake_case					(self				)					->      Any:
						UpperCAmelCase_:		List[Any]       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Tuple       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Optional[int]       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_vae
						UpperCAmelCase_:		Any       			=    self.dummy_text_encoder
						UpperCAmelCase_:		List[Any]       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		List[str]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Union[str, Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[Any]       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)

						UpperCAmelCase_:		Optional[int]       			=    output.images

						UpperCAmelCase_:		Any       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Tuple       			=    np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]				)

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

			def     __snake_case					(self				)					->      List[str]:
						UpperCAmelCase_:		Dict       			=    StableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""",      safety_checker=SCREAMING_SNAKE_CASE_				)
						assert isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
						assert isinstance(pipe.scheduler,      SCREAMING_SNAKE_CASE_				)
						assert pipe.safety_checker is None

						UpperCAmelCase_:		Optional[Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Optional[int]       			=    StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_				)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						UpperCAmelCase_:		Union[str, Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

			@unittest.skipIf(torch_device != """cuda""",      """This test requires a GPU"""				)
			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Any       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Tuple       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# put models in fp16
						UpperCAmelCase_:		Dict       			=    unet.half()
						UpperCAmelCase_:		Optional[Any]       			=    vae.half()
						UpperCAmelCase_:		Union[str, Any]       			=    bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		str       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe([prompt],      num_inference_steps=2,      output_type="""np"""				).images

						assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 							_a							(      unittest.TestCase				):

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

			def     __snake_case					(self				)					->      str:
						UpperCAmelCase_:		Optional[int]       			=    """cuda"""
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """a photo of a cat"""
						UpperCAmelCase_:		Tuple       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[str]       			=    3
						UpperCAmelCase_:		str       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.3_4_6_7_3_1_1_3,
						    0.3_8_4_9_2_7_3_3,
						    0.3_7_5_9_7_3_5_2,
						    0.3_4_0_8_6_3_3_5,
						    0.3_5_6_5_0_7_4_8,
						    0.3_5_5_7_9_2_0_5,
						    0.3_3_8_4_7_6_3,
						    0.3_4_3_4_0_2_3_6,
						    0.3_5_7_3_2_7_1,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		Any       			=    output.images
						UpperCAmelCase_:		Dict       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.4_1_8_8_7_8_2_6,
						    0.3_7_7_2_8_7_6_6,
						    0.3_0_1_3_8_2_7_2,
						    0.4_1_4_1_6_3_3_5,
						    0.4_1_6_6_4_9_8_5,
						    0.3_6_2_8_3_3_9_2,
						    0.3_6_1_9_1_2_4_6,
						    0.4_3_3_6_4_4_6_5,
						    0.4_3_0_0_1_7_3_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Tuple       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		List[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """an image of a crowded boulevard, realistic, 4k"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": """crowd, crowded, people""",
						    """reverse_editing_direction""": True,
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 8.3,
						    """edit_threshold""": 0.9,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		Dict       			=    9
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		List[Any]       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		int       			=    [
						    0.4_3_4_9_7_9_9_8,
						    0.9_1_8_1_4_0_6_5,
						    0.7_5_4_0_7_3_9,
						    0.5_5_5_8_0_2_0_5,
						    0.8_4_6_7_2_6_5,
						    0.5_3_8_9_6_9_1,
						    0.6_2_5_7_4_5_0_6,
						    0.5_8_8_9_7_7_6_3,
						    0.5_0_9_2_6_7_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[int]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		int       			=    output.images
						UpperCAmelCase_:		Any       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str       			=    [
						    0.3_0_8_9_7_1_9,
						    0.3_0_5_0_0_1_4_4,
						    0.2_9_0_1_6_0_4_2,
						    0.3_0_6_3_0_9_6_4,
						    0.3_2_5_6_8_7,
						    0.2_9_4_1_9_2_2_5,
						    0.2_9_0_8_0_9_1,
						    0.2_8_7_2_3_5_9_8,
						    0.2_7_6_9_6_2_9_4,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Union[str, Any]       			=    """cuda"""
						UpperCAmelCase_:		Optional[Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Tuple       			=    """a castle next to a river"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
						    """reverse_editing_direction""": False,
						    """edit_warmup_steps""": [15, 18],
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": [0.9, 0.8],
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		str       			=    48
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		int       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Tuple       			=    output.images
						UpperCAmelCase_:		Tuple       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    [
						    0.7_5_1_6_3_5_5_5,
						    0.7_6_0_3_7_1_4_5,
						    0.6_1_7_8_5,
						    0.9_1_8_9_6_7_3,
						    0.8_6_2_7_7_0_1,
						    0.8_5_1_8_9_6_9_4,
						    0.8_5_1_2_8_1_3,
						    0.8_7_0_1_2_0_7_6,
						    0.8_3_1_2_8_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Dict       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    output.images
						UpperCAmelCase_:		Optional[int]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[Any]       			=    [
						    0.7_3_5_5_3_3_6_5,
						    0.7_5_3_7_2_7_1,
						    0.7_4_3_4_1_9_0_5,
						    0.6_6_4_8_0_3_5_6,
						    0.6_4_7_2_9_2_5,
						    0.6_3_0_3_9_4_1_6,
						    0.6_4_8_1_2_9_0_5,
						    0.6_7_4_9_7_1_7,
						    0.6_5_1_7_1_0_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Union[str, Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""",      torch_dtype=torch.floataa				)
						UpperCAmelCase_:		Optional[int]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """a photo of a cat"""
						UpperCAmelCase_:		Dict       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[Any]       			=    3
						UpperCAmelCase_:		Dict       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		str       			=    output.images
						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.3_4_8_8_7_6_9_5,
						    0.3_8_7_6_9_5_3,
						    0.3_7_5,
						    0.3_4_4_2_3_8_2_8,
						    0.3_5_8_1_5_4_3,
						    0.3_5_7_1_7_7_7_3,
						    0.3_3_8_3_7_8_9,
						    0.3_4_5_7_0_3_1_2,
						    0.3_5_9_3_7_5,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[Any]       			=    output.images
						UpperCAmelCase_:		List[str]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.4_2_2_8_5_1_5_6,
						    0.3_6_9_1_4_0_6_2,
						    0.2_9_0_7_7_1_4_8,
						    0.4_2_0_4_1_0_1_6,
						    0.4_1_9_1_8_9_4_5,
						    0.3_5_4_9_8_0_4_7,
						    0.3_6_1_8_1_6_4,
						    0.4_4_2_3_8_2_8,
						    0.4_3_1_1_5_2_3_4,
						]

						assert image.shape == (1, 512, 512, 3)

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






