code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# clean up the VRAM after each test
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = 1
  							_snake_case			 = 3
  							_snake_case			 = (32, 32)
  
  							_snake_case			 = floats_tensor((batch_size, num_channels) + sizes,						rng=random.Random(0				)				).to(__UpperCamelCase				)
  							return image
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							torch.manual_seed(0				)
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							torch.manual_seed(0				)
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							torch.manual_seed(0				)
  							_snake_case			 = 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,						)
  							return CLIPTextModel(__UpperCamelCase				)
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							def extract(*__UpperCamelCase,						**__UpperCamelCase				):
  
  
  
  
  
  									class   lowerCamelCase_							:
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														def __init__(					self				):
  																_snake_case			 = torch.ones([0]				)
  
  
  
  
  
  
  														def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  																self.pixel_values.to(__UpperCamelCase				)
  																return self
  
  
  
  
  									return Out()
  
  							return extract
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cpu"  # ensure determinism for the device-dependent torch.Generator
  							_snake_case			 = self.dummy_cond_unet
  							_snake_case			 = DDIMScheduler(
  							    beta_start=0.0_00_85,						beta_end=0.0_12,						beta_schedule="scaled_linear",						clip_sample=__UpperCamelCase,						set_alpha_to_one=__UpperCamelCase,						)
  
  							_snake_case			 = self.dummy_vae
  							_snake_case			 = self.dummy_text_encoder
  							_snake_case			 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
  
  							# make sure here that pndm scheduler skips prk
  							_snake_case			 = StableDiffusionPipeline(
  							    unet=__UpperCamelCase,						scheduler=__UpperCamelCase,						vae=__UpperCamelCase,						text_encoder=__UpperCamelCase,						tokenizer=__UpperCamelCase,						safety_checker=__UpperCamelCase,						feature_extractor=self.dummy_extractor,						)
  							_snake_case			 = sd_pipe.to(__UpperCamelCase				)
  							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "A painting of a squirrel eating a burger"
  
  							_snake_case			 = torch.Generator(device=__UpperCamelCase				).manual_seed(0				)
  							_snake_case			 = sd_pipe([prompt],						generator=__UpperCamelCase,						guidance_scale=6.0,						num_inference_steps=2,						output_type="np"				)
  							_snake_case			 = output.images
  
  							_snake_case			 = torch.Generator(device=__UpperCamelCase				).manual_seed(0				)
  							_snake_case			 = sd_pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=6.0,						num_inference_steps=2,						output_type="np",						return_dict=__UpperCamelCase,						)[0]
  
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = image_from_tuple[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							_snake_case			 = np.array([0.57_53, 0.61_14, 0.50_01, 0.50_34, 0.54_70, 0.47_29, 0.49_71, 0.48_67, 0.48_67]				)
  
  							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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cpu"  # ensure determinism for the device-dependent torch.Generator
  							_snake_case			 = self.dummy_cond_unet
  							_snake_case			 = PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
  							_snake_case			 = self.dummy_vae
  							_snake_case			 = self.dummy_text_encoder
  							_snake_case			 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
  
  							# make sure here that pndm scheduler skips prk
  							_snake_case			 = StableDiffusionPipeline(
  							    unet=__UpperCamelCase,						scheduler=__UpperCamelCase,						vae=__UpperCamelCase,						text_encoder=__UpperCamelCase,						tokenizer=__UpperCamelCase,						safety_checker=__UpperCamelCase,						feature_extractor=self.dummy_extractor,						)
  							_snake_case			 = sd_pipe.to(__UpperCamelCase				)
  							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "A painting of a squirrel eating a burger"
  							_snake_case			 = torch.Generator(device=__UpperCamelCase				).manual_seed(0				)
  							_snake_case			 = sd_pipe([prompt],						generator=__UpperCamelCase,						guidance_scale=6.0,						num_inference_steps=2,						output_type="np"				)
  
  							_snake_case			 = output.images
  
  							_snake_case			 = torch.Generator(device=__UpperCamelCase				).manual_seed(0				)
  							_snake_case			 = sd_pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=6.0,						num_inference_steps=2,						output_type="np",						return_dict=__UpperCamelCase,						)[0]
  
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = image_from_tuple[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							_snake_case			 = np.array([0.51_22, 0.57_12, 0.48_25, 0.50_53, 0.56_46, 0.47_69, 0.51_79, 0.48_94, 0.49_94]				)
  
  							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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = StableDiffusionPipeline.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-lms-pipe",						safety_checker=__UpperCamelCase				)
  							assert isinstance(__UpperCamelCase,						__UpperCamelCase				)
  							assert isinstance(pipe.scheduler,						__UpperCamelCase				)
  							assert pipe.safety_checker is None
  
  							_snake_case			 = 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(__UpperCamelCase				)
  									_snake_case			 = StableDiffusionPipeline.from_pretrained(__UpperCamelCase				)
  
  							# sanity check that the pipeline still works
  							assert pipe.safety_checker is None
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.dummy_cond_unet
  							_snake_case			 = PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
  							_snake_case			 = self.dummy_vae
  							_snake_case			 = self.dummy_text_encoder
  							_snake_case			 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
  
  							# put models in fp16
  							_snake_case			 = unet.half()
  							_snake_case			 = vae.half()
  							_snake_case			 = bert.half()
  
  							# make sure here that pndm scheduler skips prk
  							_snake_case			 = StableDiffusionPipeline(
  							    unet=__UpperCamelCase,						scheduler=__UpperCamelCase,						vae=__UpperCamelCase,						text_encoder=__UpperCamelCase,						tokenizer=__UpperCamelCase,						safety_checker=__UpperCamelCase,						feature_extractor=self.dummy_extractor,						)
  							_snake_case			 = sd_pipe.to(__UpperCamelCase				)
  							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "A painting of a squirrel eating a burger"
  							_snake_case			 = sd_pipe([prompt],						num_inference_steps=2,						output_type="np"				).images
  
  							assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# clean up the VRAM after each test
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cuda"
  							_snake_case			 = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				)
  							_snake_case			 = pipe.to(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "a photo of a cat"
  							_snake_case			 = {
  							    "editing_prompt": ["sunglasses"],
  							    "reverse_editing_direction": [False],
  							    "edit_warmup_steps": 10,
  							    "edit_guidance_scale": 6,
  							    "edit_threshold": 0.95,
  							    "edit_momentum_scale": 0.5,
  							    "edit_mom_beta": 0.6,
  							}
  
  							_snake_case			 = 3
  							_snake_case			 = 7
  
  							# no sega enabled
  							_snake_case			 = torch.Generator(__UpperCamelCase				)
  							generator.manual_seed(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.34_67_31_13,
  							    0.38_49_27_33,
  							    0.37_59_73_52,
  							    0.34_08_63_35,
  							    0.35_65_07_48,
  							    0.35_57_92_05,
  							    0.3_38_47_63,
  							    0.34_34_02_36,
  							    0.3_57_32_71,
  							]
  
  							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(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						**__UpperCamelCase,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.41_88_78_26,
  							    0.37_72_87_66,
  							    0.30_13_82_72,
  							    0.41_41_63_35,
  							    0.41_66_49_85,
  							    0.36_28_33_92,
  							    0.36_19_12_46,
  							    0.43_36_44_65,
  							    0.43_00_17_32,
  							]
  
  							assert image.shape == (1, 512, 512, 3)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cuda"
  							_snake_case			 = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				)
  							_snake_case			 = pipe.to(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "an image of a crowded boulevard, realistic, 4k"
  							_snake_case			 = {
  							    "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,
  							}
  
  							_snake_case			 = 9
  							_snake_case			 = 7
  
  							# no sega enabled
  							_snake_case			 = torch.Generator(__UpperCamelCase				)
  							generator.manual_seed(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.43_49_79_98,
  							    0.91_81_40_65,
  							    0.7_54_07_39,
  							    0.55_58_02_05,
  							    0.8_46_72_65,
  							    0.5_38_96_91,
  							    0.62_57_45_06,
  							    0.58_89_77_63,
  							    0.50_92_67_57,
  							]
  
  							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(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						**__UpperCamelCase,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.3_08_97_19,
  							    0.30_50_01_44,
  							    0.29_01_60_42,
  							    0.30_63_09_64,
  							    0.32_56_87,
  							    0.29_41_92_25,
  							    0.2_90_80_91,
  							    0.28_72_35_98,
  							    0.27_69_62_94,
  							]
  
  							assert image.shape == (1, 512, 512, 3)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cuda"
  							_snake_case			 = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				)
  							_snake_case			 = pipe.to(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "a castle next to a river"
  							_snake_case			 = {
  							    "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,
  							}
  
  							_snake_case			 = 48
  							_snake_case			 = 7
  
  							# no sega enabled
  							_snake_case			 = torch.Generator(__UpperCamelCase				)
  							generator.manual_seed(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.75_16_35_55,
  							    0.76_03_71_45,
  							    0.6_17_85,
  							    0.9_18_96_73,
  							    0.8_62_77_01,
  							    0.85_18_96_94,
  							    0.8_51_28_13,
  							    0.87_01_20_76,
  							    0.8_31_28_57,
  							]
  
  							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(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						**__UpperCamelCase,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.73_55_33_65,
  							    0.7_53_72_71,
  							    0.74_34_19_05,
  							    0.66_48_03_56,
  							    0.6_47_29_25,
  							    0.63_03_94_16,
  							    0.64_81_29_05,
  							    0.6_74_97_17,
  							    0.6_51_71_02,
  							]
  
  							assert image.shape == (1, 512, 512, 3)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "cuda"
  							_snake_case			 = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",						torch_dtype=torch.floataa				)
  							_snake_case			 = pipe.to(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = "a photo of a cat"
  							_snake_case			 = {
  							    "editing_prompt": ["sunglasses"],
  							    "reverse_editing_direction": [False],
  							    "edit_warmup_steps": 10,
  							    "edit_guidance_scale": 6,
  							    "edit_threshold": 0.95,
  							    "edit_momentum_scale": 0.5,
  							    "edit_mom_beta": 0.6,
  							}
  
  							_snake_case			 = 3
  							_snake_case			 = 7
  
  							# no sega enabled
  							_snake_case			 = torch.Generator(__UpperCamelCase				)
  							generator.manual_seed(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.34_88_76_95,
  							    0.3_87_69_53,
  							    0.3_75,
  							    0.34_42_38_28,
  							    0.3_58_15_43,
  							    0.35_71_77_73,
  							    0.3_38_37_89,
  							    0.34_57_03_12,
  							    0.35_93_75,
  							]
  
  							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(__UpperCamelCase				)
  							_snake_case			 = pipe(
  							    [prompt],						generator=__UpperCamelCase,						guidance_scale=__UpperCamelCase,						num_inference_steps=50,						output_type="np",						width=512,						height=512,						**__UpperCamelCase,						)
  
  							_snake_case			 = output.images
  							_snake_case			 = image[0, -3:, -3:, -1]
  							_snake_case			 = [
  							    0.42_28_51_56,
  							    0.36_91_40_62,
  							    0.29_07_71_48,
  							    0.42_04_10_16,
  							    0.41_91_89_45,
  							    0.35_49_80_47,
  							    0.3_61_81_64,
  							    0.4_42_38_28,
  							    0.43_11_52_34,
  							]
  
  							assert image.shape == (1, 512, 512, 3)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  