




'''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  A		(			unittest.TestCase		):




				'''simple docstring'''





				def 	a_	(self      )     ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
				@property
				def 	a_	(self      )     ->	str:
							__UpperCamelCase				:      Optional[Any]									=       1
							__UpperCamelCase				:      int									=       3
							__UpperCamelCase				:      Any									=       (3_2, 3_2)

							__UpperCamelCase				:      Tuple									=       floats_tensor((batch_size, num_channels) + sizes			,  rng=random.Random(0      )      ).to(_UpperCAmelCase      )
							return image
				@property
				def 	a_	(self      )     ->	Any:
							torch.manual_seed(0      )
							__UpperCamelCase				:      int									=       UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,  layers_per_block=2			,  sample_size=3_2			,  in_channels=4			,  out_channels=4			,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,  cross_attention_dim=3_2			,  )
							return model
				@property
				def 	a_	(self      )     ->	Union[str, Any]:
							torch.manual_seed(0      )
							__UpperCamelCase				:      Dict									=       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			,  )
							return model
				@property
				def 	a_	(self      )     ->	Optional[int]:
							torch.manual_seed(0      )
							__UpperCamelCase				:      List[Any]									=       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			,  )
							return CLIPTextModel(_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Union[str, Any]:
							def extract(*_UpperCAmelCase			,  **_UpperCAmelCase      ):



										class  A		:




														'''simple docstring'''





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





														def 	a_	(self			,  _UpperCAmelCase      )     ->	Tuple:
																	self.pixel_values.to(_UpperCAmelCase      )
																	return self







										return Out()

							return extract
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      List[Any]									=       "cpu"  # ensure determinism for the device-dependent torch.Generator
							__UpperCamelCase				:      List[Any]									=       self.dummy_cond_unet
							__UpperCamelCase				:      Optional[int]									=       DDIMScheduler(
							    beta_start=0.00_085			,  beta_end=0.012			,  beta_schedule="scaled_linear"			,  clip_sample=_UpperCAmelCase			,  set_alpha_to_one=_UpperCAmelCase			,  )

							__UpperCamelCase				:      int									=       self.dummy_vae
							__UpperCamelCase				:      List[str]									=       self.dummy_text_encoder
							__UpperCamelCase				:      Tuple									=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

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

							__UpperCamelCase				:      str									=       "A painting of a squirrel eating a burger"

							__UpperCamelCase				:      Tuple									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      Tuple									=       sd_pipe([prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=6.0			,  num_inference_steps=2			,  output_type="np"      )
							__UpperCamelCase				:      Optional[int]									=       output.images

							__UpperCamelCase				:      Any									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      Optional[int]									=       sd_pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=6.0			,  num_inference_steps=2			,  output_type="np"			,  return_dict=_UpperCAmelCase			,  )[0]

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

							assert image.shape == (1, 6_4, 6_4, 3)
							__UpperCamelCase				:      List[str]									=       np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]      )

							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 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Dict									=       "cpu"  # ensure determinism for the device-dependent torch.Generator
							__UpperCamelCase				:      List[Any]									=       self.dummy_cond_unet
							__UpperCamelCase				:      str									=       PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       self.dummy_vae
							__UpperCamelCase				:      Tuple									=       self.dummy_text_encoder
							__UpperCamelCase				:      int									=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

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

							__UpperCamelCase				:      int									=       "A painting of a squirrel eating a burger"
							__UpperCamelCase				:      int									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      Dict									=       sd_pipe([prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=6.0			,  num_inference_steps=2			,  output_type="np"      )

							__UpperCamelCase				:      str									=       output.images

							__UpperCamelCase				:      Optional[int]									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      List[str]									=       sd_pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=6.0			,  num_inference_steps=2			,  output_type="np"			,  return_dict=_UpperCAmelCase			,  )[0]

							__UpperCamelCase				:      Optional[Any]									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Union[str, Any]									=       image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							__UpperCamelCase				:      Optional[int]									=       np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]      )

							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 	a_	(self      )     ->	Any:
							__UpperCamelCase				:      Union[str, Any]									=       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

							__UpperCamelCase				:      List[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(_UpperCAmelCase      )
										__UpperCamelCase				:      int									=       StableDiffusionPipeline.from_pretrained(_UpperCAmelCase      )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							__UpperCamelCase				:      int									=       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 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      Any									=       self.dummy_cond_unet
							__UpperCamelCase				:      Any									=       PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[Any]									=       self.dummy_vae
							__UpperCamelCase				:      int									=       self.dummy_text_encoder
							__UpperCamelCase				:      str									=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

							# put models in fp16
							__UpperCamelCase				:      Dict									=       unet.half()
							__UpperCamelCase				:      List[str]									=       vae.half()
							__UpperCamelCase				:      Tuple									=       bert.half()

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

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

							assert image.shape == (1, 6_4, 6_4, 3)







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




				'''simple docstring'''





				def 	a_	(self      )     ->	List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
				def 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      str									=       "cuda"
							__UpperCamelCase				:      Union[str, Any]									=       StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      )
							__UpperCamelCase				:      Union[str, Any]									=       pipe.to(_UpperCAmelCase      )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      List[Any]									=       "a photo of a cat"
							__UpperCamelCase				:      Union[str, Any]									=       {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.95,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCamelCase				:      Any									=       3
							__UpperCamelCase				:      List[Any]									=       7

							# no sega enabled
							__UpperCamelCase				:      Optional[int]									=       torch.Generator(_UpperCAmelCase      )
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Tuple									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  )

							__UpperCamelCase				:      Optional[Any]									=       output.images
							__UpperCamelCase				:      Tuple									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Tuple									=       [
							    0.34_673_113,
							    0.38_492_733,
							    0.37_597_352,
							    0.34_086_335,
							    0.35_650_748,
							    0.35_579_205,
							    0.3_384_763,
							    0.34_340_236,
							    0.3_573_271,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Union[str, Any]									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  **_UpperCAmelCase			,  )

							__UpperCamelCase				:      Optional[Any]									=       output.images
							__UpperCamelCase				:      List[str]									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      str									=       [
							    0.41_887_826,
							    0.37_728_766,
							    0.30_138_272,
							    0.41_416_335,
							    0.41_664_985,
							    0.36_283_392,
							    0.36_191_246,
							    0.43_364_465,
							    0.43_001_732,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Tuple									=       "cuda"
							__UpperCamelCase				:      Any									=       StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      )
							__UpperCamelCase				:      List[str]									=       pipe.to(_UpperCAmelCase      )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      int									=       "an image of a crowded boulevard, realistic, 4k"
							__UpperCamelCase				:      int									=       {
							    "editing_prompt": "crowd, crowded, people",
							    "reverse_editing_direction": True,
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 8.3,
							    "edit_threshold": 0.9,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCamelCase				:      List[Any]									=       9
							__UpperCamelCase				:      Dict									=       7

							# no sega enabled
							__UpperCamelCase				:      List[Any]									=       torch.Generator(_UpperCAmelCase      )
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Union[str, Any]									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  )

							__UpperCamelCase				:      Optional[Any]									=       output.images
							__UpperCamelCase				:      str									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Tuple									=       [
							    0.43_497_998,
							    0.91_814_065,
							    0.7_540_739,
							    0.55_580_205,
							    0.8_467_265,
							    0.5_389_691,
							    0.62_574_506,
							    0.58_897_763,
							    0.50_926_757,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Tuple									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  **_UpperCAmelCase			,  )

							__UpperCamelCase				:      Optional[Any]									=       output.images
							__UpperCamelCase				:      Dict									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Union[str, Any]									=       [
							    0.3_089_719,
							    0.30_500_144,
							    0.29_016_042,
							    0.30_630_964,
							    0.325_687,
							    0.29_419_225,
							    0.2_908_091,
							    0.28_723_598,
							    0.27_696_294,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      List[Any]									=       "cuda"
							__UpperCamelCase				:      Any									=       StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      )
							__UpperCamelCase				:      Tuple									=       pipe.to(_UpperCAmelCase      )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      Optional[Any]									=       "a castle next to a river"
							__UpperCamelCase				:      Tuple									=       {
							    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
							    "reverse_editing_direction": False,
							    "edit_warmup_steps": [1_5, 1_8],
							    "edit_guidance_scale": 6,
							    "edit_threshold": [0.9, 0.8],
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCamelCase				:      Optional[Any]									=       4_8
							__UpperCamelCase				:      Union[str, Any]									=       7

							# no sega enabled
							__UpperCamelCase				:      Any									=       torch.Generator(_UpperCAmelCase      )
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Union[str, Any]									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  )

							__UpperCamelCase				:      Any									=       output.images
							__UpperCamelCase				:      int									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Optional[int]									=       [
							    0.75_163_555,
							    0.76_037_145,
							    0.61_785,
							    0.9_189_673,
							    0.8_627_701,
							    0.85_189_694,
							    0.8_512_813,
							    0.87_012_076,
							    0.8_312_857,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Tuple									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  **_UpperCAmelCase			,  )

							__UpperCamelCase				:      Tuple									=       output.images
							__UpperCamelCase				:      str									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Any									=       [
							    0.73_553_365,
							    0.7_537_271,
							    0.74_341_905,
							    0.66_480_356,
							    0.6_472_925,
							    0.63_039_416,
							    0.64_812_905,
							    0.6_749_717,
							    0.6_517_102,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





				def 	a_	(self      )     ->	Any:
							__UpperCamelCase				:      int									=       "cuda"
							__UpperCamelCase				:      Optional[int]									=       StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			,  torch_dtype=torch.floataa      )
							__UpperCamelCase				:      Optional[Any]									=       pipe.to(_UpperCAmelCase      )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      Any									=       "a photo of a cat"
							__UpperCamelCase				:      List[str]									=       {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.95,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCamelCase				:      List[Any]									=       3
							__UpperCamelCase				:      List[str]									=       7

							# no sega enabled
							__UpperCamelCase				:      List[str]									=       torch.Generator(_UpperCAmelCase      )
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      List[str]									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  )

							__UpperCamelCase				:      Dict									=       output.images
							__UpperCamelCase				:      Optional[Any]									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      List[Any]									=       [
							    0.34_887_695,
							    0.3_876_953,
							    0.375,
							    0.34_423_828,
							    0.3_581_543,
							    0.35_717_773,
							    0.3_383_789,
							    0.34_570_312,
							    0.359_375,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       pipe(
							    [prompt]			,  generator=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  num_inference_steps=5_0			,  output_type="np"			,  width=5_1_2			,  height=5_1_2			,  **_UpperCAmelCase			,  )

							__UpperCamelCase				:      Optional[Any]									=       output.images
							__UpperCamelCase				:      List[str]									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      Tuple									=       [
							    0.42_285_156,
							    0.36_914_062,
							    0.29_077_148,
							    0.42_041_016,
							    0.41_918_945,
							    0.35_498_047,
							    0.3_618_164,
							    0.4_423_828,
							    0.43_115_234,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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









'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



