




'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class  A		(			unittest.TestCase		):




				'''simple docstring'''





				@property
				def 	a_	(self      )     ->	str:
							torch.manual_seed(0      )
							__UpperCamelCase				:      Union[str, Any]									=       UNetaDModel(
							    block_out_channels=(3_2, 6_4)			,  layers_per_block=2			,  sample_size=3_2			,  in_channels=3			,  out_channels=3			,  down_block_types=("DownBlock2D", "AttnDownBlock2D")			,  up_block_types=("AttnUpBlock2D", "UpBlock2D")			,  )
							return model
				def 	a_	(self      )     ->	Dict:
							__UpperCamelCase				:      List[str]									=       "cpu"
							__UpperCamelCase				:      Any									=       self.dummy_uncond_unet
							__UpperCamelCase				:      int									=       DDPMScheduler()

							__UpperCamelCase				:      List[Any]									=       DDPMPipeline(unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase      )
							ddpm.to(_UpperCAmelCase      )
							ddpm.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      Any									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      Union[str, Any]									=       ddpm(generator=_UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"      ).images

							__UpperCamelCase				:      Tuple									=       torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
							__UpperCamelCase				:      Optional[int]									=       ddpm(generator=_UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"			,  return_dict=_UpperCAmelCase      )[0]

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

							assert image.shape == (1, 3_2, 3_2, 3)
							__UpperCamelCase				:      Any									=       np.array(
							    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]      )

							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      )     ->	Union[str, Any]:
							__UpperCamelCase				:      Optional[int]									=       self.dummy_uncond_unet
							__UpperCamelCase				:      List[str]									=       DDPMScheduler(prediction_type="sample"      )

							__UpperCamelCase				:      int									=       DDPMPipeline(unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase      )
							ddpm.to(_UpperCAmelCase      )
							ddpm.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      List[Any]									=       torch.manual_seed(0      )
							__UpperCamelCase				:      Dict									=       ddpm(generator=_UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"      ).images

							__UpperCamelCase				:      Optional[Any]									=       torch.manual_seed(0      )
							__UpperCamelCase				:      Any									=       ddpm(generator=_UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"      )[0]

							__UpperCamelCase				:      Union[str, Any]									=       image[0, -3:, -3:, -1]
							__UpperCamelCase				:      List[Any]									=       image_eps[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							__UpperCamelCase				:      Optional[Any]									=       1E-2 if torch_device != "mps" else 3E-2
							assert np.abs(image_slice.flatten() - image_eps_slice.flatten()      ).max() < tolerance







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




				'''simple docstring'''





				def 	a_	(self      )     ->	str:
							__UpperCamelCase				:      Dict									=       "google/ddpm-cifar10-32"

							__UpperCamelCase				:      int									=       UNetaDModel.from_pretrained(_UpperCAmelCase      )
							__UpperCamelCase				:      int									=       DDPMScheduler.from_pretrained(_UpperCAmelCase      )

							__UpperCamelCase				:      Any									=       DDPMPipeline(unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase      )
							ddpm.to(_UpperCAmelCase      )
							ddpm.set_progress_bar_config(disable=_UpperCAmelCase      )

							__UpperCamelCase				:      Tuple									=       torch.manual_seed(0      )
							__UpperCamelCase				:      Optional[int]									=       ddpm(generator=_UpperCAmelCase			,  output_type="numpy"      ).images

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

							assert image.shape == (1, 3_2, 3_2, 3)
							__UpperCamelCase				:      Any									=       np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]      )
							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()



