


'''simple docstring'''

from __future__ import annotations

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

_UpperCAmelCase					:			Any				     =					TypeVar("""T""", bound=bool)


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										__lowerCAmelCase											=					value
										__lowerCAmelCase											=					None
										__lowerCAmelCase											=					None




				@property
				def        _snake_case	(self		):
										return self._value




				@staticmethod
				def        _snake_case	(__lowercase							,      __lowercase		):
										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota, roota

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

										__lowerCAmelCase											=					RandomizedHeapNode.merge(roota.left							,      __lowercase		)

										return roota


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase = ()		):
										__lowerCAmelCase											=					None

										if data:
																for item in data:
																						self.insert(__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root							,      RandomizedHeapNode(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.top()

										if self._root is None:
																return None

										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root.left							,      self._root.right		)

										return result




				def        _snake_case	(self		):
										if not self._root:
																raise IndexError('''Can\'t get top element for the empty heap.'''		)
										return self._root.value




				def        _snake_case	(self		):
										__lowerCAmelCase											=					None




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[]
										while self:
																result.append(self.pop()		)

										return result




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


if __name__ == "__main__":
	import doctest

	doctest.testmod()





'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

