


"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase__				:List[str]			   =	TypeVar("""T""")
UpperCamelCase__				:Union[str, Any]			   =	TypeVar("""U""")






class      A(  Generic[T, U]		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	key
					_UpperCamelCase					:int					     =	val
					_UpperCamelCase					:List[str]					     =	None
					_UpperCamelCase					:List[str]					     =	None





		def __repr__( self   )      ->	str:

					"""simple docstring"""



					return (
					    f"Node: key: {self.key}, val: {self.val}, "
					    f"has next: {bool(self.next   )}, has prev: {bool(self.prev   )}"
					)






class      A(  Generic[T, U]		):

		"""simple docstring"""





		def __init__( self   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	DoubleLinkedListNode(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	DoubleLinkedListNode(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	self.rear, self.head
		def __repr__( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	['''DoubleLinkedList''']
					_UpperCamelCase					:Dict					     =	self.head
					while node.next is not None:
								rep.append(str(SCREAMING_SNAKE_CASE__   )   )
								_UpperCamelCase					:int					     =	node.next
					rep.append(str(self.rear   )   )
					return ",\n    ".join(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self.rear.prev

					# All nodes other than self.head are guaranteed to have non-None previous
					assert previous is not None

					_UpperCamelCase					:int					     =	node
					_UpperCamelCase					:str					     =	previous
					_UpperCamelCase					:List[str]					     =	node
					_UpperCamelCase					:int					     =	self.rear





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	DoubleLinkedListNode[T, U] | None:

					"""simple docstring"""



					if node.prev is None or node.next is None:
								return None

					_UpperCamelCase					:Optional[Any]					     =	node.next
					_UpperCamelCase					:Union[str, Any]					     =	node.prev
					_UpperCamelCase					:Union[str, Any]					     =	None
					_UpperCamelCase					:str					     =	None
					return node






class      A(  Generic[T, U]		):

		"""simple docstring"""





		A    =							{}
		def __init__( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	DoubleLinkedList()
					_UpperCamelCase					:Tuple					     =	capacity
					_UpperCamelCase					:Any					     =	0
					_UpperCamelCase					:Tuple					     =	0
					_UpperCamelCase					:List[Any]					     =	0
					_UpperCamelCase					:List[str]					     =	{}
		def __repr__( self   )      ->	str:

					"""simple docstring"""



					return (
					    f"CacheInfo(hits={self.hits}, misses={self.miss}, "
					    f"capacity={self.capacity}, current size={self.num_keys})"
					)
		def __contains__( self					,				SCREAMING_SNAKE_CASE__   )      ->	bool:

					"""simple docstring"""



					return key in self.cache
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	U | None:

					"""simple docstring"""



					if key in self.cache:
								self.hits += 1
								_UpperCamelCase					:Union[str, Any]					     =	self.cache[key]
								_UpperCamelCase					:Optional[int]					     =	self.list.remove(self.cache[key]   )
								assert node == value_node

								# node is guaranteed not None because it is in self.cache
								assert node is not None
								self.list.add(SCREAMING_SNAKE_CASE__   )
								return node.val
					self.miss += 1
					return None
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					if key not in self.cache:
								if self.num_keys >= self.capacity:
											# delete first node (oldest) when over capacity
											_UpperCamelCase					:Dict					     =	self.list.head.next

											# guaranteed to have a non-None first node when num_keys > 0
											# explain to type checker via assertions
											assert first_node is not None
											assert first_node.key is not None
											assert (
											    self.list.remove(SCREAMING_SNAKE_CASE__   ) is not None
											)  # node guaranteed to be in list assert node.key is not None

											del self.cache[first_node.key]
											self.num_keys -= 1
								_UpperCamelCase					:Any					     =	DoubleLinkedListNode(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								self.list.add(self.cache[key]   )
								self.num_keys += 1

					else:
								# bump node to the end of the list, update value
								_UpperCamelCase					:Any					     =	self.list.remove(self.cache[key]   )
								assert node is not None  # node guaranteed to be in list
								_UpperCamelCase					:List[str]					     =	value
								self.list.add(SCREAMING_SNAKE_CASE__   )





		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__ = 1_28   )      ->	Callable[[Callable[[T], U]], Callable[..., U]]:

					"""simple docstring"""



					def cache_decorator_inner(SCREAMING_SNAKE_CASE__   ) -> Callable[..., U]:
								def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__   ) -> U:
											if func not in cls.decorator_function_to_instance_map:
														_UpperCamelCase					:Tuple					     =	LRUCache(SCREAMING_SNAKE_CASE__   )

											_UpperCamelCase					:int					     =	cls.decorator_function_to_instance_map[func].get(args[0]   )
											if result is None:
														_UpperCamelCase					:Optional[int]					     =	func(*SCREAMING_SNAKE_CASE__   )
														cls.decorator_function_to_instance_map[func].put(args[0]					,				SCREAMING_SNAKE_CASE__   )
											return result

								def cache_info() -> LRUCache[T, U]:
											return cls.decorator_function_to_instance_map[func]

								setattr(SCREAMING_SNAKE_CASE__					,				'''cache_info'''					,				SCREAMING_SNAKE_CASE__   )  # noqa: B010

								return cache_decorator_wrapper

					return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		(KDPMaDiscreteScheduler,)
						UpperCamelCase_				:			Optional[int]						=		10

						def 			A_					(      self							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						{
								    """num_train_timesteps""": 11_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**a					)
								return config

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,				[0.0002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=a							,				beta_end=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a					)

						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07					) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693_4286_5017_0972e-07					) < 1e-2
										assert abs(result_mean.item() - 0.0002					) < 1e-3

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3







						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps							,				device=a					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if str(a					).startswith("""cpu"""					):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3



