



'''simple docstring'''

from __future__ import annotations

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

SCREAMING_SNAKE_CASE__     :						int	=  TypeVar('''T''')
SCREAMING_SNAKE_CASE__     :						Tuple	=  TypeVar('''U''')

class 							a__( Generic[T, U]     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =key
					snake_case__	     =val
					snake_case__	     =None
					snake_case__	     =None






			def __repr__(     self						)							->							Any:
					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]     ):






			def __init__(     self						)							->							Optional[int]:
					snake_case__	     =DoubleLinkedListNode(lowercase__					,   lowercase__						)
					snake_case__	     =DoubleLinkedListNode(lowercase__					,   lowercase__						)
					snake_case__	     =self.rear, self.head






			def __repr__(     self						)							->							Tuple:
					snake_case__	     =['''DoubleLinkedList''']
					snake_case__	     =self.head
					while node.next is not None:
							rep.append(str(lowercase__						)						)
							snake_case__	     =node.next
					rep.append(str(self.rear						)						)
					return ",\n    ".join(lowercase__						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =self.rear.prev

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

					snake_case__	     =node
					snake_case__	     =previous
					snake_case__	     =node
					snake_case__	     =self.rear






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[int]:

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

					snake_case__	     =node.next
					snake_case__	     =node.prev
					snake_case__	     =None
					snake_case__	     =None
					return node





class 							a__( Generic[T, U]     ):
			a_	: dict[Callable[[T], U], LRUCache[T, U]]									=      {}






			def __init__(     self					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =DoubleLinkedList()
					snake_case__	     =capacity
					snake_case__	     =0
					snake_case__	     =0
					snake_case__	     =0
					snake_case__	     ={}






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






			def __contains__(     self					,   _UpperCAmelCase						)							->							int:
					return key in self.cache






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							str:
					# Note: pythonic interface would throw KeyError rather than return None

					if key in self.cache:
							self.hits += 1
							snake_case__	     =self.cache[key]
							snake_case__	     =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(lowercase__						)
							return node.val
					self.miss += 1
					return None






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:

					if key not in self.cache:
							if self.num_keys >= self.capacity:
									# delete first node (oldest) when over capacity
									snake_case__	     =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(lowercase__						) 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
							snake_case__	     =DoubleLinkedListNode(lowercase__					,   lowercase__						)
							self.list.add(self.cache[key]						)
							self.num_keys += 1

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






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase = 128						)							->							Dict:

					def cache_decorator_inner(_UpperCAmelCase						) -> Callable[..., U]:
							def cache_decorator_wrapper(*_UpperCAmelCase						) -> U:
									if func not in cls.decorator_function_to_instance_map:
											snake_case__	     =LRUCache(lowercase__						)

									snake_case__	     =cls.decorator_function_to_instance_map[func].get(args[0]						)
									if result is None:
											snake_case__	     =func(*lowercase__						)
											cls.decorator_function_to_instance_map[func].put(args[0]					,   lowercase__						)
									return result

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

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

							return cache_decorator_wrapper

					return cache_decorator_inner


if __name__ == "__main__":
			import doctest

			doctest.testmod()








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

