

'''simple docstring'''





from __future__ import annotations

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

UpperCamelCase_							=					TypeVar("""T""")
UpperCamelCase_							=					TypeVar("""U""")





class 		__SCREAMING_SNAKE_CASE       ( Generic[T, U]			):







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		T | None		,				UpperCAmelCase__    :		U | None      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=key
										lowercase   :       Optional[int]								=val
										lowercase   :       DoubleLinkedListNode[T, U] | None								=None
										lowercase   :       DoubleLinkedListNode[T, U] | None								=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 		__SCREAMING_SNAKE_CASE       ( Generic[T, U]			):







				def __init__(					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       DoubleLinkedListNode[T, U]								=DoubleLinkedListNode(UpperCAmelCase__		,				UpperCAmelCase__      )
										lowercase   :       DoubleLinkedListNode[T, U]								=DoubleLinkedListNode(UpperCAmelCase__		,				UpperCAmelCase__      )
										lowercase		,  lowercase   :       str								=self.rear, self.head







				def __repr__(					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=['''DoubleLinkedList''']
										lowercase   :       List[Any]								=self.head
										while node.next is not None:
																rep.append(str(UpperCAmelCase__      )      )
																lowercase   :       List[str]								=node.next
										rep.append(str(self.rear      )      )
										return ",\n    ".join(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		DoubleLinkedListNode[T, U]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.rear.prev

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

										lowercase   :       List[Any]								=node
										lowercase   :       Optional[int]								=previous
										lowercase   :       Optional[Any]								=node
										lowercase   :       Union[str, Any]								=self.rear







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		DoubleLinkedListNode[T, U]      ):
										'''simple docstring'''

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

										lowercase   :       Tuple								=node.next
										lowercase   :       Tuple								=node.prev
										lowercase   :       int								=None
										lowercase   :       List[Any]								=None
										return node





class 		__SCREAMING_SNAKE_CASE       ( Generic[T, U]			):
				lowerCamelCase_							=						{}







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										lowercase   :       DoubleLinkedList[T, U]								=DoubleLinkedList()
										lowercase   :       Any								=capacity
										lowercase   :       List[Any]								=0
										lowercase   :       str								=0
										lowercase   :       Any								=0
										lowercase   :       dict[T, DoubleLinkedListNode[T, U]]								={}







				def __repr__(					self    :		Tuple      ):
										'''simple docstring'''
										return (
										    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
										    F'''capacity={self.capacity}, current size={self.num_keys})'''
										)







				def __contains__(					self    :		List[str]		,				UpperCAmelCase__    :		T      ):
										'''simple docstring'''
										return key in self.cache







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		T      ):
										'''simple docstring'''
										# Note: pythonic interface would throw KeyError rather than return None

										if key in self.cache:
																self.hits += 1
																lowercase   :       DoubleLinkedListNode[T, U]								=self.cache[key]
																lowercase   :       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(UpperCAmelCase__      )
																return node.val
										self.miss += 1
										return None







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		T		,				UpperCAmelCase__    :		U      ):
										'''simple docstring'''

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

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







				@classmethod
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		int = 128      ):
										'''simple docstring'''

										def cache_decorator_inner(UpperCAmelCase__    :		Callable[[T], U]      ) -> Callable[..., U]:
																def cache_decorator_wrapper(*UpperCAmelCase__    :		T      ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																												lowercase   :       int								=LRUCache(UpperCAmelCase__      )

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

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

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

																return cache_decorator_wrapper

										return cache_decorator_inner


if __name__ == "__main__":
							import doctest

							doctest.testmod()








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





