
"""simple docstring"""


from __future__ import annotations

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

lowerCAmelCase			=		TypeVar("""T""")
lowerCAmelCase			=		TypeVar("""U""")

class 					A_			(  Generic[T, U]     ):



							"""simple docstring"""






							def __init__(						self						:Tuple  ,							lowerCamelCase_						:T | None  ,							lowerCamelCase_						:U | None ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =key
										lowerCamelCase__			:  Union[str, Any]  =val
										lowerCamelCase__			:  int  =0
										lowerCamelCase__			:  DoubleLinkedListNode[T, U] | None  =None
										lowerCamelCase__			:  DoubleLinkedListNode[T, U] | None  =None





							def __repr__(						self						:Dict ):





										"""simple docstring"""

										return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
										    self.key  ,							self.val  ,							self.freq  ,							self.next is not None  ,							self.prev is not None )






class 					A_			(  Generic[T, U]     ):



							"""simple docstring"""






							def __init__(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  DoubleLinkedListNode[T, U]  =DoubleLinkedListNode(lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  DoubleLinkedListNode[T, U]  =DoubleLinkedListNode(lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__ ,       lowerCamelCase__			:  Optional[int]  =self.rear, self.head





							def __repr__(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =['DoubleLinkedList']
										lowerCamelCase__			:  Optional[Any]  =self.head
										while node.next is not None:
													rep.append(str(lowerCamelCase_ ) )
													lowerCamelCase__			:  str  =node.next
										rep.append(str(self.rear ) )
										return ",\n    ".join(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:int  ,							lowerCamelCase_						:DoubleLinkedListNode[T, U] ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =self.rear.prev

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

										lowerCamelCase__			:  Tuple  =node
										lowerCamelCase__			:  Optional[int]  =previous
										lowerCamelCase__			:  Union[str, Any]  =node
										lowerCamelCase__			:  List[str]  =self.rear
										node.freq += 1
										self._position_node(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Tuple  ,							lowerCamelCase_						:DoubleLinkedListNode[T, U] ):





										"""simple docstring"""

										while node.prev is not None and node.prev.freq > node.freq:
													# swap node with previous node
													lowerCamelCase__			:  int  =node.prev

													lowerCamelCase__			:  str  =previous_node.prev
													lowerCamelCase__			:  Dict  =node.prev
													lowerCamelCase__			:  Tuple  =previous_node
													lowerCamelCase__			:  List[Any]  =node





							def    UpperCAmelCase__							(						self						:Dict  ,							lowerCamelCase_						:DoubleLinkedListNode[T, U] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Optional[Any]  =node.next
										lowerCamelCase__			:  Optional[int]  =node.prev
										lowerCamelCase__			:  Optional[int]  =None
										lowerCamelCase__			:  Optional[Any]  =None
										return node






class 					A_			(  Generic[T, U]     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     {}





							def __init__(						self						:List[str]  ,							lowerCamelCase_						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  DoubleLinkedList[T, U]  =DoubleLinkedList()
										lowerCamelCase__			:  Union[str, Any]  =capacity
										lowerCamelCase__			:  Tuple  =0
										lowerCamelCase__			:  str  =0
										lowerCamelCase__			:  Union[str, Any]  =0
										lowerCamelCase__			:  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						:Tuple  ,							lowerCamelCase_						:T ):





										"""simple docstring"""

										return key in self.cache





							def    UpperCAmelCase__							(						self						:Optional[Any]  ,							lowerCamelCase_						:T ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:T  ,							lowerCamelCase_						:U ):





										"""simple docstring"""

										if key not in self.cache:
													if self.num_keys >= self.capacity:
																# delete first node when over capacity
																lowerCamelCase__			:  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(lowerCamelCase_ ) is not None
																# first_node guaranteed to be in list

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

										else:
													lowerCamelCase__			:  Dict  =self.list.remove(self.cache[key] )
													assert node is not None  # node guaranteed to be in list
													lowerCamelCase__			:  List[str]  =value
													self.list.add(lowerCamelCase_ )





							@classmethod
							def    UpperCAmelCase__							(						cls						:type[LFUCache[T, U]]  ,							lowerCamelCase_						:int = 128 ):





										"""simple docstring"""

										def cache_decorator_inner(lowerCamelCase_						:Callable[[T], U] ) -> Callable[..., U]:
													def cache_decorator_wrapper(*lowerCamelCase_						:T ) -> U:
																if func not in cls.decorator_function_to_instance_map:
																			lowerCamelCase__			:  Dict  =LFUCache(lowerCamelCase_ )

																lowerCamelCase__			:  Optional[Any]  =cls.decorator_function_to_instance_map[func].get(args[0] )
																if result is None:
																			lowerCamelCase__			:  Dict  =func(*lowerCamelCase_ )
																			cls.decorator_function_to_instance_map[func].put(args[0]  ,							lowerCamelCase_ )
																return result

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

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

													return cache_decorator_wrapper

										return cache_decorator_inner


if __name__ == "__main__":
							import doctest

							doctest.testmod()

"""simple docstring"""


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase			=		logging.get_logger(__name__)  # pylint: disable=invalid-name

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     ["""safety_checker""", """feature_extractor"""]





							def __init__(						self						:Optional[Any]  ,							lowerCamelCase_						:AutoencoderKL  ,							lowerCamelCase_						:CLIPTextModel  ,							lowerCamelCase_						:CLIPTokenizer  ,							lowerCamelCase_						:UNetaDConditionModel  ,							lowerCamelCase_						:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							lowerCamelCase_						:StableDiffusionSafetyChecker  ,							lowerCamelCase_						:CLIPImageProcessor  ,							lowerCamelCase_						:bool = True  ,							):





										"""simple docstring"""

										super().__init__()
										if hasattr(scheduler.config  ,							'steps_offset' ) and scheduler.config.steps_offset != 1:
													lowerCamelCase__			:  Dict  =(
													    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
													    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
													    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
													    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
													    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
													    ' file'
													)
													deprecate('steps_offset!=1'  ,							'1.0.0'  ,							lowerCamelCase_  ,							standard_warn=lowerCamelCase_ )
													lowerCamelCase__			:  Tuple  =dict(scheduler.config )
													lowerCamelCase__			:  str  =1
													lowerCamelCase__			:  List[Any]  =FrozenDict(lowerCamelCase_ )

										self.register_modules(
										    vae=lowerCamelCase_  ,							text_encoder=lowerCamelCase_  ,							tokenizer=lowerCamelCase_  ,							unet=lowerCamelCase_  ,							scheduler=lowerCamelCase_  ,							safety_checker=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_  ,							)
										self.register_to_config(requires_safety_checker=lowerCamelCase_ )





							@property
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										return {k: getattr(self  ,							lowerCamelCase_ ) for k in self.config.keys() if not k.startswith('_' )}





							def    UpperCAmelCase__							(						self						:Optional[Any]  ,							lowerCamelCase_						:Optional[Union[str, int]] = "auto" ):





										"""simple docstring"""

										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													lowerCamelCase__			:  List[Any]  =self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										self.enable_attention_slicing(lowerCamelCase_ )





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionInpaintPipelineLegacy(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							mask_image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							**lowerCamelCase_						:str  ,							):





										"""simple docstring"""

										return StableDiffusionImgaImgPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 50  ,							lowerCamelCase_						:float = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:float = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[torch.FloatTensor] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							height=lowerCamelCase_  ,							width=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							latents=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)