
"""simple docstring"""


def      lowerCAmelCase_     (	snake_case_	:    int    )     ->int:
			if not isinstance(snake_case_							,					snake_case_    ):
						raise TypeError('recursive_lucas_number accepts only integer arguments.'    )
			if n_th_number == 0:
						return 2
			if n_th_number == 1:
						return 1

			return recursive_lucas_number(n_th_number - 1    ) + recursive_lucas_number(
			    n_th_number - 2    )






def      lowerCAmelCase_     (	snake_case_	:    int    )     ->int:
			if not isinstance(snake_case_							,					snake_case_    ):
						raise TypeError('dynamic_lucas_number accepts only integer arguments.'    )
			lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =2, 1
			for _ in range(snake_case_    ):
						lowerCamelCase__ ,       lowerCamelCase__			:  Optional[int]  =b, a + b
			return a


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							lowerCAmelCase			=		int(input("""Enter the number of terms in lucas series:\n""").strip())
							print("""Using recursive function to calculate lucas series:""")
							print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
							print("""\nUsing dynamic function to calculate lucas series:""")
							print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))

"""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_  ,							)