




'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__						):
			if common_ratio == 1:
						# Formula for sum if common ratio is 1
						return num_of_terms * first_term

			# Formula for finding sum of n terms of a GeometricProgression
			return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)









'''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		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["safety_checker", "feature_extractor"]
				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = True			,  )     ->	List[str]:
							super().__init__()
							if hasattr(scheduler.config			,  "steps_offset"      ) and scheduler.config.steps_offset != 1:
										__UpperCamelCase				:      List[str]									=       (
										    f"The configuration file of this scheduler: {scheduler} is outdated.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"			,  _UpperCAmelCase			,  standard_warn=_UpperCAmelCase      )
										__UpperCamelCase				:      str									=       dict(scheduler.config      )
										__UpperCamelCase				:      Optional[Any]									=       1
										__UpperCamelCase				:      Dict									=       FrozenDict(_UpperCAmelCase      )

							self.register_modules(
							    vae=_UpperCAmelCase			,  text_encoder=_UpperCAmelCase			,  tokenizer=_UpperCAmelCase			,  unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			,  feature_extractor=_UpperCAmelCase			,  )
							self.register_to_config(requires_safety_checker=_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict[str, Any]:
							return {k: getattr(self			,  _UpperCAmelCase      ) for k in self.config.keys() if not k.startswith("_"      )}
				def 	a_	(self			,  _UpperCAmelCase = "auto"      )     ->	List[str]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										__UpperCamelCase				:      Dict									=       self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase      )
				def 	a_	(self      )     ->	Dict:
							self.enable_attention_slicing(_UpperCAmelCase      )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	List[str]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionInpaintPipelineLegacy(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  mask_image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  **_UpperCAmelCase			,  )     ->	Optional[Any]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionImgaImgPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )





				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	Union[str, Any]:
							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
							return StableDiffusionPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  height=_UpperCAmelCase			,  width=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  latents=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )



