


'''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						(						UpperCamelCase							):



   '''simple docstring'''






   _lowercase    						= ['safety_checker', 'feature_extractor']




   def __init__(	self	:					Optional[int]					, lowerCamelCase	:					AutoencoderKL					, lowerCamelCase	:					CLIPTextModel					, lowerCamelCase	:					CLIPTokenizer					, lowerCamelCase	:					UNetaDConditionModel					, lowerCamelCase	:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					, lowerCamelCase	:					StableDiffusionSafetyChecker					, lowerCamelCase	:					CLIPImageProcessor					, lowerCamelCase	:					bool = True					, )->	int:
          super().__init__()
          if hasattr(scheduler.config					, """steps_offset"""						) and scheduler.config.steps_offset != 1:
                 snake_case__					:	Optional[int]          =					(
                     F"""The configuration file of this scheduler: {scheduler} is outdated.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						)
                 snake_case__					:	Dict          =					dict(scheduler.config						)
                 snake_case__					:	Optional[Any]          =					1
                 snake_case__					:	int          =					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   __lowerCAmelCase						(	self	:					str						)->	Dict[str, Any]:
          return {k: getattr(self					, lowerCamelCase						) for k in self.config.keys() if not k.startswith("""_"""						)}




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Optional[Union[str, int]] = "auto"						)->	Tuple:
          if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 snake_case__					:	Any          =					self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					int						)->	Optional[Any]:
          self.enable_attention_slicing(lowerCamelCase						)




   @torch.no_grad()
   def   __lowerCAmelCase						(	self	:					Optional[Any]					, 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					, )->	Tuple:
          # 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=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   __lowerCAmelCase						(	self	:					Dict					, 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	:					Dict					, )->	Tuple:
          # 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=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   __lowerCAmelCase						(	self	:					Optional[Any]					, 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					, )->	List[Any]:
          # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
          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					, )








'''simple docstring'''
from __future__ import annotations







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Any					, lowerCamelCase	:					int						)->	None:
          snake_case__					:	Optional[int]          =					value
          snake_case__					:	Node | None          =					None
          snake_case__					:	Node | None          =					None







class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42




   def __init__(	self	:					str						)->	None:
          snake_case__					:	int          =					0




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int						)->	None:
          if node is None:
                 return

          if path_sum == self.target:
                 self.paths += 1

          if node.left:
                 self.depth_first_search(node.left					, path_sum + node.left.value						)
          if node.right:
                 self.depth_first_search(node.right					, path_sum + node.right.value						)




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int | None = None						)->	int:
          if node is None:
                 return 0
          if target is not None:
                 snake_case__					:	Dict          =					target

          self.depth_first_search(lowerCamelCase					, node.value						)
          self.path_sum(node.left						)
          self.path_sum(node.right						)

          return self.paths


if __name__ == "__main__":
     import doctest

     doctest.testmod()




