



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


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







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["safety_checker", "feature_extractor"]


   def __init__(    self:     Tuple  ,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:
           UpperCAmelCase_     :       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               F'''The configuration file of this scheduler: {scheduler} is outdated.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_							)
           UpperCAmelCase_     :       int  		=					dict(scheduler.config							)
           UpperCAmelCase_     :       Any  		=					1
           UpperCAmelCase_     :       str  		=					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       A__    (    self:     int							)	->      Dict[str, Any]:
       return {k: getattr(self  ,lowerCamelCase_							) for k in self.config.keys() if not k.startswith("""_"""							)}


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[Union[str, int]] = "auto"							)	->      Dict:
       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(lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Tuple:
       self.enable_attention_slicing(lowerCamelCase_							)


   @torch.no_grad()
   def       A__    (    self:     str  ,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  ,)	->      Optional[int]:
       # 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       A__    (    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_:     List[str]  ,)	->      List[str]:
       # 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       A__    (    self:     List[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  ,)	->      Tuple:
       # 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'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



