






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__UpperCamelCase    :						Dict		     =  transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def    a_       (					_A     )	->	int:







				"""simple docstring"""

				if isinstance(__a					,      torch.Tensor     ):
								return image
				elif isinstance(__a					,      PIL.Image.Image     ):
								snake_case__													=   [image]

				snake_case__													=   [trans(img.convert('RGB'     )     ) for img in image]
				snake_case__													=   torch.stack(__a     )
				return image


class      __SCREAMING_SNAKE_CASE(		lowercase__		):
							def __init__(   self:					Optional[int]				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					List[Any]  )						->						List[str]:
											super().__init__()

											# make sure scheduler can always be converted to DDIM
											snake_case__													=   DDIMScheduler.from_config(scheduler.config  )

											self.register_modules(unet=_a				,		scheduler=_a  )
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					int  )						->						List[Any]:






											if strength < 0 or strength > 1:
															raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''  )
							def lowerCAmelCase_       (   self:					Optional[int]				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Tuple				,		UpperCamelCase:					Dict  )						->						Dict:
											snake_case__													=   min(int(num_inference_steps * strength  )				,		_a  )

											snake_case__													=   max(num_inference_steps - init_timestep				,		0  )
											snake_case__													=   self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					Union[str, Any]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					List[Any]				,		UpperCamelCase:					Optional[Any]=None  )						->						List[str]:






											if not isinstance(_a				,		(torch.Tensor, PIL.Image.Image, list)  ):
															raise ValueError(
															    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_a  )}'''  )

											snake_case__													=   image.to(device=_a				,		dtype=_a  )

											if isinstance(_a				,		_a  ) and len(_a  ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(_a  )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

											snake_case__													=   init_latents.shape
											snake_case__													=   randn_tensor(_a				,		generator=_a				,		device=_a				,		dtype=_a  )

											# get latents
											print('add noise to latents at timestep'				,		_a  )
											snake_case__													=   self.scheduler.add_noise(_a				,		_a				,		_a  )
											snake_case__													=   init_latents

											return latents


							@torch.no_grad()
							def __call__(   self:					List[Any]				,		UpperCamelCase:					Union[torch.FloatTensor, PIL.Image.Image] = None				,		UpperCamelCase:					float = 0.8				,		UpperCamelCase:					int = 1				,		UpperCamelCase:					Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		UpperCamelCase:					float = 0.0				,		UpperCamelCase:					int = 50				,		UpperCamelCase:					Optional[bool] = None				,		UpperCamelCase:					Optional[str] = "pil"				,		UpperCamelCase:					bool = True				,		)						->						Optional[Any]:
											self.check_inputs(_a  )

											# 2. Preprocess image
											snake_case__													=   preprocess(_a  )

											# 3. set timesteps
											self.scheduler.set_timesteps(_a				,		device=self.device  )
											snake_case__													=   self.get_timesteps(_a				,		_a				,		self.device  )
											snake_case__													=   timesteps[:1].repeat(_a  )

											# 4. Prepare latent variables
											snake_case__													=   self.prepare_latents(_a				,		_a				,		_a				,		self.unet.dtype				,		self.device				,		_a  )
											snake_case__													=   latents

											# 5. Denoising loop
											for t in self.progress_bar(_a  ):
															# 1. predict noise model_output
															snake_case__													=   self.unet(_a				,		_a  ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case__													=   self.scheduler.step(
															    _a				,		_a				,		_a				,		eta=_a				,		use_clipped_model_output=_a				,		generator=_a				,		).prev_sample

											snake_case__													=   (image / 2 + 0.5).clamp(0				,		1  )
											snake_case__													=   image.cpu().permute(0				,		2				,		3				,		1  ).numpy()
											if output_type == "pil":
															snake_case__													=   self.numpy_to_pil(_a  )

											if not return_dict:
															return (image, latent_timestep.item())

											return ImagePipelineOutput(images=_a  )






'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



