'''simple docstring'''







lowercase__     :  int    					=			[[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class       lowerCamelCase_							:






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			name
       UpperCamelCase__      =			[]
       UpperCamelCase__      =			[]


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       self.inbound.append(__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       self.outbound.append(__UpperCamelCase )







   def __repr__(  self ):
       return f'<node={self.name} inbound={self.inbound} outbound={self.outbound}>'





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_=3					,  lowerCAmelCase_=0.85	):
    UpperCamelCase__      =			{}
    for node in nodes:
        UpperCamelCase__      =			1

    UpperCamelCase__      =			{}
    for node in nodes:
        UpperCamelCase__      =			len(node.outbound	)

    for i in range(lowerCAmelCase_	):
        print(F'======= Iteration {i + 1} ======='	)
        for _, node in enumerate(lowerCAmelCase_	):
            UpperCamelCase__      =			(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(lowerCAmelCase_	)





def 						snake_case__ ():
    UpperCamelCase__      =			list(input("Enter Names of the Nodes: "	).split()	)

    UpperCamelCase__      =			[Node(lowerCAmelCase_	) for name in names]

    for ri, row in enumerate(lowerCAmelCase_	):
        for ci, col in enumerate(lowerCAmelCase_	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print("======= Nodes ======="	)
    for node in nodes:
        print(lowerCAmelCase_	)

    page_rank(lowerCAmelCase_	)


if __name__ == "__main__":
      main()









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

       self.register_modules(unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




