






"""simple docstring"""



_lowerCAmelCase     :     Tuple            =	[[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        lowerCAmelCase__       :
   def __init__(						self	:     int				,       snake_case__	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				name
        UpperCAmelCase__		:  Dict				=				[]
        UpperCAmelCase__		:  Tuple				=				[]
   def 	__a				(						self	:     Optional[int]				,       snake_case__	:     Optional[int]				):


        '''simple docstring'''
        self.inbound.append(snake_case__				)
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     List[Any]				):


        '''simple docstring'''
        self.outbound.append(snake_case__				)




   def __repr__(						self	:     Tuple				):


        '''simple docstring'''
        return f'<node={self.name} inbound={self.inbound} outbound={self.outbound}>'




def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]       , snake_case    :      Tuple=3       , snake_case    :      Dict=0.85							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				{}
     for node in nodes:
          UpperCAmelCase__		:  List[Any]				=				1

     UpperCAmelCase__		:  Tuple				=				{}
     for node in nodes:
          UpperCAmelCase__		:  List[Any]				=				len(node.outbound							)

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


def      SCREAMING_SNAKE_CASE__    (   )->						Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				list(input("Enter Names of the Nodes: "							).split()							)

     UpperCAmelCase__		:  Any				=				[Node(snake_case							) for name in names]

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

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

     page_rank(snake_case							)


if __name__ == "__main__":
 main()










"""simple docstring"""



import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"runwayml/stable-diffusion-v1-5"
        UpperCAmelCase__		:  Optional[Any]				=				TextToVideoZeroPipeline.from_pretrained(snake_case__				,       torch_dtype=torch.floataa				).to("cuda"				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device="cuda"				).manual_seed(0				)

        UpperCAmelCase__		:  Any				=				"A bear is playing a guitar on Times Square"
        UpperCAmelCase__		:  Dict				=				pipe(prompt=snake_case__				,       generator=snake_case__				).images

        UpperCAmelCase__		:  List[Any]				=				load_pt(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"				)

        assert_mean_pixel_difference(snake_case__				,       snake_case__				)


