__snake_case         =     [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class       __snake_case :
 def __init__(	self		, snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=name
       UpperCAmelCase     :			List[str]     	=[]
       UpperCAmelCase     :			Any     	=[]
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       self.inbound.append(snake_case_    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Any:
       '''simple docstring'''





       self.outbound.append(snake_case_    )






 def __repr__(	self    )						->				Any:
       '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=3		,       __lowerCAmelCase=0.85    )->	Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase     :			int     	={}
      for node in nodes:
            UpperCAmelCase     :			Dict     	=1

      UpperCAmelCase     :			Optional[int]     	={}
      for node in nodes:
            UpperCAmelCase     :			Optional[int]     	=len(node.outbound    )

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





def 	lowerCAmelCase_			(					)->	str:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=list(input('''Enter Names of the Nodes: '''    ).split()    )

      UpperCAmelCase     :			Union[str, Any]     	=[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"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


    def        lowerCamelCase__						(     self  ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2    ,     )

