





"""simple docstring"""




lowerCamelCase_	:		Optional[Any]         =   [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    name
        A_    :			Union[str, Any]									=    []
        A_    :			Union[str, Any]									=    []

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        self.inbound.append(snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        self.outbound.append(snake_case_    )

      def __repr__(	self    ):

        """simple docstring"""

        return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase=3					,   _UpperCAmelCase=0.85   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    {}
  for node in nodes:
    A_    :			List[Any]									=    1

  A_    :			Any									=    {}
  for node in nodes:
    A_    :			Tuple									=    len(node.outbound   )

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





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Tuple									=    list(input('Enter Names of the Nodes: '   ).split()   )

  A_    :			Dict									=    [Node(_UpperCAmelCase   ) for name in names]

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

  print('======= Nodes ======='   )
  for node in nodes:
    print(_UpperCAmelCase   )

  page_rank(_UpperCAmelCase   )


if __name__ == "__main__":
   main()





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class  __lowerCAmelCase							(				unittest.TestCase):
       _a			       =    '''hf-internal-testing/tiny-stable-diffusion-pipe'''
       _a			       =    1
       _a			       =    77
       _a			       =    32
       _a			       =    4
       _a			       =    _a			       =    64
       _a			       =    torch.manual_seed(0)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       int=0.99_99					):
         lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(self.model_id							,       subfolder="unet"					)
         lowercase     :Union[str, Any]   						=    unet.to(_lowerCAmelCase					)
         lowercase     :Any   						=    EMAModel(unet.parameters()							,       decay=_lowerCAmelCase							,       model_cls=_lowerCAmelCase							,       model_config=unet.config					)
         return unet, ema_unet






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Optional[Any]   						=    torch.randn(
             self.batch_size							,       self.num_in_channels							,       self.latent_height							,       self.latent_width							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :Dict   						=    torch.randint(0							,       10_00							,       size=(self.batch_size,)							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :List[Any]   						=    torch.randn(
             self.batch_size							,       self.prompt_length							,       self.text_encoder_hidden_dim							,       generator=self.generator					).to(_lowerCAmelCase					)
         return noisy_latents, timesteps, encoder_hidden_states






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Optional[Any]					):
         lowercase     :Any   						=    {}
         for k, param in unet.state_dict().items():
           lowercase     :Optional[int]   						=    torch.randn_like(_lowerCAmelCase					) + (param * torch.randn_like(_lowerCAmelCase					))
           updated_state_dict.update({k: updated_param}					)
         unet.load_state_dict(_lowerCAmelCase					)
         return unet






       def        SCREAMING_SNAKE_CASE     ( self:       int					):
         lowercase						,     lowercase     :List[str]   						=    self.get_models()
         # Take the first (hypothetical) EMA step.
         ema_unet.step(unet.parameters()					)
         assert ema_unet.optimization_step == 1

         # Take two more.
         for _ in range(2					):
           ema_unet.step(unet.parameters()					)
         assert ema_unet.optimization_step == 3






       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         lowercase						,     lowercase     :str   						=    self.get_models()
         # Since the `unet` is not being updated (i.e., backprop'd)
         # there won't be any difference between the `params` of `unet`
         # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
         ema_unet.step(unet.parameters()					)
         lowercase     :Tuple   						=    list(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # The above holds true even if we call `ema.step()` multiple times since
         # `unet` params are still not being updated.
         for _ in range(4					):
           ema_unet.step(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase						,     lowercase     :Optional[int]   						=    self.get_models()
         # Here we simulate the parameter updates for `unet`. Since there might
         # be some parameters which are initialized to zero we take extra care to
         # initialize their values to something non-zero before the multiplication.
         lowercase     :List[str]   						=    self.simulate_backprop(_lowerCAmelCase					)

         # Take the EMA step.
         ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

         # Now the EMA'd parameters won't be equal to the original model parameters.
         lowercase     :str   						=    list(unet_pseudo_updated_step_one.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)

         # Ensure this is the case when we take multiple EMA steps.
         for _ in range(4					):
           ema_unet.step(unet.parameters()					)
         for s_param, param in zip(ema_unet.shadow_params							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         # If we call EMA step after a backpropagation consecutively for two times,
         # the shadow params from those two steps should be different.
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()

         # First backprop + EMA
         lowercase     :Union[str, Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :int   						=    ema_unet.shadow_params

         # Second backprop + EMA
         lowercase     :Optional[int]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :Optional[int]   						=    ema_unet.shadow_params

         for step_one, step_two in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
           assert ~torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         # If there's no decay even if there are backprops, EMA steps
         # won't take any effect i.e., the shadow params would remain the
         # same.
         lowercase						,     lowercase     :Any   						=    self.get_models(decay=0.0					)
         lowercase     :List[Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         lowercase     :str   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         for step_one, step_two in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
           assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase					)






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()
         lowercase						,     lowercase						,     lowercase     :str   						=    self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(_lowerCAmelCase					)
           lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(_lowerCAmelCase							,       model_cls=_lowerCAmelCase					)
           lowercase     :Tuple   						=    loaded_unet.to(unet.device					)

         # Since no EMA step has been performed the outputs should match.
         lowercase     :Dict   						=    unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample
         lowercase     :Any   						=    loaded_unet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).sample

         assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase							,       atol=1e-4					)



