





"""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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		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()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # 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			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Any 						=		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.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

              # 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.
              UpperCamelCase     :						Any 						=		list(unet_pseudo_updated_step_one.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # 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			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

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






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

              with tempfile.TemporaryDirectory() as tmpdir:
                     ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						List[str] 						=		loaded_unet.to(unet.device    )

              # Since no EMA step has been performed the outputs should match.
              UpperCamelCase     :						Optional[Any] 						=		unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample
              UpperCamelCase     :						List[str] 						=		loaded_unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample

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

