

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				):


       '''simple docstring'''







       snake_case    							=	'hf-internal-testing/tiny-stable-diffusion-pipe'
       snake_case    							=	1
       snake_case    							=	77
       snake_case    							=	32
       snake_case    							=	4
       snake_case    							=	snake_case    							=	64
       snake_case    							=	torch.manual_seed(0				)

       def   lowerCamelCase__						(	self					:  List[Any]						,	__snake_case					:  Dict=0.9999     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    UNetaDConditionModel.from_pretrained(self.model_id						,	subfolder='unet'     )
        lowerCamelCase      						=    unet.to(__snake_case     )
        lowerCamelCase      						=    EMAModel(unet.parameters()						,	decay=__snake_case						,	model_cls=__snake_case						,	model_config=unet.config     )
        return unet, ema_unet

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase      						=    torch.randn(
            self.batch_size						,	self.num_in_channels						,	self.latent_height						,	self.latent_width						,	generator=self.generator     ).to(__snake_case     )
        lowerCamelCase      						=    torch.randint(0						,	1000						,	size=(self.batch_size,)						,	generator=self.generator     ).to(__snake_case     )
        lowerCamelCase      						=    torch.randn(
            self.batch_size						,	self.prompt_length						,	self.text_encoder_hidden_dim						,	generator=self.generator     ).to(__snake_case     )
        return noisy_latents, timesteps, encoder_hidden_states

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  int     )   -> int:







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  Dict     )   -> Any:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Dict     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    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()     )
        lowerCamelCase      						=    list(unet.parameters()     )
        for s_param, param in zip(ema_unet.shadow_params						,	__snake_case     ):
         assert torch.allclose(__snake_case						,	__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						,	__snake_case     ):
         assert torch.allclose(__snake_case						,	__snake_case     )

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Any:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    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.
        lowerCamelCase      						=    self.simulate_backprop(__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.
        lowerCamelCase      						=    list(unet_pseudo_updated_step_one.parameters()     )
        for s_param, param in zip(ema_unet.shadow_params						,	__snake_case     ):
         assert ~torch.allclose(__snake_case						,	__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						,	__snake_case     ):
         assert ~torch.allclose(__snake_case						,	__snake_case     )

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Any:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.get_models()

        # First backprop + EMA
        lowerCamelCase      						=    self.simulate_backprop(__snake_case     )
        ema_unet.step(unet_step_one.parameters()     )
        lowerCamelCase      						=    ema_unet.shadow_params

        # Second backprop + EMA
        lowerCamelCase      						=    self.simulate_backprop(__snake_case     )
        ema_unet.step(unet_step_two.parameters()     )
        lowerCamelCase      						=    ema_unet.shadow_params

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

       def   lowerCamelCase__						(	self					:  Any     )   -> List[Any]:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.get_models(decay=0.0     )
        lowerCamelCase      						=    self.simulate_backprop(__snake_case     )
        ema_unet.step(unet_step_one.parameters()     )
        lowerCamelCase      						=    ema_unet.shadow_params

        lowerCamelCase      						=    self.simulate_backprop(__snake_case     )
        ema_unet.step(unet_step_two.parameters()     )
        lowerCamelCase      						=    ema_unet.shadow_params

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





       @skip_mps
       def   lowerCamelCase__						(	self					:  str     )   -> int:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.get_models()
        lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase      						=    self.get_dummy_inputs()

        with tempfile.TemporaryDirectory() as tmpdir:
         ema_unet.save_pretrained(__snake_case     )
         lowerCamelCase      						=    UNetaDConditionModel.from_pretrained(__snake_case						,	model_cls=__snake_case     )
         lowerCamelCase      						=    loaded_unet.to(unet.device     )

        # Since no EMA step has been performed the outputs should match.
        lowerCamelCase      						=    unet(__snake_case						,	__snake_case						,	__snake_case     ).sample
        lowerCamelCase      						=    loaded_unet(__snake_case						,	__snake_case						,	__snake_case     ).sample

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







from __future__ import annotations


class     lowerCAmelCase				:


       '''simple docstring'''







       def __init__(	self					:  Dict						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    vertices
        lowerCamelCase      						=    [[0] * vertices for _ in range(__snake_case     )]

       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  list[int]     )   -> None:







        '''simple docstring'''

        print('Vertex \t Distance from Source'     )
        for vertex in range(self.vertices     ):
         print(__snake_case						,	'\t\t'						,	distances_from_source[vertex]     )

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  list[int]						,	__snake_case					:  list[bool]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    1e7
        lowerCamelCase      						=    0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices     ):
         if distances_from_source[vertex] < minimum and visited[vertex] is False:
          lowerCamelCase      						=    distances_from_source[vertex]
          lowerCamelCase      						=    vertex
        return min_index





       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    [int(1e7     )] * self.vertices  # distances from the source
        lowerCamelCase      						=    0
        lowerCamelCase      						=    [False] * self.vertices

        for _ in range(self.vertices     ):
         lowerCamelCase      						=    self.minimum_distance(__snake_case						,	__snake_case     )
         lowerCamelCase      						=    True

         # Update dist value of the adjacent vertices
         # of the picked vertex only if the current
         # distance is greater than new distance and
         # the vertex in not in the shortest path tree
         for v in range(self.vertices     ):
          if (
              self.graph[u][v] > 0
              and visited[v] is False
              and distances[v] > distances[u] + self.graph[u][v]
          ):
           lowerCamelCase      						=    distances[u] + self.graph[u][v]

        self.print_solution(__snake_case     )


if __name__ == "__main__":
     _lowerCAmelCase :	Dict     				=    Graph(9)
     _lowerCAmelCase :	int     				=    [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)




