






import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 							__a							(  unittest.TestCase						):





						'''simple docstring'''



						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_activation('swish'       )

										self.assertIsInstance(_A ,    nn.SiLU       )

										self.assertEqual(act(torch.tensor(-100 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertNotEqual(act(torch.tensor(-1 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(0 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(20 ,    dtype=torch.floataa       )       ).item() ,    20       )


						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	str													=   get_activation('silu'       )

										self.assertIsInstance(_A ,    nn.SiLU       )

										self.assertEqual(act(torch.tensor(-100 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertNotEqual(act(torch.tensor(-1 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(0 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(20 ,    dtype=torch.floataa       )       ).item() ,    20       )


						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   get_activation('mish'       )

										self.assertIsInstance(_A ,    nn.Mish       )

										self.assertEqual(act(torch.tensor(-200 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertNotEqual(act(torch.tensor(-1 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(0 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(20 ,    dtype=torch.floataa       )       ).item() ,    20       )






						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_activation('gelu'       )

										self.assertIsInstance(_A ,    nn.GELU       )

										self.assertEqual(act(torch.tensor(-100 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertNotEqual(act(torch.tensor(-1 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(0 ,    dtype=torch.floataa       )       ).item() ,    0       )
										self.assertEqual(act(torch.tensor(20 ,    dtype=torch.floataa       )       ).item() ,    20       )







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()