
import math

import flax.linen as nn
import jax.numpy as jnp
def 							A_     (__a				,   __a				,   __a = 1				,   __a = 1				,   __a = 1.0e4				,   __a = False				,   __a = 1.0				,   ):

					'''simple docstring'''





					assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
					assert embedding_dim % 2 == 0, f'Embedding dimension {embedding_dim} should be even'
					A_				 =						float(embedding_dim // 2				)
					A_				 =						math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
					A_				 =						min_timescale * jnp.exp(jnp.arange(__a				,   dtype=jnp.floataa				) * -log_timescale_increment				)
					A_				 =						jnp.expand_dims(__a				,   1				) * jnp.expand_dims(__a				,   0				)

					# scale embeddings
					A_				 =						scale * emb

					if flip_sin_to_cos:
										A_				 =						jnp.concatenate([jnp.cos(__a				), jnp.sin(__a				)]				,   axis=1				)
					else:
										A_				 =						jnp.concatenate([jnp.sin(__a				), jnp.cos(__a				)]				,   axis=1				)
					A_				 =						jnp.reshape(__a				,   [jnp.shape(__a				)[0], embedding_dim]				)
					return signal



class __lowerCAmelCase				(					nn.Module      ):




			"""simple docstring"""







			snake_case	      =				32
			snake_case	      =				jnp.floataa




			@nn.compact
			def __call__(				self						:					List[str]       ,   _snake_case						:					List[str]	)      -> Any:







								"""simple docstring"""
								A_				 =						nn.Dense(self.time_embed_dim       ,   dtype=self.dtype       ,   name="linear_1"	)(_snake_case	)
								A_				 =						nn.silu(_snake_case	)
								A_				 =						nn.Dense(self.time_embed_dim       ,   dtype=self.dtype       ,   name="linear_2"	)(_snake_case	)
								return temb





class __lowerCAmelCase				(					nn.Module      ):




			"""simple docstring"""







			snake_case	      =				32
			snake_case	      =				False
			snake_case	      =				1




			@nn.compact
			def __call__(				self						:					int       ,   _snake_case						:					Dict	)      -> List[Any]:







								"""simple docstring"""
								return get_sinusoidal_embeddings(
								    _snake_case       ,   embedding_dim=self.dim       ,   flip_sin_to_cos=self.flip_sin_to_cos       ,   freq_shift=self.freq_shift	)






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






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







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

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





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

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

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




