





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class        __UpperCamelCase				:
							# setable values
							lowerCamelCase   :					Optional[int]      					=None
							lowerCamelCase   :					Optional[jnp.ndarray]      					=None
							lowerCamelCase   :					Optional[jnp.ndarray]      					=None  # sigma(t_i)






							@classmethod
							def     __a   (   cls      )	-> Optional[int]:
								return cls()


@dataclass
class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					jnp.ndarray
							lowerCamelCase   :					jnp.ndarray
							lowerCamelCase   :					KarrasVeSchedulerState


class        __UpperCamelCase				(						a__		,       a__						):






							@property
							def     __a   (   self      )	-> Optional[int]:
								return True






							@register_to_config
							def __init__(   self      ,					lowerCAmelCase__ = 0.02      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 1.007      ,					lowerCAmelCase__ = 80      ,					lowerCAmelCase__ = 0.05      ,					lowerCAmelCase__ = 50      ,					)	-> int:
								pass






							def     __a   (   self      )	-> Tuple:
								return KarrasVeSchedulerState.create()






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = ()      )	-> KarrasVeSchedulerState:
								a      :		List[Any]	=				jnp.arange(0      ,					lowerCAmelCase__      )[::-1].copy()
								a      :		Optional[Any]	=				[
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in timesteps
								]

								return state.replace(
								    num_inference_steps=lowerCAmelCase__      ,					schedule=jnp.array(lowerCAmelCase__      ,					dtype=jnp.floataa      )      ,					timesteps=lowerCAmelCase__      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Tuple[jnp.ndarray, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a      :		int	=				min(self.config.s_churn / state.num_inference_steps      ,					2**0.5 - 1      )
								else:
									a      :		Any	=				0

								# sample eps ~ N(0, S_noise^2 * I)
								a      :		Tuple	=				random.split(lowerCAmelCase__      ,					num=1      )
								a      :		List[str]	=				self.config.s_noise * random.normal(key=lowerCAmelCase__      ,					shape=sample.shape      )
								a      :		str	=				sigma + gamma * sigma
								a      :		Any	=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = True      ,					)	-> Union[FlaxKarrasVeOutput, Tuple]:
								a      :		int	=				sample_hat + sigma_hat * model_output
								a      :		Optional[Any]	=				(sample_hat - pred_original_sample) / sigma_hat
								a      :		Union[str, Any]	=				sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative, state)

								return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase__      ,					derivative=lowerCAmelCase__      ,					state=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = True      ,					)	-> Union[FlaxKarrasVeOutput, Tuple]:
								a      :		Dict	=				sample_prev + sigma_prev * model_output
								a      :		Any	=				(sample_prev - pred_original_sample) / sigma_prev
								a      :		Tuple	=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative, state)

								return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase__      ,					derivative=lowerCAmelCase__      ,					state=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								raise NotImplementedError()









"""simple docstring"""




import heapq as hq
import math
from collections.abc import Iterator


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		List[str]	=				str(id_      )
								a      :		Optional[Any]	=				None
								a      :		Tuple	=				None
								a      :		str	=				[]
								a      :		Any	=				{}  # {vertex:distance}






							def __lt__(   self      ,					lowerCAmelCase__      )	-> Any:
								return self.key < other.key






							def __repr__(   self      )	-> Optional[Any]:
								return self.id






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								self.neighbors.append(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								a      :		Optional[Any]	=				weight






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	graph[a - 1].add_neighbor(graph[b - 1]					)
	graph[b - 1].add_neighbor(graph[a - 1]					)
	# add the edges:
	graph[a - 1].add_edge(graph[b - 1]    ,       _lowercase					)
	graph[b - 1].add_edge(graph[a - 1]    ,       _lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->list:






	'''simple docstring'''






	a      :		int	=				[]
	for u in graph:
		a      :		List[str]	=				math.inf
		a      :		int	=				None
	a      :		str	=				0
	a      :		Union[str, Any]	=				graph[:]
	while q:
		a      :		List[Any]	=				min(_lowercase					)
		q.remove(_lowercase					)
		for v in u.neighbors:
			if (v in q) and (u.edges[v.id] < v.key):
				a      :		List[Any]	=				u
				a      :		Optional[int]	=				u.edges[v.id]
	for i in range(1    ,       len(_lowercase					)					):
		a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
	return a






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->Iterator[tuple]:






	'''simple docstring'''






	for u in graph:
		a      :		str	=				math.inf
		a      :		Dict	=				None
	a      :		Dict	=				0

	a      :		List[Any]	=				list(_lowercase					)
	hq.heapify(_lowercase					)

	while h:
		a      :		Dict	=				hq.heappop(_lowercase					)
		for v in u.neighbors:
			if (v in h) and (u.edges[v.id] < v.key):
				a      :		Dict	=				u
				a      :		Optional[Any]	=				u.edges[v.id]
				hq.heapify(_lowercase					)

	for i in range(1    ,       len(_lowercase					)					):
		yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)






def       _SCREAMING_SNAKE_CASE		(	) ->None:






	'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()


