"""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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			# setable values
			_UpperCamelCase     :	Optional[jnp.ndarray]				    =							None
			_UpperCamelCase     :	Optional[jnp.ndarray]				    =							None
			_UpperCamelCase     :	Optional[jnp.ndarray]				    =							None





			@classmethod
			def 		lowerCamelCase				( cls       ):







										'''simple docstring'''


										return cls()





@dataclass
class 	__UpperCAmelCase						(__UpperCAmelCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	ScoreSdeVeSchedulerState
			_UpperCamelCase     :	jnp.ndarray
			_UpperCamelCase     :	Optional[jnp.ndarray]				    =							None





class 	__UpperCAmelCase						(__UpperCAmelCase       ,      __UpperCAmelCase    ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return True





			@register_to_config
			def __init__( self      ,			snake_case_ = 2_000      ,			snake_case_ = 0.15      ,			snake_case_ = 0.01      ,			snake_case_ = 1_348.0      ,			snake_case_ = 1E-5      ,			snake_case_ = 1      ,			):







										'''simple docstring'''


										pass





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= ScoreSdeVeSchedulerState.create()
										return self.set_sigmas(
										    lowerCamelCase__      ,			self.config.num_train_timesteps      ,			self.config.sigma_min      ,			self.config.sigma_max      ,			self.config.sampling_eps      ,			)





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_ = ()      ,			snake_case_ = None       ):







										'''simple docstring'''


										A__					:    Dict														= sampling_eps if sampling_eps is not None else self.config.sampling_eps

										A__					:    Tuple														= jnp.linspace(1      ,			lowerCamelCase__      ,			lowerCamelCase__       )
										return state.replace(timesteps=lowerCamelCase__       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			):







										'''simple docstring'''


										A__					:    int														= sigma_min if sigma_min is not None else self.config.sigma_min
										A__					:    List[Any]														= sigma_max if sigma_max is not None else self.config.sigma_max
										A__					:    Tuple														= sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if state.timesteps is None:
																	A__					:    Optional[Any]														= self.set_timesteps(lowerCamelCase__      ,			lowerCamelCase__      ,			lowerCamelCase__       )

										A__					:    Tuple														= jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )      ,			jnp.log(lowerCamelCase__       )      ,			lowerCamelCase__       )       )
										A__					:    Any														= jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )

										return state.replace(discrete_sigmas=lowerCamelCase__      ,			sigmas=lowerCamelCase__       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										return jnp.where(timesteps == 0      ,			jnp.zeros_like(lowerCamelCase__       )      ,			state.discrete_sigmas[timesteps - 1]       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_ = True      ,			):







										'''simple docstring'''


										if state.timesteps is None:
																	raise ValueError(
																	    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )

										A__					:    Union[str, Any]														= timestep * jnp.ones(
										    sample.shape[0]      ,			)
										A__					:    Union[str, Any]														= (timestep * (len(state.timesteps       ) - 1)).long()

										A__					:    Optional[Any]														= state.discrete_sigmas[timesteps]
										A__					:    Dict														= self.get_adjacent_sigma(lowerCamelCase__      ,			lowerCamelCase__      ,			lowerCamelCase__       )
										A__					:    Dict														= jnp.zeros_like(lowerCamelCase__       )
										A__					:    Optional[int]														= (sigma**2 - adjacent_sigma**2) ** 0.5

										# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
										# also equation 47 shows the analog from SDE models to ancestral sampling methods
										A__					:    str														= diffusion.flatten()
										A__					:    Tuple														= broadcast_to_shape_from_left(lowerCamelCase__      ,			sample.shape       )
										A__					:    Union[str, Any]														= drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										A__					:    Union[str, Any]														= random.split(lowerCamelCase__      ,			num=1       )
										A__					:    Tuple														= random.normal(key=lowerCamelCase__      ,			shape=sample.shape       )
										A__					:    Optional[Any]														= sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										A__					:    Optional[Any]														= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

										if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

										return FlaxSdeVeOutput(prev_sample=lowerCamelCase__      ,			prev_sample_mean=lowerCamelCase__      ,			state=lowerCamelCase__       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_ = True      ,			):







										'''simple docstring'''


										if state.timesteps is None:
																	raise ValueError(
																	    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )

										# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
										# sample noise for correction
										A__					:    List[Any]														= random.split(lowerCamelCase__      ,			num=1       )
										A__					:    List[Any]														= random.normal(key=lowerCamelCase__      ,			shape=sample.shape       )

										# compute step size from the model_output, the noise, and the snr
										A__					:    Optional[int]														= jnp.linalg.norm(lowerCamelCase__       )
										A__					:    Union[str, Any]														= jnp.linalg.norm(lowerCamelCase__       )
										A__					:    List[Any]														= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
										A__					:    Tuple														= step_size * jnp.ones(sample.shape[0]       )

										# compute corrected sample: model_output term and noise term
										A__					:    int														= step_size.flatten()
										A__					:    Tuple														= broadcast_to_shape_from_left(lowerCamelCase__      ,			sample.shape       )
										A__					:    List[Any]														= sample + step_size * model_output
										A__					:    Dict														= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
																	return (prev_sample, state)

										return FlaxSdeVeOutput(prev_sample=lowerCamelCase__      ,			state=lowerCamelCase__       )





			def __len__( self       ):







										'''simple docstring'''


										return self.config.num_train_timesteps






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase         =		TypeVar("T", bound=bool)
class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

										if random.choice([True, False]       ):
																	A__							,       A__					:    Any														= roota.right, roota.left

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

										if data:
																	for item in data:
																								self.insert(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self._root:
																	raise IndexError("""Can't get top element for the empty heap."""       )
										return self._root.value





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





