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+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/ekf/dense_fisher/index.html b/api/ekf/dense_fisher/index.html new file mode 100644 index 00000000..312d9fe3 --- /dev/null +++ b/api/ekf/dense_fisher/index.html @@ -0,0 +1,2211 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + EKF Dense Fisher - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + + +

EKF Dense Fisher𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.ekf.dense_fisher.build(log_likelihood, lr, transition_cov=0.0, per_sample=False, init_cov=1.0) + +𝞡

+ + +
+ +

Builds a transform to implement an extended Kalman Filter update.

+

EKF applies an online update to a Gaussian posterior over the parameters.

+

The approximate Bayesian update is based on the linearization +$$ +\log p(θ | y) ≈ \log p(θ) + ε g(μ)ᵀ(θ - μ) + \frac12 ε (θ - μ)^T F(μ) (θ - μ) +$$ +where \(μ\) is the mean of the prior distribution, \(ε\) is the learning rate +(or equivalently the likelihood inverse temperature), +\(g(μ)\) is the gradient of the log likelihood at μ and \(F(μ)\) is the +empirical Fisher information matrix at \(μ\) for data \(y\).

+

For more information on extended Kalman filtering as well as an equivalence +to (online) natural gradient descent see Ollivier, 2019.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_likelihood + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log-likelihood value as well as auxiliary information, +e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Inverse temperature of the update, which behaves like a learning rate.

+
+ 		+ required +
transition_cov + Tensor | float + +
+

Covariance of the transition noise, to additively +inflate the covariance before the update.

+
+ 		+ 0.0 +
per_sample + bool + +
+

If True, then log_likelihood is assumed to return a vector of +log likelihoods for each sample in the batch. If False, then log_likelihood +is assumed to return a scalar log likelihood for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_likelihood to be per sample.

+
+ 		+ False +
init_cov + Tensor | float + +
+

Initial covariance of the Normal distribution. Can be torch.Tensor or scalar.

+
+ 		+ 1.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

EKF transform instance.

+
+
+ +
+ Source code in posteriors/ekf/dense_fisher.py + 
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def build(
+    log_likelihood: LogProbFn,
+    lr: float,
+    transition_cov: torch.Tensor | float = 0.0,
+    per_sample: bool = False,
+    init_cov: torch.Tensor | float = 1.0,
+) -> Transform:
+    """Builds a transform to implement an extended Kalman Filter update.
+
+    EKF applies an online update to a Gaussian posterior over the parameters.
+
+    The approximate Bayesian update is based on the linearization
+    $$
+    \\log p(θ | y) ≈ \\log p(θ) +  ε g(μ)ᵀ(θ - μ) +  \\frac12 ε (θ - μ)^T F(μ) (θ - μ)
+    $$
+    where $μ$ is the mean of the prior distribution, $ε$ is the learning rate
+    (or equivalently the likelihood inverse temperature),
+    $g(μ)$ is the gradient of the log likelihood at μ and $F(μ)$ is the
+    empirical Fisher information matrix at $μ$ for data $y$.
+
+    For more information on extended Kalman filtering as well as an equivalence
+    to (online) natural gradient descent see [Ollivier, 2019](https://arxiv.org/abs/1703.00209).
+
+    Args:
+        log_likelihood: Function that takes parameters and input batch and
+            returns the log-likelihood value as well as auxiliary information,
+            e.g. from the model call.
+        lr: Inverse temperature of the update, which behaves like a learning rate.
+        transition_cov: Covariance of the transition noise, to additively
+            inflate the covariance before the update.
+        per_sample: If True, then log_likelihood is assumed to return a vector of
+            log likelihoods for each sample in the batch. If False, then log_likelihood
+            is assumed to return a scalar log likelihood for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_likelihood to be per sample.
+        init_cov: Initial covariance of the Normal distribution. Can be torch.Tensor or scalar.
+
+    Returns:
+        EKF transform instance.
+    """
+    init_fn = partial(init, init_cov=init_cov)
+    update_fn = partial(
+        update,
+        log_likelihood=log_likelihood,
+        lr=lr,
+        transition_cov=transition_cov,
+        per_sample=per_sample,
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.ekf.dense_fisher.EKFDenseState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a Normal distribution over parameters.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
cov + Tensor + +
+

Covariance matrix of the +Normal distribution.

+
+
log_likelihood + Tensor + +
+

Log likelihood of the data given the parameters.

+
+
aux + Any + +
+

Auxiliary information from the log_likelihood call.

+
+
+ +
+ Source code in posteriors/ekf/dense_fisher.py + 
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class EKFDenseState(NamedTuple):
+    """State encoding a Normal distribution over parameters.
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        cov: Covariance matrix of the
+            Normal distribution.
+        log_likelihood: Log likelihood of the data given the parameters.
+        aux: Auxiliary information from the log_likelihood call.
+    """
+
+    params: TensorTree
+    cov: torch.Tensor
+    log_likelihood: torch.Tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.dense_fisher.init(params, init_cov=1.0) + +𝞡

+ + +
+ +

Initialise Multivariate Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Initial mean of the Normal distribution.

+
+ 		+ required +
init_cov + Tensor | float + +
+

Initial covariance matrix of the Multivariate Normal distribution. +If it is a float, it is defined as an identity matrix scaled by that float.

+
+ 		+ 1.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ EKFDenseState + +
+

Initial EKFDenseState.

+
+
+ +
+ Source code in posteriors/ekf/dense_fisher.py + 
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def init(
+    params: TensorTree,
+    init_cov: torch.Tensor | float = 1.0,
+) -> EKFDenseState:
+    """Initialise Multivariate Normal distribution over parameters.
+
+    Args:
+        params: Initial mean of the Normal distribution.
+        init_cov: Initial covariance matrix of the Multivariate Normal distribution.
+            If it is a float, it is defined as an identity matrix scaled by that float.
+
+    Returns:
+        Initial EKFDenseState.
+    """
+    if is_scalar(init_cov):
+        num_params = tree_size(params)
+        init_cov = init_cov * torch.eye(num_params, requires_grad=False)
+
+    return EKFDenseState(params, init_cov)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.dense_fisher.update(state, batch, log_likelihood, lr, transition_cov=0.0, per_sample=False, inplace=False) + +𝞡

+ + +
+ +

Applies an extended Kalman Filter update to the Multivariate Normal distribution. +The approximate Bayesian update is based on the linearization +$$ +\log p(θ | y) ≈ \log p(θ) + ε g(μ)ᵀ(θ - μ) + \frac12 ε (θ - μ)^T F(μ) (θ - μ) +$$ +where \(μ\) is the mean of the prior distribution, \(ε\) is the learning rate +(or equivalently the likelihood inverse temperature), +\(g(μ)\) is the gradient of the log likelihood at μ and \(F(μ)\) is the +empirical Fisher information matrix at \(μ\) for data \(y\).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + EKFDenseState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to log_likelihood.

+
+ 		+ required +
log_likelihood + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log-likelihood value as well as auxiliary information, +e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Inverse temperature of the update, which behaves like a learning rate.

+
+ 		+ required +
transition_cov + Tensor | float + +
+

Covariance of the transition noise, to additively +inflate the covariance before the update.

+
+ 		+ 0.0 +
per_sample + bool + +
+

If True, then log_likelihood is assumed to return a vector of +log likelihoods for each sample in the batch. If False, then log_likelihood +is assumed to return a scalar log likelihood for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_likelihood to be per sample.

+
+ 		+ False +
inplace + bool + +
+

Whether to update the state parameters in-place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ EKFDenseState + +
+

Updated EKFDenseState.

+
+
+ +
+ Source code in posteriors/ekf/dense_fisher.py + 
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def update(
+    state: EKFDenseState,
+    batch: Any,
+    log_likelihood: LogProbFn,
+    lr: float,
+    transition_cov: torch.Tensor | float = 0.0,
+    per_sample: bool = False,
+    inplace: bool = False,
+) -> EKFDenseState:
+    """Applies an extended Kalman Filter update to the Multivariate Normal distribution.
+    The approximate Bayesian update is based on the linearization
+    $$
+    \\log p(θ | y) ≈ \\log p(θ) +  ε g(μ)ᵀ(θ - μ) +  \\frac12 ε (θ - μ)^T F(μ) (θ - μ)
+    $$
+    where $μ$ is the mean of the prior distribution, $ε$ is the learning rate
+    (or equivalently the likelihood inverse temperature),
+    $g(μ)$ is the gradient of the log likelihood at μ and $F(μ)$ is the
+    empirical Fisher information matrix at $μ$ for data $y$.
+
+    Args:
+        state: Current state.
+        batch: Input data to log_likelihood.
+        log_likelihood: Function that takes parameters and input batch and
+            returns the log-likelihood value as well as auxiliary information,
+            e.g. from the model call.
+        lr: Inverse temperature of the update, which behaves like a learning rate.
+        transition_cov: Covariance of the transition noise, to additively
+            inflate the covariance before the update.
+        per_sample: If True, then log_likelihood is assumed to return a vector of
+            log likelihoods for each sample in the batch. If False, then log_likelihood
+            is assumed to return a scalar log likelihood for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_likelihood to be per sample.
+        inplace: Whether to update the state parameters in-place.
+
+    Returns:
+        Updated EKFDenseState.
+    """
+    if not per_sample:
+        log_likelihood = per_samplify(log_likelihood)
+
+    with torch.no_grad(), CatchAuxError():
+
+        def log_likelihood_reduced(params, batch):
+            per_samp_log_lik, internal_aux = log_likelihood(params, batch)
+            return per_samp_log_lik.mean(), internal_aux
+
+        grad, (log_liks, aux) = grad_and_value(log_likelihood_reduced, has_aux=True)(
+            state.params, batch
+        )
+        fisher, _ = empirical_fisher(
+            lambda p: log_likelihood(p, batch), has_aux=True, normalize=True
+        )(state.params)
+
+        predict_cov = state.cov + transition_cov
+        predict_cov_inv = torch.cholesky_inverse(torch.linalg.cholesky(predict_cov))
+        update_cov_inv = predict_cov_inv - lr * fisher
+        update_cov = torch.cholesky_inverse(torch.linalg.cholesky(update_cov_inv))
+
+        mu_raveled, mu_unravel_f = tree_ravel(state.params)
+        update_mean = mu_raveled + lr * update_cov @ tree_ravel(grad)[0]
+        update_mean = mu_unravel_f(update_mean)
+
+    if inplace:
+        tree_insert_(state.params, update_mean)
+        tree_insert_(state.cov, update_cov)
+        tree_insert_(state.log_likelihood, log_liks.mean().detach())
+        return state._replace(aux=aux)
+
+    return EKFDenseState(update_mean, update_cov, log_liks.mean().detach(), aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.dense_fisher.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Single sample from Multivariate Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + EKFDenseState + +
+

State encoding mean and covariance.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from Multivariate Normal distribution.

+
+
+ +
+ Source code in posteriors/ekf/dense_fisher.py + 
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def sample(
+    state: EKFDenseState, sample_shape: torch.Size = torch.Size([])
+) -> TensorTree:
+    """Single sample from Multivariate Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and covariance.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from Multivariate Normal distribution.
+    """
+    mean_flat, unravel_func = tree_ravel(state.params)
+
+    samples = torch.distributions.MultivariateNormal(
+        loc=mean_flat,
+        covariance_matrix=state.cov,
+        validate_args=False,
+    ).sample(sample_shape)
+
+    samples = torch.vmap(unravel_func)(samples)
+    return samples
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/ekf/diag_fisher/index.html b/api/ekf/diag_fisher/index.html new file mode 100644 index 00000000..fa7cbffe --- /dev/null +++ b/api/ekf/diag_fisher/index.html @@ -0,0 +1,2206 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + EKF Diagonal Fisher - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

EKF Diagonal Fisher𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.ekf.diag_fisher.build(log_likelihood, lr, transition_sd=0.0, per_sample=False, init_sds=1.0) + +𝞡

+ + +
+ +

Builds a transform to implement an extended Kalman Filter update.

+

EKF applies an online update to a (diagonal) Gaussian posterior over the parameters.

+

The approximate Bayesian update is based on the linearization +$$ +\log p(θ | y) ≈ \log p(θ) + ε g(μ)ᵀ(θ - μ) + \frac12 ε (θ - μ)^T F_d(μ) (θ - μ) +$$ +where \(μ\) is the mean of the prior distribution, \(ε\) is the learning rate +(or equivalently the likelihood inverse temperature), +\(g(μ)\) is the gradient of the log likelihood at μ and \(F_d(μ)\) is the diagonal +empirical Fisher information matrix at \(μ\) for data \(y\). Completing the square +regains a diagonal Normal distribution over the parameters.

+

For more information on extended Kalman filtering as well as an equivalence +to (online) natural gradient descent see Ollivier, 2019.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_likelihood + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log-likelihood value as well as auxiliary information, +e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Inverse temperature of the update, which behaves like a learning rate.

+
+ 		+ required +
transition_sd + float + +
+

Standard deviation of the transition noise, to additively +inflate the diagonal covariance before the update.

+
+ 		+ 0.0 +
per_sample + bool + +
+

If True, then log_likelihood is assumed to return a vector of +log likelihoods for each sample in the batch. If False, then log_likelihood +is assumed to return a scalar log likelihood for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_likelihood to be per sample.

+
+ 		+ False +
init_sds + TensorTree | float + +
+

Initial square-root diagonal of the covariance matrix +of the Normal distribution. Can be tree like params or scalar.

+
+ 		+ 1.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Diagonal EKF transform instance.

+
+
+ +
+ Source code in posteriors/ekf/diag_fisher.py + 
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def build(
+    log_likelihood: LogProbFn,
+    lr: float,
+    transition_sd: float = 0.0,
+    per_sample: bool = False,
+    init_sds: TensorTree | float = 1.0,
+) -> Transform:
+    """Builds a transform to implement an extended Kalman Filter update.
+
+    EKF applies an online update to a (diagonal) Gaussian posterior over the parameters.
+
+    The approximate Bayesian update is based on the linearization
+    $$
+    \\log p(θ | y) ≈ \\log p(θ) +  ε g(μ)ᵀ(θ - μ) +  \\frac12 ε (θ - μ)^T F_d(μ) (θ - μ)
+    $$
+    where $μ$ is the mean of the prior distribution, $ε$ is the learning rate
+    (or equivalently the likelihood inverse temperature),
+    $g(μ)$ is the gradient of the log likelihood at μ and $F_d(μ)$ is the diagonal
+    empirical Fisher information matrix at $μ$ for data $y$. Completing the square
+    regains a diagonal Normal distribution over the parameters.
+
+    For more information on extended Kalman filtering as well as an equivalence
+    to (online) natural gradient descent see [Ollivier, 2019](https://arxiv.org/abs/1703.00209).
+
+    Args:
+        log_likelihood: Function that takes parameters and input batch and
+            returns the log-likelihood value as well as auxiliary information,
+            e.g. from the model call.
+        lr: Inverse temperature of the update, which behaves like a learning rate.
+        transition_sd: Standard deviation of the transition noise, to additively
+            inflate the diagonal covariance before the update.
+        per_sample: If True, then log_likelihood is assumed to return a vector of
+            log likelihoods for each sample in the batch. If False, then log_likelihood
+            is assumed to return a scalar log likelihood for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_likelihood to be per sample.
+        init_sds: Initial square-root diagonal of the covariance matrix
+            of the Normal distribution. Can be tree like params or scalar.
+
+    Returns:
+        Diagonal EKF transform instance.
+    """
+    init_fn = partial(init, init_sds=init_sds)
+    update_fn = partial(
+        update,
+        log_likelihood=log_likelihood,
+        lr=lr,
+        transition_sd=transition_sd,
+        per_sample=per_sample,
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.ekf.diag_fisher.EKFDiagState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a diagonal Normal distribution over parameters.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
sd_diag + TensorTree + +
+

Square-root diagonal of the covariance matrix of the +Normal distribution.

+
+
log_likelihood + Tensor + +
+

Log likelihood of the data given the parameters.

+
+
aux + Any + +
+

Auxiliary information from the log_likelihood call.

+
+
+ +
+ Source code in posteriors/ekf/diag_fisher.py + 
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class EKFDiagState(NamedTuple):
+    """State encoding a diagonal Normal distribution over parameters.
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        sd_diag: Square-root diagonal of the covariance matrix of the
+            Normal distribution.
+        log_likelihood: Log likelihood of the data given the parameters.
+        aux: Auxiliary information from the log_likelihood call.
+    """
+
+    params: TensorTree
+    sd_diag: TensorTree
+    log_likelihood: torch.Tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.diag_fisher.init(params, init_sds=1.0) + +𝞡

+ + +
+ +

Initialise diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Initial mean of the Normal distribution.

+
+ 		+ required +
init_sds + TensorTree | float + +
+

Initial square-root diagonal of the covariance matrix +of the Normal distribution. Can be tree like params or scalar.

+
+ 		+ 1.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ EKFDiagState + +
+

Initial EKFDiagState.

+
+
+ +
+ Source code in posteriors/ekf/diag_fisher.py + 
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def init(
+    params: TensorTree,
+    init_sds: TensorTree | float = 1.0,
+) -> EKFDiagState:
+    """Initialise diagonal Normal distribution over parameters.
+
+    Args:
+        params: Initial mean of the Normal distribution.
+        init_sds: Initial square-root diagonal of the covariance matrix
+            of the Normal distribution. Can be tree like params or scalar.
+
+    Returns:
+        Initial EKFDiagState.
+    """
+    if is_scalar(init_sds):
+        init_sds = tree_map(
+            lambda x: torch.full_like(x, init_sds, requires_grad=x.requires_grad),
+            params,
+        )
+
+    return EKFDiagState(params, init_sds)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.diag_fisher.update(state, batch, log_likelihood, lr, transition_sd=0.0, per_sample=False, inplace=False) + +𝞡

+ + +
+ +

Applies an extended Kalman Filter update to the diagonal Normal distribution. +The approximate Bayesian update is based on the linearization +$$ +\log p(θ | y) ≈ \log p(θ) + ε g(μ)ᵀ(θ - μ) + \frac12 ε (θ - μ)^T F_d(μ) (θ - μ) +$$ +where \(μ\) is the mean of the prior distribution, \(ε\) is the learning rate +(or equivalently the likelihood inverse temperature), +\(g(μ)\) is the gradient of the log likelihood at μ and \(F_d(μ)\) is the diagonal +empirical Fisher information matrix at \(μ\) for data \(y\). Completing the square +regains a diagonal Normal distribution over the parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + EKFDiagState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to log_likelihood.

+
+ 		+ required +
log_likelihood + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log-likelihood value as well as auxiliary information, +e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Inverse temperature of the update, which behaves like a learning rate.

+
+ 		+ required +
transition_sd + float + +
+

Standard deviation of the transition noise, to additively +inflate the diagonal covariance before the update.

+
+ 		+ 0.0 +
per_sample + bool + +
+

If True, then log_likelihood is assumed to return a vector of +log likelihoods for each sample in the batch. If False, then log_likelihood +is assumed to return a scalar log likelihood for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_likelihood to be per sample.

+
+ 		+ False +
inplace + bool + +
+

Whether to update the state parameters in-place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ EKFDiagState + +
+

Updated EKFDiagState.

+
+
+ +
+ Source code in posteriors/ekf/diag_fisher.py + 
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def update(
+    state: EKFDiagState,
+    batch: Any,
+    log_likelihood: LogProbFn,
+    lr: float,
+    transition_sd: float = 0.0,
+    per_sample: bool = False,
+    inplace: bool = False,
+) -> EKFDiagState:
+    """Applies an extended Kalman Filter update to the diagonal Normal distribution.
+    The approximate Bayesian update is based on the linearization
+    $$
+    \\log p(θ | y) ≈ \\log p(θ) +  ε g(μ)ᵀ(θ - μ) +  \\frac12 ε (θ - μ)^T F_d(μ) (θ - μ)
+    $$
+    where $μ$ is the mean of the prior distribution, $ε$ is the learning rate
+    (or equivalently the likelihood inverse temperature),
+    $g(μ)$ is the gradient of the log likelihood at μ and $F_d(μ)$ is the diagonal
+    empirical Fisher information matrix at $μ$ for data $y$. Completing the square
+    regains a diagonal Normal distribution over the parameters.
+
+    Args:
+        state: Current state.
+        batch: Input data to log_likelihood.
+        log_likelihood: Function that takes parameters and input batch and
+            returns the log-likelihood value as well as auxiliary information,
+            e.g. from the model call.
+        lr: Inverse temperature of the update, which behaves like a learning rate.
+        transition_sd: Standard deviation of the transition noise, to additively
+            inflate the diagonal covariance before the update.
+        per_sample: If True, then log_likelihood is assumed to return a vector of
+            log likelihoods for each sample in the batch. If False, then log_likelihood
+            is assumed to return a scalar log likelihood for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_likelihood to be per sample.
+        inplace: Whether to update the state parameters in-place.
+
+    Returns:
+        Updated EKFDiagState.
+    """
+
+    if not per_sample:
+        log_likelihood = per_samplify(log_likelihood)
+
+    predict_sd_diag = flexi_tree_map(
+        lambda x: (x**2 + transition_sd**2) ** 0.5, state.sd_diag, inplace=inplace
+    )
+    with torch.no_grad(), CatchAuxError():
+        log_liks, aux = log_likelihood(state.params, batch)
+        jac, _ = jacrev(log_likelihood, has_aux=True)(state.params, batch)
+        grad = tree_map(lambda x: x.mean(0), jac)
+        diag_lik_hessian_approx = tree_map(lambda x: -(x**2).mean(0), jac)
+
+    update_sd_diag = flexi_tree_map(
+        lambda sig, h: (sig**-2 - lr * h) ** -0.5,
+        predict_sd_diag,
+        diag_lik_hessian_approx,
+        inplace=inplace,
+    )
+    update_mean = flexi_tree_map(
+        lambda mu, sig, g: mu + sig**2 * lr * g,
+        state.params,
+        update_sd_diag,
+        grad,
+        inplace=inplace,
+    )
+
+    if inplace:
+        tree_insert_(state.log_likelihood, log_liks.mean().detach())
+        return state._replace(aux=aux)
+
+    return EKFDiagState(update_mean, update_sd_diag, log_liks.mean().detach(), aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.ekf.diag_fisher.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Single sample from diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + EKFDiagState + +
+

State encoding mean and standard deviations.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from Normal distribution.

+
+
+ +
+ Source code in posteriors/ekf/diag_fisher.py + 
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def sample(
+    state: EKFDiagState, sample_shape: torch.Size = torch.Size([])
+) -> TensorTree:
+    """Single sample from diagonal Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and standard deviations.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from Normal distribution.
+    """
+    return diag_normal_sample(state.params, state.sd_diag, sample_shape=sample_shape)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/index.html b/api/index.html new file mode 100644 index 00000000..1324c93a --- /dev/null +++ b/api/index.html @@ -0,0 +1,1176 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + API - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
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+ + + +
+
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+ + + +
+
+
+ + + +
+
+ + + + + + + +

API𝞡

+

Extended Kalman filter (EKF)𝞡

+
    +
  • ekf.dense_fisher applies an online Bayesian update based +on a Taylor approximation of the log-likelihood. Uses the empirical Fisher +information matrix as a positive-definite alternative to the Hessian. +Natural gradient descent equivalence following Ollivier, 2019.
    • +
  • ekf.diag_fisher same as ekf.dense_fisher but +uses the diagonal of the empirical Fisher information matrix instead.
    • +
+

Laplace approximation𝞡

+
    +
  • laplace.dense_fisher calculates the empirical Fisher +information matrix and uses it to approximate the posterior precision, i.e. a Laplace +approximation.
    • +
  • laplace.dense_ggn calculates the Generalised +Gauss-Newton matrix which is equivalent to the non-empirical Fisher in most +neural network settings - see Martens, 2020.
    • +
  • laplace.diag_fisher same as laplace.dense_fisher but +uses the diagonal of the empirical Fisher information matrix instead.
    • +
  • laplace.diag_ggn same as laplace.dense_ggn but +uses the diagonal of the Generalised Gauss-Newton matrix instead.
    • +
+

All Laplace transforms leave the parameters unmodified. Comprehensive details on Laplace approximations can be found in Daxberger et al, 2021.

+

Stochastic gradient Markov chain Monte Carlo (SGMCMC)𝞡

+ +

For an overview and unifying framework for SGMCMC methods, see Ma et al, 2015.

+

Variational inference (VI)𝞡

+
    +
  • vi.diag implements a diagonal Gaussian variational distribution. +Expects a torchopt optimizer for handling the +minimization of the NELBO. Also find vi.diag.nelbo for simply calculating the NELBO +with respect to a log_posterior and diagonal Gaussian distribution.
    • +
+

A review of variational inference can be found in Blei et al, 2017.

+

Optim𝞡

+
    +
  • optim wrapper for torch.optim optimizers within the unified posteriors +API that allows for easy swapping with UQ methods.
    • +
+

TorchOpt𝞡

+
    +
  • torchopt wrapper for torchopt +optimizers within the unified posteriors API that allows for easy swapping with UQ +methods.
    • +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/laplace/dense_fisher/index.html b/api/laplace/dense_fisher/index.html new file mode 100644 index 00000000..4d9f13f9 --- /dev/null +++ b/api/laplace/dense_fisher/index.html @@ -0,0 +1,2056 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Laplace Dense Fisher - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Laplace Dense Fisher𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.laplace.dense_fisher.build(log_posterior, per_sample=False, init_prec=0.0) + +𝞡

+ + +
+ +

Builds a transform for dense empirical Fisher information +Laplace approximation.

+

The empirical Fisher is defined here as: +$$ +F(θ) = \sum_i ∇_θ \log p(y_i, θ | x_i) ∇_θ \log p(y_i, θ | x_i)^T +$$ +where \(p(y_i, θ | x_i)\) is the joint model distribution (equivalent to the posterior +up to proportionality) with parameters \(θ\), inputs \(x_i\) and labels \(y_i\).

+

More info on empirical Fisher matrices can be found in +Martens, 2020 and +their use within a Laplace approximation in Daxberger et al, 2021.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
per_sample + bool + +
+

If True, then log_posterior is assumed to return a vector of +log posteriors for each sample in the batch. If False, then log_posterior +is assumed to return a scalar log posterior for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_posterior to be per sample.

+
+ 		+ False +
init_prec + Tensor | float + +
+

Initial precision matrix. +If it is a float, it is defined as an identity matrix +scaled by that float.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Empirical Fisher information Laplace approximation transform instance.

+
+
+ +
+ Source code in posteriors/laplace/dense_fisher.py + 
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def build(
+    log_posterior: LogProbFn,
+    per_sample: bool = False,
+    init_prec: torch.Tensor | float = 0.0,
+) -> Transform:
+    """Builds a transform for dense empirical Fisher information
+    Laplace approximation.
+
+    The empirical Fisher is defined here as:
+    $$
+    F(θ) = \\sum_i ∇_θ \\log p(y_i, θ | x_i) ∇_θ \\log p(y_i, θ | x_i)^T
+    $$
+    where $p(y_i, θ | x_i)$ is the joint model distribution (equivalent to the posterior
+    up to proportionality) with parameters $θ$, inputs $x_i$ and labels $y_i$.
+
+    More info on empirical Fisher matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf) and
+    their use within a Laplace approximation in [Daxberger et al, 2021](https://arxiv.org/abs/2106.14806).
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        per_sample: If True, then log_posterior is assumed to return a vector of
+            log posteriors for each sample in the batch. If False, then log_posterior
+            is assumed to return a scalar log posterior for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_posterior to be per sample.
+        init_prec: Initial precision matrix.
+            If it is a float, it is defined as an identity matrix
+            scaled by that float.
+
+    Returns:
+        Empirical Fisher information Laplace approximation transform instance.
+    """
+    init_fn = partial(init, init_prec=init_prec)
+    update_fn = partial(update, log_posterior=log_posterior, per_sample=per_sample)
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.laplace.dense_fisher.DenseLaplaceState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a Normal distribution over parameters, +with a dense precision matrix

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
prec + Tensor + +
+

Precision matrix of the Normal distribution.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/laplace/dense_fisher.py + 
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class DenseLaplaceState(NamedTuple):
+    """State encoding a Normal distribution over parameters,
+    with a dense precision matrix
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        prec: Precision matrix of the Normal distribution.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    prec: torch.Tensor
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_fisher.init(params, init_prec=0.0) + +𝞡

+ + +
+ +

Initialise Normal distribution over parameters +with a dense precision matrix.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Mean of the Normal distribution.

+
+ 		+ required +
init_prec + Tensor | float + +
+

Initial precision matrix. +If it is a float, it is defined as an identity matrix +scaled by that float.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DenseLaplaceState + +
+

Initial DenseLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/dense_fisher.py + 
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def init(
+    params: TensorTree,
+    init_prec: torch.Tensor | float = 0.0,
+) -> DenseLaplaceState:
+    """Initialise Normal distribution over parameters
+    with a dense precision matrix.
+
+    Args:
+        params: Mean of the Normal distribution.
+        init_prec: Initial precision matrix.
+            If it is a float, it is defined as an identity matrix
+            scaled by that float.
+
+    Returns:
+        Initial DenseLaplaceState.
+    """
+
+    if is_scalar(init_prec):
+        num_params = tree_size(params)
+        init_prec = init_prec * torch.eye(num_params, requires_grad=False)
+
+    return DenseLaplaceState(params, init_prec)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_fisher.update(state, batch, log_posterior, per_sample=False, inplace=False) + +𝞡

+ + +
+ +

Adds empirical Fisher information matrix of covariance summed over +given batch.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DenseLaplaceState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised)

+
+ 		+ required +
per_sample + bool + +
+

If True, then log_posterior is assumed to return a vector of +log posteriors for each sample in the batch. If False, then log_posterior +is assumed to return a scalar log posterior for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_posterior to be per sample.

+
+ 		+ False +
inplace + bool + +
+

If True, the state is updated in place. Otherwise, a new +state is returned.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DenseLaplaceState + +
+

Updated DenseLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/dense_fisher.py + 
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def update(
+    state: DenseLaplaceState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    per_sample: bool = False,
+    inplace: bool = False,
+) -> DenseLaplaceState:
+    """Adds empirical Fisher information matrix of covariance summed over
+    given batch.
+
+    Args:
+        state: Current state.
+        batch: Input data to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+        per_sample: If True, then log_posterior is assumed to return a vector of
+            log posteriors for each sample in the batch. If False, then log_posterior
+            is assumed to return a scalar log posterior for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_posterior to be per sample.
+        inplace: If True, the state is updated in place. Otherwise, a new
+            state is returned.
+
+    Returns:
+        Updated DenseLaplaceState.
+    """
+    if not per_sample:
+        log_posterior = per_samplify(log_posterior)
+
+    with torch.no_grad(), CatchAuxError():
+        fisher, aux = empirical_fisher(
+            lambda p: log_posterior(p, batch), has_aux=True, normalize=False
+        )(state.params)
+
+    if inplace:
+        state.prec.data += fisher
+        return state._replace(aux=aux)
+    else:
+        return DenseLaplaceState(state.params, state.prec + fisher, aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_fisher.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Sample from Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DenseLaplaceState + +
+

State encoding mean and precision matrix.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from the Normal distribution.

+
+
+ +
+ Source code in posteriors/laplace/dense_fisher.py + 
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def sample(
+    state: DenseLaplaceState,
+    sample_shape: torch.Size = torch.Size([]),
+) -> TensorTree:
+    """Sample from Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and precision matrix.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from the Normal distribution.
+    """
+    samples = torch.distributions.MultivariateNormal(
+        loc=torch.zeros(state.prec.shape[0], device=state.prec.device),
+        precision_matrix=state.prec,
+        validate_args=False,
+    ).sample(sample_shape)
+    samples = samples.flatten(end_dim=-2)  # ensure samples is 2D
+    mean_flat, unravel_func = tree_ravel(state.params)
+    samples += mean_flat
+    samples = torch.vmap(unravel_func)(samples)
+    samples = tree_map(lambda x: x.reshape(sample_shape + x.shape[-1:]), samples)
+    return samples
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/laplace/dense_ggn/index.html b/api/laplace/dense_ggn/index.html new file mode 100644 index 00000000..b6a053d5 --- /dev/null +++ b/api/laplace/dense_ggn/index.html @@ -0,0 +1,2073 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Laplace Dense GGN - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Laplace Dense GGN𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.laplace.dense_ggn.build(forward, outer_log_likelihood, init_prec=0.0) + +𝞡

+ + +
+ +

Builds a transform for a Generalized Gauss-Newton (GGN) +Laplace approximation.

+

Equivalent to the (non-empirical) Fisher information matrix when +the outer_log_likelihood is exponential family with natural parameter equal to +the output from forward.

+

forward should output auxiliary information (or torch.tensor([])), +outer_log_likelihood should not.

+

The GGN is defined as +$$ +G(θ) = J_f(θ) H_l(z) J_f(θ)^T +$$ +where \(z = f(θ)\) is the output of the forward function \(f\) and \(l(z)\) +is a loss (negative log-likelihood) that maps the output of \(f\) to a scalar output.

+

More info on Fisher and GGN matrices can be found in +Martens, 2020 and +their use within a Laplace approximation in Daxberger et al, 2021.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward + ForwardFn + +
+

Function that takes parameters and input batch and +returns a forward value (e.g. logits), not reduced over the batch, +as well as auxiliary information.

+
+ 		+ required +
outer_log_likelihood + OuterLogProbFn + +
+

A function that takes the output of forward and batch +then returns the log likelihood of the model output, +with no auxiliary information.

+
+ 		+ required +
init_prec + TensorTree | float + +
+

Initial precision matrix. +If it is a float, it is defined as an identity matrix +scaled by that float.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

GGN Laplace approximation transform instance.

+
+
+ +
+ Source code in posteriors/laplace/dense_ggn.py + 
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def build(
+    forward: ForwardFn,
+    outer_log_likelihood: OuterLogProbFn,
+    init_prec: TensorTree | float = 0.0,
+) -> Transform:
+    """Builds a transform for a Generalized Gauss-Newton (GGN)
+    Laplace approximation.
+
+    Equivalent to the (non-empirical) Fisher information matrix when
+    the `outer_log_likelihood` is exponential family with natural parameter equal to
+    the output from `forward`.
+
+    `forward` should output auxiliary information (or `torch.tensor([])`),
+    `outer_log_likelihood` should not.
+
+    The GGN is defined as
+    $$
+    G(θ) = J_f(θ) H_l(z) J_f(θ)^T
+    $$
+    where $z = f(θ)$ is the output of the forward function $f$ and $l(z)$
+    is a loss (negative log-likelihood) that maps the output of $f$ to a scalar output.
+
+    More info on Fisher and GGN matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf) and
+    their use within a Laplace approximation in [Daxberger et al, 2021](https://arxiv.org/abs/2106.14806).
+
+    Args:
+        forward: Function that takes parameters and input batch and
+            returns a forward value (e.g. logits), not reduced over the batch,
+            as well as auxiliary information.
+        outer_log_likelihood: A function that takes the output of `forward` and batch
+            then returns the log likelihood of the model output,
+            with no auxiliary information.
+        init_prec: Initial precision matrix.
+            If it is a float, it is defined as an identity matrix
+            scaled by that float.
+
+    Returns:
+        GGN Laplace approximation transform instance.
+    """
+    init_fn = partial(init, init_prec=init_prec)
+    update_fn = partial(
+        update, forward=forward, outer_log_likelihood=outer_log_likelihood
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.laplace.dense_ggn.DenseLaplaceState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a Normal distribution over parameters, +with a dense precision matrix

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
prec + Tensor + +
+

Precision matrix of the Normal distribution.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/laplace/dense_ggn.py + 
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class DenseLaplaceState(NamedTuple):
+    """State encoding a Normal distribution over parameters,
+    with a dense precision matrix
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        prec: Precision matrix of the Normal distribution.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    prec: torch.Tensor
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_ggn.init(params, init_prec=0.0) + +𝞡

+ + +
+ +

Initialise Normal distribution over parameters +with a dense precision matrix.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Mean of the Normal distribution.

+
+ 		+ required +
init_prec + Tensor | float + +
+

Initial precision matrix. +If it is a float, it is defined as an identity matrix +scaled by that float.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DenseLaplaceState + +
+

Initial DenseLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/dense_ggn.py + 
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def init(
+    params: TensorTree,
+    init_prec: torch.Tensor | float = 0.0,
+) -> DenseLaplaceState:
+    """Initialise Normal distribution over parameters
+    with a dense precision matrix.
+
+    Args:
+        params: Mean of the Normal distribution.
+        init_prec: Initial precision matrix.
+            If it is a float, it is defined as an identity matrix
+            scaled by that float.
+
+    Returns:
+        Initial DenseLaplaceState.
+    """
+
+    if is_scalar(init_prec):
+        num_params = tree_size(params)
+        init_prec = init_prec * torch.eye(num_params, requires_grad=False)
+
+    return DenseLaplaceState(params, init_prec)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_ggn.update(state, batch, forward, outer_log_likelihood, inplace=False) + +𝞡

+ + +
+ +

Adds GGN matrix over given batch.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DenseLaplaceState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to model.

+
+ 		+ required +
forward + ForwardFn + +
+

Function that takes parameters and input batch and +returns a forward value (e.g. logits), not reduced over the batch, +as well as auxiliary information.

+
+ 		+ required +
outer_log_likelihood + OuterLogProbFn + +
+

A function that takes the output of forward and batch +then returns the log likelihood of the model output, +with no auxiliary information.

+
+ 		+ required +
inplace + bool + +
+

If True, then the state is updated in place, otherwise a new state +is returned.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DenseLaplaceState + +
+

Updated DenseLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/dense_ggn.py + 
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def update(
+    state: DenseLaplaceState,
+    batch: Any,
+    forward: ForwardFn,
+    outer_log_likelihood: OuterLogProbFn,
+    inplace: bool = False,
+) -> DenseLaplaceState:
+    """Adds GGN matrix over given batch.
+
+    Args:
+        state: Current state.
+        batch: Input data to model.
+        forward: Function that takes parameters and input batch and
+            returns a forward value (e.g. logits), not reduced over the batch,
+            as well as auxiliary information.
+        outer_log_likelihood: A function that takes the output of `forward` and batch
+            then returns the log likelihood of the model output,
+            with no auxiliary information.
+        inplace: If True, then the state is updated in place, otherwise a new state
+            is returned.
+
+    Returns:
+        Updated DenseLaplaceState.
+    """
+
+    def outer_loss(z, batch):
+        return -outer_log_likelihood(z, batch)
+
+    with torch.no_grad(), CatchAuxError():
+        ggn_batch, aux = ggn(
+            lambda params: forward(params, batch),
+            lambda z: outer_loss(z, batch),
+            forward_has_aux=True,
+            loss_has_aux=False,
+            normalize=False,
+        )(state.params)
+
+    if inplace:
+        state.prec.data += ggn_batch
+        return state._replace(aux=aux)
+    else:
+        return DenseLaplaceState(state.params, state.prec + ggn_batch, aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.dense_ggn.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Sample from Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DenseLaplaceState + +
+

State encoding mean and precision matrix.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from the Normal distribution.

+
+
+ +
+ Source code in posteriors/laplace/dense_ggn.py + 
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def sample(
+    state: DenseLaplaceState,
+    sample_shape: torch.Size = torch.Size([]),
+) -> TensorTree:
+    """Sample from Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and precision matrix.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from the Normal distribution.
+    """
+    samples = torch.distributions.MultivariateNormal(
+        loc=torch.zeros(state.prec.shape[0], device=state.prec.device),
+        precision_matrix=state.prec,
+        validate_args=False,
+    ).sample(sample_shape)
+    samples = samples.flatten(end_dim=-2)  # ensure samples is 2D
+    mean_flat, unravel_func = tree_ravel(state.params)
+    samples += mean_flat
+    samples = torch.vmap(unravel_func)(samples)
+    samples = tree_map(lambda x: x.reshape(sample_shape + x.shape[-1:]), samples)
+    return samples
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/laplace/diag_fisher/index.html b/api/laplace/diag_fisher/index.html new file mode 100644 index 00000000..09add459 --- /dev/null +++ b/api/laplace/diag_fisher/index.html @@ -0,0 +1,2037 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Laplace Diagonal Fisher - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Laplace Diagonal Fisher𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.laplace.diag_fisher.build(log_posterior, per_sample=False, init_prec_diag=0.0) + +𝞡

+ + +
+ +

Builds a transform for diagonal empirical Fisher information +Laplace approximation.

+

The empirical Fisher is defined here as: +$$ +F(θ) = \sum_i ∇_θ \log p(y_i, θ | x_i) ∇_θ \log p(y_i, θ | x_i)^T +$$ +where \(p(y_i, θ | x_i)\) is the joint model distribution (equivalent to the posterior +up to proportionality) with parameters \(θ\), inputs \(x_i\) and labels \(y_i\).

+

More info on empirical Fisher matrices can be found in +Martens, 2020 and +their use within a Laplace approximation in Daxberger et al, 2021.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
per_sample + bool + +
+

If True, then log_posterior is assumed to return a vector of +log posteriors for each sample in the batch. If False, then log_posterior +is assumed to return a scalar log posterior for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_posterior to be per sample.

+
+ 		+ False +
init_prec_diag + TensorTree | float + +
+

Initial diagonal precision matrix. +Can be tree like params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Diagonal empirical Fisher information Laplace approximation transform instance.

+
+
+ +
+ Source code in posteriors/laplace/diag_fisher.py + 
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def build(
+    log_posterior: LogProbFn,
+    per_sample: bool = False,
+    init_prec_diag: TensorTree | float = 0.0,
+) -> Transform:
+    """Builds a transform for diagonal empirical Fisher information
+    Laplace approximation.
+
+    The empirical Fisher is defined here as:
+    $$
+    F(θ) = \\sum_i ∇_θ \\log p(y_i, θ | x_i) ∇_θ \\log p(y_i, θ | x_i)^T
+    $$
+    where $p(y_i, θ | x_i)$ is the joint model distribution (equivalent to the posterior
+    up to proportionality) with parameters $θ$, inputs $x_i$ and labels $y_i$.
+
+    More info on empirical Fisher matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf) and
+    their use within a Laplace approximation in [Daxberger et al, 2021](https://arxiv.org/abs/2106.14806).
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        per_sample: If True, then log_posterior is assumed to return a vector of
+            log posteriors for each sample in the batch. If False, then log_posterior
+            is assumed to return a scalar log posterior for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_posterior to be per sample.
+        init_prec_diag: Initial diagonal precision matrix.
+            Can be tree like params or scalar.
+
+    Returns:
+        Diagonal empirical Fisher information Laplace approximation transform instance.
+    """
+    init_fn = partial(init, init_prec_diag=init_prec_diag)
+    update_fn = partial(update, log_posterior=log_posterior, per_sample=per_sample)
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.laplace.diag_fisher.DiagLaplaceState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a diagonal Normal distribution over parameters.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
prec_diag + TensorTree + +
+

Diagonal of the precision matrix of the Normal distribution.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/laplace/diag_fisher.py + 
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class DiagLaplaceState(NamedTuple):
+    """State encoding a diagonal Normal distribution over parameters.
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        prec_diag: Diagonal of the precision matrix of the Normal distribution.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    prec_diag: TensorTree
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_fisher.init(params, init_prec_diag=0.0) + +𝞡

+ + +
+ +

Initialise diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Mean of the Normal distribution.

+
+ 		+ required +
init_prec_diag + TensorTree | float + +
+

Initial diagonal precision matrix. +Can be tree like params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DiagLaplaceState + +
+

Initial DiagLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/diag_fisher.py + 
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def init(
+    params: TensorTree,
+    init_prec_diag: TensorTree | float = 0.0,
+) -> DiagLaplaceState:
+    """Initialise diagonal Normal distribution over parameters.
+
+    Args:
+        params: Mean of the Normal distribution.
+        init_prec_diag: Initial diagonal precision matrix.
+            Can be tree like params or scalar.
+
+    Returns:
+        Initial DiagLaplaceState.
+    """
+    if is_scalar(init_prec_diag):
+        init_prec_diag = tree_map(
+            lambda x: torch.full_like(x, init_prec_diag, requires_grad=x.requires_grad),
+            params,
+        )
+
+    return DiagLaplaceState(params, init_prec_diag)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_fisher.update(state, batch, log_posterior, per_sample=False, inplace=False) + +𝞡

+ + +
+ +

Adds diagonal empirical Fisher information matrix of covariance summed over +given batch.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DiagLaplaceState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
per_sample + bool + +
+

If True, then log_posterior is assumed to return a vector of +log posteriors for each sample in the batch. If False, then log_posterior +is assumed to return a scalar log posterior for the whole batch, in this +case torch.func.vmap will be called, this is typically slower than +directly writing log_posterior to be per sample.

+
+ 		+ False +
inplace + bool + +
+

If True, then the state is updated in place, otherwise a new state +is returned.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DiagLaplaceState + +
+

Updated DiagLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/diag_fisher.py + 
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def update(
+    state: DiagLaplaceState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    per_sample: bool = False,
+    inplace: bool = False,
+) -> DiagLaplaceState:
+    """Adds diagonal empirical Fisher information matrix of covariance summed over
+    given batch.
+
+    Args:
+        state: Current state.
+        batch: Input data to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        per_sample: If True, then log_posterior is assumed to return a vector of
+            log posteriors for each sample in the batch. If False, then log_posterior
+            is assumed to return a scalar log posterior for the whole batch, in this
+            case torch.func.vmap will be called, this is typically slower than
+            directly writing log_posterior to be per sample.
+        inplace: If True, then the state is updated in place, otherwise a new state
+            is returned.
+
+    Returns:
+        Updated DiagLaplaceState.
+    """
+    if not per_sample:
+        log_posterior = per_samplify(log_posterior)
+
+    with torch.no_grad(), CatchAuxError():
+        jac, aux = jacrev(log_posterior, has_aux=True)(state.params, batch)
+        batch_diag_score_sq = tree_map(lambda j: j.square().sum(0), jac)
+
+    def update_func(x, y):
+        return x + y
+
+    prec_diag = flexi_tree_map(
+        update_func, state.prec_diag, batch_diag_score_sq, inplace=inplace
+    )
+
+    if inplace:
+        return state._replace(aux=aux)
+    return DiagLaplaceState(state.params, prec_diag, aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_fisher.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Sample from diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DiagLaplaceState + +
+

State encoding mean and diagonal precision.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from Normal distribution.

+
+
+ +
+ Source code in posteriors/laplace/diag_fisher.py + 
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def sample(
+    state: DiagLaplaceState, sample_shape: torch.Size = torch.Size([])
+) -> TensorTree:
+    """Sample from diagonal Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and diagonal precision.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from Normal distribution.
+    """
+    sd_diag = tree_map(lambda x: x.sqrt().reciprocal(), state.prec_diag)
+    return diag_normal_sample(state.params, sd_diag, sample_shape=sample_shape)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/laplace/diag_ggn/index.html b/api/laplace/diag_ggn/index.html new file mode 100644 index 00000000..e5fcae97 --- /dev/null +++ b/api/laplace/diag_ggn/index.html @@ -0,0 +1,2057 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Laplace Diagonal GGN - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Laplace Diagonal GGN𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.laplace.diag_ggn.build(forward, outer_log_likelihood, init_prec_diag=0.0) + +𝞡

+ + +
+ +

Builds a transform for a diagonal Generalized Gauss-Newton (GGN) +Laplace approximation.

+

Equivalent to the diagonal of the (non-empirical) Fisher information matrix when +the outer_log_likelihood is exponential family with natural parameter equal to +the output from forward.

+

forward should output auxiliary information (or torch.tensor([])), +outer_log_likelihood should not.

+

The GGN is defined as +$$ +G(θ) = J_f(θ) H_l(z) J_f(θ)^T +$$ +where \(z = f(θ)\) is the output of the forward function \(f\) and \(l(z)\) +is a loss (negative log-likelihood) that maps the output of \(f\) to a scalar output.

+

More info on Fisher and GGN matrices can be found in +Martens, 2020 and +their use within a Laplace approximation in Daxberger et al, 2021.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward + ForwardFn + +
+

Function that takes parameters and input batch and +returns a forward value (e.g. logits), not reduced over the batch, +as well as auxiliary information.

+
+ 		+ required +
outer_log_likelihood + OuterLogProbFn + +
+

A function that takes the output of forward and batch +then returns the log likelihood of the model output, +with no auxiliary information.

+
+ 		+ required +
init_prec_diag + TensorTree | float + +
+

Initial diagonal precision matrix. +Can be tree like params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Diagonal GGN Laplace approximation transform instance.

+
+
+ +
+ Source code in posteriors/laplace/diag_ggn.py + 
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def build(
+    forward: ForwardFn,
+    outer_log_likelihood: OuterLogProbFn,
+    init_prec_diag: TensorTree | float = 0.0,
+) -> Transform:
+    """Builds a transform for a diagonal Generalized Gauss-Newton (GGN)
+    Laplace approximation.
+
+    Equivalent to the diagonal of the (non-empirical) Fisher information matrix when
+    the `outer_log_likelihood` is exponential family with natural parameter equal to
+    the output from `forward`.
+
+    `forward` should output auxiliary information (or `torch.tensor([])`),
+    `outer_log_likelihood` should not.
+
+    The GGN is defined as
+    $$
+    G(θ) = J_f(θ) H_l(z) J_f(θ)^T
+    $$
+    where $z = f(θ)$ is the output of the forward function $f$ and $l(z)$
+    is a loss (negative log-likelihood) that maps the output of $f$ to a scalar output.
+
+    More info on Fisher and GGN matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf) and
+    their use within a Laplace approximation in [Daxberger et al, 2021](https://arxiv.org/abs/2106.14806).
+
+    Args:
+        forward: Function that takes parameters and input batch and
+            returns a forward value (e.g. logits), not reduced over the batch,
+            as well as auxiliary information.
+        outer_log_likelihood: A function that takes the output of `forward` and batch
+            then returns the log likelihood of the model output,
+            with no auxiliary information.
+        init_prec_diag: Initial diagonal precision matrix.
+            Can be tree like params or scalar.
+
+    Returns:
+        Diagonal GGN Laplace approximation transform instance.
+    """
+    init_fn = partial(init, init_prec_diag=init_prec_diag)
+    update_fn = partial(
+        update, forward=forward, outer_log_likelihood=outer_log_likelihood
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.laplace.diag_ggn.DiagLaplaceState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a diagonal Normal distribution over parameters.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the Normal distribution.

+
+
prec_diag + TensorTree + +
+

Diagonal of the precision matrix of the Normal distribution.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/laplace/diag_ggn.py + 
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class DiagLaplaceState(NamedTuple):
+    """State encoding a diagonal Normal distribution over parameters.
+
+    Attributes:
+        params: Mean of the Normal distribution.
+        prec_diag: Diagonal of the precision matrix of the Normal distribution.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    prec_diag: TensorTree
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_ggn.init(params, init_prec_diag=0.0) + +𝞡

+ + +
+ +

Initialise diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Mean of the Normal distribution.

+
+ 		+ required +
init_prec_diag + TensorTree | float + +
+

Initial diagonal precision matrix. +Can be tree like params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DiagLaplaceState + +
+

Initial DiagLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/diag_ggn.py + 
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def init(
+    params: TensorTree,
+    init_prec_diag: TensorTree | float = 0.0,
+) -> DiagLaplaceState:
+    """Initialise diagonal Normal distribution over parameters.
+
+    Args:
+        params: Mean of the Normal distribution.
+        init_prec_diag: Initial diagonal precision matrix.
+            Can be tree like params or scalar.
+
+    Returns:
+        Initial DiagLaplaceState.
+    """
+    if is_scalar(init_prec_diag):
+        init_prec_diag = tree_map(
+            lambda x: torch.full_like(x, init_prec_diag, requires_grad=x.requires_grad),
+            params,
+        )
+
+    return DiagLaplaceState(params, init_prec_diag)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_ggn.update(state, batch, forward, outer_log_likelihood, inplace=False) + +𝞡

+ + +
+ +

Adds diagonal GGN matrix of covariance summed over given batch.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DiagLaplaceState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to model.

+
+ 		+ required +
forward + ForwardFn + +
+

Function that takes parameters and input batch and +returns a forward value (e.g. logits), not reduced over the batch, +as well as auxiliary information.

+
+ 		+ required +
outer_log_likelihood + OuterLogProbFn + +
+

A function that takes the output of forward and batch +then returns the log likelihood of the model output, +with no auxiliary information.

+
+ 		+ required +
inplace + bool + +
+

If True, then the state is updated in place, otherwise a new state +is returned.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DiagLaplaceState + +
+

Updated DiagLaplaceState.

+
+
+ +
+ Source code in posteriors/laplace/diag_ggn.py + 
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def update(
+    state: DiagLaplaceState,
+    batch: Any,
+    forward: ForwardFn,
+    outer_log_likelihood: OuterLogProbFn,
+    inplace: bool = False,
+) -> DiagLaplaceState:
+    """Adds diagonal GGN matrix of covariance summed over given batch.
+
+    Args:
+        state: Current state.
+        batch: Input data to model.
+        forward: Function that takes parameters and input batch and
+            returns a forward value (e.g. logits), not reduced over the batch,
+            as well as auxiliary information.
+        outer_log_likelihood: A function that takes the output of `forward` and batch
+            then returns the log likelihood of the model output,
+            with no auxiliary information.
+        inplace: If True, then the state is updated in place, otherwise a new state
+            is returned.
+
+    Returns:
+        Updated DiagLaplaceState.
+    """
+
+    def outer_loss(z, batch):
+        return -outer_log_likelihood(z, batch)
+
+    with torch.no_grad(), CatchAuxError():
+        diag_ggn_batch, aux = diag_ggn(
+            lambda params: forward(params, batch),
+            lambda z: outer_loss(z, batch),
+            forward_has_aux=True,
+            loss_has_aux=False,
+            normalize=False,
+        )(state.params)
+
+    def update_func(x, y):
+        return x + y
+
+    prec_diag = flexi_tree_map(
+        update_func, state.prec_diag, diag_ggn_batch, inplace=inplace
+    )
+
+    if inplace:
+        return state._replace(aux=aux)
+    return DiagLaplaceState(state.params, prec_diag, aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.laplace.diag_ggn.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Sample from diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + DiagLaplaceState + +
+

State encoding mean and diagonal precision.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from Normal distribution.

+
+
+ +
+ Source code in posteriors/laplace/diag_ggn.py + 
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def sample(
+    state: DiagLaplaceState, sample_shape: torch.Size = torch.Size([])
+) -> TensorTree:
+    """Sample from diagonal Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and diagonal precision.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from Normal distribution.
+    """
+    sd_diag = tree_map(lambda x: x.sqrt().reciprocal(), state.prec_diag)
+    return diag_normal_sample(state.params, sd_diag, sample_shape=sample_shape)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/optim/index.html b/api/optim/index.html new file mode 100644 index 00000000..104567ea --- /dev/null +++ b/api/optim/index.html @@ -0,0 +1,1844 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Optim - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Optim

+ +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.optim.build(loss_fn, optimizer, **kwargs) + +𝞡

+ + +
+ +

Builds an optimizer transform from torch.optim

+
transform = build(loss_fn, torch.optim.Adam, lr=0.1)
+state = transform.init(params)
+
+for batch in dataloader:
+    state = transform.update(state, batch)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
loss_fn + LogProbFn + +
+

Function that takes the parameters and returns the loss. +of the form loss, aux = fn(params, batch).

+
+ 		+ required +
optimizer + Type[Optimizer] + +
+

Optimizer class from torch.optim.

+
+ 		+ required +
**kwargs + Any + +
+

Keyword arguments to pass to the optimizer class.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

torch.optim transform instance.

+
+
+ +
+ Source code in posteriors/optim.py + 
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def build(
+    loss_fn: LogProbFn,
+    optimizer: Type[torch.optim.Optimizer],
+    **kwargs: Any,
+) -> Transform:
+    """Builds an optimizer transform from [torch.optim](https://pytorch.org/docs/stable/optim.html)
+
+    ```
+    transform = build(loss_fn, torch.optim.Adam, lr=0.1)
+    state = transform.init(params)
+
+    for batch in dataloader:
+        state = transform.update(state, batch)
+    ```
+
+    Args:
+        loss_fn: Function that takes the parameters and returns the loss.
+            of the form `loss, aux = fn(params, batch)`.
+        optimizer: Optimizer class from torch.optim.
+        **kwargs: Keyword arguments to pass to the optimizer class.
+
+    Returns:
+        `torch.optim` transform instance.
+    """
+    init_fn = partial(init, optimizer_cls=optimizer, **kwargs)
+    update_fn = partial(update, loss_fn=loss_fn)
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.optim.OptimState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State of an optimizer from torch.optim.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Parameters to be optimized.

+
+
optimizer + Optimizer + +
+

torch.optim optimizer instance.

+
+
loss + tensor + +
+

Loss value.

+
+
aux + Any + +
+

Auxiliary information from the loss function call.

+
+
+ +
+ Source code in posteriors/optim.py + 
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class OptimState(NamedTuple):
+    """State of an optimizer from [torch.optim](https://pytorch.org/docs/stable/optim.html).
+
+    Attributes:
+        params: Parameters to be optimized.
+        optimizer: torch.optim optimizer instance.
+        loss: Loss value.
+        aux: Auxiliary information from the loss function call.
+    """
+
+    params: TensorTree
+    optimizer: torch.optim.Optimizer
+    loss: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.optim.init(params, optimizer_cls, *args, **kwargs) + +𝞡

+ + +
+ +

Initialise a torch.optim optimizer +state.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Parameters to be optimized.

+
+ 		+ required +
optimizer_cls + Type[Optimizer] + +
+

Optimizer class from torch.optim.

+
+ 		+ required +
*args + Any + +
+

Positional arguments to pass to the optimizer class.

+
+ 		+ () +
**kwargs + Any + +
+

Keyword arguments to pass to the optimizer class.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ OptimState + +
+

Initial OptimState.

+
+
+ +
+ Source code in posteriors/optim.py + 
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def init(
+    params: TensorTree,
+    optimizer_cls: Type[torch.optim.Optimizer],
+    *args: Any,
+    **kwargs: Any,
+) -> OptimState:
+    """Initialise a [torch.optim](https://pytorch.org/docs/stable/optim.html) optimizer
+    state.
+
+    Args:
+        params: Parameters to be optimized.
+        optimizer_cls: Optimizer class from torch.optim.
+        *args: Positional arguments to pass to the optimizer class.
+        **kwargs: Keyword arguments to pass to the optimizer class.
+
+    Returns:
+        Initial OptimState.
+    """
+    opt_params = [params] if isinstance(params, torch.Tensor) else params
+
+    optimizer = optimizer_cls(opt_params, *args, **kwargs)
+    return OptimState(params, optimizer)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.optim.update(state, batch, loss_fn, inplace=True) + +𝞡

+ + +
+ +

Perform a single update step of a torch.optim +optimizer.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + OptimState + +
+

Current optimizer state.

+
+ 		+ required +
batch + TensorTree + +
+

Input data to loss_fn.

+
+ 		+ required +
loss_fn + LogProbFn + +
+

Function that takes the parameters and returns the loss. +of the form loss, aux = fn(params, batch).

+
+ 		+ required +
inplace + bool + +
+

Whether to update the parameters in place. +inplace=False not supported for posteriors.optim

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ OptimState + +
+

Updated OptimState.

+
+
+ +
+ Source code in posteriors/optim.py + 
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def update(
+    state: OptimState,
+    batch: TensorTree,
+    loss_fn: LogProbFn,
+    inplace: bool = True,
+) -> OptimState:
+    """Perform a single update step of a [torch.optim](https://pytorch.org/docs/stable/optim.html)
+    optimizer.
+
+    Args:
+        state: Current optimizer state.
+        batch: Input data to loss_fn.
+        loss_fn: Function that takes the parameters and returns the loss.
+            of the form `loss, aux = fn(params, batch)`.
+        inplace: Whether to update the parameters in place.
+            inplace=False not supported for posteriors.optim
+
+    Returns:
+        Updated OptimState.
+    """
+    if not inplace:
+        raise NotImplementedError("inplace=False not supported for posteriors.optim")
+    state.optimizer.zero_grad()
+    with CatchAuxError():
+        loss, aux = loss_fn(state.params, batch)
+    loss.backward()
+    state.optimizer.step()
+    tree_insert_(state.loss, loss.detach())
+    return state._replace(aux=aux)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/sgmcmc/sghmc/index.html b/api/sgmcmc/sghmc/index.html new file mode 100644 index 00000000..e1cddd9d --- /dev/null +++ b/api/sgmcmc/sghmc/index.html @@ -0,0 +1,2112 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + SGHMC - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

SGHMC𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.sgmcmc.sghmc.build(log_posterior, lr, alpha=0.01, beta=0.0, sigma=1.0, temperature=1.0, momenta=None) + +𝞡

+ + +
+ +

Builds SGHMC transform.

+

Algorithm from Chen et al, 2014:

+
\[\begin{align} +θ_{t+1} &= θ_t + ε σ^{-2} m_t \\ +m_{t+1} &= m_t + ε \nabla \log p(θ_t, \text{batch}) - ε σ^{-2} α m_t ++ N(0, ε T (2 α - ε β T) \mathbb{I})\ +\end{align}\]
+

for learning rate \(\epsilon\) and temperature \(T\)

+

Targets \(p_T(θ, m) \propto \exp( (\log p(θ) - \frac{1}{2σ^2} m^Tm) / T)\) +with temperature \(T\).

+

The log posterior and temperature are recommended to be constructed in tandem +to ensure robust scaling for a large amount of data and variable batch size.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
alpha + float + +
+

Friction coefficient.

+
+ 		+ 0.01 +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
sigma + float + +
+

Standard deviation of momenta target distribution.

+
+ 		+ 1.0 +
temperature + float + +
+

Temperature of the joint parameter + momenta distribution.

+
+ 		+ 1.0 +
momenta + TensorTree | float | None + +
+

Initial momenta. Can be tree like params or scalar. +Defaults to random iid samples from N(0, 1).

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

SGHMC transform instance.

+
+
+ +
+ Source code in posteriors/sgmcmc/sghmc.py + 
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def build(
+    log_posterior: LogProbFn,
+    lr: float,
+    alpha: float = 0.01,
+    beta: float = 0.0,
+    sigma: float = 1.0,
+    temperature: float = 1.0,
+    momenta: TensorTree | float | None = None,
+) -> Transform:
+    """Builds SGHMC transform.
+
+    Algorithm from [Chen et al, 2014](https://arxiv.org/abs/1402.4102):
+
+    \\begin{align}
+    θ_{t+1} &= θ_t + ε σ^{-2} m_t \\\\
+    m_{t+1} &= m_t + ε \\nabla \\log p(θ_t, \\text{batch}) - ε σ^{-2} α m_t
+    + N(0, ε T (2 α - ε β T) \\mathbb{I})\\
+    \\end{align}
+
+    for learning rate $\\epsilon$ and temperature $T$
+
+    Targets $p_T(θ, m) \\propto \\exp( (\\log p(θ) - \\frac{1}{2σ^2} m^Tm) / T)$
+    with temperature $T$.
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data and variable batch size.
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        alpha: Friction coefficient.
+        beta: Gradient noise coefficient (estimated variance).
+        sigma: Standard deviation of momenta target distribution.
+        temperature: Temperature of the joint parameter + momenta distribution.
+        momenta: Initial momenta. Can be tree like params or scalar.
+            Defaults to random iid samples from N(0, 1).
+
+    Returns:
+        SGHMC transform instance.
+    """
+    init_fn = partial(init, momenta=momenta)
+    update_fn = partial(
+        update,
+        log_posterior=log_posterior,
+        lr=lr,
+        alpha=alpha,
+        beta=beta,
+        sigma=sigma,
+        temperature=temperature,
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.sgmcmc.sghmc.SGHMCState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding params and momenta for SGHMC.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Parameters.

+
+
momenta + TensorTree + +
+

Momenta for each parameter.

+
+
log_posterior + tensor + +
+

Log posterior evaluation.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/sgmcmc/sghmc.py + 
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class SGHMCState(NamedTuple):
+    """State encoding params and momenta for SGHMC.
+
+    Attributes:
+        params: Parameters.
+        momenta: Momenta for each parameter.
+        log_posterior: Log posterior evaluation.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    momenta: TensorTree
+    log_posterior: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sghmc.init(params, momenta=None) + +𝞡

+ + +
+ +

Initialise momenta for SGHMC.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Parameters for which to initialise.

+
+ 		+ required +
momenta + TensorTree | float | None + +
+

Initial momenta. Can be tree like params or scalar. +Defaults to random iid samples from N(0, 1).

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SGHMCState + +
+

Initial SGHMCState containing momenta.

+
+
+ +
+ Source code in posteriors/sgmcmc/sghmc.py + 
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def init(params: TensorTree, momenta: TensorTree | float | None = None) -> SGHMCState:
+    """Initialise momenta for SGHMC.
+
+    Args:
+        params: Parameters for which to initialise.
+        momenta: Initial momenta. Can be tree like params or scalar.
+            Defaults to random iid samples from N(0, 1).
+
+    Returns:
+        Initial SGHMCState containing momenta.
+    """
+    if momenta is None:
+        momenta = tree_map(
+            lambda x: torch.randn_like(x, requires_grad=x.requires_grad),
+            params,
+        )
+    elif is_scalar(momenta):
+        momenta = tree_map(
+            lambda x: torch.full_like(x, momenta, requires_grad=x.requires_grad),
+            params,
+        )
+
+    return SGHMCState(params, momenta)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sghmc.update(state, batch, log_posterior, lr, alpha=0.01, beta=0.0, sigma=1.0, temperature=1.0, inplace=False) + +𝞡

+ + +
+ +

Updates parameters and momenta for SGHMC.

+

Update rule from Chen et al, 2014:

+
\[\begin{align} +θ_{t+1} &= θ_t + ε σ^{-2} m_t \\ +m_{t+1} &= m_t + ε \nabla \log p(θ_t, \text{batch}) - ε σ^{-2} α m_t ++ N(0, ε T (2 α - ε β T) \mathbb{I})\ +\end{align}\]
+

for learning rate \(\epsilon\) and temperature \(T\)

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + SGHMCState + +
+

SGHMCState containing params and momenta.

+
+ 		+ required +
batch + Any + +
+

Data batch to be send to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
alpha + float + +
+

Friction coefficient.

+
+ 		+ 0.01 +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
sigma + float + +
+

Standard deviation of momenta target distribution.

+
+ 		+ 1.0 +
temperature + float + +
+

Temperature of the joint parameter + momenta distribution.

+
+ 		+ 1.0 +
inplace + bool + +
+

Whether to modify state in place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ SGHMCState + +
+

Updated state

+
+
+ SGHMCState + +
+

(which are pointers to the inputted state tensors if inplace=True).

+
+
+ +
+ Source code in posteriors/sgmcmc/sghmc.py + 
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def update(
+    state: SGHMCState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    lr: float,
+    alpha: float = 0.01,
+    beta: float = 0.0,
+    sigma: float = 1.0,
+    temperature: float = 1.0,
+    inplace: bool = False,
+) -> SGHMCState:
+    """Updates parameters and momenta for SGHMC.
+
+    Update rule from [Chen et al, 2014](https://arxiv.org/abs/1402.4102):
+
+    \\begin{align}
+    θ_{t+1} &= θ_t + ε σ^{-2} m_t \\\\
+    m_{t+1} &= m_t + ε \\nabla \\log p(θ_t, \\text{batch}) - ε σ^{-2} α m_t
+    + N(0, ε T (2 α - ε β T) \\mathbb{I})\\
+    \\end{align}
+
+    for learning rate $\\epsilon$ and temperature $T$
+
+    Args:
+        state: SGHMCState containing params and momenta.
+        batch: Data batch to be send to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        alpha: Friction coefficient.
+        beta: Gradient noise coefficient (estimated variance).
+        sigma: Standard deviation of momenta target distribution.
+        temperature: Temperature of the joint parameter + momenta distribution.
+        inplace: Whether to modify state in place.
+
+    Returns:
+        Updated state
+        (which are pointers to the inputted state tensors if inplace=True).
+    """
+    with torch.no_grad(), CatchAuxError():
+        grads, (log_post, aux) = grad_and_value(log_posterior, has_aux=True)(
+            state.params, batch
+        )
+
+    prec = sigma**-2
+
+    def transform_params(p, m):
+        return p + lr * prec * m
+
+    def transform_momenta(m, g):
+        return (
+            m
+            + lr * g
+            - lr * prec * alpha * m
+            + (temperature * lr * (2 * alpha - temperature * lr * beta)) ** 0.5
+            * torch.randn_like(m)
+        )
+
+    params = flexi_tree_map(
+        transform_params, state.params, state.momenta, inplace=inplace
+    )
+    momenta = flexi_tree_map(transform_momenta, state.momenta, grads, inplace=inplace)
+
+    if inplace:
+        tree_insert_(state.log_posterior, log_post.detach())
+        return state._replace(aux=aux)
+    return SGHMCState(params, momenta, log_post.detach(), aux)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/sgmcmc/sgld/index.html b/api/sgmcmc/sgld/index.html new file mode 100644 index 00000000..2427f5e9 --- /dev/null +++ b/api/sgmcmc/sgld/index.html @@ -0,0 +1,1902 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + SGLD - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

SGLD𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.sgmcmc.sgld.build(log_posterior, lr, beta=0.0, temperature=1.0) + +𝞡

+ + +
+ +

Builds SGLD transform.

+

Algorithm from Welling and Teh, 2011: +$$ +θ_{t+1} = θ_t + ε \nabla \log p(θ_t, \text{batch}) + N(0, ε (2 - ε β) T \mathbb{I}) +$$ +for learning rate \(\epsilon\) and temperature \(T\).

+

Targets \(p_T(θ) \propto \exp( \log p(θ) / T)\) with temperature \(T\).

+

The log posterior and temperature are recommended to be constructed in tandem +to ensure robust scaling for a large amount of data and variable batch size.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
temperature + float + +
+

Temperature of the sampling distribution.

+
+ 		+ 1.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

SGLD transform (posteriors.types.Transform instance).

+
+
+ +
+ Source code in posteriors/sgmcmc/sgld.py + 
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def build(
+    log_posterior: LogProbFn,
+    lr: float,
+    beta: float = 0.0,
+    temperature: float = 1.0,
+) -> Transform:
+    """Builds SGLD transform.
+
+    Algorithm from [Welling and Teh, 2011](https://www.stats.ox.ac.uk/~teh/research/compstats/WelTeh2011a.pdf):
+    $$
+    θ_{t+1} = θ_t + ε \\nabla \\log p(θ_t, \\text{batch}) + N(0, ε  (2 - ε β) T \\mathbb{I})
+    $$
+    for learning rate $\\epsilon$ and temperature $T$.
+
+    Targets $p_T(θ) \\propto \\exp( \\log p(θ) / T)$ with temperature $T$.
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data and variable batch size.
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        beta: Gradient noise coefficient (estimated variance).
+        temperature: Temperature of the sampling distribution.
+
+    Returns:
+        SGLD transform (posteriors.types.Transform instance).
+    """
+    update_fn = partial(
+        update,
+        log_posterior=log_posterior,
+        lr=lr,
+        beta=beta,
+        temperature=temperature,
+    )
+    return Transform(init, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.sgmcmc.sgld.SGLDState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding params for SGLD.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Parameters.

+
+
log_posterior + tensor + +
+

Log posterior evaluation.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/sgmcmc/sgld.py + 
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class SGLDState(NamedTuple):
+    """State encoding params for SGLD.
+
+    Attributes:
+        params: Parameters.
+        log_posterior: Log posterior evaluation.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    log_posterior: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sgld.init(params) + +𝞡

+ + +
+ +

Initialise SGLD.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Parameters for which to initialise.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SGLDState + +
+

Initial SGLDState.

+
+
+ +
+ Source code in posteriors/sgmcmc/sgld.py + 
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def init(params: TensorTree) -> SGLDState:
+    """Initialise SGLD.
+
+    Args:
+        params: Parameters for which to initialise.
+
+    Returns:
+        Initial SGLDState.
+    """
+
+    return SGLDState(params)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sgld.update(state, batch, log_posterior, lr, beta=0.0, temperature=1.0, inplace=False) + +𝞡

+ + +
+ +

Updates parameters for SGLD.

+

Update rule from Welling and Teh, 2011: +$$ +θ_{t+1} = θ_t + ε \nabla \log p(θ_t, \text{batch}) + N(0, ε (2 - ε β) T \mathbb{I}) +$$ +for lr \(\epsilon\) and temperature \(T\).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + SGLDState + +
+

SGLDState containing params.

+
+ 		+ required +
batch + Any + +
+

Data batch to be send to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
temperature + float + +
+

Temperature of the sampling distribution.

+
+ 		+ 1.0 +
inplace + bool + +
+

Whether to modify state in place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SGLDState + +
+

Updated state (which are pointers to the input state tensors if inplace=True).

+
+
+ +
+ Source code in posteriors/sgmcmc/sgld.py + 
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def update(
+    state: SGLDState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    lr: float,
+    beta: float = 0.0,
+    temperature: float = 1.0,
+    inplace: bool = False,
+) -> SGLDState:
+    """Updates parameters for SGLD.
+
+    Update rule from [Welling and Teh, 2011](https://www.stats.ox.ac.uk/~teh/research/compstats/WelTeh2011a.pdf):
+    $$
+    θ_{t+1} = θ_t + ε \\nabla \\log p(θ_t, \\text{batch}) + N(0, ε  (2 - ε β) T \\mathbb{I})
+    $$
+    for lr $\\epsilon$ and temperature $T$.
+
+    Args:
+        state: SGLDState containing params.
+        batch: Data batch to be send to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        beta: Gradient noise coefficient (estimated variance).
+        temperature: Temperature of the sampling distribution.
+        inplace: Whether to modify state in place.
+
+    Returns:
+        Updated state (which are pointers to the input state tensors if inplace=True).
+    """
+    with torch.no_grad(), CatchAuxError():
+        grads, (log_post, aux) = grad_and_value(log_posterior, has_aux=True)(
+            state.params, batch
+        )
+
+    def transform_params(p, g):
+        return (
+            p
+            + lr * g
+            + (temperature * lr * (2 - temperature * lr * beta)) ** 0.5
+            * torch.randn_like(p)
+        )
+
+    params = flexi_tree_map(transform_params, state.params, grads, inplace=inplace)
+
+    if inplace:
+        tree_insert_(state.log_posterior, log_post.detach())
+        return state._replace(aux=aux)
+    return SGLDState(params, log_post.detach(), aux)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/sgmcmc/sgnht/index.html b/api/sgmcmc/sgnht/index.html new file mode 100644 index 00000000..193cc433 --- /dev/null +++ b/api/sgmcmc/sgnht/index.html @@ -0,0 +1,2167 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + SGNHT - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

SGNHT𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.sgmcmc.sgnht.build(log_posterior, lr, alpha=0.01, beta=0.0, sigma=1.0, temperature=1.0, momenta=None, xi=None) + +𝞡

+ + +
+ +

Builds SGNHT transform.

+

Algorithm from Ding et al, 2014:

+
\[\begin{align} +θ_{t+1} &= θ_t + ε σ^{-2} m_t \\ +m_{t+1} &= m_t + ε \nabla \log p(θ_t, \text{batch}) - ε σ^{-2} ξ_t m_t ++ N(0, ε T (2 α - ε β T) \mathbb{I})\\ +ξ_{t+1} &= ξ_t + ε (σ^{-2} d^{-1} m_t^T m_t - T) +\end{align}\]
+

for learning rate \(\epsilon\), temperature \(T\) and parameter dimension \(d\).

+

Targets \(p_T(θ, m, ξ) \propto \exp( (\log p(θ) - \frac{1}{2σ^2} m^Tm - \frac{d}{2}(ξ - α)^2) / T)\).

+

The log posterior and temperature are recommended to be constructed in tandem +to ensure robust scaling for a large amount of data and variable batch size.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
alpha + float + +
+

Friction coefficient.

+
+ 		+ 0.01 +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
sigma + float + +
+

Standard deviation of momenta target distribution.

+
+ 		+ 1.0 +
temperature + float + +
+

Temperature of the joint parameter + momenta distribution.

+
+ 		+ 1.0 +
momenta + TensorTree | float | None + +
+

Initial momenta. Can be tree like params or scalar. +Defaults to random iid samples from N(0, 1).

+
+ 		+ None +
xi + float + +
+

Initial value for scalar thermostat ξ. Defaults to alpha.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

SGNHT transform instance.

+
+
+ +
+ Source code in posteriors/sgmcmc/sgnht.py + 
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def build(
+    log_posterior: LogProbFn,
+    lr: float,
+    alpha: float = 0.01,
+    beta: float = 0.0,
+    sigma: float = 1.0,
+    temperature: float = 1.0,
+    momenta: TensorTree | float | None = None,
+    xi: float = None,
+) -> Transform:
+    """Builds SGNHT transform.
+
+    Algorithm from [Ding et al, 2014](https://proceedings.neurips.cc/paper/2014/file/21fe5b8ba755eeaece7a450849876228-Paper.pdf):
+
+    \\begin{align}
+    θ_{t+1} &= θ_t + ε σ^{-2} m_t \\\\
+    m_{t+1} &= m_t + ε \\nabla \\log p(θ_t, \\text{batch}) - ε σ^{-2} ξ_t m_t
+    + N(0, ε T (2 α - ε β T) \\mathbb{I})\\\\
+    ξ_{t+1} &= ξ_t + ε (σ^{-2} d^{-1} m_t^T m_t - T)
+    \\end{align}
+
+    for learning rate $\\epsilon$, temperature $T$ and parameter dimension $d$.
+
+    Targets $p_T(θ, m, ξ) \\propto \\exp( (\\log p(θ) - \\frac{1}{2σ^2} m^Tm - \\frac{d}{2}(ξ - α)^2) / T)$.
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data and variable batch size.
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        alpha: Friction coefficient.
+        beta: Gradient noise coefficient (estimated variance).
+        sigma: Standard deviation of momenta target distribution.
+        temperature: Temperature of the joint parameter + momenta distribution.
+        momenta: Initial momenta. Can be tree like params or scalar.
+            Defaults to random iid samples from N(0, 1).
+        xi: Initial value for scalar thermostat ξ. Defaults to `alpha`.
+
+    Returns:
+        SGNHT transform instance.
+    """
+    init_fn = partial(init, momenta=momenta, xi=xi or alpha)
+    update_fn = partial(
+        update,
+        log_posterior=log_posterior,
+        lr=lr,
+        alpha=alpha,
+        beta=beta,
+        sigma=sigma,
+        temperature=temperature,
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.sgmcmc.sgnht.SGNHTState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding params and momenta for SGNHT.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Parameters.

+
+
momenta + TensorTree + +
+

Momenta for each parameter.

+
+
log_posterior + tensor + +
+

Log posterior evaluation.

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/sgmcmc/sgnht.py + 
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class SGNHTState(NamedTuple):
+    """State encoding params and momenta for SGNHT.
+
+    Attributes:
+        params: Parameters.
+        momenta: Momenta for each parameter.
+        log_posterior: Log posterior evaluation.
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    momenta: TensorTree
+    xi: torch.tensor = torch.tensor([])
+    log_posterior: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sgnht.init(params, momenta=None, xi=0.01) + +𝞡

+ + +
+ +

Initialise momenta for SGNHT.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Parameters for which to initialise.

+
+ 		+ required +
momenta + TensorTree | float | None + +
+

Initial momenta. Can be tree like params or scalar. +Defaults to random iid samples from N(0, 1).

+
+ 		+ None +
xi + float | Tensor + +
+

Initial value for scalar thermostat ξ.

+
+ 		+ 0.01 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SGNHTState + +
+

Initial SGNHTState containing momenta.

+
+
+ +
+ Source code in posteriors/sgmcmc/sgnht.py + 
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def init(
+    params: TensorTree,
+    momenta: TensorTree | float | None = None,
+    xi: float | torch.Tensor = 0.01,
+) -> SGNHTState:
+    """Initialise momenta for SGNHT.
+
+    Args:
+        params: Parameters for which to initialise.
+        momenta: Initial momenta. Can be tree like params or scalar.
+            Defaults to random iid samples from N(0, 1).
+        xi: Initial value for scalar thermostat ξ.
+
+    Returns:
+        Initial SGNHTState containing momenta.
+    """
+    if momenta is None:
+        momenta = tree_map(
+            lambda x: torch.randn_like(x, requires_grad=x.requires_grad),
+            params,
+        )
+    elif is_scalar(momenta):
+        momenta = tree_map(
+            lambda x: torch.full_like(x, momenta, requires_grad=x.requires_grad),
+            params,
+        )
+
+    return SGNHTState(params, momenta, torch.tensor(xi))
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.sgmcmc.sgnht.update(state, batch, log_posterior, lr, alpha=0.01, beta=0.0, sigma=1.0, temperature=1.0, inplace=False) + +𝞡

+ + +
+ +

Updates parameters, momenta and xi for SGNHT.

+

Update rule from Ding et al, 2014:

+
\[\begin{align} +θ_{t+1} &= θ_t + ε σ^{-2} m_t \\ +m_{t+1} &= m_t + ε \nabla \log p(θ_t, \text{batch}) - ε σ^{-2} ξ_t m_t ++ N(0, ε T (2 α - ε β T) \mathbb{I})\\ +ξ_{t+1} &= ξ_t + ε (σ^{-2} d^{-1} m_t^T m_t - T) +\end{align}\]
+

for learning rate \(\epsilon\) and temperature \(T\)

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + SGNHTState + +
+

SGNHTState containing params, momenta and xi.

+
+ 		+ required +
batch + Any + +
+

Data batch to be send to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior value (which can be unnormalised) +as well as auxiliary information, e.g. from the model call.

+
+ 		+ required +
lr + float + +
+

Learning rate.

+
+ 		+ required +
alpha + float + +
+

Friction coefficient.

+
+ 		+ 0.01 +
beta + float + +
+

Gradient noise coefficient (estimated variance).

+
+ 		+ 0.0 +
sigma + float + +
+

Standard deviation of momenta target distribution.

+
+ 		+ 1.0 +
temperature + float + +
+

Temperature of the joint parameter + momenta distribution.

+
+ 		+ 1.0 +
inplace + bool + +
+

Whether to modify state in place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ SGNHTState + +
+

Updated SGNHTState

+
+
+ SGNHTState + +
+

(which are pointers to the inputted state tensors if inplace=True).

+
+
+ +
+ Source code in posteriors/sgmcmc/sgnht.py + 
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def update(
+    state: SGNHTState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    lr: float,
+    alpha: float = 0.01,
+    beta: float = 0.0,
+    sigma: float = 1.0,
+    temperature: float = 1.0,
+    inplace: bool = False,
+) -> SGNHTState:
+    """Updates parameters, momenta and xi for SGNHT.
+
+    Update rule from [Ding et al, 2014](https://proceedings.neurips.cc/paper/2014/file/21fe5b8ba755eeaece7a450849876228-Paper.pdf):
+
+    \\begin{align}
+    θ_{t+1} &= θ_t + ε σ^{-2} m_t \\\\
+    m_{t+1} &= m_t + ε \\nabla \\log p(θ_t, \\text{batch}) - ε σ^{-2} ξ_t m_t
+    + N(0, ε T (2 α - ε β T) \\mathbb{I})\\\\
+    ξ_{t+1} &= ξ_t + ε (σ^{-2} d^{-1} m_t^T m_t - T)
+    \\end{align}
+
+    for learning rate $\\epsilon$ and temperature $T$
+
+    Args:
+        state: SGNHTState containing params, momenta and xi.
+        batch: Data batch to be send to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate.
+        alpha: Friction coefficient.
+        beta: Gradient noise coefficient (estimated variance).
+        sigma: Standard deviation of momenta target distribution.
+        temperature: Temperature of the joint parameter + momenta distribution.
+        inplace: Whether to modify state in place.
+
+    Returns:
+        Updated SGNHTState
+        (which are pointers to the inputted state tensors if inplace=True).
+    """
+    with torch.no_grad(), CatchAuxError():
+        grads, (log_post, aux) = grad_and_value(log_posterior, has_aux=True)(
+            state.params, batch
+        )
+
+    prec = sigma**-2
+
+    def transform_params(p, m):
+        return p + lr * prec * m
+
+    def transform_momenta(m, g):
+        return (
+            m
+            + lr * g
+            - lr * prec * state.xi * m
+            + (temperature * lr * (2 * alpha - temperature * lr * beta)) ** 0.5
+            * torch.randn_like(m)
+        )
+
+    m_flat, _ = tree_ravel(state.momenta)
+    xi_new = state.xi + lr * (prec * torch.mean(m_flat**2) - temperature)
+
+    params = flexi_tree_map(
+        transform_params, state.params, state.momenta, inplace=inplace
+    )
+    momenta = flexi_tree_map(transform_momenta, state.momenta, grads, inplace=inplace)
+
+    if inplace:
+        tree_insert_(state.xi, xi_new)
+        tree_insert_(state.log_posterior, log_post.detach())
+        return state._replace(aux=aux)
+    return SGNHTState(params, momenta, xi_new, log_post.detach(), aux)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/torchopt/index.html b/api/torchopt/index.html new file mode 100644 index 00000000..8d91ea1b --- /dev/null +++ b/api/torchopt/index.html @@ -0,0 +1,1829 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + TorchOpt - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
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+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

TorchOpt

+ +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.torchopt.build(loss_fn, optimizer) + +𝞡

+ + +
+ +

Build a TorchOpt optimizer transformation.

+

Make sure to use the lower case functional optimizers e.g. torchopt.adam().

+
transform = build(loss_fn, torchopt.adam(lr=0.1))
+state = transform.init(params)
+
+for batch in dataloader:
+    state = transform.update(state, batch)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
loss_fn + LogProbFn + +
+

Loss function.

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer. +Make sure to use lower case e.g. torchopt.adam()

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Torchopt optimizer transform instance.

+
+
+ +
+ Source code in posteriors/torchopt.py + 
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def build(
+    loss_fn: LogProbFn,
+    optimizer: torchopt.base.GradientTransformation,
+) -> Transform:
+    """Build a [TorchOpt](https://github.com/metaopt/torchopt) optimizer transformation.
+
+    Make sure to use the lower case functional optimizers e.g. `torchopt.adam()`.
+
+    ```
+    transform = build(loss_fn, torchopt.adam(lr=0.1))
+    state = transform.init(params)
+
+    for batch in dataloader:
+        state = transform.update(state, batch)
+    ```
+
+    Args:
+        loss_fn: Loss function.
+        optimizer: TorchOpt functional optimizer.
+            Make sure to use lower case e.g. torchopt.adam()
+
+    Returns:
+        Torchopt optimizer transform instance.
+    """
+    init_fn = partial(init, optimizer=optimizer)
+    update_fn = partial(update, optimizer=optimizer, loss_fn=loss_fn)
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.torchopt.TorchOptState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State of a TorchOpt optimizer.

+

Contains the parameters, the optimizer state for the TorchOpt optimizer, +loss value, and auxiliary information.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Parameters to be optimized.

+
+
opt_state + OptState + +
+

TorchOpt optimizer state.

+
+
loss + tensor + +
+

Loss value.

+
+
aux + Any + +
+

Auxiliary information from the loss function call.

+
+
+ +
+ Source code in posteriors/torchopt.py + 
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class TorchOptState(NamedTuple):
+    """State of a [TorchOpt](https://github.com/metaopt/torchopt) optimizer.
+
+    Contains the parameters, the optimizer state for the TorchOpt optimizer,
+    loss value, and auxiliary information.
+
+    Attributes:
+        params: Parameters to be optimized.
+        opt_state: TorchOpt optimizer state.
+        loss: Loss value.
+        aux: Auxiliary information from the loss function call.
+    """
+
+    params: TensorTree
+    opt_state: torchopt.typing.OptState
+    loss: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.torchopt.init(params, optimizer) + +𝞡

+ + +
+ +

Initialise a TorchOpt optimizer.

+

Make sure to use the lower case functional optimizers e.g. torchopt.adam().

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Parameters to be optimized.

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer. +Make sure to use lower case e.g. torchopt.adam()

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TorchOptState + +
+

Initial TorchOptState.

+
+
+ +
+ Source code in posteriors/torchopt.py + 
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def init(
+    params: TensorTree,
+    optimizer: torchopt.base.GradientTransformation,
+) -> TorchOptState:
+    """Initialise a [TorchOpt](https://github.com/metaopt/torchopt) optimizer.
+
+    Make sure to use the lower case functional optimizers e.g. `torchopt.adam()`.
+
+    Args:
+        params: Parameters to be optimized.
+        optimizer: TorchOpt functional optimizer.
+            Make sure to use lower case e.g. torchopt.adam()
+
+    Returns:
+        Initial TorchOptState.
+    """
+    opt_state = optimizer.init(params)
+    return TorchOptState(params, opt_state)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.torchopt.update(state, batch, loss_fn, optimizer, inplace=False) + +𝞡

+ + +
+ +

Update the TorchOpt optimizer state.

+

Make sure to use the lower case functional optimizers e.g. torchopt.adam().

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + TorchOptState + +
+

Current state.

+
+ 		+ required +
batch + TensorTree + +
+

Batch of data.

+
+ 		+ required +
loss_fn + LogProbFn + +
+

Loss function.

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer. +Make sure to use lower case like torchopt.adam()

+
+ 		+ required +
inplace + bool + +
+

Whether to update the state in place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TorchOptState + +
+

Updated TorchOptState.

+
+
+ +
+ Source code in posteriors/torchopt.py + 
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def update(
+    state: TorchOptState,
+    batch: TensorTree,
+    loss_fn: LogProbFn,
+    optimizer: torchopt.base.GradientTransformation,
+    inplace: bool = False,
+) -> TorchOptState:
+    """Update the [TorchOpt](https://github.com/metaopt/torchopt) optimizer state.
+
+    Make sure to use the lower case functional optimizers e.g. `torchopt.adam()`.
+
+    Args:
+        state: Current state.
+        batch: Batch of data.
+        loss_fn: Loss function.
+        optimizer: TorchOpt functional optimizer.
+            Make sure to use lower case like torchopt.adam()
+        inplace: Whether to update the state in place.
+
+    Returns:
+        Updated TorchOptState.
+    """
+    params = state.params
+    opt_state = state.opt_state
+    with torch.no_grad(), CatchAuxError():
+        grads, (loss, aux) = torch.func.grad_and_value(loss_fn, has_aux=True)(
+            params, batch
+        )
+    updates, opt_state = optimizer.update(grads, opt_state, params=params)
+    params = torchopt.apply_updates(params, updates, inplace=inplace)
+    if inplace:
+        tree_insert_(state.loss, loss.detach())
+        return state._replace(aux=aux)
+
+    return TorchOptState(params, opt_state, loss, aux)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/tree_utils/index.html b/api/tree_utils/index.html new file mode 100644 index 00000000..05fbd4d1 --- /dev/null +++ b/api/tree_utils/index.html @@ -0,0 +1,3023 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Tree Utils - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Tree Utils

+ +
+ + + + +
+ + + +
+ + + + + + + +
+ + +

+ posteriors.tree_utils.tree_size(tree) + +𝞡

+ + +
+ +

Returns the total number of elements in a PyTree. +Not the number of leaves, but the total number of elements for all tensors in the +tree.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
tree + TensorTree + +
+

A PyTree of tensors.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int + +
+

Number of elements in the PyTree.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def tree_size(tree: TensorTree) -> int:
+    """Returns the total number of elements in a PyTree.
+    Not the number of leaves, but the total number of elements for all tensors in the
+    tree.
+
+    Args:
+        tree: A PyTree of tensors.
+
+    Returns:
+        Number of elements in the PyTree.
+    """
+
+    def ensure_tensor(x):
+        return x if isinstance(x, torch.Tensor) else torch.tensor(x)
+
+    return tree_reduce(torch.add, tree_map(lambda x: ensure_tensor(x).numel(), tree))
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.tree_extract(tree, f) + +𝞡

+ + +
+ +

Extracts values from a PyTree where f returns True. +False values are replaced with empty tensors.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
tree + TensorTree + +
+

A PyTree.

+
+ 		+ required +
f + Callable[[tensor], bool] + +
+

A function that takes a PyTree element and returns True or False.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A PyTree with the same structure as tree where f returns True.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def tree_extract(
+    tree: TensorTree,
+    f: Callable[[torch.tensor], bool],
+) -> TensorTree:
+    """Extracts values from a PyTree where f returns True.
+    False values are replaced with empty tensors.
+
+    Args:
+        tree: A PyTree.
+        f: A function that takes a PyTree element and returns True or False.
+
+    Returns:
+        A PyTree with the same structure as tree where f returns True.
+    """
+    return tree_map(lambda x: x if f(x) else torch.tensor([], device=x.device), tree)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.tree_insert(full_tree, sub_tree, f=lambda _: True) + +𝞡

+ + +
+ +

Inserts sub_tree into full_tree where full_tree tensors evaluate f to True. +Both PyTrees must have the same structure.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
full_tree + TensorTree + +
+

A PyTree to insert sub_tree into.

+
+ 		+ required +
sub_tree + TensorTree + +
+

A PyTree to insert into full_tree.

+
+ 		+ required +
f + Callable[[tensor], bool] + +
+

A function that takes a PyTree element and returns True or False. +Defaults to lambda _: True. I.e. insert on all leaves.

+
+ 		+ lambda _: True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A PyTree with sub_tree inserted into full_tree.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def tree_insert(
+    full_tree: TensorTree,
+    sub_tree: TensorTree,
+    f: Callable[[torch.tensor], bool] = lambda _: True,
+) -> TensorTree:
+    """Inserts sub_tree into full_tree where full_tree tensors evaluate f to True.
+    Both PyTrees must have the same structure.
+
+    Args:
+        full_tree: A PyTree to insert sub_tree into.
+        sub_tree: A PyTree to insert into full_tree.
+        f: A function that takes a PyTree element and returns True or False.
+            Defaults to lambda _: True. I.e. insert on all leaves.
+
+    Returns:
+        A PyTree with sub_tree inserted into full_tree.
+    """
+    return tree_map(
+        lambda sub, full: sub if f(full) else full,
+        sub_tree,
+        full_tree,
+    )
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.tree_insert_(full_tree, sub_tree, f=lambda _: True) + +𝞡

+ + +
+ +

Inserts sub_tree into full_tree in-place where full_tree tensors evaluate +f to True. Both PyTrees must have the same structure.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
full_tree + TensorTree + +
+

A PyTree to insert sub_tree into.

+
+ 		+ required +
sub_tree + TensorTree + +
+

A PyTree to insert into full_tree.

+
+ 		+ required +
f + Callable[[tensor], bool] + +
+

A function that takes a PyTree element and returns True or False. +Defaults to lambda _: True. I.e. insert on all leaves.

+
+ 		+ lambda _: True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A pointer to full_tree with sub_tree inserted.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def tree_insert_(
+    full_tree: TensorTree,
+    sub_tree: TensorTree,
+    f: Callable[[torch.tensor], bool] = lambda _: True,
+) -> TensorTree:
+    """Inserts sub_tree into full_tree in-place where full_tree tensors evaluate
+    f to True. Both PyTrees must have the same structure.
+
+    Args:
+        full_tree: A PyTree to insert sub_tree into.
+        sub_tree: A PyTree to insert into full_tree.
+        f: A function that takes a PyTree element and returns True or False.
+            Defaults to lambda _: True. I.e. insert on all leaves.
+
+    Returns:
+        A pointer to full_tree with sub_tree inserted.
+    """
+
+    def insert_(full, sub):
+        if f(full):
+            full.data = sub.data
+
+    return tree_map_(insert_, full_tree, sub_tree)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.extract_requires_grad(tree) + +𝞡

+ + +
+ +

Extracts only parameters that require gradients.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
tree + TensorTree + +
+

A PyTree of tensors.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A PyTree of tensors that require gradients.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def extract_requires_grad(tree: TensorTree) -> TensorTree:
+    """Extracts only parameters that require gradients.
+
+    Args:
+        tree: A PyTree of tensors.
+
+    Returns:
+        A PyTree of tensors that require gradients.
+    """
+    return tree_extract(tree, lambda x: x.requires_grad)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.insert_requires_grad(full_tree, sub_tree) + +𝞡

+ + +
+ +

Inserts sub_tree into full_tree where full_tree tensors requires_grad. +Both PyTrees must have the same structure.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
full_tree + TensorTree + +
+

A PyTree to insert sub_tree into.

+
+ 		+ required +
sub_tree + TensorTree + +
+

A PyTree to insert into full_tree.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A PyTree with sub_tree inserted into full_tree.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def insert_requires_grad(full_tree: TensorTree, sub_tree: TensorTree) -> TensorTree:
+    """Inserts sub_tree into full_tree where full_tree tensors requires_grad.
+    Both PyTrees must have the same structure.
+
+    Args:
+        full_tree: A PyTree to insert sub_tree into.
+        sub_tree: A PyTree to insert into full_tree.
+
+    Returns:
+        A PyTree with sub_tree inserted into full_tree.
+    """
+    return tree_insert(full_tree, sub_tree, lambda x: x.requires_grad)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.insert_requires_grad_(full_tree, sub_tree) + +𝞡

+ + +
+ +

Inserts sub_tree into full_tree in-place where full_tree tensors requires_grad. +Both PyTrees must have the same structure.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
full_tree + TensorTree + +
+

A PyTree to insert sub_tree into.

+
+ 		+ required +
sub_tree + TensorTree + +
+

A PyTree to insert into full_tree.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

A pointer to full_tree with sub_tree inserted.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def insert_requires_grad_(full_tree: TensorTree, sub_tree: TensorTree) -> TensorTree:
+    """Inserts sub_tree into full_tree in-place where full_tree tensors requires_grad.
+    Both PyTrees must have the same structure.
+
+    Args:
+        full_tree: A PyTree to insert sub_tree into.
+        sub_tree: A PyTree to insert into full_tree.
+
+    Returns:
+        A pointer to full_tree with sub_tree inserted.
+    """
+    return tree_insert_(full_tree, sub_tree, lambda x: x.requires_grad)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.extract_requires_grad_and_func(tree, func, inplace=False) + +𝞡

+ + +
+ +

Extracts only parameters that require gradients and converts a function +that takes the full parameter tree (in its first argument) +into one that takes the subtree.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
tree + TensorTree + +
+

A PyTree of tensors.

+
+ 		+ required +
func + Callable + +
+

A function that takes tree in its first argument.

+
+ 		+ required +
inplace + bool + +
+

Whether to modify the tree inplace or not whe the new function +is called.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[TensorTree, Callable] + +
+

A PyTree of tensors that require gradients and a modified func that takes the +subtree structure rather than full tree in its first argument.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def extract_requires_grad_and_func(
+    tree: TensorTree, func: Callable, inplace: bool = False
+) -> Tuple[TensorTree, Callable]:
+    """Extracts only parameters that require gradients and converts a function
+    that takes the full parameter tree (in its first argument)
+    into one that takes the subtree.
+
+    Args:
+        tree: A PyTree of tensors.
+        func: A function that takes tree in its first argument.
+        inplace: Whether to modify the tree inplace or not whe the new function
+            is called.
+
+    Returns:
+        A PyTree of tensors that require gradients and a modified func that takes the
+            subtree structure rather than full tree in its first argument.
+    """
+    subtree = extract_requires_grad(tree)
+
+    insert = insert_requires_grad_ if inplace else insert_requires_grad
+
+    def subfunc(subtree, *args, **kwargs):
+        return func(insert(tree, subtree), *args, **kwargs)
+
+    return subtree, subfunc
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.inplacify(func) + +𝞡

+ + +
+ +

Converts a function that takes a tensor as its first argument +into one that takes the same arguments but modifies the first argument +tensor in-place with the output of the function.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
func + Callable + +
+

A function that takes a tensor as its first argument and a returns +a modified version of said tensor.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable + +
+

A function that takes a tensor as its first argument and modifies it +in-place.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def inplacify(func: Callable) -> Callable:
+    """Converts a function that takes a tensor as its first argument
+    into one that takes the same arguments but modifies the first argument
+    tensor in-place with the output of the function.
+
+    Args:
+        func: A function that takes a tensor as its first argument and a returns
+            a modified version of said tensor.
+
+    Returns:
+        A function that takes a tensor as its first argument and modifies it
+            in-place.
+    """
+
+    def func_(tens, *args, **kwargs):
+        tens.data = func(tens, *args, **kwargs)
+        return tens
+
+    return func_
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.tree_map_inplacify_(func, tree, *rests, is_leaf=None, none_is_leaf=False, namespace='') + +𝞡

+ + +
+ +

Applies a pure function to each tensor in a PyTree in-place.

+

Like optree.tree_map_ +but takes a pure function as input (and takes replaces its first argument with its +output in-place) rather than a side-effect function.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
func + Callable + +
+

A function that takes a tensor as its first argument and a returns +a modified version of said tensor.

+
+ 		+ required +
tree + pytree + +
+

A pytree to be mapped over, with each leaf providing the first +positional argument to function func.

+
+ 		+ required +
rests + tuple of pytree + +
+

A tuple of pytrees, each of which has the same +structure as tree or has tree as a prefix.

+
+ 		+ () +
is_leaf + callable + +
+

An optionally specified function that will be +called at each flattening step. It should return a boolean, with +True stopping the traversal and the whole subtree being treated as a +leaf, and False indicating the flattening should traverse the +current object.

+
+ 		+ None +
none_is_leaf + bool + +
+

Whether to treat None as a leaf. If +False, None is a non-leaf node with arity 0. Thus None is contained in +the treespec rather than in the leaves list and None will be remain in the +result pytree. (default: False)

+
+ 		+ False +
namespace + str + +
+

The registry namespace used for custom pytree node +types. (default: :const:'', i.e., the global namespace)

+
+ 		+ '' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

The original tree with the value at each leaf is given by the side-effect of +function func(x, *xs) (not the return value) where x is the value at +the corresponding leaf in tree and xs is the tuple of values at +values at corresponding nodes in rests.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def tree_map_inplacify_(
+    func: Callable,
+    tree: TensorTree,
+    *rests: TensorTree,
+    is_leaf: Callable[[TensorTree], bool] | None = None,
+    none_is_leaf: bool = False,
+    namespace: str = "",
+) -> TensorTree:
+    """Applies a pure function to each tensor in a PyTree in-place.
+
+    Like [optree.tree_map_](https://optree.readthedocs.io/en/latest/ops.html#optree.tree_map_)
+    but takes a pure function as input (and takes replaces its first argument with its
+    output in-place) rather than a side-effect function.
+
+    Args:
+        func: A function that takes a tensor as its first argument and a returns
+            a modified version of said tensor.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first
+            positional argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same
+            structure as ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An optionally specified function that will be
+            called at each flattening step. It should return a boolean, with
+            `True` stopping the traversal and the whole subtree being treated as a
+            leaf, and `False` indicating the flattening should traverse the
+            current object.
+        none_is_leaf (bool, optional): Whether to treat `None` as a leaf. If
+            `False`, `None` is a non-leaf node with arity 0. Thus `None` is contained in
+            the treespec rather than in the leaves list and `None` will be remain in the
+            result pytree. (default: `False`)
+        namespace (str, optional): The registry namespace used for custom pytree node
+            types. (default: :const:`''`, i.e., the global namespace)
+
+    Returns:
+        The original ``tree`` with the value at each leaf is given by the side-effect of
+            function ``func(x, *xs)`` (not the return value) where ``x`` is the value at
+            the corresponding leaf in ``tree`` and ``xs`` is the tuple of values at
+            values at corresponding nodes in ``rests``.
+    """
+    return tree_map_(
+        inplacify(func),
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=none_is_leaf,
+        namespace=namespace,
+    )
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.tree_utils.flexi_tree_map(func, tree, *rests, inplace=False, is_leaf=None, none_is_leaf=False, namespace='') + +𝞡

+ + +
+ +

Applies a pure function to each tensor in a PyTree, with inplace argument.

+
out_tensor = func(tensor, *rest_tensors)
+
+

where out_tensor is of the same shape as tensor. +Therefore

+
out_tree = func(tree, *rests, inplace=True)
+
+

will return out_tree a pointer to the original tree with leaves (tensors) +modified in place. +If inplace=False, flexi_tree_map is equivalent to optree.tree_map +and returns a new tree.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
func + Callable + +
+

A pure function that takes a tensor as its first argument and a returns +a modified version of said tensor.

+
+ 		+ required +
tree + pytree + +
+

A pytree to be mapped over, with each leaf providing the first +positional argument to function func.

+
+ 		+ required +
rests + tuple of pytree + +
+

A tuple of pytrees, each of which has the same +structure as tree or has tree as a prefix.

+
+ 		+ () +
inplace + bool + +
+

Whether to modify the tree in-place or not.

+
+ 		+ False +
is_leaf + callable + +
+

An optionally specified function that will be +called at each flattening step. It should return a boolean, with True +stopping the traversal and the whole subtree being treated as a leaf, and +False indicating the flattening should traverse the current object.

+
+ 		+ None +
none_is_leaf + bool + +
+

Whether to treat None as a leaf. If False, +None is a non-leaf node with arity 0. Thus None is contained in the +treespec rather than in the leaves list and None will be remain in the +result pytree. (default: False)

+
+ 		+ False +
namespace + str + +
+

The registry namespace used for custom pytree node +types. (default: :const:'', i.e., the global namespace)

+
+ 		+ '' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Either the original tree modified in-place or a new tree depending on the +inplace argument.

+
+
+ +
+ Source code in posteriors/tree_utils.py + 
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def flexi_tree_map(
+    func: Callable,
+    tree: TensorTree,
+    *rests: TensorTree,
+    inplace: bool = False,
+    is_leaf: Callable[[TensorTree], bool] | None = None,
+    none_is_leaf: bool = False,
+    namespace: str = "",
+) -> TensorTree:
+    """Applies a pure function to each tensor in a PyTree, with inplace argument.
+
+    ```
+    out_tensor = func(tensor, *rest_tensors)
+    ```
+
+    where `out_tensor` is of the same shape as `tensor`.
+    Therefore
+
+    ```
+    out_tree = func(tree, *rests, inplace=True)
+    ```
+
+    will return `out_tree` a pointer to the original `tree` with leaves (tensors)
+    modified in place.
+    If `inplace=False`, `flexi_tree_map` is equivalent to [`optree.tree_map`](https://optree.readthedocs.io/en/latest/ops.html#optree.tree_map)
+    and returns a new tree.
+
+    Args:
+        func: A pure function that takes a tensor as its first argument and a returns
+            a modified version of said tensor.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first
+            positional argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same
+            structure as ``tree`` or has ``tree`` as a prefix.
+        inplace (bool, optional): Whether to modify the tree in-place or not.
+        is_leaf (callable, optional): An optionally specified function that will be
+            called at each flattening step. It should return a boolean, with `True`
+            stopping the traversal and the whole subtree being treated as a leaf, and
+            `False` indicating the flattening should traverse the current object.
+        none_is_leaf (bool, optional): Whether to treat `None` as a leaf. If `False`,
+            `None` is a non-leaf node with arity 0. Thus `None` is contained in the
+            treespec rather than in the leaves list and `None` will be remain in the
+            result pytree. (default: `False`)
+        namespace (str, optional): The registry namespace used for custom pytree node
+            types. (default: :const:`''`, i.e., the global namespace)
+
+    Returns:
+        Either the original tree modified in-place or a new tree depending on the
+            `inplace` argument.
+    """
+    tm = tree_map_inplacify_ if inplace else tree_map
+    return tm(
+        func,
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=none_is_leaf,
+        namespace=namespace,
+    )
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/types/index.html b/api/types/index.html new file mode 100644 index 00000000..1509e769 --- /dev/null +++ b/api/types/index.html @@ -0,0 +1,2008 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + Types - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Types

+ +
+ + + + +
+ + + +
+ + + + + + + +
+ + + +

+ posteriors.types.TransformState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

A posteriors transform state is a NamedTuple containing the required +information for the posteriors iterative algorithm defined by the init and +update functions.

+

Inherit the NamedTuple class when defining a new algorithm state, just make sure +to include the params and aux fields.

+
class AlgorithmState(NamedTuple):
+    params: TensorTree
+    algorithm_info: Any
+    aux: Any
+
+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

PyTree containing the current value of parameters.

+
+
aux + Any + +
+

Auxiliary information from the model call.

+
+
+ +
+ Source code in posteriors/types.py + 
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class TransformState(NamedTuple):
+    """A `posteriors` transform state is a `NamedTuple` containing the required
+    information for the posteriors iterative algorithm defined by the `init` and
+    `update` functions.
+
+    Inherit the `NamedTuple` class when defining a new algorithm state, just make sure
+    to include the `params` and `aux` fields.
+
+    ```
+    class AlgorithmState(NamedTuple):
+        params: TensorTree
+        algorithm_info: Any
+        aux: Any
+    ```
+
+    Attributes:
+        params: PyTree containing the current value of parameters.
+        aux: Auxiliary information from the model call.
+    """
+
+    params: TensorTree
+    aux: Any
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + + +

+ posteriors.types.InitFn + + +𝞡

+ + +
+

+ Bases: Protocol

+ + +
+ Source code in posteriors/types.py + 
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+63
class InitFn(Protocol):
+    @staticmethod
+    def __call__(
+        params: TensorTree,
+    ) -> TransformState:
+        """Initiate a posteriors state with unified API:
+
+        ```
+        state = init(params)
+        ```
+
+        where params is a PyTree of parameters. The produced `state` is a
+        `TransformState` (`NamedTuple`) containing the required information for the
+        posteriors iterative algorithm defined by the `init` and `update` functions.
+
+        Note that this represents the `init` function as stored in a `Transform`
+        returned by an algorithm's `build` function, the internal `init` function in
+        the algorithm module can and likely will have additional arguments.
+
+        Args:
+            params: PyTree containing initial value of parameters.
+
+        Returns:
+            The initial state (`NamedTuple`).
+        """
+
+
+ + + +
+ + + + + + + +
+ + +

+ __call__(params) + + + staticmethod + + +𝞡

+ + +
+ +

Initiate a posteriors state with unified API:

+
state = init(params)
+
+

where params is a PyTree of parameters. The produced state is a +TransformState (NamedTuple) containing the required information for the +posteriors iterative algorithm defined by the init and update functions.

+

Note that this represents the init function as stored in a Transform +returned by an algorithm's build function, the internal init function in +the algorithm module can and likely will have additional arguments.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

PyTree containing initial value of parameters.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TransformState + +
+

The initial state (NamedTuple).

+
+
+ +
+ Source code in posteriors/types.py + 
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+62
+63
@staticmethod
+def __call__(
+    params: TensorTree,
+) -> TransformState:
+    """Initiate a posteriors state with unified API:
+
+    ```
+    state = init(params)
+    ```
+
+    where params is a PyTree of parameters. The produced `state` is a
+    `TransformState` (`NamedTuple`) containing the required information for the
+    posteriors iterative algorithm defined by the `init` and `update` functions.
+
+    Note that this represents the `init` function as stored in a `Transform`
+    returned by an algorithm's `build` function, the internal `init` function in
+    the algorithm module can and likely will have additional arguments.
+
+    Args:
+        params: PyTree containing initial value of parameters.
+
+    Returns:
+        The initial state (`NamedTuple`).
+    """
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + +
+ + + +

+ posteriors.types.UpdateFn + + +𝞡

+ + +
+

+ Bases: Protocol

+ + +
+ Source code in posteriors/types.py + 
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+93
class UpdateFn(Protocol):
+    @staticmethod
+    def __call__(
+        state: TransformState,
+        batch: Any,
+        inplace: bool = False,
+    ) -> TransformState:
+        """Transform a posteriors state with unified API:
+
+        ```
+        state = update(state, batch, inplace=False)
+        ```
+
+        where state is a `NamedTuple` containing the required information for the
+        posteriors iterative algorithm defined by the `init` and `update` functions.
+
+        Note that this represents the `update` function as stored in a `Transform`
+        returned by an algorithm's `build` function, the internal `update` function in
+        the algorithm module can and likely will have additional arguments.
+
+        Args:
+            state: The state of the iterative algorithm.
+            batch: The data batch.
+            inplace: Whether to modify state using inplace operations. Defaults to True.
+
+        Returns:
+            The transformed state (`NamedTuple`).
+        """
+
+
+ + + +
+ + + + + + + +
+ + +

+ __call__(state, batch, inplace=False) + + + staticmethod + + +𝞡

+ + +
+ +

Transform a posteriors state with unified API:

+
state = update(state, batch, inplace=False)
+
+

where state is a NamedTuple containing the required information for the +posteriors iterative algorithm defined by the init and update functions.

+

Note that this represents the update function as stored in a Transform +returned by an algorithm's build function, the internal update function in +the algorithm module can and likely will have additional arguments.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + TransformState + +
+

The state of the iterative algorithm.

+
+ 		+ required +
batch + Any + +
+

The data batch.

+
+ 		+ required +
inplace + bool + +
+

Whether to modify state using inplace operations. Defaults to True.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TransformState + +
+

The transformed state (NamedTuple).

+
+
+ +
+ Source code in posteriors/types.py + 
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+93
@staticmethod
+def __call__(
+    state: TransformState,
+    batch: Any,
+    inplace: bool = False,
+) -> TransformState:
+    """Transform a posteriors state with unified API:
+
+    ```
+    state = update(state, batch, inplace=False)
+    ```
+
+    where state is a `NamedTuple` containing the required information for the
+    posteriors iterative algorithm defined by the `init` and `update` functions.
+
+    Note that this represents the `update` function as stored in a `Transform`
+    returned by an algorithm's `build` function, the internal `update` function in
+    the algorithm module can and likely will have additional arguments.
+
+    Args:
+        state: The state of the iterative algorithm.
+        batch: The data batch.
+        inplace: Whether to modify state using inplace operations. Defaults to True.
+
+    Returns:
+        The transformed state (`NamedTuple`).
+    """
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + +
+ + + +

+ posteriors.types.Transform + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

A transform contains init and update functions defining an iterative + algorithm.

+

Within the Transform all algorithm specific arguments are predefined, so that the +init and update functions have a unified API: +

state = transform.init(params)
+state = transform.update(state, batch, inplace=False)
+

+

Note that this represents the Transform function is returned by an algorithm's +build function, the internal init and update functions in the +algorithm module can and likely will have additional arguments.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
init + InitFn + +
+

The init function.

+
+
update + UpdateFn + +
+

The update function.

+
+
+ +
+ Source code in posteriors/types.py + 
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+ 97
+ 98
+ 99
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+116
+117
+118
class Transform(NamedTuple):
+    """A transform contains `init` and `update` functions defining an iterative
+        algorithm.
+
+    Within the `Transform` all algorithm specific arguments are predefined, so that the
+    `init` and `update` functions have a unified API:
+    ```
+    state = transform.init(params)
+    state = transform.update(state, batch, inplace=False)
+    ```
+
+    Note that this represents the `Transform` function is returned by an algorithm's
+    `build` function, the internal `init` and `update` functions in the
+    algorithm module can and likely will have additional arguments.
+
+    Attributes:
+        init: The init function.
+        update: The update function.
+
+    """
+
+    init: InitFn
+    update: UpdateFn
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/utils/index.html b/api/utils/index.html new file mode 100644 index 00000000..6e403e3c --- /dev/null +++ b/api/utils/index.html @@ -0,0 +1,4793 @@ + + + + + + + + + + + + + + + + + + + + + + + + + Utils - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Utils

+ +
+ + + + +
+ + + +
+ + + + + + + + + + + +
+ + + +

+ posteriors.utils.CatchAuxError + + +𝞡

+ + +
+

+ Bases: AbstractContextManager

+ + +

Context manager to catch errors when auxiliary output is not found.

+ +
+ Source code in posteriors/utils.py + 
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+20
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+39
class CatchAuxError(contextlib.AbstractContextManager):
+    """Context manager to catch errors when auxiliary output is not found."""
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        if exc_type is not None:
+            if NO_AUX_ERROR_MSG in str(exc_value):
+                raise RuntimeError(
+                    "Auxiliary output not found. Perhaps you have forgotten to return "
+                    "the aux output?\n"
+                    "\tIf you don't have any auxiliary info, simply amend to e.g. "
+                    "log_posterior(params, batch) -> Tuple[float, torch.tensor([])].\n"
+                    "\tMore info at https://normal-computing.github.io/posteriors/log_posteriors"
+                )
+            elif NON_TENSOR_AUX_ERROR_MSG in str(exc_value):
+                raise RuntimeError(
+                    "Auxiliary output should be a TensorTree. If you don't have any "
+                    "auxiliary info, simply amend to e.g. "
+                    "log_posterior(params, batch) -> Tuple[float, torch.tensor([])].\n"
+                    "\tMore info at https://normal-computing.github.io/posteriors/log_posteriors"
+                )
+        return False
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.utils.model_to_function(model) + +𝞡

+ + +
+ +

Converts a model into a function that maps parameters and inputs to outputs.

+

Convenience wrapper around torch.functional_call.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
model + Module + +
+

torch.nn.Module with parameters stored in .named_parameters().

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable[[TensorTree, Any], Any] + +
+

Function that takes a PyTree of parameters as well as any input +arg or kwargs and returns the output of the model.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def model_to_function(model: torch.nn.Module) -> Callable[[TensorTree, Any], Any]:
+    """Converts a model into a function that maps parameters and inputs to outputs.
+
+    Convenience wrapper around [torch.functional_call](https://pytorch.org/docs/stable/generated/torch.func.functional_call.html).
+
+    Args:
+        model: torch.nn.Module with parameters stored in .named_parameters().
+
+    Returns:
+        Function that takes a PyTree of parameters as well as any input
+            arg or kwargs and returns the output of the model.
+    """
+
+    def func_model(p_dict, *args, **kwargs):
+        return functional_call(model, p_dict, args=args, kwargs=kwargs)
+
+    return func_model
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.linearized_forward_diag(forward_func, params, batch, sd_diag) + +𝞡

+ + +
+ +

Compute the linearized forward mean and its square root covariance, assuming +posterior covariance over parameters is diagonal.

+

$$ +f(x | θ) \sim N(x | f(x | θₘ), J(x | θₘ) \Sigma J(x | θₘ)^T) +$$ +where \(θₘ\) is the MAP estimate, \(\Sigma\) is the diagonal covariance approximation +at the MAP and \(J(x | θₘ)\) is the Jacobian of the forward function \(f(x | θₘ)\) with +respect to \(θₘ\).

+

For more info on linearized models see Foong et al, 2019.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward_func + ForwardFn + +
+

A function that takes params and batch and returns the forward +values and any auxiliary information. Forward values must be a dim=2 Tensor +with batch dimension in its first axis.

+
+ 		+ required +
params + TensorTree + +
+

PyTree of tensors.

+
+ 		+ required +
batch + TensorTree + +
+

PyTree of tensors.

+
+ 		+ required +
sd_diag + TensorTree + +
+

PyTree of tensors of same shape as params.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[TensorTree, Tensor, TensorTree] + +
+

A tuple of (forward_vals, chol, aux) where forward_vals is the output of the +forward function (mean), chol is the tensor square root of the covariance +matrix (non-diagonal) and aux is auxiliary info from the forward function.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def linearized_forward_diag(
+    forward_func: ForwardFn, params: TensorTree, batch: TensorTree, sd_diag: TensorTree
+) -> Tuple[TensorTree, Tensor, TensorTree]:
+    """Compute the linearized forward mean and its square root covariance, assuming
+    posterior covariance over parameters is diagonal.
+
+    $$
+    f(x | θ) \\sim N(x | f(x | θₘ), J(x | θₘ) \\Sigma J(x | θₘ)^T)
+    $$
+    where $θₘ$ is the MAP estimate, $\\Sigma$ is the diagonal covariance approximation
+    at the MAP and $J(x | θₘ)$ is the Jacobian of the forward function $f(x | θₘ)$ with
+    respect to $θₘ$.
+
+    For more info on linearized models see [Foong et al, 2019](https://arxiv.org/abs/1906.11537).
+
+    Args:
+        forward_func: A function that takes params and batch and returns the forward
+            values and any auxiliary information. Forward values must be a dim=2 Tensor
+            with batch dimension in its first axis.
+        params: PyTree of tensors.
+        batch: PyTree of tensors.
+        sd_diag: PyTree of tensors of same shape as params.
+
+    Returns:
+        A tuple of (forward_vals, chol, aux) where forward_vals is the output of the
+            forward function (mean), chol is the tensor square root of the covariance
+            matrix (non-diagonal) and aux is auxiliary info from the forward function.
+    """
+    forward_vals, aux = forward_func(params, batch)
+
+    with torch.no_grad(), CatchAuxError():
+        jac, _ = jacrev(forward_func, has_aux=True)(params, batch)
+
+    # Convert Jacobian to be flat in parameter dimension
+    jac = tree_flatten(jac)[0]
+    jac = torch.cat([x.flatten(start_dim=2) for x in jac], dim=2)
+
+    # Flatten the diagonal square root covariance
+    sd_diag = tree_flatten(sd_diag)[0]
+    sd_diag = torch.cat([x.flatten() for x in sd_diag])
+
+    # Cholesky of J @ Σ @ J^T
+    linearised_chol = torch.linalg.cholesky((jac * sd_diag**2) @ jac.transpose(-1, -2))
+
+    return forward_vals, linearised_chol, aux
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.hvp(f, primals, tangents, has_aux=False) + +𝞡

+ + +
+ +

Hessian vector product.

+

H(primals) @ tangents

+

where H(primals) is the Hessian of f evaluated at primals.

+

Taken from jacobians_hessians.html. +Follows API from torch.func.jvp.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
f + Callable + +
+

A function with scalar output.

+
+ 		+ required +
primals + tuple + +
+

Tuple of e.g. tensor or dict with tensor values to evalute f at.

+
+ 		+ required +
tangents + tuple + +
+

Tuple matching structure of primals.

+
+ 		+ required +
has_aux + bool + +
+

Whether f returns auxiliary information.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[float, TensorTree] | Tuple[float, TensorTree, Any] + +
+

Returns a (gradient, hvp_out) tuple containing the gradient of func evaluated at +primals and the Hessian-vector product. If has_aux is True, then instead +returns a (gradient, hvp_out, aux) tuple.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def hvp(
+    f: Callable, primals: tuple, tangents: tuple, has_aux: bool = False
+) -> Tuple[float, TensorTree] | Tuple[float, TensorTree, Any]:
+    """Hessian vector product.
+
+    H(primals) @ tangents
+
+    where H(primals) is the Hessian of f evaluated at primals.
+
+    Taken from [jacobians_hessians.html](https://pytorch.org/functorch/nightly/notebooks/jacobians_hessians.html).
+    Follows API from [`torch.func.jvp`](https://pytorch.org/docs/stable/generated/torch.func.jvp.html).
+
+    Args:
+        f: A function with scalar output.
+        primals: Tuple of e.g. tensor or dict with tensor values to evalute f at.
+        tangents: Tuple matching structure of primals.
+        has_aux: Whether f returns auxiliary information.
+
+    Returns:
+        Returns a (gradient, hvp_out) tuple containing the gradient of func evaluated at
+            primals and the Hessian-vector product. If has_aux is True, then instead
+            returns a (gradient, hvp_out, aux) tuple.
+    """
+    return jvp(grad(f, has_aux=has_aux), primals, tangents, has_aux=has_aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.fvp(f, primals, tangents, has_aux=False, normalize=False) + +𝞡

+ + +
+ +

Empirical Fisher vector product.

+

F(primals) @ tangents

+

where F(primals) is the empirical Fisher of f evaluated at primals.

+

The empirical Fisher is defined as: +$$ +F(θ) = J_f(θ) J_f(θ)^T +$$ +where typically \(f_θ\) is the per-sample log likelihood (with elements +\(\log p(y_i | x_i, θ)\) for a model with primals \(θ\) given inputs \(x_i\) and +labels \(y_i\)).

+

If normalize=True, then \(F(θ)\) is divided by the number of outputs from f +(i.e. batchsize).

+

Follows API from torch.func.jvp.

+

More info on empirical Fisher matrices can be found in +Martens, 2020.

+ + +

Examples:

+ 
from functools import partial
+from optree import tree_map
+import torch
+from posteriors import fvp
+
+# Load model that outputs logits
+# Load batch = {'inputs': ..., 'labels': ...}
+
+def log_likelihood_per_sample(params, batch):
+    output = torch.func.functional_call(model, params, batch["inputs"])
+    return -torch.nn.functional.cross_entropy(
+        output, batch["labels"], reduction="none"
+    )
+
+params = dict(model.parameters())
+v = tree_map(lambda x: torch.randn_like(x), params)
+fvp_result = fvp(
+    partial(log_likelihood_per_sample, batch=batch),
+    (params,),
+    (v,)
+)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
f + Callable + +
+

A function with tensor output. +Typically this is the per-sample log likelihood of a model.

+
+ 		+ required +
primals + tuple + +
+

Tuple of e.g. tensor or dict with tensor values to evaluate f at.

+
+ 		+ required +
tangents + tuple + +
+

Tuple matching structure of primals.

+
+ 		+ required +
has_aux + bool + +
+

Whether f returns auxiliary information.

+
+ 		+ False +
normalize + bool + +
+

Whether to normalize, divide by the dimension of the output from f.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[float, TensorTree] | Tuple[float, TensorTree, Any] + +
+

Returns a (output, fvp_out) tuple containing the output of func evaluated at +primals and the empirical Fisher-vector product. If has_aux is True, then +instead returns a (output, fvp_out, aux) tuple.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def fvp(
+    f: Callable,
+    primals: tuple,
+    tangents: tuple,
+    has_aux: bool = False,
+    normalize: bool = False,
+) -> Tuple[float, TensorTree] | Tuple[float, TensorTree, Any]:
+    """Empirical Fisher vector product.
+
+    F(primals) @ tangents
+
+    where F(primals) is the empirical Fisher of f evaluated at primals.
+
+    The empirical Fisher is defined as:
+    $$
+    F(θ) = J_f(θ) J_f(θ)^T
+    $$
+    where typically $f_θ$ is the per-sample log likelihood (with elements
+    $\\log p(y_i | x_i, θ)$ for a model with `primals` $θ$ given inputs $x_i$ and
+    labels $y_i$).
+
+    If `normalize=True`, then $F(θ)$ is divided by the number of outputs from f
+    (i.e. batchsize).
+
+    Follows API from [`torch.func.jvp`](https://pytorch.org/docs/stable/generated/torch.func.jvp.html).
+
+    More info on empirical Fisher matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf).
+
+    Examples:
+        ```python
+        from functools import partial
+        from optree import tree_map
+        import torch
+        from posteriors import fvp
+
+        # Load model that outputs logits
+        # Load batch = {'inputs': ..., 'labels': ...}
+
+        def log_likelihood_per_sample(params, batch):
+            output = torch.func.functional_call(model, params, batch["inputs"])
+            return -torch.nn.functional.cross_entropy(
+                output, batch["labels"], reduction="none"
+            )
+
+        params = dict(model.parameters())
+        v = tree_map(lambda x: torch.randn_like(x), params)
+        fvp_result = fvp(
+            partial(log_likelihood_per_sample, batch=batch),
+            (params,),
+            (v,)
+        )
+        ```
+
+    Args:
+        f: A function with tensor output.
+            Typically this is the [per-sample log likelihood of a model](https://pytorch.org/tutorials/intermediate/per_sample_grads.html).
+        primals: Tuple of e.g. tensor or dict with tensor values to evaluate f at.
+        tangents: Tuple matching structure of primals.
+        has_aux: Whether f returns auxiliary information.
+        normalize: Whether to normalize, divide by the dimension of the output from f.
+
+    Returns:
+        Returns a (output, fvp_out) tuple containing the output of func evaluated at
+            primals and the empirical Fisher-vector product. If has_aux is True, then
+            instead returns a (output, fvp_out, aux) tuple.
+    """
+    jvp_output = jvp(f, primals, tangents, has_aux=has_aux)
+    Jv = jvp_output[1]
+    f_vjp = vjp(f, *primals, has_aux=has_aux)[1]
+    Fv = f_vjp(Jv)[0]
+
+    if normalize:
+        output_dim = tree_flatten(jvp_output[0])[0][0].shape[0]
+        Fv = tree_map(lambda x: x / output_dim, Fv)
+
+    return jvp_output[0], Fv, *jvp_output[2:]
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.empirical_fisher(f, argnums=0, has_aux=False, normalize=False) + +𝞡

+ + +
+ +

Constructs function to compute the empirical Fisher information matrix of a function +f with respect to its parameters, defined as (unnormalized): +$$ +F(θ) = J_f(θ) J_f(θ)^T +$$ +where typically \(f_θ\) is the per-sample log likelihood (with elements +\(\log p(y_i | x_i, θ)\) for a model with primals \(θ\) given inputs \(x_i\) and +labels \(y_i\)).

+

If normalize=True, then \(F(θ)\) is divided by the number of outputs from f +(i.e. batchsize).

+

The empirical Fisher will be provided as a square tensor with respect to the +ravelled parameters. +flat_params, params_unravel = optree.tree_ravel(params).

+

Follows API from torch.func.jacrev.

+

More info on empirical Fisher matrices can be found in +Martens, 2020.

+ + +

Examples:

+ 
import torch
+from posteriors import empirical_fisher, per_samplify
+
+# Load model that outputs logits
+# Load batch = {'inputs': ..., 'labels': ...}
+
+def log_likelihood(params, batch):
+    output = torch.func.functional_call(model, params, batch['inputs'])
+    return -torch.nn.functional.cross_entropy(output, batch['labels'])
+
+likelihood_per_sample = per_samplify(log_likelihood)
+params = dict(model.parameters())
+ef_result = empirical_fisher(log_likelihood_per_sample)(params, batch)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
f + Callable + +
+

A Python function that takes one or more arguments, one of which must be a +Tensor, and returns one or more Tensors. +Typically this is the per-sample log likelihood of a model.

+
+ 		+ required +
argnums + int | Sequence[int] + +
+

Optional, integer or sequence of integers. Specifies which +positional argument(s) to differentiate with respect to.

+
+ 		+ 0 +
has_aux + bool + +
+

Whether f returns auxiliary information.

+
+ 		+ False +
normalize + bool + +
+

Whether to normalize, divide by the dimension of the output from f.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable + +
+

A function with the same arguments as f that returns the empirical Fisher, F. +If has_aux is True, then the function instead returns a tuple of (F, aux).

+
+
+ +
+ Source code in posteriors/utils.py + 
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def empirical_fisher(
+    f: Callable,
+    argnums: int | Sequence[int] = 0,
+    has_aux: bool = False,
+    normalize: bool = False,
+) -> Callable:
+    """
+    Constructs function to compute the empirical Fisher information matrix of a function
+    f with respect to its parameters, defined as (unnormalized):
+    $$
+    F(θ) = J_f(θ) J_f(θ)^T
+    $$
+    where typically $f_θ$ is the per-sample log likelihood (with elements
+    $\\log p(y_i | x_i, θ)$ for a model with `primals` $θ$ given inputs $x_i$ and
+    labels $y_i$).
+
+    If `normalize=True`, then $F(θ)$ is divided by the number of outputs from f
+    (i.e. batchsize).
+
+    The empirical Fisher will be provided as a square tensor with respect to the
+    ravelled parameters.
+    `flat_params, params_unravel = optree.tree_ravel(params)`.
+
+    Follows API from [`torch.func.jacrev`](https://pytorch.org/functorch/stable/generated/functorch.jacrev.html).
+
+    More info on empirical Fisher matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf).
+
+    Examples:
+        ```python
+        import torch
+        from posteriors import empirical_fisher, per_samplify
+
+        # Load model that outputs logits
+        # Load batch = {'inputs': ..., 'labels': ...}
+
+        def log_likelihood(params, batch):
+            output = torch.func.functional_call(model, params, batch['inputs'])
+            return -torch.nn.functional.cross_entropy(output, batch['labels'])
+
+        likelihood_per_sample = per_samplify(log_likelihood)
+        params = dict(model.parameters())
+        ef_result = empirical_fisher(log_likelihood_per_sample)(params, batch)
+        ```
+
+    Args:
+        f:  A Python function that takes one or more arguments, one of which must be a
+            Tensor, and returns one or more Tensors.
+            Typically this is the [per-sample log likelihood of a model](https://pytorch.org/tutorials/intermediate/per_sample_grads.html).
+        argnums: Optional, integer or sequence of integers. Specifies which
+            positional argument(s) to differentiate with respect to.
+        has_aux: Whether f returns auxiliary information.
+        normalize: Whether to normalize, divide by the dimension of the output from f.
+
+    Returns:
+        A function with the same arguments as f that returns the empirical Fisher, F.
+            If has_aux is True, then the function instead returns a tuple of (F, aux).
+    """
+
+    def f_to_flat(*args, **kwargs):
+        f_out = f(*args, **kwargs)
+        f_out_val = f_out[0] if has_aux else f_out
+        f_out_val = tree_ravel(f_out_val)[0]
+        return (f_out_val, f_out[1]) if has_aux else f_out_val
+
+    def fisher(*args, **kwargs):
+        jac_output = jacrev(f_to_flat, argnums=argnums, has_aux=has_aux)(
+            *args, **kwargs
+        )
+        jac = jac_output[0] if has_aux else jac_output
+
+        # Convert Jacobian to tensor, flat in parameter dimension
+        jac = torch.vmap(lambda x: tree_ravel(x)[0])(jac)
+
+        rescale = 1 / jac.shape[0] if normalize else 1
+
+        if has_aux:
+            return jac.T @ jac * rescale, jac_output[1]
+        else:
+            return jac.T @ jac * rescale
+
+    return fisher
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.ggnvp(forward, loss, primals, tangents, forward_has_aux=False, loss_has_aux=False, normalize=False) + +𝞡

+ + +
+ +

Generalised Gauss-Newton vector product.

+

Equivalent to the (non-empirical) Fisher vector product when loss is the negative +log likelihood of an exponential family distribution as a function of its natural +parameter.

+

Defined as +$$ +G(θ) = J_f(θ) H_l(z) J_f(θ)^T +$$ +where \(z = f(θ)\) is the output of the forward function \(f\) and \(l(z)\) +is a loss function with scalar output.

+

Thus \(J_f(θ)\) is the Jacobian of the forward function \(f\) evaluated +at primals \(θ\), with dimensions (dz, dθ). +And \(H_l(z)\) is the Hessian of the loss function \(l\) evaluated at z = f(θ), with +dimensions (dz, dz).

+

Follows API from torch.func.jvp.

+

More info on Fisher and GGN matrices can be found in +Martens, 2020.

+ + +

Examples:

+ 
from functools import partial
+from optree import tree_map
+import torch
+from posteriors import ggnvp
+
+# Load model that outputs logits
+# Load batch = {'inputs': ..., 'labels': ...}
+
+def forward(params, inputs):
+    return torch.func.functional_call(model, params, inputs)
+
+def loss(logits, labels):
+    return torch.nn.functional.cross_entropy(logits, labels)
+
+params = dict(model.parameters())
+v = tree_map(lambda x: torch.randn_like(x), params)
+ggnvp_result = ggnvp(
+    partial(forward, inputs=batch['inputs']),
+    partial(loss, labels=batch['labels']),
+    (params,),
+    (v,),
+)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward + Callable + +
+

A function with tensor output.

+
+ 		+ required +
loss + Callable + +
+

A function that maps the output of forward to a scalar output.

+
+ 		+ required +
primals + tuple + +
+

Tuple of e.g. tensor or dict with tensor values to evaluate f at.

+
+ 		+ required +
tangents + tuple + +
+

Tuple matching structure of primals.

+
+ 		+ required +
forward_has_aux + bool + +
+

Whether forward returns auxiliary information.

+
+ 		+ False +
loss_has_aux + bool + +
+

Whether loss returns auxiliary information.

+
+ 		+ False +
normalize + bool + +
+

Whether to normalize, divide by the first dimension of the output +from f.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[float, TensorTree] | Tuple[float, TensorTree, Any] | Tuple[float, TensorTree, Any, Any] + +
+

Returns a (output, ggnvp_out) tuple, where output is a tuple of +(forward(primals), grad(loss)(forward(primals))). +If forward_has_aux or loss_has_aux is True, then instead returns a +(output, ggnvp_out, aux) or +(output, ggnvp_out, forward_aux, loss_aux) tuple accordingly.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def ggnvp(
+    forward: Callable,
+    loss: Callable,
+    primals: tuple,
+    tangents: tuple,
+    forward_has_aux: bool = False,
+    loss_has_aux: bool = False,
+    normalize: bool = False,
+) -> (
+    Tuple[float, TensorTree]
+    | Tuple[float, TensorTree, Any]
+    | Tuple[float, TensorTree, Any, Any]
+):
+    """Generalised Gauss-Newton vector product.
+
+    Equivalent to the (non-empirical) Fisher vector product when `loss` is the negative
+    log likelihood of an exponential family distribution as a function of its natural
+    parameter.
+
+    Defined as
+    $$
+    G(θ) = J_f(θ) H_l(z) J_f(θ)^T
+    $$
+    where $z = f(θ)$ is the output of the forward function $f$ and $l(z)$
+    is a loss function with scalar output.
+
+    Thus $J_f(θ)$ is the Jacobian of the forward function $f$ evaluated
+    at `primals` $θ$, with dimensions `(dz, dθ)`.
+    And $H_l(z)$ is the Hessian of the loss function $l$ evaluated at `z = f(θ)`, with
+    dimensions `(dz, dz)`.
+
+    Follows API from [`torch.func.jvp`](https://pytorch.org/docs/stable/generated/torch.func.jvp.html).
+
+    More info on Fisher and GGN matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf).
+
+    Examples:
+        ```python
+        from functools import partial
+        from optree import tree_map
+        import torch
+        from posteriors import ggnvp
+
+        # Load model that outputs logits
+        # Load batch = {'inputs': ..., 'labels': ...}
+
+        def forward(params, inputs):
+            return torch.func.functional_call(model, params, inputs)
+
+        def loss(logits, labels):
+            return torch.nn.functional.cross_entropy(logits, labels)
+
+        params = dict(model.parameters())
+        v = tree_map(lambda x: torch.randn_like(x), params)
+        ggnvp_result = ggnvp(
+            partial(forward, inputs=batch['inputs']),
+            partial(loss, labels=batch['labels']),
+            (params,),
+            (v,),
+        )
+        ```
+
+    Args:
+        forward: A function with tensor output.
+        loss: A function that maps the output of forward to a scalar output.
+        primals: Tuple of e.g. tensor or dict with tensor values to evaluate f at.
+        tangents: Tuple matching structure of primals.
+        forward_has_aux: Whether forward returns auxiliary information.
+        loss_has_aux: Whether loss returns auxiliary information.
+        normalize: Whether to normalize, divide by the first dimension of the output
+            from f.
+
+    Returns:
+        Returns a (output, ggnvp_out) tuple, where output is a tuple of
+            `(forward(primals), grad(loss)(forward(primals)))`.
+            If forward_has_aux or loss_has_aux is True, then instead returns a
+            (output, ggnvp_out, aux) or
+            (output, ggnvp_out, forward_aux, loss_aux) tuple accordingly.
+    """
+
+    jvp_output = jvp(forward, primals, tangents, has_aux=forward_has_aux)
+    z = jvp_output[0]
+    Jv = jvp_output[1]
+    HJv_output = hvp(loss, (z,), (Jv,), has_aux=loss_has_aux)
+    HJv = HJv_output[1]
+
+    if normalize:
+        output_dim = tree_flatten(jvp_output[0])[0][0].shape[0]
+        HJv = tree_map(lambda x: x / output_dim, HJv)
+
+    forward_vjp = vjp(forward, *primals, has_aux=forward_has_aux)[1]
+    JTHJv = forward_vjp(HJv)[0]
+
+    return (jvp_output[0], HJv_output[0]), JTHJv, *jvp_output[2:], *HJv_output[2:]
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.ggn(forward, loss, argnums=0, forward_has_aux=False, loss_has_aux=False, normalize=False) + +𝞡

+ + +
+ +

Constructs function to compute the Generalised Gauss-Newton matrix.

+

Equivalent to the (non-empirical) Fisher when loss is the negative +log likelihood of an exponential family distribution as a function of its natural +parameter.

+

Defined as +$$ +G(θ) = J_f(θ) H_l(z) J_f(θ)^T +$$ +where \(z = f(θ)\) is the output of the forward function \(f\) and \(l(z)\) +is a loss function with scalar output.

+

Thus \(J_f(θ)\) is the Jacobian of the forward function \(f\) evaluated +at primals \(θ\). And \(H_l(z)\) is the Hessian of the loss function \(l\) evaluated +at z = f(θ).

+

Requires output from forward to be a tensor and therefore loss takes a tensor as +input. Although both support aux output.

+

If normalize=True, then \(G(θ)\) is divided by the size of the leading dimension of +outputs from forward (i.e. batchsize).

+

The GGN will be provided as a square tensor with respect to the +ravelled parameters. +flat_params, params_unravel = optree.tree_ravel(params).

+

Follows API from torch.func.jacrev.

+

More info on Fisher and GGN matrices can be found in +Martens, 2020.

+ + +

Examples:

+ 
from functools import partial
+import torch
+from posteriors import ggn
+
+# Load model that outputs logits
+# Load batch = {'inputs': ..., 'labels': ...}
+
+def forward(params, inputs):
+    return torch.func.functional_call(model, params, inputs)
+
+def loss(logits, labels):
+    return torch.nn.functional.cross_entropy(logits, labels)
+
+params = dict(model.parameters())
+ggn_result = ggn(
+    partial(forward, inputs=batch['inputs']),
+    partial(loss, labels=batch['labels']),
+)(params)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward + Callable + +
+

A function with tensor output.

+
+ 		+ required +
loss + Callable + +
+

A function that maps the output of forward to a scalar output. +Takes a single input and returns a scalar (and possibly aux).

+
+ 		+ required +
argnums + int | Sequence[int] + +
+

Optional, integer or sequence of integers. Specifies which +positional argument(s) to differentiate forward with respect to.

+
+ 		+ 0 +
forward_has_aux + bool + +
+

Whether forward returns auxiliary information.

+
+ 		+ False +
loss_has_aux + bool + +
+

Whether loss returns auxiliary information.

+
+ 		+ False +
normalize + bool + +
+

Whether to normalize, divide by the first dimension of the output +from f.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable + +
+

A function with the same arguments as f that returns the tensor GGN. +If has_aux is True, then the function instead returns a tuple of (F, aux).

+
+
+ +
+ Source code in posteriors/utils.py + 
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def ggn(
+    forward: Callable,
+    loss: Callable,
+    argnums: int | Sequence[int] = 0,
+    forward_has_aux: bool = False,
+    loss_has_aux: bool = False,
+    normalize: bool = False,
+) -> Callable:
+    """
+    Constructs function to compute the Generalised Gauss-Newton matrix.
+
+    Equivalent to the (non-empirical) Fisher when `loss` is the negative
+    log likelihood of an exponential family distribution as a function of its natural
+    parameter.
+
+    Defined as
+    $$
+    G(θ) = J_f(θ) H_l(z) J_f(θ)^T
+    $$
+    where $z = f(θ)$ is the output of the forward function $f$ and $l(z)$
+    is a loss function with scalar output.
+
+    Thus $J_f(θ)$ is the Jacobian of the forward function $f$ evaluated
+    at `primals` $θ$. And $H_l(z)$ is the Hessian of the loss function $l$ evaluated
+    at `z = f(θ)`.
+
+    Requires output from `forward` to be a tensor and therefore `loss` takes a tensor as
+    input. Although both support `aux` output.
+
+    If `normalize=True`, then $G(θ)$ is divided by the size of the leading dimension of
+    outputs from `forward` (i.e. batchsize).
+
+    The GGN will be provided as a square tensor with respect to the
+    ravelled parameters.
+    `flat_params, params_unravel = optree.tree_ravel(params)`.
+
+    Follows API from [`torch.func.jacrev`](https://pytorch.org/functorch/stable/generated/functorch.jacrev.html).
+
+    More info on Fisher and GGN matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf).
+
+    Examples:
+        ```python
+        from functools import partial
+        import torch
+        from posteriors import ggn
+
+        # Load model that outputs logits
+        # Load batch = {'inputs': ..., 'labels': ...}
+
+        def forward(params, inputs):
+            return torch.func.functional_call(model, params, inputs)
+
+        def loss(logits, labels):
+            return torch.nn.functional.cross_entropy(logits, labels)
+
+        params = dict(model.parameters())
+        ggn_result = ggn(
+            partial(forward, inputs=batch['inputs']),
+            partial(loss, labels=batch['labels']),
+        )(params)
+        ```
+
+    Args:
+        forward: A function with tensor output.
+        loss: A function that maps the output of forward to a scalar output.
+            Takes a single input and returns a scalar (and possibly aux).
+        argnums: Optional, integer or sequence of integers. Specifies which
+            positional argument(s) to differentiate `forward` with respect to.
+        forward_has_aux: Whether forward returns auxiliary information.
+        loss_has_aux: Whether loss returns auxiliary information.
+        normalize: Whether to normalize, divide by the first dimension of the output
+            from f.
+
+    Returns:
+        A function with the same arguments as f that returns the tensor GGN.
+            If has_aux is True, then the function instead returns a tuple of (F, aux).
+    """
+    assert argnums == 0, "Only argnums=0 is supported for now."
+
+    def internal_ggn(params):
+        flat_params, params_unravel = tree_ravel(params)
+
+        def flat_params_to_forward(fps):
+            return forward(params_unravel(fps))
+
+        jac, hess, aux = _hess_and_jac_for_ggn(
+            flat_params_to_forward,
+            loss,
+            argnums,
+            forward_has_aux,
+            loss_has_aux,
+            normalize,
+            flat_params,
+        )
+
+        if aux:
+            return jac.T @ (hess @ jac), *aux
+        else:
+            return jac.T @ (hess @ jac)
+
+    return internal_ggn
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.diag_ggn(forward, loss, argnums=0, forward_has_aux=False, loss_has_aux=False, normalize=False) + +𝞡

+ + +
+ +

Constructs function to compute the diagonal of the Generalised Gauss-Newton matrix.

+

Equivalent to the (non-empirical) diagonal Fisher when loss is the negative +log likelihood of an exponential family distribution as a function of its natural +parameter.

+

The GGN is defined as +$$ +G(θ) = J_f(θ) H_l(z) J_f(θ)^T +$$ +where \(z = f(θ)\) is the output of the forward function \(f\) and \(l(z)\) +is a loss function with scalar output.

+

Thus \(J_f(θ)\) is the Jacobian of the forward function \(f\) evaluated +at primals \(θ\). And \(H_l(z)\) is the Hessian of the loss function \(l\) evaluated +at z = f(θ).

+

Requires output from forward to be a tensor and therefore loss takes a tensor as +input. Although both support aux output.

+

If normalize=True, then \(G(θ)\) is divided by the size of the leading dimension of +outputs from forward (i.e. batchsize).

+

Unlike posteriors.ggn, the output will be in PyTree form matching the input.

+

Follows API from torch.func.jacrev.

+

More info on Fisher and GGN matrices can be found in +Martens, 2020.

+ + +

Examples:

+ 
from functools import partial
+import torch
+from posteriors import diag_ggn
+
+# Load model that outputs logits
+# Load batch = {'inputs': ..., 'labels': ...}
+
+def forward(params, inputs):
+    return torch.func.functional_call(model, params, inputs)
+
+def loss(logits, labels):
+    return torch.nn.functional.cross_entropy(logits, labels)
+
+params = dict(model.parameters())
+ggndiag_result = diag_ggn(
+    partial(forward, inputs=batch['inputs']),
+    partial(loss, labels=batch['labels']),
+)(params)
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
forward + Callable + +
+

A function with tensor output.

+
+ 		+ required +
loss + Callable + +
+

A function that maps the output of forward to a scalar output. +Takes a single input and returns a scalar (and possibly aux).

+
+ 		+ required +
argnums + int | Sequence[int] + +
+

Optional, integer or sequence of integers. Specifies which +positional argument(s) to differentiate forward with respect to.

+
+ 		+ 0 +
forward_has_aux + bool + +
+

Whether forward returns auxiliary information.

+
+ 		+ False +
loss_has_aux + bool + +
+

Whether loss returns auxiliary information.

+
+ 		+ False +
normalize + bool + +
+

Whether to normalize, divide by the first dimension of the output +from f.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable + +
+

A function with the same arguments as f that returns the diagonal GGN. +If has_aux is True, then the function instead returns a tuple of (F, aux).

+
+
+ +
+ Source code in posteriors/utils.py + 
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def diag_ggn(
+    forward: Callable,
+    loss: Callable,
+    argnums: int | Sequence[int] = 0,
+    forward_has_aux: bool = False,
+    loss_has_aux: bool = False,
+    normalize: bool = False,
+) -> Callable:
+    """
+    Constructs function to compute the diagonal of the Generalised Gauss-Newton matrix.
+
+    Equivalent to the (non-empirical) diagonal Fisher when `loss` is the negative
+    log likelihood of an exponential family distribution as a function of its natural
+    parameter.
+
+    The GGN is defined as
+    $$
+    G(θ) = J_f(θ) H_l(z) J_f(θ)^T
+    $$
+    where $z = f(θ)$ is the output of the forward function $f$ and $l(z)$
+    is a loss function with scalar output.
+
+    Thus $J_f(θ)$ is the Jacobian of the forward function $f$ evaluated
+    at `primals` $θ$. And $H_l(z)$ is the Hessian of the loss function $l$ evaluated
+    at `z = f(θ)`.
+
+    Requires output from `forward` to be a tensor and therefore `loss` takes a tensor as
+    input. Although both support `aux` output.
+
+    If `normalize=True`, then $G(θ)$ is divided by the size of the leading dimension of
+    outputs from `forward` (i.e. batchsize).
+
+    Unlike `posteriors.ggn`, the output will be in PyTree form matching the input.
+
+    Follows API from [`torch.func.jacrev`](https://pytorch.org/functorch/stable/generated/functorch.jacrev.html).
+
+    More info on Fisher and GGN matrices can be found in
+    [Martens, 2020](https://jmlr.org/papers/volume21/17-678/17-678.pdf).
+
+    Examples:
+        ```python
+        from functools import partial
+        import torch
+        from posteriors import diag_ggn
+
+        # Load model that outputs logits
+        # Load batch = {'inputs': ..., 'labels': ...}
+
+        def forward(params, inputs):
+            return torch.func.functional_call(model, params, inputs)
+
+        def loss(logits, labels):
+            return torch.nn.functional.cross_entropy(logits, labels)
+
+        params = dict(model.parameters())
+        ggndiag_result = diag_ggn(
+            partial(forward, inputs=batch['inputs']),
+            partial(loss, labels=batch['labels']),
+        )(params)
+        ```
+
+    Args:
+        forward: A function with tensor output.
+        loss: A function that maps the output of forward to a scalar output.
+            Takes a single input and returns a scalar (and possibly aux).
+        argnums: Optional, integer or sequence of integers. Specifies which
+            positional argument(s) to differentiate `forward` with respect to.
+        forward_has_aux: Whether forward returns auxiliary information.
+        loss_has_aux: Whether loss returns auxiliary information.
+        normalize: Whether to normalize, divide by the first dimension of the output
+            from f.
+
+    Returns:
+        A function with the same arguments as f that returns the diagonal GGN.
+            If has_aux is True, then the function instead returns a tuple of (F, aux).
+    """
+    assert argnums == 0, "Only argnums=0 is supported for now."
+
+    def internal_ggn(params):
+        flat_params, params_unravel = tree_ravel(params)
+
+        def flat_params_to_forward(fps):
+            return forward(params_unravel(fps))
+
+        jac, hess, aux = _hess_and_jac_for_ggn(
+            flat_params_to_forward,
+            loss,
+            argnums,
+            forward_has_aux,
+            loss_has_aux,
+            normalize,
+            flat_params,
+        )
+
+        G_diag = torch.einsum("ji,jk,ki->i", jac, hess, jac)
+        G_diag = params_unravel(G_diag)
+
+        if aux:
+            return G_diag, *aux
+        else:
+            return G_diag
+
+    return internal_ggn
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.cg(A, b, x0=None, *, maxiter=None, damping=0.0, tol=1e-05, atol=0.0, M=_identity) + +𝞡

+ + +
+ +

Use Conjugate Gradient iteration to solve Ax = b. +A is supplied as a function instead of a matrix.

+

Adapted from jax.scipy.sparse.linalg.cg.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
A + Callable + +
+

Callable that calculates the linear map (matrix-vector +product) Ax when called like A(x). A must represent +a hermitian, positive definite matrix, and must return array(s) with the +same structure and shape as its argument.

+
+ 		+ required +
b + TensorTree + +
+

Right hand side of the linear system representing a single vector.

+
+ 		+ required +
x0 + TensorTree + +
+

Starting guess for the solution. Must have the same structure as b.

+
+ 		+ None +
maxiter + int + +
+

Maximum number of iterations. Iteration will stop after maxiter +steps even if the specified tolerance has not been achieved.

+
+ 		+ None +
damping + float + +
+

damping term for the mvp function. Acts as regularization.

+
+ 		+ 0.0 +
tol + float + +
+

Tolerance for convergence.

+
+ 		+ 1e-05 +
atol + float + +
+

Tolerance for convergence. norm(residual) <= max(tol*norm(b), atol). +The behaviour will differ from SciPy unless you explicitly pass +atol to SciPy's cg.

+
+ 		+ 0.0 +
M + Callable + +
+

Preconditioner for A. +See the preconditioned CG method.

+
+ 		+ _identity +
+ + +

Returns:

+ + + + + + + + + + + + + + + + + +
Name TypeDescription
x + TensorTree + +
+

The converged solution. Has the same structure as b.

+
+
info + Any + +
+

Placeholder for convergence information.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def cg(
+    A: Callable,
+    b: TensorTree,
+    x0: TensorTree = None,
+    *,
+    maxiter: int = None,
+    damping: float = 0.0,
+    tol: float = 1e-5,
+    atol: float = 0.0,
+    M: Callable = _identity,
+) -> Tuple[TensorTree, Any]:
+    """Use Conjugate Gradient iteration to solve ``Ax = b``.
+    ``A`` is supplied as a function instead of a matrix.
+
+    Adapted from [`jax.scipy.sparse.linalg.cg`](https://jax.readthedocs.io/en/latest/_autosummary/jax.scipy.sparse.linalg.cg.html).
+
+    Args:
+        A:  Callable that calculates the linear map (matrix-vector
+            product) ``Ax`` when called like ``A(x)``. ``A`` must represent
+            a hermitian, positive definite matrix, and must return array(s) with the
+            same structure and shape as its argument.
+        b:  Right hand side of the linear system representing a single vector.
+        x0: Starting guess for the solution. Must have the same structure as ``b``.
+        maxiter: Maximum number of iterations.  Iteration will stop after maxiter
+            steps even if the specified tolerance has not been achieved.
+        damping: damping term for the mvp function. Acts as regularization.
+        tol: Tolerance for convergence.
+        atol: Tolerance for convergence. ``norm(residual) <= max(tol*norm(b), atol)``.
+            The behaviour will differ from SciPy unless you explicitly pass
+            ``atol`` to SciPy's ``cg``.
+        M: Preconditioner for A.
+            See [the preconditioned CG method.](https://en.wikipedia.org/wiki/Conjugate_gradient_method#The_preconditioned_conjugate_gradient_method)
+
+    Returns:
+        x : The converged solution. Has the same structure as ``b``.
+        info : Placeholder for convergence information.
+    """
+    if x0 is None:
+        x0 = tree_map(torch.zeros_like, b)
+
+    if maxiter is None:
+        maxiter = 10 * tree_size(b)  # copied from scipy
+
+    tol *= torch.tensor([1.0])
+    atol *= torch.tensor([1.0])
+
+    # tolerance handling uses the "non-legacy" behavior of scipy.sparse.linalg.cg
+    bs = _vdot_real_tree(b, b)
+    atol2 = torch.maximum(torch.square(tol) * bs, torch.square(atol))
+
+    def A_damped(p):
+        return _add(A(p), _mul(damping, p))
+
+    def cond_fun(value):
+        _, r, gamma, _, k = value
+        rs = gamma.real if M is _identity else _vdot_real_tree(r, r)
+        return (rs > atol2) & (k < maxiter)
+
+    def body_fun(value):
+        x, r, gamma, p, k = value
+        Ap = A_damped(p)
+        alpha = gamma / _vdot_real_tree(p, Ap)
+        x_ = _add(x, _mul(alpha, p))
+        r_ = _sub(r, _mul(alpha, Ap))
+        z_ = M(r_)
+        gamma_ = _vdot_real_tree(r_, z_)
+        beta_ = gamma_ / gamma
+        p_ = _add(z_, _mul(beta_, p))
+        return x_, r_, gamma_, p_, k + 1
+
+    r0 = _sub(b, A_damped(x0))
+    p0 = z0 = r0
+    gamma0 = _vdot_real_tree(r0, z0)
+    initial_value = (x0, r0, gamma0, p0, 0)
+
+    value = initial_value
+
+    while cond_fun(value):
+        value = body_fun(value)
+
+    x_final, r, gamma, _, k = value
+    # compute the final error and whether it has converged.
+    rs = gamma if M is _identity else _vdot_real_tree(r, r)
+    converged = rs <= atol2
+
+    # additional info output structure
+    info = {"error": rs, "converged": converged, "niter": k}
+
+    return x_final, info
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.diag_normal_log_prob(x, mean=0.0, sd_diag=1.0, normalize=True) + +𝞡

+ + +
+ +

Evaluate multivariate normal log probability for a diagonal covariance matrix.

+

If either mean or sd_diag are scalars, they will be broadcast to the same shape as x +(in a memory efficient manner).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + TensorTree + +
+

Value to evaluate log probability at.

+
+ 		+ required +
mean + float | TensorTree + +
+

Mean of the distribution.

+
+ 		+ 0.0 +
sd_diag + float | TensorTree + +
+

Square-root diagonal of the covariance matrix.

+
+ 		+ 1.0 +
normalize + bool + +
+

Whether to compute normalized log probability. +If False the elementwise log prob is -0.5 * ((x - mean) / sd_diag)**2.

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ float + +
+

Scalar log probability.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def diag_normal_log_prob(
+    x: TensorTree,
+    mean: float | TensorTree = 0.0,
+    sd_diag: float | TensorTree = 1.0,
+    normalize: bool = True,
+) -> float:
+    """Evaluate multivariate normal log probability for a diagonal covariance matrix.
+
+    If either mean or sd_diag are scalars, they will be broadcast to the same shape as x
+    (in a memory efficient manner).
+
+    Args:
+        x: Value to evaluate log probability at.
+        mean: Mean of the distribution.
+        sd_diag: Square-root diagonal of the covariance matrix.
+        normalize: Whether to compute normalized log probability.
+            If False the elementwise log prob is -0.5 * ((x - mean) / sd_diag)**2.
+
+    Returns:
+        Scalar log probability.
+    """
+    if tree_size(mean) == 1:
+        mean = tree_map(lambda t: torch.tensor(mean, device=t.device), x)
+    if tree_size(sd_diag) == 1:
+        sd_diag = tree_map(lambda t: torch.tensor(sd_diag, device=t.device), x)
+
+    if normalize:
+
+        def univariate_norm_and_sum(v, m, sd):
+            return Normal(m, sd, validate_args=False).log_prob(v).sum()
+    else:
+
+        def univariate_norm_and_sum(v, m, sd):
+            return (-0.5 * ((v - m) / sd) ** 2).sum()
+
+    log_probs = tree_map(
+        univariate_norm_and_sum,
+        x,
+        mean,
+        sd_diag,
+    )
+    log_prob = tree_reduce(torch.add, log_probs)
+    return log_prob
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.diag_normal_sample(mean, sd_diag, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Sample from multivariate normal with diagonal covariance matrix.

+

If sd_diag is scalar, it will be broadcast to the same shape as mean +(in a memory efficient manner).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
mean + TensorTree + +
+

Mean of the distribution.

+
+ 		+ required +
sd_diag + float | TensorTree + +
+

Square-root diagonal of the covariance matrix.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the sample.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ dict + +
+

Sample(s) from normal distribution with the same structure as mean and sd_diag.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def diag_normal_sample(
+    mean: TensorTree,
+    sd_diag: float | TensorTree,
+    sample_shape: torch.Size = torch.Size([]),
+) -> dict:
+    """Sample from multivariate normal with diagonal covariance matrix.
+
+    If sd_diag is scalar, it will be broadcast to the same shape as mean
+    (in a memory efficient manner).
+
+    Args:
+        mean: Mean of the distribution.
+        sd_diag: Square-root diagonal of the covariance matrix.
+        sample_shape: Shape of the sample.
+
+    Returns:
+        Sample(s) from normal distribution with the same structure as mean and sd_diag.
+    """
+    if tree_size(sd_diag) == 1:
+        sd_diag = tree_map(lambda t: torch.tensor(sd_diag, device=t.device), mean)
+
+    return tree_map(
+        lambda m, sd: m + torch.randn(sample_shape + m.shape, device=m.device) * sd,
+        mean,
+        sd_diag,
+    )
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.per_samplify(f) + +𝞡

+ + +
+ +

Converts a function that takes params and batch into one that provides an output +for each batch sample.

+
output = f(params, batch)
+per_sample_output = per_samplify(f)(params, batch)
+
+

For more info see per_sample_grads.html

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
f + Callable[[TensorTree, TensorTree], Any] + +
+

A function that takes params and batch provides an output with size +independent of batchsize (i.e. averaged).

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable[[TensorTree, TensorTree], Any] + +
+

A new function that provides an output for each batch sample. +per_sample_output = per_samplify(f)(params, batch)

+
+
+ +
+ Source code in posteriors/utils.py + 
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def per_samplify(
+    f: Callable[[TensorTree, TensorTree], Any],
+) -> Callable[[TensorTree, TensorTree], Any]:
+    """Converts a function that takes params and batch into one that provides an output
+    for each batch sample.
+
+    ```
+    output = f(params, batch)
+    per_sample_output = per_samplify(f)(params, batch)
+    ```
+
+    For more info see [per_sample_grads.html](https://pytorch.org/tutorials/intermediate/per_sample_grads.html)
+
+    Args:
+        f: A function that takes params and batch provides an output with size
+            independent of batchsize (i.e. averaged).
+
+    Returns:
+        A new function that provides an output for each batch sample.
+            `per_sample_output  = per_samplify(f)(params, batch)`
+    """
+
+    @partial(torch.vmap, in_dims=(None, 0))
+    def f_per_sample(params, batch):
+        batch = tree_map(lambda x: x.unsqueeze(0), batch)
+        return f(params, batch)
+
+    @wraps(f)
+    def f_per_sample_ensure_no_kwargs(params, batch):
+        return f_per_sample(params, batch)  # vmap in_dims requires no kwargs
+
+    return f_per_sample_ensure_no_kwargs
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.utils.is_scalar(x) + +𝞡

+ + +
+ +

Returns True if x is a scalar (int, float, bool) or a tensor with a single element.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Any + +
+

Any object.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

True if x is a scalar.

+
+
+ +
+ Source code in posteriors/utils.py + 
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def is_scalar(x: Any) -> bool:
+    """Returns True if x is a scalar (int, float, bool) or a tensor with a single element.
+
+    Args:
+        x: Any object.
+
+    Returns:
+        True if x is a scalar.
+    """
+    return isinstance(x, (int, float)) or (torch.is_tensor(x) and x.numel() == 1)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/vi/diag/index.html b/api/vi/diag/index.html new file mode 100644 index 00000000..8ab99375 --- /dev/null +++ b/api/vi/diag/index.html @@ -0,0 +1,2524 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + VI Diag - posteriors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

VI Diag𝞡

+ + +
+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +

+ posteriors.vi.diag.build(log_posterior, optimizer, temperature=1.0, n_samples=1, stl=True, init_log_sds=0.0) + +𝞡

+ + +
+ +

Builds a transform for variational inference with a diagonal Normal +distribution over parameters.

+

Find \(\mu\) and diagonal \(\Sigma\) that mimimize \(\text{KL}(N(θ| \mu, \Sigma) || p_T(θ))\) +where \(p_T(θ) \propto \exp( \log p(θ) / T)\) with temperature \(T\).

+

The log posterior and temperature are recommended to be constructed in tandem +to ensure robust scaling for a large amount of data.

+

For more information on variational inference see Blei et al, 2017.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
log_posterior + Callable[[TensorTree, Any], float] + +
+

Function that takes parameters and input batch and +returns the log posterior (which can be unnormalised).

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer for updating the variational +parameters. Make sure to use lower case like torchopt.adam()

+
+ 		+ required +
temperature + float + +
+

Temperature to rescale (divide) log_posterior.

+
+ 		+ 1.0 +
n_samples + int + +
+

Number of samples to use for Monte Carlo estimate.

+
+ 		+ 1 +
stl + bool + +
+

Whether to use the stick-the-landing estimator +from (Roeder et al](https://arxiv.org/abs/1703.09194).

+
+ 		+ True +
init_log_sds + TensorTree | float + +
+

Initial log of the square-root diagonal of the covariance matrix +of the variational distribution. Can be a tree matching params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Transform + +
+

Diagonal VI transform instance.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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def build(
+    log_posterior: Callable[[TensorTree, Any], float],
+    optimizer: torchopt.base.GradientTransformation,
+    temperature: float = 1.0,
+    n_samples: int = 1,
+    stl: bool = True,
+    init_log_sds: TensorTree | float = 0.0,
+) -> Transform:
+    """Builds a transform for variational inference with a diagonal Normal
+    distribution over parameters.
+
+    Find $\\mu$ and diagonal $\\Sigma$ that mimimize $\\text{KL}(N(θ| \\mu, \\Sigma) || p_T(θ))$
+    where $p_T(θ) \\propto \\exp( \\log p(θ) / T)$ with temperature $T$.
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data.
+
+    For more information on variational inference see [Blei et al, 2017](https://arxiv.org/abs/1601.00670).
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior (which can be unnormalised).
+        optimizer: TorchOpt functional optimizer for updating the variational
+            parameters. Make sure to use lower case like torchopt.adam()
+        temperature: Temperature to rescale (divide) log_posterior.
+        n_samples: Number of samples to use for Monte Carlo estimate.
+        stl: Whether to use the stick-the-landing estimator
+            from (Roeder et al](https://arxiv.org/abs/1703.09194).
+        init_log_sds: Initial log of the square-root diagonal of the covariance matrix
+            of the variational distribution. Can be a tree matching params or scalar.
+
+    Returns:
+        Diagonal VI transform instance.
+    """
+    init_fn = partial(init, optimizer=optimizer, init_log_sds=init_log_sds)
+    update_fn = partial(
+        update,
+        log_posterior=log_posterior,
+        optimizer=optimizer,
+        temperature=temperature,
+        n_samples=n_samples,
+        stl=stl,
+    )
+    return Transform(init_fn, update_fn)
+
+
+
+ +
+ + + + + + +
+ + + +

+ posteriors.vi.diag.VIDiagState + + +𝞡

+ + +
+

+ Bases: NamedTuple

+ + +

State encoding a diagonal Normal variational distribution over parameters.

+ + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
params + TensorTree + +
+

Mean of the variational distribution.

+
+
log_sd_diag + TensorTree + +
+

Log of the square-root diagonal of the covariance matrix of the +variational distribution.

+
+
opt_state + OptState + +
+

TorchOpt state storing optimizer data for updating the +variational parameters.

+
+
nelbo + tensor + +
+

Negative evidence lower bound (lower is better).

+
+
aux + Any + +
+

Auxiliary information from the log_posterior call.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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class VIDiagState(NamedTuple):
+    """State encoding a diagonal Normal variational distribution over parameters.
+
+    Attributes:
+        params: Mean of the variational distribution.
+        log_sd_diag: Log of the square-root diagonal of the covariance matrix of the
+            variational distribution.
+        opt_state: TorchOpt state storing optimizer data for updating the
+            variational parameters.
+        nelbo: Negative evidence lower bound (lower is better).
+        aux: Auxiliary information from the log_posterior call.
+    """
+
+    params: TensorTree
+    log_sd_diag: TensorTree
+    opt_state: torchopt.typing.OptState
+    nelbo: torch.tensor = torch.tensor([])
+    aux: Any = None
+
+
+ + + +
+ + + + + +
+ +
+ +
+ + + + + + +
+ + +

+ posteriors.vi.diag.init(params, optimizer, init_log_sds=0.0) + +𝞡

+ + +
+ +

Initialise diagonal Normal variational distribution over parameters.

+

optimizer.init will be called on flattened variational parameters so hyperparameters +such as learning rate need to pre-specified through TorchOpt's functional API:

+
import torchopt
+
+optimizer = torchopt.adam(lr=1e-2)
+vi_state = init(init_mean, optimizer)
+
+

It's assumed maximize=False for the optimizer, so that we minimize the NELBO.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
params + TensorTree + +
+

Initial mean of the variational distribution.

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer for updating the variational +parameters. Make sure to use lower case like torchopt.adam()

+
+ 		+ required +
init_log_sds + TensorTree | float + +
+

Initial log of the square-root diagonal of the covariance matrix +of the variational distribution. Can be a tree matching params or scalar.

+
+ 		+ 0.0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ VIDiagState + +
+

Initial DiagVIState.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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def init(
+    params: TensorTree,
+    optimizer: torchopt.base.GradientTransformation,
+    init_log_sds: TensorTree | float = 0.0,
+) -> VIDiagState:
+    """Initialise diagonal Normal variational distribution over parameters.
+
+    optimizer.init will be called on flattened variational parameters so hyperparameters
+    such as learning rate need to pre-specified through TorchOpt's functional API:
+
+    ```
+    import torchopt
+
+    optimizer = torchopt.adam(lr=1e-2)
+    vi_state = init(init_mean, optimizer)
+    ```
+
+    It's assumed maximize=False for the optimizer, so that we minimize the NELBO.
+
+    Args:
+        params: Initial mean of the variational distribution.
+        optimizer: TorchOpt functional optimizer for updating the variational
+            parameters. Make sure to use lower case like torchopt.adam()
+        init_log_sds: Initial log of the square-root diagonal of the covariance matrix
+            of the variational distribution. Can be a tree matching params or scalar.
+
+    Returns:
+        Initial DiagVIState.
+    """
+    if is_scalar(init_log_sds):
+        init_log_sds = tree_map(
+            lambda x: torch.full_like(x, init_log_sds, requires_grad=x.requires_grad),
+            params,
+        )
+
+    opt_state = optimizer.init([params, init_log_sds])
+    return VIDiagState(params, init_log_sds, opt_state)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.vi.diag.update(state, batch, log_posterior, optimizer, temperature=1.0, n_samples=1, stl=True, inplace=False) + +𝞡

+ + +
+ +

Updates the variational parameters to minimize the NELBO.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + VIDiagState + +
+

Current state.

+
+ 		+ required +
batch + Any + +
+

Input data to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior (which can be unnormalised).

+
+ 		+ required +
optimizer + GradientTransformation + +
+

TorchOpt functional optimizer for updating the variational +parameters. Make sure to use lower case like torchopt.adam()

+
+ 		+ required +
temperature + float + +
+

Temperature to rescale (divide) log_posterior.

+
+ 		+ 1.0 +
n_samples + int + +
+

Number of samples to use for Monte Carlo estimate.

+
+ 		+ 1 +
stl + bool + +
+

Whether to use the stick-the-landing estimator +from (Roeder et al](https://arxiv.org/abs/1703.09194).

+
+ 		+ True +
inplace + bool + +
+

Whether to modify state in place.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ VIDiagState + +
+

Updated DiagVIState.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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def update(
+    state: VIDiagState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    optimizer: torchopt.base.GradientTransformation,
+    temperature: float = 1.0,
+    n_samples: int = 1,
+    stl: bool = True,
+    inplace: bool = False,
+) -> VIDiagState:
+    """Updates the variational parameters to minimize the NELBO.
+
+    Args:
+        state: Current state.
+        batch: Input data to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior (which can be unnormalised).
+        optimizer: TorchOpt functional optimizer for updating the variational
+            parameters. Make sure to use lower case like torchopt.adam()
+        temperature: Temperature to rescale (divide) log_posterior.
+        n_samples: Number of samples to use for Monte Carlo estimate.
+        stl: Whether to use the stick-the-landing estimator
+            from (Roeder et al](https://arxiv.org/abs/1703.09194).
+        inplace: Whether to modify state in place.
+
+    Returns:
+        Updated DiagVIState.
+    """
+
+    def nelbo_log_sd(m, lsd):
+        sd_diag = tree_map(torch.exp, lsd)
+        return nelbo(m, sd_diag, batch, log_posterior, temperature, n_samples, stl)
+
+    with torch.no_grad(), CatchAuxError():
+        nelbo_grads, (nelbo_val, aux) = grad_and_value(
+            nelbo_log_sd, argnums=(0, 1), has_aux=True
+        )(state.params, state.log_sd_diag)
+
+    updates, opt_state = optimizer.update(
+        nelbo_grads,
+        state.opt_state,
+        params=[state.params, state.log_sd_diag],
+        inplace=inplace,
+    )
+    mean, log_sd_diag = torchopt.apply_updates(
+        (state.params, state.log_sd_diag), updates, inplace=inplace
+    )
+
+    if inplace:
+        tree_insert_(state.nelbo, nelbo_val.detach())
+        return state._replace(aux=aux)
+
+    return VIDiagState(mean, log_sd_diag, opt_state, nelbo_val.detach(), aux)
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.vi.diag.nelbo(mean, sd_diag, batch, log_posterior, temperature=1.0, n_samples=1, stl=True) + +𝞡

+ + +
+ +

Returns the negative evidence lower bound (NELBO) for a diagonal Normal +variational distribution over the parameters of a model.

+

Monte Carlo estimate with n_samples from q. +$$ +\text{NELBO} = - 𝔼_{q(θ)}[\log p(y|x, θ) + \log p(θ) - \log q(θ) * T]) +$$ +for temperature \(T\).

+

log_posterior expects to take parameters and input batch and return a scalar +as well as a TensorTree of any auxiliary information:

+
log_posterior_eval, aux = log_posterior(params, batch)
+
+

The log posterior and temperature are recommended to be constructed in tandem +to ensure robust scaling for a large amount of data and variable batch size.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
mean + dict + +
+

Mean of the variational distribution.

+
+ 		+ required +
sd_diag + dict + +
+

Square-root diagonal of the covariance matrix of the +variational distribution.

+
+ 		+ required +
batch + Any + +
+

Input data to log_posterior.

+
+ 		+ required +
log_posterior + LogProbFn + +
+

Function that takes parameters and input batch and +returns the log posterior (which can be unnormalised).

+
+ 		+ required +
temperature + float + +
+

Temperature to rescale (divide) log_posterior.

+
+ 		+ 1.0 +
n_samples + int + +
+

Number of samples to use for Monte Carlo estimate.

+
+ 		+ 1 +
stl + bool + +
+

Whether to use the stick-the-landing estimator +from (Roeder et al](https://arxiv.org/abs/1703.09194).

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tuple[float, Any] + +
+

The sampled approximate NELBO averaged over the batch.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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def nelbo(
+    mean: dict,
+    sd_diag: dict,
+    batch: Any,
+    log_posterior: LogProbFn,
+    temperature: float = 1.0,
+    n_samples: int = 1,
+    stl: bool = True,
+) -> Tuple[float, Any]:
+    """Returns the negative evidence lower bound (NELBO) for a diagonal Normal
+    variational distribution over the parameters of a model.
+
+    Monte Carlo estimate with `n_samples` from q.
+    $$
+    \\text{NELBO} = - 𝔼_{q(θ)}[\\log p(y|x, θ) + \\log p(θ) - \\log q(θ) * T])
+    $$
+    for temperature $T$.
+
+    `log_posterior` expects to take parameters and input batch and return a scalar
+    as well as a TensorTree of any auxiliary information:
+
+    ```
+    log_posterior_eval, aux = log_posterior(params, batch)
+    ```
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data and variable batch size.
+
+    Args:
+        mean: Mean of the variational distribution.
+        sd_diag: Square-root diagonal of the covariance matrix of the
+            variational distribution.
+        batch: Input data to log_posterior.
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior (which can be unnormalised).
+        temperature: Temperature to rescale (divide) log_posterior.
+        n_samples: Number of samples to use for Monte Carlo estimate.
+        stl: Whether to use the stick-the-landing estimator
+            from (Roeder et al](https://arxiv.org/abs/1703.09194).
+
+    Returns:
+        The sampled approximate NELBO averaged over the batch.
+    """
+    sampled_params = diag_normal_sample(mean, sd_diag, sample_shape=(n_samples,))
+    if stl:
+        mean = tree_map(lambda x: x.detach(), mean)
+        sd_diag = tree_map(lambda x: x.detach(), sd_diag)
+
+    log_p, aux = vmap(log_posterior, (0, None), (0, 0))(sampled_params, batch)
+    log_q = vmap(diag_normal_log_prob, (0, None, None))(sampled_params, mean, sd_diag)
+    return -(log_p - log_q * temperature).mean(), aux
+
+
+
+ +
+ + + + + + +
+ + +

+ posteriors.vi.diag.sample(state, sample_shape=torch.Size([])) + +𝞡

+ + +
+ +

Single sample from diagonal Normal distribution over parameters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
state + VIDiagState + +
+

State encoding mean and log standard deviations.

+
+ 		+ required +
sample_shape + Size + +
+

Shape of the desired samples.

+
+ 		+ Size([]) +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ TensorTree + +
+

Sample(s) from Normal distribution.

+
+
+ +
+ Source code in posteriors/vi/diag.py + 
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def sample(state: VIDiagState, sample_shape: torch.Size = torch.Size([])) -> TensorTree:
+    """Single sample from diagonal Normal distribution over parameters.
+
+    Args:
+        state: State encoding mean and log standard deviations.
+        sample_shape: Shape of the desired samples.
+
+    Returns:
+        Sample(s) from Normal distribution.
+    """
+    sd_diag = tree_map(torch.exp, state.log_sd_diag)
+    return diag_normal_sample(state.params, sd_diag, sample_shape=sample_shape)
+
+
+
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 00000000..85449ec7 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,119 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Backward-compatibility: docstring section titles in bold. */ +.doc-section-title { + font-weight: bold; +} + +/* Symbols in Navigation and ToC. */ +:root, +[data-md-color-scheme="default"] { + --doc-symbol-attribute-fg-color: #953800; + --doc-symbol-function-fg-color: #8250df; + --doc-symbol-method-fg-color: #8250df; + --doc-symbol-class-fg-color: #0550ae; + --doc-symbol-module-fg-color: #5cad0f; + + --doc-symbol-attribute-bg-color: #9538001a; + --doc-symbol-function-bg-color: #8250df1a; + --doc-symbol-method-bg-color: #8250df1a; + --doc-symbol-class-bg-color: #0550ae1a; + --doc-symbol-module-bg-color: #5cad0f1a; +} + +[data-md-color-scheme="slate"] { + --doc-symbol-attribute-fg-color: #ffa657; + --doc-symbol-function-fg-color: #d2a8ff; + --doc-symbol-method-fg-color: #d2a8ff; + --doc-symbol-class-fg-color: #79c0ff; + --doc-symbol-module-fg-color: #baff79; + + --doc-symbol-attribute-bg-color: #ffa6571a; + --doc-symbol-function-bg-color: #d2a8ff1a; + --doc-symbol-method-bg-color: #d2a8ff1a; + --doc-symbol-class-bg-color: #79c0ff1a; + --doc-symbol-module-bg-color: #baff791a; +} + +code.doc-symbol { + border-radius: .1rem; + font-size: .85em; + padding: 0 .3em; + font-weight: bold; +} + +code.doc-symbol-attribute { + color: var(--doc-symbol-attribute-fg-color); + background-color: var(--doc-symbol-attribute-bg-color); +} + +code.doc-symbol-attribute::after { + content: "attr"; +} + +code.doc-symbol-function { + color: var(--doc-symbol-function-fg-color); + background-color: var(--doc-symbol-function-bg-color); +} + +code.doc-symbol-function::after { + content: "func"; +} + +code.doc-symbol-method { + color: var(--doc-symbol-method-fg-color); + background-color: var(--doc-symbol-method-bg-color); +} + +code.doc-symbol-method::after { + content: "meth"; +} + +code.doc-symbol-class { + color: var(--doc-symbol-class-fg-color); + background-color: var(--doc-symbol-class-bg-color); +} + +code.doc-symbol-class::after { + content: "class"; +} + +code.doc-symbol-module { + color: var(--doc-symbol-module-fg-color); + background-color: var(--doc-symbol-module-bg-color); +} + +code.doc-symbol-module::after { + content: "mod"; +} + +.doc-signature .autorefs { + color: inherit; + border-bottom: 1px dotted currentcolor; +} diff --git a/assets/images/favicon.png b/assets/images/favicon.png new file mode 100644 index 00000000..1cf13b9f Binary files /dev/null and b/assets/images/favicon.png differ diff --git a/assets/javascripts/bundle.fe8b6f2b.min.js b/assets/javascripts/bundle.fe8b6f2b.min.js new file mode 100644 index 00000000..cf778d42 --- /dev/null +++ b/assets/javascripts/bundle.fe8b6f2b.min.js @@ -0,0 +1,29 @@ +"use strict";(()=>{var Fi=Object.create;var gr=Object.defineProperty;var ji=Object.getOwnPropertyDescriptor;var Wi=Object.getOwnPropertyNames,Dt=Object.getOwnPropertySymbols,Ui=Object.getPrototypeOf,xr=Object.prototype.hasOwnProperty,no=Object.prototype.propertyIsEnumerable;var oo=(e,t,r)=>t in e?gr(e,t,{enumerable:!0,configurable:!0,writable:!0,value:r}):e[t]=r,R=(e,t)=>{for(var r in t||(t={}))xr.call(t,r)&&oo(e,r,t[r]);if(Dt)for(var r of Dt(t))no.call(t,r)&&oo(e,r,t[r]);return e};var io=(e,t)=>{var r={};for(var o in 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.state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var Br,Da=0;function Va(){return typeof mermaid=="undefined"||mermaid instanceof Element?wt("https://unpkg.com/mermaid@10/dist/mermaid.min.js"):I(void 0)}function In(e){return e.classList.remove("mermaid"),Br||(Br=Va().pipe(E(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Rn,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),G(1))),Br.subscribe(()=>ao(this,null,function*(){e.classList.add("mermaid");let t=`__mermaid_${Da++}`,r=x("div",{class:"mermaid"}),o=e.textContent,{svg:n,fn:i}=yield mermaid.render(t,o),a=r.attachShadow({mode:"closed"});a.innerHTML=n,e.replaceWith(r),i==null||i(a)})),Br.pipe(m(()=>({ref:e})))}var Fn=x("table");function jn(e){return e.replaceWith(Fn),Fn.replaceWith(On(e)),I({ref:e})}function Na(e){let t=e.find(r=>r.checked)||e[0];return S(...e.map(r=>d(r,"change").pipe(m(()=>P(`label[for="${r.id}"]`))))).pipe(Q(P(`label[for="${t.id}"]`)),m(r=>({active:r})))}function Wn(e,{viewport$:t,target$:r}){let o=P(".tabbed-labels",e),n=$(":scope > input",e),i=Qr("prev");e.append(i);let a=Qr("next");return e.append(a),C(()=>{let s=new g,p=s.pipe(X(),ne(!0));z([s,ge(e),tt(e)]).pipe(U(p),Le(1,me)).subscribe({next([{active:c},l]){let f=Ue(c),{width:u}=ce(c);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let h=pr(o);(f.xh.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),z([De(o),ge(o)]).pipe(U(p)).subscribe(([c,l])=>{let f=Tt(o);i.hidden=c.x<16,a.hidden=c.x>f.width-l.width-16}),S(d(i,"click").pipe(m(()=>-1)),d(a,"click").pipe(m(()=>1))).pipe(U(p)).subscribe(c=>{let{width:l}=ce(o);o.scrollBy({left:l*c,behavior:"smooth"})}),r.pipe(U(p),b(c=>n.includes(c))).subscribe(c=>c.click()),o.classList.add("tabbed-labels--linked");for(let c of n){let l=P(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),d(l.firstElementChild,"click").pipe(U(p),b(f=>!(f.metaKey||f.ctrlKey)),E(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),ee(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let w of $("[data-tabs]"))for(let A of $(":scope > input",w)){let te=P(`label[for="${A.id}"]`);if(te!==c&&te.innerText.trim()===f){te.setAttribute("data-md-switching",""),A.click();break}}window.scrollTo({top:e.offsetTop-u});let h=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...h])])}}),s.pipe(U(p)).subscribe(()=>{for(let c of $("audio, video",e))c.pause()}),Na(n).pipe(E(c=>s.next(c)),L(()=>s.complete()),m(c=>R({ref:e},c)))}).pipe(Qe(se))}function Un(e,{viewport$:t,target$:r,print$:o}){return S(...$(".annotate:not(.highlight)",e).map(n=>Cn(n,{target$:r,print$:o})),...$("pre:not(.mermaid) > code",e).map(n=>$n(n,{target$:r,print$:o})),...$("pre.mermaid",e).map(n=>In(n)),...$("table:not([class])",e).map(n=>jn(n)),...$("details",e).map(n=>Pn(n,{target$:r,print$:o})),...$("[data-tabs]",e).map(n=>Wn(n,{viewport$:t,target$:r})),...$("[title]",e).filter(()=>B("content.tooltips")).map(n=>lt(n,{viewport$:t})))}function za(e,{alert$:t}){return t.pipe(v(r=>S(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function Dn(e,t){let r=P(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),za(e,t).pipe(E(n=>o.next(n)),L(()=>o.complete()),m(n=>R({ref:e},n)))})}var qa=0;function Qa(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?De(o):I({x:0,y:0}),i=S(et(t),kt(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ue(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Vn(e){let t=e.title;if(!t.length)return O;let r=`__tooltip_${qa++}`,o=Pt(r,"inline"),n=P(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),S(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Le(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(ct(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Qa(o,e).pipe(E(a=>i.next(a)),L(()=>i.complete()),m(a=>R({ref:e},a)))}).pipe(Qe(se))}function Ka({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Ye(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=Ve("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Nn(e,t){return C(()=>z([ge(e),Ka(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function zn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(X(),ne(!0));o.pipe(Z("active"),We(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue($("[title]",e)).pipe(b(()=>B("content.tooltips")),oe(a=>Vn(a)));return r.subscribe(o),t.pipe(U(n),m(a=>R({ref:e},a)),Pe(i.pipe(U(n))))})}function Ya(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),Z("active"))}function qn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?O:Ya(o,t).pipe(E(n=>r.next(n)),L(()=>r.complete()),m(n=>R({ref:e},n)))})}function Qn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),Z("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function Ba(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(oe(o=>d(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Kn(e){let t=$("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=$t("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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Gr=Vt(Yr());function Ga(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r.trimEnd()}function Bn({alert$:e}){Gr.default.isSupported()&&new F(t=>{new Gr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Ga(P(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(E(t=>{t.trigger.focus()}),m(()=>Ee("clipboard.copied"))).subscribe(e)}function Gn(e,t){return e.protocol=t.protocol,e.hostname=t.hostname,e}function Ja(e,t){let r=new Map;for(let o of $("url",e)){let n=P("loc",o),i=[Gn(new URL(n.textContent),t)];r.set(`${i[0]}`,i);for(let a of $("[rel=alternate]",o)){let s=a.getAttribute("href");s!=null&&i.push(Gn(new URL(s),t))}}return r}function ur(e){return mn(new URL("sitemap.xml",e)).pipe(m(t=>Ja(t,new URL(e))),ve(()=>I(new Map)))}function Xa(e,t){if(!(e.target instanceof Element))return O;let r=e.target.closest("a");if(r===null)return O;if(r.target||e.metaKey||e.ctrlKey)return O;let o=new URL(r.href);return o.search=o.hash="",t.has(`${o}`)?(e.preventDefault(),I(new URL(r.href))):O}function Jn(e){let t=new Map;for(let r of $(":scope > *",e.head))t.set(r.outerHTML,r);return t}function Xn(e){for(let t of $("[href], [src]",e))for(let r of["href","src"]){let o=t.getAttribute(r);if(o&&!/^(?:[a-z]+:)?\/\//i.test(o)){t[r]=t[r];break}}return I(e)}function Za(e){for(let o of["[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...B("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let n=fe(o),i=fe(o,e);typeof n!="undefined"&&typeof i!="undefined"&&n.replaceWith(i)}let t=Jn(document);for(let[o,n]of Jn(e))t.has(o)?t.delete(o):document.head.appendChild(n);for(let o of t.values()){let n=o.getAttribute("name");n!=="theme-color"&&n!=="color-scheme"&&o.remove()}let r=Se("container");return je($("script",r)).pipe(v(o=>{let n=e.createElement("script");if(o.src){for(let i of o.getAttributeNames())n.setAttribute(i,o.getAttribute(i));return o.replaceWith(n),new F(i=>{n.onload=()=>i.complete()})}else return n.textContent=o.textContent,o.replaceWith(n),O}),X(),ne(document))}function Zn({location$:e,viewport$:t,progress$:r}){let o=ye();if(location.protocol==="file:")return O;let n=ur(o.base);I(document).subscribe(Xn);let i=d(document.body,"click").pipe(We(n),v(([p,c])=>Xa(p,c)),pe()),a=d(window,"popstate").pipe(m(xe),pe());i.pipe(ee(t)).subscribe(([p,{offset:c}])=>{history.replaceState(c,""),history.pushState(null,"",p)}),S(i,a).subscribe(e);let s=e.pipe(Z("pathname"),v(p=>ln(p,{progress$:r}).pipe(ve(()=>(pt(p,!0),O)))),v(Xn),v(Za),pe());return 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p=o.pipe(E(()=>s.innerHTML=""),v(({items:l})=>S(I(...l.slice(0,10)),I(...l.slice(10)).pipe(Ye(4),Vr(n),v(([f])=>f)))),m(Tn),pe());return p.subscribe(l=>s.appendChild(l)),p.pipe(oe(l=>{let f=fe("details",l);return typeof f=="undefined"?O:d(f,"toggle").pipe(U(o),m(()=>f))})).subscribe(l=>{l.open===!1&&l.offsetTop<=i.scrollTop&&i.scrollTo({top:l.offsetTop})}),t.pipe(b(dr),m(({data:l})=>l)).pipe(E(l=>o.next(l)),L(()=>o.complete()),m(l=>R({ref:e},l)))}function is(e,{query$:t}){return t.pipe(m(({value:r})=>{let o=xe();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function ci(e,t){let r=new g,o=r.pipe(X(),ne(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),d(e,"click").pipe(U(o)).subscribe(n=>n.preventDefault()),is(e,t).pipe(E(n=>r.next(n)),L(()=>r.complete()),m(n=>R({ref:e},n)))}function pi(e,{worker$:t,keyboard$:r}){let o=new g,n=Se("search-query"),i=S(d(n,"keydown"),d(n,"focus")).pipe(be(se),m(()=>n.value),K());return o.pipe(We(i),m(([{suggest:s},p])=>{let c=p.split(/([\s-]+)/);if(s!=null&&s.length&&c[c.length-1]){let l=s[s.length-1];l.startsWith(c[c.length-1])&&(c[c.length-1]=l)}else c.length=0;return c})).subscribe(s=>e.innerHTML=s.join("").replace(/\s/g," ")),r.pipe(b(({mode:s})=>s==="search")).subscribe(s=>{switch(s.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(b(dr),m(({data:s})=>s)).pipe(E(s=>o.next(s)),L(()=>o.complete()),m(()=>({ref:e})))}function li(e,{index$:t,keyboard$:r}){let o=ye();try{let n=ni(o.search,t),i=Se("search-query",e),a=Se("search-result",e);d(e,"click").pipe(b(({target:p})=>p instanceof Element&&!!p.closest("a"))).subscribe(()=>Je("search",!1)),r.pipe(b(({mode:p})=>p==="search")).subscribe(p=>{let c=Re();switch(p.type){case"Enter":if(c===i){let l=new Map;for(let f of $(":first-child [href]",a)){let 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z([t,r.pipe(Q(xe()),b(o=>!!o.searchParams.get("h")))]).pipe(m(([o,n])=>oi(o.config)(n.searchParams.get("h"))),m(o=>{var a;let n=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let s=i.nextNode();s;s=i.nextNode())if((a=s.parentElement)!=null&&a.offsetHeight){let p=s.textContent,c=o(p);c.length>p.length&&n.set(s,c)}for(let[s,p]of n){let{childNodes:c}=x("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function as(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return z([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Jr(e,o){var n=o,{header$:t}=n,r=io(n,["header$"]);let i=P(".md-sidebar__scrollwrap",e),{y:a}=Ue(i);return C(()=>{let s=new g,p=s.pipe(X(),ne(!0)),c=s.pipe(Le(0,me));return 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r=`https://api.github.com/users/${e}`;return Ne(r).pipe(m(o=>({repositories:o.public_repos})),Be({}))}}function ui(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ne(r).pipe(ve(()=>O),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),Be({}))}function di(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return fi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return ui(r,o)}return O}var ss;function cs(e){return ss||(ss=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return O}return di(e.href).pipe(E(o=>__md_set("__source",o,sessionStorage)))}).pipe(ve(()=>O),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),G(1)))}function hi(e){let t=P(":scope > :last-child",e);return C(()=>{let r=new g;return 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n=t.pipe(m(({offset:{y:a}})=>a),Ye(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return z([i,n]).pipe(m(([a,s])=>!(a&&s)),K(),U(o.pipe(Ce(1))),ne(!0),st({delay:250}),m(a=>({hidden:a})))}function gi(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(X(),ne(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(U(a),Z("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),d(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),ms(e,{viewport$:t,main$:o,target$:n}).pipe(E(s=>i.next(s)),L(()=>i.complete()),m(s=>R({ref:e},s)))}function xi({document$:e,viewport$:t}){e.pipe(v(()=>$(".md-ellipsis")),oe(r=>tt(r).pipe(U(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?lt(n,{viewport$:t}).pipe(U(e.pipe(Ce(1))),L(()=>n.removeAttribute("title"))):O})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>$(".md-status")),oe(r=>lt(r,{viewport$:t}))).subscribe()}function yi({document$:e,tablet$:t}){e.pipe(v(()=>$(".md-toggle--indeterminate")),E(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>d(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),ee(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function fs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ei({document$:e}){e.pipe(v(()=>$("[data-md-scrollfix]")),E(t=>t.removeAttribute("data-md-scrollfix")),b(fs),oe(t=>d(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function wi({viewport$:e,tablet$:t}){z([Ve("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),ee(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let n=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",n&&window.scrollTo(0,n)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let o=e[r];typeof o=="string"?o=document.createTextNode(o):o.parentNode&&o.parentNode.removeChild(o),r?t.insertBefore(this.previousSibling,o):t.replaceChild(o,this)}}}));function us(){return location.protocol==="file:"?wt(`${new URL("search/search_index.js",Xr.base)}`).pipe(m(()=>__index),G(1)):Ne(new URL("search/search_index.json",Xr.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Yo(),jt=nn(),Ot=cn(jt),Zr=on(),Oe=bn(),hr=$t("(min-width: 960px)"),Si=$t("(min-width: 1220px)"),Oi=pn(),Xr=ye(),Mi=document.forms.namedItem("search")?us():Ke,eo=new g;Bn({alert$:eo});var to=new g;B("navigation.instant")&&Zn({location$:jt,viewport$:Oe,progress$:to}).subscribe(ot);var Ti;((Ti=Xr.version)==null?void 0:Ti.provider)==="mike"&&ii({document$:ot});S(jt,Ot).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});Zr.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&&pt(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&&pt(r);break;case"Enter":let o=Re();o instanceof HTMLLabelElement&&o.click()}});xi({viewport$:Oe,document$:ot});yi({document$:ot,tablet$:hr});Ei({document$:ot});wi({viewport$:Oe,tablet$:hr});var rt=Nn(Se("header"),{viewport$:Oe}),Ft=ot.pipe(m(()=>Se("main")),v(e=>Qn(e,{viewport$:Oe,header$:rt})),G(1)),ds=S(...ae("consent").map(e=>xn(e,{target$:Ot})),...ae("dialog").map(e=>Dn(e,{alert$:eo})),...ae("header").map(e=>zn(e,{viewport$:Oe,header$:rt,main$:Ft})),...ae("palette").map(e=>Kn(e)),...ae("progress").map(e=>Yn(e,{progress$:to})),...ae("search").map(e=>li(e,{index$:Mi,keyboard$:Zr})),...ae("source").map(e=>hi(e))),hs=C(()=>S(...ae("announce").map(e=>gn(e)),...ae("content").map(e=>Un(e,{viewport$:Oe,target$:Ot,print$:Oi})),...ae("content").map(e=>B("search.highlight")?mi(e,{index$:Mi,location$:jt}):O),...ae("header-title").map(e=>qn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Si,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft})):Nr(hr,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft}))),...ae("tabs").map(e=>bi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>vi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})),...ae("top").map(e=>gi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})))),Li=ot.pipe(v(()=>hs),Pe(ds),G(1));Li.subscribe();window.document$=ot;window.location$=jt;window.target$=Ot;window.keyboard$=Zr;window.viewport$=Oe;window.tablet$=hr;window.screen$=Si;window.print$=Oi;window.alert$=eo;window.progress$=to;window.component$=Li;})(); +//# sourceMappingURL=bundle.fe8b6f2b.min.js.map + diff --git a/assets/javascripts/bundle.fe8b6f2b.min.js.map b/assets/javascripts/bundle.fe8b6f2b.min.js.map new file mode 100644 index 00000000..82635852 --- /dev/null +++ b/assets/javascripts/bundle.fe8b6f2b.min.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/rxjs/node_modules/tslib/tslib.es6.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/BehaviorSubject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/QueueAction.ts", "node_modules/rxjs/src/internal/scheduler/QueueScheduler.ts", "node_modules/rxjs/src/internal/scheduler/queue.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/EmptyError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/debounce.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/templates/assets/javascripts/browser/document/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/browser/element/focus/index.ts", "src/templates/assets/javascripts/browser/element/hover/index.ts", "src/templates/assets/javascripts/utilities/h/index.ts", "src/templates/assets/javascripts/utilities/round/index.ts", "src/templates/assets/javascripts/browser/script/index.ts", "src/templates/assets/javascripts/browser/element/size/_/index.ts", "src/templates/assets/javascripts/browser/element/size/content/index.ts", "src/templates/assets/javascripts/browser/element/offset/_/index.ts", "src/templates/assets/javascripts/browser/element/offset/content/index.ts", "src/templates/assets/javascripts/browser/element/visibility/index.ts", "src/templates/assets/javascripts/browser/toggle/index.ts", "src/templates/assets/javascripts/browser/keyboard/index.ts", "src/templates/assets/javascripts/browser/location/_/index.ts", "src/templates/assets/javascripts/browser/location/hash/index.ts", "src/templates/assets/javascripts/browser/media/index.ts", "src/templates/assets/javascripts/browser/request/index.ts", "src/templates/assets/javascripts/browser/viewport/offset/index.ts", "src/templates/assets/javascripts/browser/viewport/size/index.ts", "src/templates/assets/javascripts/browser/viewport/_/index.ts", "src/templates/assets/javascripts/browser/viewport/at/index.ts", "src/templates/assets/javascripts/browser/worker/index.ts", "src/templates/assets/javascripts/_/index.ts", "src/templates/assets/javascripts/components/_/index.ts", "src/templates/assets/javascripts/components/announce/index.ts", "src/templates/assets/javascripts/components/consent/index.ts", "src/templates/assets/javascripts/templates/tooltip/index.tsx", "src/templates/assets/javascripts/templates/annotation/index.tsx", "src/templates/assets/javascripts/templates/clipboard/index.tsx", "src/templates/assets/javascripts/templates/search/index.tsx", "src/templates/assets/javascripts/templates/source/index.tsx", "src/templates/assets/javascripts/templates/tabbed/index.tsx", "src/templates/assets/javascripts/templates/table/index.tsx", "src/templates/assets/javascripts/templates/version/index.tsx", "src/templates/assets/javascripts/components/tooltip2/index.ts", "src/templates/assets/javascripts/components/content/annotation/_/index.ts", "src/templates/assets/javascripts/components/content/annotation/list/index.ts", "src/templates/assets/javascripts/components/content/annotation/block/index.ts", "src/templates/assets/javascripts/components/content/code/_/index.ts", "src/templates/assets/javascripts/components/content/details/index.ts", "src/templates/assets/javascripts/components/content/mermaid/index.css", "src/templates/assets/javascripts/components/content/mermaid/index.ts", "src/templates/assets/javascripts/components/content/table/index.ts", "src/templates/assets/javascripts/components/content/tabs/index.ts", "src/templates/assets/javascripts/components/content/_/index.ts", "src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n __assign = Object.assign || function __assign(t) {\r\n for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n s = arguments[i];\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n }\r\n return t;\r\n }\r\n return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n var t = {};\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n t[p] = s[p];\r\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n t[p[i]] = s[p[i]];\r\n }\r\n return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n return new (P || (P = Promise))(function (resolve, reject) {\r\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n function verb(n) { return function (v) { return step([n, v]); }; }\r\n function step(op) {\r\n if (f) throw new TypeError(\"Generator is already executing.\");\r\n while (_) try {\r\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n if (y = 0, t) op = [op[0] & 2, t.value];\r\n switch (op[0]) {\r\n case 0: case 1: t = op; break;\r\n case 4: _.label++; return { value: op[1], done: false };\r\n case 5: _.label++; y = op[1]; op = [0]; continue;\r\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n default:\r\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n if (t[2]) _.ops.pop();\r\n _.trys.pop(); continue;\r\n }\r\n op = body.call(thisArg, _);\r\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n if (m) return m.call(o);\r\n if (o && typeof o.length === \"number\") return {\r\n next: function () {\r\n if (o && i >= o.length) o = void 0;\r\n return { value: o && o[i++], done: !o };\r\n }\r\n };\r\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n if (!m) return o;\r\n var i = m.call(o), r, ar = [], e;\r\n try {\r\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n }\r\n catch (error) { e = { error: error }; }\r\n finally {\r\n try {\r\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n }\r\n finally { if (e) throw e.error; }\r\n }\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n for (var ar = [], i = 0; i < arguments.length; i++)\r\n ar = ar.concat(__read(arguments[i]));\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n r[k] = a[j];\r\n return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n if (ar || !(i in from)) {\r\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n ar[i] = from[i];\r\n }\r\n }\r\n return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n function fulfill(value) { resume(\"next\", value); }\r\n function reject(value) { resume(\"throw\", value); }\r\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n var i, p;\r\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var m = o[Symbol.asyncIterator], i;\r\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n if (mod && mod.__esModule) return mod;\r\n var result = {};\r\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n __setModuleDefault(result, mod);\r\n return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an