Add Types to numpyro.distributions.distribution

numpyro/_typing.py

@@ -58,59 +58,6 @@ def check(self, value: NumLike) -> ArrayLike: ...
     def feasible_like(self, prototype: NumLike) -> NumLike: ...
 
 
-@runtime_checkable
-class DistributionT(Protocol):
-    """A protocol for typing distributions.
-
-    Used to type object of type numpyro.distributions.Distribution, funsor.Funsor
-    or tensorflow_probability.distributions.Distribution.
-    """
-
-    arg_constraints: dict[str, ConstraintT] = ...
-    support: ConstraintT = ...
-    has_enumerate_support: bool = ...
-    reparametrized_params: list[str] = ...
-    _validate_args: bool = ...
-    pytree_data_fields: tuple = ...
-    pytree_aux_fields: tuple = ...
-
-    def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
-
-    def rsample(
-        self, key: jax.dtypes.prng_key, sample_shape: tuple[int, ...] = ()
-    ) -> ArrayLike: ...
-    def sample(
-        self, key: jax.dtypes.prng_key, sample_shape: tuple[int, ...] = ()
-    ) -> ArrayLike: ...
-    def log_prob(self, value: ArrayLike) -> ArrayLike: ...
-    def cdf(self, value: ArrayLike) -> ArrayLike: ...
-    def icdf(self, q: ArrayLike) -> ArrayLike: ...
-    def entropy(self) -> ArrayLike: ...
-    def enumerate_support(self, expand: bool = True) -> ArrayLike: ...
-    def shape(self, sample_shape: tuple[int, ...] = ()) -> tuple[int, ...]: ...
-
-    @property
-    def batch_shape(self) -> tuple[int, ...]: ...
-    @property
-    def event_shape(self) -> tuple[int, ...]: ...
-    @property
-    def event_dim(self) -> int: ...
-    @property
-    def has_rsample(self) -> bool: ...
-
-    @property
-    def mean(self) -> ArrayLike: ...
-    @property
-    def variance(self) -> ArrayLike: ...
-
-    @property
-    def is_discrete(self) -> bool: ...
-
-
-# To avoid breaking changes for user code that uses `DistributionLike`
-DistributionLike = DistributionT
-
-
 @runtime_checkable
 class TransformT(Protocol):
     _inv: Optional[Union["TransformT", weakref.ref]] = ...

numpyro/distributions/censored.py

@@ -12,7 +12,7 @@
 import jax.numpy as jnp
 from jax.typing import ArrayLike
 
-from numpyro._typing import ConstraintT, DistributionT
+from numpyro._typing import ConstraintT
 from numpyro.distributions import constraints
 from numpyro.distributions.distribution import Distribution
 from numpyro.distributions.util import log1mexp, promote_shapes, validate_sample
@@ -78,7 +78,7 @@ class LeftCensoredDistribution(Distribution):
 
     def __init__(
         self,
-        base_dist: DistributionT,
+        base_dist: Distribution,
         censored: ArrayLike = False,
         *,
         validate_args: bool = False,
@@ -194,7 +194,7 @@ class RightCensoredDistribution(Distribution):
 
     def __init__(
         self,
-        base_dist: DistributionT,
+        base_dist: Distribution,
         censored: ArrayLike = False,
         *,
         validate_args: bool = False,
@@ -331,7 +331,7 @@ class IntervalCensoredDistribution(Distribution):
 
     def __init__(
         self,
-        base_dist: DistributionT,
+        base_dist: Distribution,
         left_censored: ArrayLike,
         right_censored: ArrayLike,
         *,

numpyro/distributions/constraints.py

@@ -825,7 +825,7 @@ def tree_flatten(self):
 complex = _Complex()
 corr_cholesky = _CorrCholesky()
 corr_matrix = _CorrMatrix()
-dependent: Constraint = _Dependent()
+dependent: _Dependent = _Dependent()
 greater_than = _GreaterThan
 greater_than_eq = _GreaterThanEq
 less_than = _LessThan

numpyro/distributions/continuous.py

@@ -59,7 +59,6 @@
 from numpyro.distributions.discrete import _to_logits_bernoulli
 from numpyro.distributions.distribution import (
     Distribution,
-    DistributionT,
     TransformedDistribution,
 )
 from numpyro.distributions.transforms import (
@@ -409,7 +408,7 @@ def __init__(
         self,
         t: Array,
         sde_fn: Callable[[Array, Array], tuple[Array, Array]],
-        init_dist: DistributionT,
+        init_dist: Distribution,
         *,
         validate_args: Optional[bool] = None,
     ) -> None:

numpyro/distributions/copula.py

@@ -8,7 +8,7 @@
 from jax import Array, lax, numpy as jnp
 from jax.typing import ArrayLike
 
-from numpyro._typing import ConstraintT, DistributionT
+from numpyro._typing import ConstraintT
 import numpyro.distributions.constraints as constraints
 from numpyro.distributions.continuous import Beta, MultivariateNormal, Normal
 from numpyro.distributions.distribution import Distribution
@@ -39,7 +39,7 @@ class GaussianCopula(Distribution):
 
     def __init__(
         self,
-        marginal_dist: DistributionT,
+        marginal_dist: Distribution,
         correlation_matrix: Optional[Array] = None,
         correlation_cholesky: Optional[Array] = None,
         *,

numpyro/distributions/discrete.py

@@ -39,7 +39,7 @@
 from jax.typing import ArrayLike
 
 from numpyro.distributions import constraints, transforms
-from numpyro.distributions.distribution import Distribution, DistributionT
+from numpyro.distributions.distribution import Distribution
 from numpyro.distributions.util import (
     assert_one_of,
     binary_cross_entropy_with_logits,
@@ -845,7 +845,7 @@ class ZeroInflatedProbs(Distribution):
 
     def __init__(
         self,
-        base_dist: DistributionT,
+        base_dist: Distribution,
         gate: ArrayLike,
         *,
         validate_args: Optional[bool] = None,
@@ -908,7 +908,7 @@ class ZeroInflatedLogits(ZeroInflatedProbs):
 
     def __init__(
         self,
-        base_dist: DistributionT,
+        base_dist: Distribution,
         gate_logits: ArrayLike,
         *,
         validate_args: Optional[bool] = None,
@@ -928,7 +928,7 @@ def log_prob(self, value: ArrayLike) -> ArrayLike:
 
 
 def ZeroInflatedDistribution(
-    base_dist: DistributionT,
+    base_dist: Distribution,
     *,
     gate: Optional[ArrayLike] = None,
     gate_logits: Optional[ArrayLike] = None,