Use JAX type annotation for random keys. Fix pypi links to tests.

PiperOrigin-RevId: 430463106

neural_tangents/_src/monte_carlo.py

@@ -37,7 +37,7 @@
 from jax.tree_util import tree_map
 from jax.tree_util import tree_multimap
 from .utils import utils
-from .utils.typing import ApplyFn, Axes, EmpiricalGetKernelFn, Get, InitFn, MonteCarloKernelFn, NTTree, PRNGKey, PyTree, VMapAxes
+from .utils.typing import ApplyFn, Axes, EmpiricalGetKernelFn, Get, InitFn, MonteCarloKernelFn, NTTree, PyTree, VMapAxes
 
 
 def _sample_once_kernel_fn(kernel_fn: EmpiricalGetKernelFn,
@@ -52,7 +52,7 @@ def _sample_once_kernel_fn(kernel_fn: EmpiricalGetKernelFn,
   def kernel_fn_sample_once(
       x1: NTTree[np.ndarray],
       x2: Optional[NTTree[np.ndarray]],
-      key: PRNGKey,
+      key: random.KeyArray,
       get: Get,
       **apply_fn_kwargs):
     init_key, dropout_key = random.split(key, 2)
@@ -64,7 +64,7 @@ def kernel_fn_sample_once(
 
 def _sample_many_kernel_fn(
     kernel_fn_sample_once,
-    key: PRNGKey,
+    key: random.KeyArray,
     n_samples: Set[int],
     get_generator: bool):
   def normalize(sample: PyTree, n: int) -> PyTree:
@@ -115,7 +115,7 @@ def get_sampled_kernel(
 def monte_carlo_kernel_fn(
     init_fn: InitFn,
     apply_fn: ApplyFn,
-    key: PRNGKey,
+    key: random.KeyArray,
     n_samples: Union[int, Iterable[int]],
     batch_size: int = 0,
     device_count: int = -1,

neural_tangents/_src/utils/typing.py

@@ -17,6 +17,7 @@
 from typing import Any, Dict, Generator, List, Optional, Sequence, TYPE_CHECKING, Tuple, TypeVar, Union
 
 import jax.numpy as np
+from jax import random
 from .kernel import Kernel
 from typing_extensions import Protocol
 
@@ -29,14 +30,6 @@
 PyTree = Any
 
 
-"""A type alias for PRNGKeys.
-
-  See https://jax.readthedocs.io/en/latest/jax.random.html#jax.random.PRNGKey
-  for details.
-"""
-PRNGKey = np.ndarray
-
-
 """A type alias for axes specification.
 
   Axes can be specified as integers (`axis=-1`) or sequences (`axis=(1, 3)`).
@@ -81,7 +74,7 @@ class InitFn(Protocol):
 
   def __call__(
       self,
-      rng: PRNGKey,
+      rng: random.KeyArray,
       input_shape: Shapes,
       **kwargs
   ) -> Tuple[Shapes, PyTree]:

setup.py

@@ -80,7 +80,10 @@ def _get_version() -> str:
         'Bug Tracker': 'https://github.com/google/neural-tangents/issues',
         'Release Notes': 'https://github.com/google/neural-tangents/releases',
         'PyPi': 'https://pypi.org/project/neural-tangents/',
-        'Tests': 'https://travis-ci.org/github/google/neural-tangents',
+        'Linux Tests': 'https://github.com/google/neural-tangents/actions/workflows/linux.yml',
+        'macOS Tests': 'https://github.com/google/neural-tangents/actions/workflows/macos.yml',
+        'Pytype': 'https://github.com/google/neural-tangents/actions/workflows/pytype.yml',
+        'Coverage': 'https://app.codecov.io/gh/google/neural-tangents'
     },
     packages=setuptools.find_packages(exclude=('presentation',)),
     long_description=long_description,