Improve random seed processing for SMC sampling (#6298)

* improve random seed processing
* improve type-hint

Author: Juan Orduz

pymc/smc/sampling.py

@@ -18,7 +18,6 @@
 import warnings
 
 from collections import defaultdict
-from collections.abc import Iterable
 from itertools import repeat
 
 import cloudpickle
@@ -34,6 +33,7 @@
 from pymc.model import modelcontext
 from pymc.sampling.parallel import _cpu_count
 from pymc.smc.kernels import IMH
+from pymc.util import RandomState, _get_seeds_per_chain
 
 
 def sample_smc(
@@ -42,7 +42,7 @@ def sample_smc(
     *,
     start=None,
     model=None,
-    random_seed=None,
+    random_seed: RandomState = None,
     chains=None,
     cores=None,
     compute_convergence_checks=True,
@@ -64,8 +64,10 @@ def sample_smc(
         Starting point in parameter space. It should be a list of dict with length `chains`.
         When None (default) the starting point is sampled from the prior distribution.
     model: Model (optional if in ``with`` context)).
-    random_seed: int
-        random seed
+    random_seed : int, array-like of int, RandomState or Generator, optional
+        Random seed(s) used by the sampling steps. If a list, tuple or array of ints
+        is passed, each entry will be used to seed each chain. A ValueError will be
+        raised if the length does not match the number of chains.
     chains : int
         The number of chains to sample. Running independent chains is important for some
         convergence statistics. If ``None`` (default), then set to either ``cores`` or 2, whichever
@@ -183,20 +185,7 @@ def sample_smc(
     else:
         cores = min(chains, cores)
 
-    if random_seed == -1:
-        raise FutureWarning(
-            f"random_seed should be a non-negative integer or None, got: {random_seed}"
-            "This will raise a ValueError in the Future"
-        )
-        random_seed = None
-    if isinstance(random_seed, int) or random_seed is None:
-        rng = np.random.default_rng(seed=random_seed)
-        random_seed = list(rng.integers(2**30, size=chains))
-    elif isinstance(random_seed, Iterable):
-        if len(random_seed) != chains:
-            raise ValueError(f"Length of seeds ({len(seeds)}) must match number of chains {chains}")
-    else:
-        raise TypeError("Invalid value for `random_seed`. Must be tuple, list, int or None")
+    random_seed = _get_seeds_per_chain(random_state=random_seed, chains=chains)
 
     model = modelcontext(model)