Fix metropolis.py type hints

Author: ricardoV94

pymc/step_methods/metropolis.py

@@ -59,7 +59,7 @@
  "MultivariateNormalProposal",
 ]
 
-from pymc.util import get_value_vars_from_user_vars
+from pymc.util import RandomGenerator, get_value_vars_from_user_vars
 
 # Available proposal distributions for Metropolis
 
@@ -302,7 +302,7 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
  accept_rate = self.delta_logp(q, q0d)
  q, accepted = metrop_select(accept_rate, q, q0d, rng=self.rng)
  self.accept_rate_iter = accept_rate
- self.accepted_iter = accepted
+ self.accepted_iter[0] = accepted
  self.accepted_sum += accepted
 
  self.steps_until_tune -= 1
@@ -622,14 +622,16 @@ class CategoricalGibbsMetropolis(ArrayStep):
 
  _state_class = CategoricalGibbsMetropolisState
 
- def __init__(self, vars, proposal="uniform", order="random", model=None, rng=None):
+ def __init__(
+ self, vars, proposal="uniform", order="random", model=None, rng: RandomGenerator = None
+ ):
  model = pm.modelcontext(model)
 
  vars = get_value_vars_from_user_vars(vars, model)
 
  initial_point = model.initial_point()
 
- dimcats = []
+ dimcats: list[tuple[int, int]] = []
  # The above variable is a list of pairs (aggregate dimension, number
  # of categories). For example, if vars = [x, y] with x being a 2-D
  # variable with M categories and y being a 3-D variable with N
@@ -665,10 +667,10 @@ def __init__(self, vars, proposal="uniform", order="random", model=None, rng=Non
  self.dimcats = [dimcats[j] for j in order]
 
  if proposal == "uniform":
- self.astep = self.astep_unif
+ self.astep = self.astep_unif # type: ignore[assignment]
  elif proposal == "proportional":
  # Use the optimized "Metropolized Gibbs Sampler" described in Liu96.
- self.astep = self.astep_prop
+ self.astep = self.astep_prop # type: ignore[assignment]
  else:
  raise ValueError("Argument 'proposal' should either be 'uniform' or 'proportional'")