Bring back testing utilities used in downstream packages

Follow up to
* 534a9ae
* e1d36ca

.github/workflows/tests.yml

@@ -47,6 +47,7 @@ jobs:
             tests/test_func_utils.py
             tests/distributions/test_shape_utils.py
             tests/distributions/test_mixture.py
+            tests/test_testing.py
 
           - |
             tests/distributions/test_continuous.py

docs/source/contributing/implementing_distribution.md

@@ -240,10 +240,11 @@ Most tests can be accommodated by the default `BaseTestDistributionRandom` class
 1. Shape variable inference is correct, via `check_rv_size`
 
 ```python
-from tests.distributions.util import BaseTestDistributionRandom, seeded_scipy_distribution_builder
 
-class TestBlah(BaseTestDistributionRandom):
+from pymc.testing import BaseTestDistributionRandom, seeded_scipy_distribution_builder
+
 
+class TestBlah(BaseTestDistributionRandom):
     pymc_dist = pm.Blah
     # Parameters with which to test the blah pymc Distribution
     pymc_dist_params = {"param1": 0.25, "param2": 2.0}
@@ -311,38 +312,36 @@ Tests for the `logp` and `logcdf` mostly make use of the helpers `check_logp`, `
 `check_selfconsistency_discrete_logcdf` implemented in `~tests.distributions.util`
 
 ```python
-from tests.helpers import select_by_precision
-from tests.distributions.util import check_logp, check_logcdf, Domain
+
+from pymc.testing import Domain, check_logp, check_logcdf, select_by_precision
 
 R = Domain([-np.inf, -2.1, -1, -0.01, 0.0, 0.01, 1, 2.1, np.inf])
 Rplus = Domain([0, 0.01, 0.1, 0.9, 0.99, 1, 1.5, 2, 100, np.inf])
 
 
-
 def test_blah():
-
-  check_logp(
-      pymc_dist=pm.Blah,
-      # Domain of the distribution values
-      domain=R,
-      # Domains of the distribution parameters
-      paramdomains={"mu": R, "sigma": Rplus},
-      # Reference scipy (or other) logp function
-      scipy_logp = lambda value, mu, sigma: sp.norm.logpdf(value, mu, sigma),
-      # Number of decimal points expected to match between the pymc and reference functions
-      decimal=select_by_precision(float64=6, float32=3),
-      # Maximum number of combinations of domain * paramdomains to test
-      n_samples=100,
-  )
-
-  check_logcdf(
-      pymc_dist=pm.Blah,
-      domain=R,
-      paramdomains={"mu": R, "sigma": Rplus},
-      scipy_logcdf=lambda value, mu, sigma: sp.norm.logcdf(value, mu, sigma),
-      decimal=select_by_precision(float64=6, float32=1),
-      n_samples=-1,
-  )
+    check_logp(
+        pymc_dist=pm.Blah,
+        # Domain of the distribution values
+        domain=R,
+        # Domains of the distribution parameters
+        paramdomains={"mu": R, "sigma": Rplus},
+        # Reference scipy (or other) logp function
+        scipy_logp=lambda value, mu, sigma: sp.norm.logpdf(value, mu, sigma),
+        # Number of decimal points expected to match between the pymc and reference functions
+        decimal=select_by_precision(float64=6, float32=3),
+        # Maximum number of combinations of domain * paramdomains to test
+        n_samples=100,
+    )
+
+    check_logcdf(
+        pymc_dist=pm.Blah,
+        domain=R,
+        paramdomains={"mu": R, "sigma": Rplus},
+        scipy_logcdf=lambda value, mu, sigma: sp.norm.logcdf(value, mu, sigma),
+        decimal=select_by_precision(float64=6, float32=1),
+        n_samples=-1,
+    )
 
 ```
 
@@ -382,7 +381,8 @@ which checks if:
 
 import pytest
 from pymc.distributions import Blah
-from tests.distributions.util import assert_moment_is_expected
+from pymc.testing import assert_moment_is_expected
+
 
 @pytest.mark.parametrize(
     "param1, param2, size, expected",