Add public testing function to mock sample

pymc/testing.py

@@ -18,6 +18,8 @@
 from collections.abc import Callable, Sequence
 from typing import Any
 
+from arviz import InferenceData
+from xarray import DataArray
 import numpy as np
 import pytensor
 import pytensor.tensor as pt
@@ -982,3 +984,53 @@ def assert_no_rvs(vars: Sequence[Variable]) -> None:
     rvs = rvs_in_graph(vars)
     if rvs:
         raise AssertionError(f"RV found in graph: {rvs}")
+
+
+def mock_sample(*args, **kwargs):
+    """Mock the pm.sample function by returning prior predictive samples as posterior.
+
+    Useful for testing models that use pm.sample without running MCMC sampling.
+
+    Examples
+    --------
+    Using mock_sample with pytest
+
+    .. code-block:: python
+
+        import pytest
+
+        import pymc as pm
+        from pymc.testing import mock_sample
+
+
+        @pytest.fixture(scope="module")
+        def mock_pymc_sample():
+            original_sample = pm.sample
+            pm.sample = mock_sample
+
+            yield
+
+            pm.sample = original_sample
+
+    """
+    random_seed = kwargs.get("random_seed", None)
+    model = kwargs.get("model", None)
+    draws = kwargs.get("draws", 10)
+    n_chains = kwargs.get("chains", 1)
+    idata: InferenceData = pm.sample_prior_predictive(
+        model=model,
+        random_seed=random_seed,
+        draws=draws,
+    )
+
+    expanded_chains = DataArray(
+        np.ones(n_chains),
+        coords={"chain": np.arange(n_chains)},
+    )
+    idata.add_groups(
+        posterior=(idata.prior.mean("chain") * expanded_chains).transpose("chain", "draw", ...)
+    )
+    del idata.prior
+    if "prior_predictive" in idata:
+        del idata.prior_predictive
+    return idata