Allow for generation of a constrained float with multiple_of argument for hypothesis plugin

changes/2442-tobi-lipede-oodle.md

@@ -0,0 +1 @@
+enable the Hypothesis plugin to generate a constrained float when the `multiple_of` argument is specified.

pydantic/_hypothesis_plugin.py

@@ -276,6 +276,7 @@ def resolve_confloat(cls):  # type: ignore[no-untyped-def]
     max_value = cls.le
     exclude_min = False
     exclude_max = False
+
     if cls.gt is not None:
         assert min_value is None, 'Set `gt` or `ge`, but not both'
         min_value = cls.gt
@@ -284,7 +285,21 @@ def resolve_confloat(cls):  # type: ignore[no-untyped-def]
         assert max_value is None, 'Set `lt` or `le`, but not both'
         max_value = cls.lt
         exclude_max = True
-    return st.floats(min_value, max_value, exclude_min=exclude_min, exclude_max=exclude_max, allow_nan=False)
+
+    if cls.multiple_of is None:
+        return st.floats(min_value, max_value, exclude_min=exclude_min, exclude_max=exclude_max, allow_nan=False)
+
+    if min_value is not None:
+        min_value = math.ceil(min_value / cls.multiple_of)
+        if exclude_min:
+            min_value = min_value + 1
+    if max_value is not None:
+        assert max_value >= cls.multiple_of, 'Cannot build model with max value smaller than multiple of'
+        max_value = math.floor(max_value / cls.multiple_of)
+        if exclude_max:
+            max_value = max_value - 1
+
+    return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of)
 
 
 @resolves(pydantic.ConstrainedInt)

tests/test_hypothesis_plugin.py

@@ -74,6 +74,8 @@ class ConstrainedNumbersModel(pydantic.BaseModel):
         conintmul: pydantic.conint(ge=10, le=100, multiple_of=7)
         confloatt: pydantic.confloat(gt=10, lt=100)
         confloate: pydantic.confloat(ge=10, le=100)
+        confloatemul: pydantic.confloat(ge=10, le=100, multiple_of=4.2)
+        confloattmul: pydantic.confloat(gt=10, lt=100, multiple_of=10)
         condecimalt: pydantic.condecimal(gt=10, lt=100)
         condecimale: pydantic.condecimal(ge=10, le=100)