From 5cffef7f07a1007681b671304e4ee69864c13940 Mon Sep 17 00:00:00 2001
From: poulami-sau <109125687+poulami-sau@users.noreply.github.com>
Date: Tue, 23 Apr 2024 04:45:33 -0400
Subject: [PATCH] Fixed Bug Regarding Attribute Error in pytest.approx For
 Types Implicitly Convertible to Numpy Arrays (#12232)

* added test case in testing/python/approx.py based on test case provided by reporter in issue #12114
* test cases pass for pytest testing/python/approx.py
* expanded the type annotation to include objects which may cast to a array and renamed other_side to other_side_as_array and asserted that it is not none
---
 AUTHORS                    |  1 +
 changelog/12114.bugfix.rst |  1 +
 src/_pytest/python_api.py  | 14 +++++++++-----
 testing/python/approx.py   | 17 +++++++++++++++++
 4 files changed, 28 insertions(+), 5 deletions(-)
 create mode 100644 changelog/12114.bugfix.rst

diff --git a/AUTHORS b/AUTHORS
index 7c35a6152..d7148acfc 100644
--- a/AUTHORS
+++ b/AUTHORS
@@ -321,6 +321,7 @@ Pierre Sassoulas
 Pieter Mulder
 Piotr Banaszkiewicz
 Piotr Helm
+Poulami Sau
 Prakhar Gurunani
 Prashant Anand
 Prashant Sharma
diff --git a/changelog/12114.bugfix.rst b/changelog/12114.bugfix.rst
new file mode 100644
index 000000000..798496925
--- /dev/null
+++ b/changelog/12114.bugfix.rst
@@ -0,0 +1 @@
+Fixed attribute error in pytest.approx for types implicitly convertible to numpy arrays by converting other_side to a numpy array so that np_array_shape != other_side.shape can be properly checked.
diff --git a/src/_pytest/python_api.py b/src/_pytest/python_api.py
index 0ba86e816..7d89fdd80 100644
--- a/src/_pytest/python_api.py
+++ b/src/_pytest/python_api.py
@@ -142,7 +142,7 @@ class ApproxNumpy(ApproxBase):
         )
         return f"approx({list_scalars!r})"
 
-    def _repr_compare(self, other_side: "ndarray") -> List[str]:
+    def _repr_compare(self, other_side: Union["ndarray", List[Any]]) -> List[str]:
         import itertools
         import math
 
@@ -163,10 +163,14 @@ class ApproxNumpy(ApproxBase):
             self._approx_scalar, self.expected.tolist()
         )
 
-        if np_array_shape != other_side.shape:
+        # convert other_side to numpy array to ensure shape attribute is available
+        other_side_as_array = _as_numpy_array(other_side)
+        assert other_side_as_array is not None
+
+        if np_array_shape != other_side_as_array.shape:
             return [
                 "Impossible to compare arrays with different shapes.",
-                f"Shapes: {np_array_shape} and {other_side.shape}",
+                f"Shapes: {np_array_shape} and {other_side_as_array.shape}",
             ]
 
         number_of_elements = self.expected.size
@@ -175,7 +179,7 @@ class ApproxNumpy(ApproxBase):
         different_ids = []
         for index in itertools.product(*(range(i) for i in np_array_shape)):
             approx_value = get_value_from_nested_list(approx_side_as_seq, index)
-            other_value = get_value_from_nested_list(other_side, index)
+            other_value = get_value_from_nested_list(other_side_as_array, index)
             if approx_value != other_value:
                 abs_diff = abs(approx_value.expected - other_value)
                 max_abs_diff = max(max_abs_diff, abs_diff)
@@ -188,7 +192,7 @@ class ApproxNumpy(ApproxBase):
         message_data = [
             (
                 str(index),
-                str(get_value_from_nested_list(other_side, index)),
+                str(get_value_from_nested_list(other_side_as_array, index)),
                 str(get_value_from_nested_list(approx_side_as_seq, index)),
             )
             for index in different_ids
diff --git a/testing/python/approx.py b/testing/python/approx.py
index 079667bd0..968e88285 100644
--- a/testing/python/approx.py
+++ b/testing/python/approx.py
@@ -763,6 +763,23 @@ class TestApprox:
         assert a12 != approx(a21)
         assert a21 != approx(a12)
 
+    def test_numpy_array_implicit_conversion(self):
+        np = pytest.importorskip("numpy")
+
+        class ImplicitArray:
+            """Type which is implicitly convertible to a numpy array."""
+
+            def __init__(self, vals):
+                self.vals = vals
+
+            def __array__(self, dtype=None, copy=None):
+                return np.array(self.vals)
+
+        vec1 = ImplicitArray([1.0, 2.0, 3.0])
+        vec2 = ImplicitArray([1.0, 2.0, 4.0])
+        # see issue #12114 for test case
+        assert vec1 != approx(vec2)
+
     def test_numpy_array_protocol(self):
         """
         array-like objects such as tensorflow's DeviceArray are handled like ndarray.