Satisfy new flake8 type check rules (#1056)

cunumeric/array.py

@@ -2554,7 +2554,7 @@ def diagonal(
                 raise ValueError("extract can be true only for Ndim >=2")
             axes = None
         else:
-            if type(axis1) == int and type(axis2) == int:
+            if isinstance(axis1, int) and isinstance(axis2, int):
                 if axes is not None:
                     raise ValueError(
                         "Either axis1/axis2 or axes must be supplied"
@@ -3102,7 +3102,7 @@ def mean(
         Multiple GPUs, Multiple CPUs
 
         """
-        if axis is not None and type(axis) != int:
+        if axis is not None and not isinstance(axis, int):
             raise NotImplementedError(
                 "cunumeric.mean only supports int types for "
                 "'axis' currently"

cunumeric/module.py

@@ -4760,7 +4760,7 @@ def einsum_path(
     """
     computed_operands = [convert_to_cunumeric_ndarray(op) for op in operands]
     memory_limit = _builtin_max(op.size for op in computed_operands)
-    if type(optimize) == tuple:
+    if isinstance(optimize, tuple):
         if len(optimize) != 2:
             raise ValueError("einsum_path expects optimize tuples of size 2")
         optimize, memory_limit = optimize
@@ -4771,7 +4771,7 @@ def einsum_path(
     elif optimize in ["greedy", "optimal"]:
         pass
     elif (
-        type(optimize) == list
+        isinstance(optimize, list)
         and len(optimize) > 1
         and optimize[0] == "einsum_path"
     ):

cunumeric/utils.py

@@ -234,11 +234,11 @@ def deep_apply(obj: Any, func: Callable[[Any], Any]) -> Any:
     primarily meant to be used for arguments of NumPy API calls, which
     shouldn't nest their arrays very deep.
     """
-    if type(obj) == list:
+    if isinstance(obj, list):
         return [deep_apply(x, func) for x in obj]
-    elif type(obj) == tuple:
+    elif isinstance(obj, tuple):
         return tuple(deep_apply(x, func) for x in obj)
-    elif type(obj) == dict:
+    elif isinstance(obj, dict):
         return {k: deep_apply(v, func) for k, v in obj.items()}
     else:
         return func(obj)

tests/integration/test_prod.py

@@ -263,7 +263,7 @@ def test_out_axis(self, size):
         ndim = arr_np.ndim
         for axis in range(-ndim + 1, ndim, 1):
             out_shape = ()
-            if type(size) == tuple:
+            if isinstance(size, tuple):
                 out_shape_list = list(size)
                 del out_shape_list[axis]
                 out_shape = tuple(out_shape_list)
@@ -283,7 +283,7 @@ def test_out_axis_dtype(self, size):
         ndim = arr_np.ndim
         for axis in range(-ndim + 1, ndim, 1):
             out_shape = ()
-            if type(size) == tuple:
+            if isinstance(size, tuple):
                 out_shape_list = list(size)
                 del out_shape_list[axis]
                 out_shape = tuple(out_shape_list)

tests/integration/test_reduction.py

@@ -219,7 +219,7 @@ def test_out_axis(self, size):
         ndim = arr_np.ndim
         for axis in range(-ndim + 1, ndim, 1):
             out_shape = ()
-            if type(size) == tuple:
+            if isinstance(size, tuple):
                 out_shape_list = list(size)
                 del out_shape_list[axis]
                 out_shape = tuple(out_shape_list)
@@ -239,7 +239,7 @@ def test_out_axis_dtype(self, size):
         ndim = arr_np.ndim
         for axis in range(-ndim + 1, ndim, 1):
             out_shape = ()
-            if type(size) == tuple:
+            if isinstance(size, tuple):
                 out_shape_list = list(size)
                 del out_shape_list[axis]
                 out_shape = tuple(out_shape_list)

tests/integration/test_squeeze.py

@@ -125,7 +125,7 @@ def test_array_basic(size):
 
 
 @pytest.mark.parametrize(
-    "size", (s for s in SIZES if type(s) == tuple if 1 in s), ids=str
+    "size", (s for s in SIZES if isinstance(s, tuple) if 1 in s), ids=str
 )
 def test_num_axis(size):
     a = np.random.randint(low=-10, high=10, size=size)
@@ -139,7 +139,7 @@ def test_num_axis(size):
 
 
 @pytest.mark.parametrize(
-    "size", (s for s in SIZES if type(s) == tuple if 1 in s), ids=str
+    "size", (s for s in SIZES if isinstance(s, tuple) if 1 in s), ids=str
 )
 def test_array_axis(size):
     a = np.random.randint(low=-10, high=10, size=size)

tests/unit/cunumeric/test_coverage.py

@@ -388,8 +388,8 @@ def __array_prepare__(self):
         return "I am now ready"
 
     def foo(self, other):
-        assert type(self) == _Orig_ndarray
-        assert type(other) == _Orig_ndarray
+        assert type(self) == _Orig_ndarray  # noqa
+        assert type(other) == _Orig_ndarray  # noqa
         return "original foo"
 
     def bar(self, other):