[CodeStyle][PLR1701] unify multiple isinstance expressions as one (#52150)

python/paddle/audio/functional/functional.py

@@ -250,7 +250,7 @@ def compute_fbank_matrix(
     if norm == 'slaney':
         enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
         weights *= enorm.unsqueeze(1)
-    elif isinstance(norm, int) or isinstance(norm, float):
+    elif isinstance(norm, (int, float)):
         weights = paddle.nn.functional.normalize(weights, p=norm, axis=-1)
 
     return weights

python/paddle/distributed/auto_parallel/dist_op.py

@@ -323,7 +323,7 @@ class DistributedOperatorHelper:
         output = self._serial_op(*args, **kwargs)
         new_op_size = len(cur_block.ops)
 
-        if isinstance(output, tuple) or isinstance(output, list):
+        if isinstance(output, (tuple, list)):
             new_output = list(output)
         elif isinstance(output, Variable):
             new_output = [output]

python/paddle/distributed/auto_parallel/reshard.py

@@ -1948,9 +1948,7 @@ class Resharder:
                     )
                 idx = idx_list[0]
 
-            elif isinstance(op_desc, SliceOpDesc) or isinstance(
-                op_desc, AllGatherConcatOpDesc
-            ):
+            elif isinstance(op_desc, (SliceOpDesc, AllGatherConcatOpDesc)):
                 target_tensor = None
                 if isinstance(op_desc, SliceOpDesc):
                     assert (

python/paddle/distributed/fleet/base/util_factory.py

@@ -425,7 +425,7 @@ class UtilBase:
         def feed_gen(batch_size, feeded_vars_dims, feeded_vars_filelist):
             def reader(batch_size, fn, dim):
                 data = []
-                if isinstance(dim, list) or isinstance(dim, tuple):
+                if isinstance(dim, (list, tuple)):
                     shape = list(dim)
                     _temp = 1
                     for x in dim:

python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py

@@ -45,11 +45,14 @@ class LocalSGDOptimizer(MetaOptimizerBase):
         if self.role_maker._worker_num() <= 1:
             return False
 
-        return (
-            isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum)
-            or isinstance(self.inner_opt, paddle.fluid.optimizer.Momentum)
-            or isinstance(self.inner_opt, paddle.optimizer.sgd.SGD)
-            or isinstance(self.inner_opt, paddle.fluid.optimizer.SGD)
+        return isinstance(
+            self.inner_opt,
+            (
+                paddle.optimizer.momentum.Momentum,
+                paddle.fluid.optimizer.Momentum,
+                paddle.optimizer.sgd.SGD,
+                paddle.fluid.optimizer.SGD,
+            ),
         )
 
     def _disable_strategy(self, dist_strategy):
@@ -228,11 +231,14 @@ class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
         if self.role_maker._worker_num() <= 1:
             return False
 
-        return (
-            isinstance(self.inner_opt, paddle.optimizer.Momentum)
-            or isinstance(self.inner_opt, paddle.fluid.optimizer.Momentum)
-            or isinstance(self.inner_opt, paddle.optimizer.sgd.SGD)
-            or isinstance(self.inner_opt, paddle.fluid.optimizer.SGD)
+        return isinstance(
+            self.inner_opt,
+            (
+                paddle.optimizer.Momentum,
+                paddle.fluid.optimizer.Momentum,
+                paddle.optimizer.sgd.SGD,
+                paddle.fluid.optimizer.SGD,
+            ),
         )
 
     def _disable_strategy(self, dist_strategy):

python/paddle/fluid/tests/unittests/ir/inference/auto_scan_test.py

@@ -795,9 +795,7 @@ class TrtLayerAutoScanTest(AutoScanTest):
                 if isinstance(threshold, float):
                     atol = threshold
                     rtol = 1e-8
-                elif isinstance(threshold, list) or isinstance(
-                    threshold, tuple
-                ):
+                elif isinstance(threshold, (list, tuple)):
                     atol = threshold[0]
                     rtol = threshold[1]
                 else:

python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_v2_mkldnn_op.py

@@ -110,7 +110,7 @@ class TestBilinearInterpOneDNNOp(OpTest):
         scale_w = 0
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 scale_h = float(self.scale)
                 scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.py

@@ -104,7 +104,7 @@ class TestNearestInterpV2MKLDNNOp(OpTest):
         scale_w = 0
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 scale_h = float(self.scale)
                 scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/test_bicubic_interp_v2_op.py

@@ -37,17 +37,15 @@ def bicubic_interp_test(
     align_corners=True,
     align_mode=0,
 ):
-    if isinstance(scale, float) or isinstance(scale, int):
+    if isinstance(scale, (float, int)):
         scale_list = []
         for _ in range(len(x.shape) - 2):
             scale_list.append(scale)
         scale = list(map(float, scale_list))
-    elif isinstance(scale, list) or isinstance(scale, tuple):
+    elif isinstance(scale, (list, tuple)):
         scale = list(map(float, scale))
     if SizeTensor is not None:
-        if not isinstance(SizeTensor, list) and not isinstance(
-            SizeTensor, tuple
-        ):
+        if not isinstance(SizeTensor, (list, tuple)):
             SizeTensor = [SizeTensor]
     return paddle._C_ops.bicubic_interp(
         x,
@@ -197,7 +195,7 @@ class TestBicubicInterpOp(OpTest):
             in_w = self.input_shape[2]
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0.0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -236,7 +234,7 @@ class TestBicubicInterpOp(OpTest):
             'data_layout': self.data_layout,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0.0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/test_bilinear_interp_v2_op.py

@@ -37,12 +37,12 @@ def bilinear_interp_test(
     align_corners=True,
     align_mode=0,
 ):
-    if isinstance(scale, float) or isinstance(scale, int):
+    if isinstance(scale, (float, int)):
         scale_list = []
         for _ in range(len(x.shape) - 2):
             scale_list.append(scale)
         scale = list(map(float, scale_list))
-    elif isinstance(scale, list) or isinstance(scale, tuple):
+    elif isinstance(scale, (list, tuple)):
         scale = list(map(float, scale))
     if SizeTensor is not None:
         if not isinstance(SizeTensor, list) and not isinstance(
@@ -169,7 +169,7 @@ class TestBilinearInterpOp(OpTest):
         scale_h = 0
         scale_w = 0
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0.0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -210,7 +210,7 @@ class TestBilinearInterpOp(OpTest):
             'data_layout': self.data_layout,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0.0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -363,7 +363,7 @@ class TestBilinearInterpOpUint8(OpTest):
         ).astype("uint8")
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -400,7 +400,7 @@ class TestBilinearInterpOpUint8(OpTest):
             'align_mode': self.align_mode,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -537,7 +537,7 @@ class TestBilinearInterpOp_attr_tensor(OpTest):
         if self.scale_by_1Dtensor:
             self.inputs['Scale'] = np.array([self.scale]).astype("float32")
         elif self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -564,7 +564,7 @@ class TestBilinearInterpOp_attr_tensor(OpTest):
         self.attrs['out_h'] = self.out_h
         self.attrs['out_w'] = self.out_w
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/test_desc_clone.py

@@ -114,9 +114,7 @@ def operator_equal(a, b):
         raise ValueError("In operator_equal not equal\n")
 
     for k, v in a.__dict__.items():
-        if isinstance(v, fluid.framework.Program) or isinstance(
-            v, fluid.framework.Block
-        ):
+        if isinstance(v, (fluid.framework.Program, fluid.framework.Block)):
             continue
 
         elif isinstance(v, core.OpDesc):
@@ -137,13 +135,10 @@ def operator_equal(a, b):
 
 def block_equal(a, b):
     for k, v in a.__dict__.items():
-        if (
-            isinstance(v, core.ProgramDesc)
-            or isinstance(v, fluid.framework.Program)
-            or isinstance(v, core.BlockDesc)
+        if isinstance(
+            v, (core.ProgramDesc, fluid.framework.Program, core.BlockDesc)
         ):
             continue
-
         elif k == "ops":
             assert len(a.ops) == len(b.ops)
             for i in range(0, len(a.ops)):

python/paddle/fluid/tests/unittests/test_linear_interp_v2_op.py

@@ -38,12 +38,12 @@ def linear_interp_test(
     align_corners=True,
     align_mode=0,
 ):
-    if isinstance(scale, float) or isinstance(scale, int):
+    if isinstance(scale, (float, int)):
         scale_list = []
         for _ in range(len(x.shape) - 2):
             scale_list.append(scale)
         scale = list(map(float, scale_list))
-    elif isinstance(scale, list) or isinstance(scale, tuple):
+    elif isinstance(scale, (list, tuple)):
         scale = list(map(float, scale))
     if SizeTensor is not None:
         if not isinstance(SizeTensor, list) and not isinstance(
@@ -138,7 +138,7 @@ class TestLinearInterpOp(OpTest):
             in_w = self.input_shape[1]
 
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = float(self.scale)
             if isinstance(self.scale, list):
                 self.scale = float(self.scale[0])
@@ -170,7 +170,7 @@ class TestLinearInterpOp(OpTest):
             'data_layout': self.data_layout,
         }
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = [float(self.scale)]
             self.attrs['scale'] = self.scale
         self.outputs = {'Out': output_np}
@@ -262,7 +262,7 @@ class TestLinearInterpOpSizeTensor(TestLinearInterpOp):
             in_w = self.input_shape[1]
 
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = float(self.scale)
             if isinstance(self.scale, list):
                 self.scale = float(self.scale[0])
@@ -302,7 +302,7 @@ class TestLinearInterpOpSizeTensor(TestLinearInterpOp):
             'data_layout': self.data_layout,
         }
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
                 self.scale = [self.scale[0], self.scale[0]]
@@ -343,7 +343,7 @@ class TestResizeLinearOpUint8(OpTest):
         input_np = np.random.random(self.input_shape).astype("uint8")
 
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = float(self.scale)
             if isinstance(self.scale, list):
                 self.scale = float(self.scale[0])
@@ -371,7 +371,7 @@ class TestResizeLinearOpUint8(OpTest):
             'align_mode': self.align_mode,
         }
         if self.scale > 0:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
                 self.scale = [self.scale[0], self.scale[0]]

python/paddle/fluid/tests/unittests/test_nearest_interp_v2_op.py

@@ -39,12 +39,12 @@ def nearest_interp_test(
     align_corners=True,
     align_mode=0,
 ):
-    if isinstance(scale, float) or isinstance(scale, int):
+    if isinstance(scale, (float, int)):
         scale_list = []
         for _ in range(len(x.shape) - 2):
             scale_list.append(scale)
         scale = list(map(float, scale_list))
-    elif isinstance(scale, list) or isinstance(scale, tuple):
+    elif isinstance(scale, (list, tuple)):
         scale = list(map(float, scale))
     if SizeTensor is not None:
         if not isinstance(SizeTensor, list) and not isinstance(
@@ -233,7 +233,7 @@ class TestNearestInterpOp(OpTest):
         scale_h = 0
         scale_w = 0
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_d = scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -305,7 +305,7 @@ class TestNearestInterpOp(OpTest):
                 'data_layout': self.data_layout,
             }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -438,7 +438,7 @@ class TestNearestInterpOpUint8(OpTest):
         ).astype("uint8")
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -472,7 +472,7 @@ class TestNearestInterpOpUint8(OpTest):
             'align_corners': self.align_corners,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -583,7 +583,7 @@ class TestNearestInterpOp_attr_tensor(OpTest):
         if self.scale_by_1Dtensor:
             self.inputs['Scale'] = np.array([self.scale]).astype("float64")
         elif self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -610,7 +610,7 @@ class TestNearestInterpOp_attr_tensor(OpTest):
         self.attrs['out_h'] = self.out_h
         self.attrs['out_w'] = self.out_w
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/test_trilinear_interp_v2_op.py

@@ -39,12 +39,12 @@ def trilinear_interp_test(
     align_corners=True,
     align_mode=0,
 ):
-    if isinstance(scale, float) or isinstance(scale, int):
+    if isinstance(scale, (float, int)):
         scale_list = []
         for _ in range(len(x.shape) - 2):
             scale_list.append(scale)
         scale = list(map(float, scale_list))
-    elif isinstance(scale, list) or isinstance(scale, tuple):
+    elif isinstance(scale, (list, tuple)):
         scale = list(map(float, scale))
     if SizeTensor is not None:
         if not isinstance(SizeTensor, list) and not isinstance(
@@ -219,7 +219,7 @@ class TestTrilinearInterpOp(OpTest):
             in_w = self.input_shape[3]
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_d = scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -270,7 +270,7 @@ class TestTrilinearInterpOp(OpTest):
             'data_layout': data_layout,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -434,7 +434,7 @@ class TestTrilinearInterpOpUint8(OpTest):
         ).astype("uint8")
 
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_d = scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -477,7 +477,7 @@ class TestTrilinearInterpOpUint8(OpTest):
             'align_mode': self.align_mode,
         }
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -611,7 +611,7 @@ class TestTrilinearInterpOp_attr_tensor(OpTest):
         if self.scale_by_1Dtensor:
             self.inputs['Scale'] = np.array([self.scale]).astype("float32")
         elif self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     scale_d = scale_h = scale_w = float(self.scale)
             if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -642,7 +642,7 @@ class TestTrilinearInterpOp_attr_tensor(OpTest):
         self.attrs['out_h'] = self.out_h
         self.attrs['out_w'] = self.out_w
         if self.scale:
-            if isinstance(self.scale, float) or isinstance(self.scale, int):
+            if isinstance(self.scale, (float, int)):
                 if self.scale > 0:
                     self.scale = [self.scale]
             if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/testsuite.py

@@ -71,7 +71,7 @@ def create_op(scope, op_type, inputs, outputs, attrs, cache_list=None):
 
 def set_input(scope, op, inputs, place):
     def __set_input__(var_name, var):
-        if isinstance(var, tuple) or isinstance(var, np.ndarray):
+        if isinstance(var, (tuple, np.ndarray)):
             tensor = scope.find_var(var_name).get_tensor()
             if isinstance(var, tuple):
                 tensor.set_recursive_sequence_lengths(var[1])

python/paddle/fluid/tests/unittests/xpu/test_bilinear_interp_v2_op_xpu.py

@@ -141,7 +141,7 @@ class XPUTestBilinearInterpV2Op(XPUOpTestWrapper):
             scale_h = 0
             scale_w = 0
             if self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0.0:
                         scale_h = scale_w = float(self.scale)
                 if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -182,7 +182,7 @@ class XPUTestBilinearInterpV2Op(XPUOpTestWrapper):
                 'data_layout': self.data_layout,
             }
             if self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0.0:
                         self.scale = [self.scale]
                 if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -389,7 +389,7 @@ class XPUTestBilinearInterpV2Op(XPUOpTestWrapper):
             if self.scale_by_1Dtensor:
                 self.inputs['Scale'] = np.array([self.scale]).astype("float32")
             elif self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0:
                         scale_h = scale_w = float(self.scale)
                 if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -416,7 +416,7 @@ class XPUTestBilinearInterpV2Op(XPUOpTestWrapper):
             self.attrs['out_h'] = self.out_h
             self.attrs['out_w'] = self.out_w
             if self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0:
                         self.scale = [self.scale]
                 if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/fluid/tests/unittests/xpu/test_nearest_interp_v2_op_xpu.py

@@ -212,7 +212,7 @@ class XPUNearestInterpOpWrapper(XPUOpTestWrapper):
             scale_h = 0
             scale_w = 0
             if self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0:
                         scale_d = scale_h = scale_w = float(self.scale)
                         self.scale = [self.scale]
@@ -450,7 +450,7 @@ class XPUNearestInterpOpWrapper(XPUOpTestWrapper):
             if self.scale_by_1Dtensor:
                 self.inputs['Scale'] = np.array([self.scale]).astype("float32")
             elif self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0:
                         scale_h = scale_w = float(self.scale)
                 if isinstance(self.scale, list) and len(self.scale) == 1:
@@ -477,7 +477,7 @@ class XPUNearestInterpOpWrapper(XPUOpTestWrapper):
             self.attrs['out_h'] = self.out_h
             self.attrs['out_w'] = self.out_w
             if self.scale:
-                if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if isinstance(self.scale, (float, int)):
                     if self.scale > 0:
                         self.scale = [self.scale]
                 if isinstance(self.scale, list) and len(self.scale) == 1:

python/paddle/hapi/progressbar.py

@@ -81,11 +81,7 @@ class ProgressBar:
 
         for i, (k, val) in enumerate(values):
             if k == "loss":
-                val = (
-                    val
-                    if isinstance(val, list) or isinstance(val, np.ndarray)
-                    else [val]
-                )
+                val = val if isinstance(val, (list, np.ndarray)) else [val]
                 if isinstance(val[0], np.uint16):
                     values[i] = ("loss", list(convert_uint16_to_float(val)))
 

python/paddle/incubate/distributed/fleet/utils.py

@@ -192,7 +192,7 @@ def load_var(var_name, shape_list, dtype, save_path):
 
 def reader(batch_size, fn, dim):
     data = []
-    if isinstance(dim, list) or isinstance(dim, tuple):
+    if isinstance(dim, (list, tuple)):
         shape = list(dim)
         _temp = 1
         for x in dim:

python/paddle/jit/api.py

@@ -915,9 +915,7 @@ def save(layer, path, input_spec=None, **configs):
         )
 
     if not (
-        isinstance(layer, Layer)
-        or inspect.isfunction(layer)
-        or isinstance(layer, StaticFunction)
+        isinstance(layer, (Layer, StaticFunction)) or inspect.isfunction(layer)
     ):
         raise TypeError(
             "The input of paddle.jit.save should be 'Layer' or 'Function', but received input type is %s."

python/paddle/jit/dy2static/static_analysis.py

@@ -179,9 +179,7 @@ class StaticAnalysisVisitor:
 
         self.ancestor_wrappers.append(cur_wrapper)
         for child in gast.iter_child_nodes(node):
-            if isinstance(child, gast.FunctionDef) or isinstance(
-                child, gast.AsyncFunctionDef
-            ):
+            if isinstance(child, (gast.FunctionDef, gast.AsyncFunctionDef)):
                 # TODO: current version is function name mapping to its type
                 # consider complex case involving parameters
                 self.var_env.enter_scope(

python/paddle/nn/functional/common.py

@@ -399,9 +399,7 @@ def interpolate(
     if size is None and scale_factor is None:
         raise ValueError("One of size and scale_factor must not be None.")
 
-    if (isinstance(size, list) or isinstance(size, tuple)) and len(
-        size
-    ) != x.ndim - 2:
+    if isinstance(size, (tuple, list)) and (len(size) != x.ndim - 2):
         raise ValueError(
             'The x and size should satisfy rank(x) - 2 == len(size).'
         )
@@ -427,11 +425,7 @@ def interpolate(
         )
 
     if resample == 'AREA':
-        if (
-            isinstance(size, list)
-            or isinstance(size, tuple)
-            or isinstance(size, Variable)
-        ):
+        if isinstance(size, (list, tuple, Variable)):
             if len(size) == 0:
                 raise ValueError("output size can not be empty")
         if size is None:
@@ -464,7 +458,7 @@ def interpolate(
         )
 
     def _is_list_or_turple_(data):
-        return isinstance(data, list) or isinstance(data, tuple)
+        return isinstance(data, (list, tuple))
 
     if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW':
         data_layout = 'NCHW'
@@ -581,18 +575,14 @@ def interpolate(
         if isinstance(scale, Variable):
             scale.stop_gradient = True
             inputs["Scale"] = scale
-        elif (
-            isinstance(scale, float)
-            or isinstance(scale, int)
-            or isinstance(scale, numpy.ndarray)
-        ):
+        elif isinstance(scale, (float, int, numpy.ndarray)):
             if scale <= 0:
                 raise ValueError("Attr(scale) should be greater than zero.")
             scale_list = []
             for i in range(len(x.shape) - 2):
                 scale_list.append(scale)
             attrs['scale'] = list(map(float, scale_list))
-        elif isinstance(scale, list) or isinstance(scale, tuple):
+        elif isinstance(scale, (list, tuple)):
             if len(scale) != len(x.shape) - 2:
                 raise ValueError(
                     "scale_shape length should be {} for "
@@ -2275,7 +2265,7 @@ def fold(
     assert len(x.shape) == 3, "input should be the format of [N, C, L]"
 
     def _is_list_or_turple_(data):
-        return isinstance(data, list) or isinstance(data, tuple)
+        return isinstance(data, (list, tuple))
 
     if isinstance(output_sizes, int):
         output_sizes = [output_sizes, output_sizes]

python/paddle/nn/initializer/constant.py

@@ -50,9 +50,7 @@ class ConstantInitializer(Initializer):
         """
         block = self._check_block(block)
 
-        assert isinstance(var, framework.Variable) or isinstance(
-            var, framework.EagerParamBase
-        )
+        assert isinstance(var, (framework.Variable, framework.EagerParamBase))
         assert isinstance(block, framework.Block)
 
         if in_dygraph_mode():

python/paddle/nn/layer/layers.py

@@ -1860,7 +1860,7 @@ class Layer:
                 raise ValueError(
                     "{} is not found in the provided dict.".format(key)
                 )
-            if isinstance(state, dict) or isinstance(state, list):
+            if isinstance(state, (dict, list)):
                 if len(state) != len(param):
                     missing_keys.append(key)
                     raise ValueError(

python/paddle/nn/layer/rnn.py

@@ -593,7 +593,7 @@ class RNNCellBase(Layer):
 
         def _is_shape_sequence(seq):
             """For shape, list/tuple of integer is the finest-grained objection"""
-            if isinstance(seq, list) or isinstance(seq, tuple):
+            if isinstance(seq, (list, tuple)):
                 if reduce(
                     lambda flag, x: isinstance(x, int) and flag, seq, True
                 ):

python/paddle/nn/quant/quant_layers.py

@@ -1087,11 +1087,7 @@ class MAOutputScaleLayer(Layer):
     def forward(self, *inputs, **kwargs):
         out = self._layer(*inputs, **kwargs)
         # TODO (jc): support the ops of several outputs
-        if (
-            isinstance(out, list)
-            or isinstance(out, tuple)
-            or isinstance(out, dict)
-        ):
+        if isinstance(out, (list, tuple, dict)):
             return out
         else:
             return self._ma_output_scale(out)
@@ -1129,7 +1125,7 @@ class FakeQuantMAOutputScaleLayer(Layer):
     def forward(self, *inputs, **kwargs):
         out = self._layer(*inputs, **kwargs)
         # TODO (jc): support the ops of several outputs
-        if (isinstance(out, list) or isinstance(out, tuple)) and len(out) > 1:
+        if (isinstance(out, (list, tuple))) and len(out) > 1:
             return out
         else:
             return self._fake_quant_output(out)

python/paddle/optimizer/lr.py

@@ -791,11 +791,7 @@ class LinearWarmup(LRScheduler):
         last_epoch=-1,
         verbose=False,
     ):
-        type_check = (
-            isinstance(learning_rate, float)
-            or isinstance(learning_rate, int)
-            or isinstance(learning_rate, LRScheduler)
-        )
+        type_check = isinstance(learning_rate, (float, int, LRScheduler))
         if not type_check:
             raise TypeError(
                 "the type of learning_rate should be [int, float or LRScheduler], the current type is {}".format(

python/paddle/static/nn/common.py

@@ -961,13 +961,11 @@ def conv2d(
 
     # padding
     def _update_padding(padding, data_format):
-        def is_list_or_tuple(ele):
-            if isinstance(ele, list) or isinstance(ele, tuple):
-                return True
-            return False
 
-        if is_list_or_tuple(padding) and len(padding) == 4:
-            if is_list_or_tuple(padding[0]) and (data_format == "NCHW"):
+        if isinstance(padding, (list, tuple)) and len(padding) == 4:
+            if isinstance(padding[0], (list, tuple)) and (
+                data_format == "NCHW"
+            ):
                 if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -975,7 +973,9 @@ def conv2d(
                     )
                 padding = padding[2:4]
                 padding = [ele for a_list in padding for ele in a_list]
-            elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"):
+            elif isinstance(padding[0], (list, tuple)) and (
+                data_format == "NHWC"
+            ):
                 if not (padding[0] == [0, 0] and padding[3] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1257,13 +1257,11 @@ def conv3d(
     dilation = paddle.utils.convert_to_list(dilation, 3, 'dilation')
 
     def _update_padding(padding, data_format):
-        def is_list_or_tuple(ele):
-            if isinstance(ele, list) or isinstance(ele, tuple):
-                return True
-            return False
 
-        if is_list_or_tuple(padding) and len(padding) == 5:
-            if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
+        if isinstance(padding, (list, tuple)) and len(padding) == 5:
+            if isinstance(padding[0], (list, tuple)) and (
+                data_format == "NCDHW"
+            ):
                 if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1271,7 +1269,9 @@ def conv3d(
                     )
                 padding = padding[2:5]
                 padding = [ele for a_list in padding for ele in a_list]
-            elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
+            elif isinstance(padding[0], (list, tuple)) and (
+                data_format == "NDHWC"
+            ):
                 if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1282,7 +1282,7 @@ def conv3d(
             padding = paddle.utils.convert_to_list(padding, 6, 'padding')
             if paddle.utils._is_symmetric_padding(padding, 3):
                 padding = [padding[0], padding[2], padding[4]]
-        elif is_list_or_tuple(padding) and len(padding) == 6:
+        elif isinstance(padding, (list, tuple)) and len(padding) == 6:
             padding = paddle.utils.convert_to_list(padding, 6, 'padding')
             if paddle.utils._is_symmetric_padding(padding, 3):
                 padding = [padding[0], padding[2], padding[4]]
@@ -1580,13 +1580,11 @@ def conv2d_transpose(
         raise ValueError("use_cudnn should be True or False")
 
     def _update_padding(padding, data_format):
-        def is_list_or_tuple(ele):
-            if isinstance(ele, list) or isinstance(ele, tuple):
-                return True
-            return False
 
-        if is_list_or_tuple(padding) and len(padding) == 4:
-            if is_list_or_tuple(padding[0]) and (data_format == "NCHW"):
+        if isinstance(padding, (list, tuple)) and len(padding) == 4:
+            if isinstance(padding[0], (list, tuple)) and (
+                data_format == "NCHW"
+            ):
                 if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1594,7 +1592,9 @@ def conv2d_transpose(
                     )
                 padding = padding[2:4]
                 padding = [ele for a_list in padding for ele in a_list]
-            elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"):
+            elif isinstance(padding[0], (list, tuple)) and (
+                data_format == "NHWC"
+            ):
                 if not (padding[0] == [0, 0] and padding[3] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1951,13 +1951,11 @@ def conv3d_transpose(
         raise ValueError("use_cudnn should be True or False")
 
     def _update_padding(padding, data_format):
-        def is_list_or_tuple(ele):
-            if isinstance(ele, list) or isinstance(ele, tuple):
-                return True
-            return False
 
-        if is_list_or_tuple(padding) and len(padding) == 5:
-            if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
+        if isinstance(padding, (list, tuple)) and len(padding) == 5:
+            if isinstance(padding[0], (list, tuple)) and (
+                data_format == "NCDHW"
+            ):
                 if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1965,7 +1963,9 @@ def conv3d_transpose(
                     )
                 padding = padding[2:5]
                 padding = [ele for a_list in padding for ele in a_list]
-            elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
+            elif isinstance(padding[0], (list, tuple)) and (
+                data_format == "NDHWC"
+            ):
                 if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
                     raise ValueError(
                         "Non-zero padding(%s) in the batch or channel dimensions "
@@ -1975,7 +1975,7 @@ def conv3d_transpose(
                 padding = [ele for a_list in padding for ele in a_list]
             padding = paddle.utils.convert_to_list(padding, 6, 'padding')
 
-        elif is_list_or_tuple(padding) and len(padding) == 6:
+        elif isinstance(padding, (list, tuple)) and len(padding) == 6:
             padding = paddle.utils.convert_to_list(padding, 6, 'padding')
 
         else:

python/paddle/static/quantization/quantization_pass.py

@@ -1438,12 +1438,7 @@ class QuantizationFreezePass:
         return "%s.dequantized" % (var_name)
 
     def _is_float(self, v):
-        return (
-            isinstance(v, float)
-            or isinstance(v, np.float16)
-            or isinstance(v, np.float32)
-            or isinstance(v, np.float64)
-        )
+        return isinstance(v, (float, np.float16, np.float32, np.float64))
 
 
 class ConvertToInt8Pass:

python/paddle/static/quantization/tests/test_imperative_ptq.py

@@ -60,10 +60,7 @@ class TestFuseLinearBn(unittest.TestCase):
         quant_h = ptq.quantize(model_h, fuse=True, fuse_list=f_l)
         for name, layer in quant_model.named_sublayers():
             if name in f_l:
-                assert not (
-                    isinstance(layer, nn.BatchNorm1D)
-                    or isinstance(layer, nn.BatchNorm2D)
-                )
+                assert not (isinstance(layer, (nn.BatchNorm1D, nn.BatchNorm2D)))
         out = model(inputs)
         out_h = model_h(inputs)
         out_quant = quant_model(inputs)
@@ -294,10 +291,7 @@ class TestImperativePTQfuse(TestImperativePTQ):
         quant_model = self.ptq.quantize(model, fuse=True, fuse_list=f_l)
         for name, layer in quant_model.named_sublayers():
             if name in f_l:
-                assert not (
-                    isinstance(layer, nn.BatchNorm1D)
-                    or isinstance(layer, nn.BatchNorm2D)
-                )
+                assert not (isinstance(layer, (nn.BatchNorm1D, nn.BatchNorm2D)))
         before_acc_top1 = self.model_test(
             quant_model, self.batch_num, self.batch_size
         )

python/paddle/tensor/layer_function_generator.py

@@ -238,11 +238,7 @@ def generate_layer_fn(op_type):
         outputs = {}
         out = kwargs.pop(_convert_(o_name), [])
         if out:
-            out_var = (
-                out[0]
-                if (isinstance(out, list) or isinstance(out, tuple))
-                else out
-            )
+            out_var = out[0] if isinstance(out, (list, tuple)) else out
         else:
             out_var = helper.create_variable_for_type_inference(dtype=dtype)
         outputs[o_name] = [out_var]

python/paddle/tensor/math.py

@@ -1651,7 +1651,7 @@ def add_n(inputs, name=None):
     else:
         helper = LayerHelper('add_n', **locals())
         check_type(inputs, 'inputs', (Variable, tuple, list), 'add_n')
-        if isinstance(inputs, list) or isinstance(inputs, tuple):
+        if isinstance(inputs, (list, tuple)):
             if len(inputs) > 0:
                 for input in inputs:
                     check_variable_and_dtype(

python/paddle/vision/ops.py

@@ -495,7 +495,7 @@ def prior_box(
     """
 
     def _is_list_or_tuple_(data):
-        return isinstance(data, list) or isinstance(data, tuple)
+        return isinstance(data, (list, tuple))
 
     if not _is_list_or_tuple_(min_sizes):
         min_sizes = [min_sizes]