CodeStyle Task No.5 - Enable Ruff PLR1701 rule in python/paddle and t…

…est/ (PaddlePaddle#57391)

pyproject.toml

@@ -69,7 +69,7 @@ select = [
     "PLC3002",
     "PLR0206",
     "PLR0402",
-    # "PLR1701",
+    "PLR1701",
     # "PLR1711",  # Confirmation required
     "PLR1722",
     "PLW3301",  # Confirmation required

python/paddle/base/compiler.py

@@ -206,9 +206,9 @@ def _compile_data_parallel(self, places, use_device, scope=None):
             assert scope is not None, ""
             self._local_scopes = []
 
-        assert isinstance(places, tuple) or isinstance(
-            places, list
-        ), "Currently , The places type can only be list or tuple, but the input type is {}.".format(
+        assert isinstance(
+            places, (list, tuple)
+        ), "Currently, The places type can only be list or tuple, but the input type is {}.".format(
             type(places)
         )
 

python/paddle/base/executor.py

@@ -495,8 +495,8 @@ def _add_feed_fetch_ops(
         global_block, fetch_list, fetch_var_name, fetch_op
     ):
         for i, var in enumerate(fetch_list):
-            assert isinstance(var, Variable) or isinstance(
-                var, str
+            assert isinstance(
+                var, (Variable, str)
             ), "Wrong type for fetch_list[%s]: %s" % (i, type(var))
             global_block.append_op(
                 type=fetch_op,
@@ -664,7 +664,7 @@ def _get_program_cache_key(feed, fetch_list):
     feed_var_names = []
     if isinstance(feed, dict):
         feed_var_names = list(feed.keys())
-    elif isinstance(feed, list) or isinstance(feed, tuple):
+    elif isinstance(feed, (list, tuple)):
         for i, each in enumerate(feed):
             feed_var_names += list(each.keys())
     fetch_var_names = list(map(_to_name_str, fetch_list))
@@ -1295,11 +1295,7 @@ def _get_targets(_optimize_ops, _fetch_list, item):
                     raise TypeError(
                         "The operator in fetch_list is not an optimize_op"
                     )
-            elif (
-                isinstance(item, Variable)
-                or isinstance(item, str)
-                or isinstance(item, str)
-            ):
+            elif isinstance(item, (Variable, str)):
                 _fetch_list.append(item)
             else:
                 raise TypeError(
@@ -1365,7 +1361,7 @@ def _prune_program(
         feed_names = []
         if isinstance(feed, dict):
             feed_names = list(feed.keys())
-        elif isinstance(feed, list) or isinstance(feed, tuple):
+        elif isinstance(feed, (list, tuple)):
             for i, each in enumerate(feed):
                 feed_names += list(each.keys())
 
@@ -1426,7 +1422,7 @@ def _update_feed(cls, program, feed):
                         % feed_name
                     )
 
-        elif isinstance(feed, list) or isinstance(feed, tuple):
+        elif isinstance(feed, (list, tuple)):
             for i, each in enumerate(feed):
                 for feed_name in list(each.keys()):
                     if not global_block.has_var(feed_name):
@@ -2805,8 +2801,8 @@ def _add_fetch_ops(
             global_block, fetch_list, fetch_var_name, fetch_op
         ):
             for i, var in enumerate(fetch_list):
-                assert isinstance(var, Variable) or isinstance(
-                    var, str
+                assert isinstance(
+                    var, (Variable, str)
                 ), "Wrong type for fetch_list[%s]: %s" % (i, type(var))
                 global_block.append_op(
                     type=fetch_op,

python/paddle/base/framework.py

@@ -3430,9 +3430,7 @@ def _update_desc_attr(self, name, val):
             self.desc.set_block_attr(name, val.desc)
         elif isinstance(val, list) and val and _all_is_type(val, Block):
             self.desc.set_blocks_attr(name, [v.desc for v in val])
-        elif isinstance(val, core.BlockDesc) or isinstance(
-            val, core.ProgramDesc
-        ):
+        elif isinstance(val, (core.BlockDesc, core.ProgramDesc)):
             self.desc.set_serialized_attr(name, val.serialize_to_string())
         else:
             self._update_desc_plain_attr(name, val)
@@ -5068,9 +5066,7 @@ def _update_desc_attr(self, name, val):
             desc.set_block_attr(name, val.desc)
         elif isinstance(val, list) and val and _all_is_type(val, Block):
             desc.set_blocks_attr(name, [v.desc for v in val])
-        elif isinstance(val, core.BlockDesc) or isinstance(
-            val, core.ProgramDesc
-        ):
+        elif isinstance(val, (core.BlockDesc, core.ProgramDesc)):
             desc.set_serialized_attr(name, val.serialize_to_string())
         else:
             desc._set_attr(name, val)
@@ -5547,9 +5543,7 @@ def _update_desc_attr(self, desc, name, val):
             desc.set_block_attr(name, val.desc)
         elif isinstance(val, list) and val and _all_is_type(val, Block):
             desc.set_blocks_attr(name, [v.desc for v in val])
-        elif isinstance(val, core.BlockDesc) or isinstance(
-            val, core.ProgramDesc
-        ):
+        elif isinstance(val, (core.BlockDesc, core.ProgramDesc)):
             desc.set_serialized_attr(name, val.serialize_to_string())
         else:
             desc._set_attr(name, val)

python/paddle/base/layer_helper.py

@@ -45,7 +45,7 @@ def append_op(self, *args, **kwargs):
     def multiple_input(self, input_param_name='input'):
         inputs = self.kwargs.get(input_param_name, [])
         ret = []
-        if isinstance(inputs, list) or isinstance(inputs, tuple):
+        if isinstance(inputs, (list, tuple)):
             for inp in inputs:
                 ret.append(self.to_variable(inp))
         else:

python/paddle/base/layers/layer_function_generator.py

@@ -235,11 +235,7 @@ def func(*args, **kwargs):
         outputs = dict()
         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/base/param_attr.py

@@ -174,7 +174,7 @@ def _to_attr(arg):
         """
         if arg is None:
             return ParamAttr()
-        elif isinstance(arg, list) or isinstance(arg, tuple):
+        elif isinstance(arg, (list, tuple)):
             return [ParamAttr._to_attr(a) for a in arg]
         elif isinstance(arg, ParamAttr):
             return arg

python/paddle/incubate/optimizer/recompute.py

@@ -109,8 +109,8 @@ def _set_checkpoints(self, checkpoints):
             checkpoints, list
         ), "_checkpoints should be a list of Variable or a list of String"
         for ckpt in checkpoints:
-            assert isinstance(ckpt, str) or isinstance(
-                ckpt, Variable
+            assert isinstance(
+                ckpt, (Variable, str)
             ), "_checkpoints should be a list of Variable or a list of String"
         self._checkpoints = checkpoints
 

python/paddle/io/dataloader/dataloader_iter.py

@@ -711,7 +711,7 @@ def _get_data(self):
                     )
 
                 # get(timeout) will call _poll(timeout) and may raise IOError
-                if isinstance(e, queue.Empty) or isinstance(e, IOError):
+                if isinstance(e, (IOError, queue.Empty)):
                     # continue on timeout to keep getting data from queue
                     continue
 

python/paddle/tensor/manipulation.py

@@ -3607,9 +3607,7 @@ def reshape(x, shape, name=None):
                 out = x
             else:
                 out = _C_ops.reshape(x, new_shape)
-        elif isinstance(shape, core.eager.Tensor) or isinstance(
-            shape, paddle.ir.OpResult
-        ):
+        elif isinstance(shape, (core.eager.Tensor, paddle.ir.OpResult)):
             shape.stop_gradient = True
             out = _C_ops.reshape(x, shape)
         else:

test/ir/inference/auto_scan_test.py

@@ -772,9 +772,7 @@ def random_to_skip():
                 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:

test/legacy_test/nets.py

@@ -222,9 +222,7 @@ def img_conv_group(
                                                   pool_stride=2)
     """
     tmp = input
-    assert isinstance(conv_num_filter, list) or isinstance(
-        conv_num_filter, tuple
-    )
+    assert isinstance(conv_num_filter, (list, tuple))
 
     def __extend_list__(obj):
         if not hasattr(obj, '__len__'):

test/legacy_test/test_bicubic_interp_v2_op.py

@@ -104,12 +104,12 @@ 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(
@@ -266,7 +266,7 @@ def setUp(self):
             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:
@@ -305,7 +305,7 @@ def setUp(self):
             '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:
@@ -433,7 +433,7 @@ def setUp(self):
             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:
@@ -472,7 +472,7 @@ def setUp(self):
             '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:

test/legacy_test/test_bilinear_interp_v2_op.py

@@ -121,12 +121,12 @@ 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(
@@ -254,7 +254,7 @@ def setUp(self):
         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:
@@ -295,7 +295,7 @@ def setUp(self):
             '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:
@@ -463,7 +463,7 @@ def setUp(self):
         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:
@@ -504,7 +504,7 @@ def setUp(self):
             '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:
@@ -604,7 +604,7 @@ def setUp(self):
         ).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:
@@ -641,7 +641,7 @@ def setUp(self):
             '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:
@@ -778,7 +778,7 @@ def setUp(self):
         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:
@@ -805,7 +805,7 @@ def setUp(self):
         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: