[CodeStyle][UP027] Replace unpacked list comprehension with a generator expression (#52025)

* codestyle up027

* add to pyproject.toml

paddle/fluid/operators/generator/parse_utils.py

@@ -45,7 +45,7 @@ def parse_arg(op_name: str, s: str) -> Dict[str, str]:
     1. typename name
     2. typename name = default_value
     """
-    typename, rest = [item.strip() for item in s.split(" ", 1)]
+    typename, rest = (item.strip() for item in s.split(" ", 1))
     assert (
         len(typename) > 0
     ), f"The arg typename should not be empty. Please check the args of {op_name} in yaml."
@@ -54,7 +54,7 @@ def parse_arg(op_name: str, s: str) -> Dict[str, str]:
         rest.count("=") <= 1
     ), f"There is more than 1 = in an arg in {op_name}"
     if rest.count("=") == 1:
-        name, default_value = [item.strip() for item in rest.split("=", 1)]
+        name, default_value = (item.strip() for item in rest.split("=", 1))
         assert (
             len(name) > 0
         ), f"The arg name should not be empty. Please check the args of {op_name} in yaml."

pyproject.toml

@@ -66,7 +66,7 @@ select = [
     "UP021",
     "UP022",
     "UP023",
-    # "UP024",
+    "UP024",
     "UP025",
     "UP026",
     # "UP027",

python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py

@@ -594,9 +594,9 @@ class TestLACModel(unittest.TestCase):
                             input=crf_decode, label=targets, seq_length=length
                         )
                         outputs = [avg_cost, precision, recall, f1_score]
-                        avg_cost, precision, recall, f1_score = [
+                        avg_cost, precision, recall, f1_score = (
                             np.mean(x.numpy()) for x in outputs
-                        ]
+                        )
 
                         print(
                             "[train] step = %d, loss = %f, P: %f, R: %f, F1: %f, elapsed time %f"

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

@@ -195,7 +195,7 @@ class TestDygraphDeepCF(unittest.TestCase):
         num_items = -1
         with open(self.data_path, 'r') as f:
             for l in f.readlines():
-                uid, iid, rating = [int(v) for v in l.split('\t')]
+                uid, iid, rating = (int(v) for v in l.split('\t'))
                 num_users = max(num_users, uid + 1)
                 num_items = max(num_items, iid + 1)
                 if float(rating) > 0.0:

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

@@ -67,9 +67,9 @@ class TestSampleLogitsOp(OpTest):
     def test_check_output(self):
         places = self._get_places()
         for p in places:
-            (Samples, Probabilities, SampledLogits, SampledLabels) = [
+            (Samples, Probabilities, SampledLogits, SampledLabels) = (
                 np.array(o) for o in self.calc_output(p)
-            ]
+            )
 
             assert (
                 Samples.dtype == np.int64

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

@@ -29,8 +29,8 @@ from paddle.nn.layer.transformer import (
 
 
 def generate_basic_params(mode="attn", self_attention=True):
-    batch_size, query_length = [np.random.randint(2, 10) for _ in range(2)]
-    d_head, num_heads = [np.random.randint(3, 10) for _ in range(2)]
+    batch_size, query_length = (np.random.randint(2, 10) for _ in range(2))
+    d_head, num_heads = (np.random.randint(3, 10) for _ in range(2))
     attn_dropout = 0.0
     embed_dim = d_head * num_heads
     if mode == "attn":
@@ -38,7 +38,7 @@ def generate_basic_params(mode="attn", self_attention=True):
             kdim, vdim = embed_dim, embed_dim
             key_length, value_length = query_length, query_length
         else:
-            kdim, vdim = [np.random.randint(5, 20) for _ in range(2)]
+            kdim, vdim = (np.random.randint(5, 20) for _ in range(2))
             key_length = np.random.randint(2, 10)
             value_length = key_length
         return (

python/paddle/tensor/einsum.py

@@ -337,7 +337,7 @@ def plan_matmul(plan, g_view, op1, op2, g_supports, g_shape, I, J1, J2, K):
     # Then apply matmul(x, y, transpose_x=False, tranpose_y=True)
     var1, var2 = f'op{op1}', f'op{op2}'
 
-    op1_view, op2_view = [g_view[op] for op in (op1, op2)]
+    op1_view, op2_view = (g_view[op] for op in (op1, op2))
 
     I1 = [idx for idx in I if op1_view[idx] >= 0]
     I2 = [idx for idx in I if op2_view[idx] >= 0]
@@ -347,7 +347,7 @@ def plan_matmul(plan, g_view, op1, op2, g_supports, g_shape, I, J1, J2, K):
     op2_view = np.array(op2_view)
     op2_dims = op2_view[I2 + J2 + K]
 
-    op1_mask, op2_mask = [g_supports[op] for op in (op1, op2)]
+    op1_mask, op2_mask = (g_supports[op] for op in (op1, op2))
     op1_vshape = np.array([s if m else 1 for s, m in zip(g_shape, op1_mask)])
     op2_vshape = np.array([s if m else 1 for s, m in zip(g_shape, op2_mask)])
     vshape = np.maximum(op1_vshape, op2_vshape)