chore: import upstream snapshot with attribution

test/tensorrt/test_converter_input.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from tensorrt_test_base import TensorRTBaseTest
+
+import paddle
+
+
+class TestOneHotCase1TRTPattern(TensorRTBaseTest):
+    def setUp(self):
+        self.python_api = paddle.nn.functional.one_hot
+        self.api_args = {
+            "x": np.random.randint(0, 2, size=(3, 1)).astype("int64"),
+            "num_classes": np.array([2], dtype="int64"),
+        }
+        self.dynamic_shape_data = {
+            "x": lambda shape: np.random.randint(0, 2, size=shape).astype(
+                "int64"
+            ),
+            "num_classes": lambda shape: np.array([2], dtype="int64"),
+        }
+        self.program_config = {"feed_list": ["x", "num_classes"]}
+        self.min_shape = {"x": [1, 1]}
+        self.opt_shape = {"x": [3, 1]}
+        self.max_shape = {"x": [6, 1]}
+
+    def test_trt_result(self):
+        self.check_trt_result()
+
+
+class TestOneHotCase2TRTPattern(TensorRTBaseTest):
+    def setUp(self):
+        self.python_api = paddle.nn.functional.one_hot
+        self.num_classes = 2
+        self.api_args = {
+            "x": np.random.randint(0, 2, size=(3, 1)).astype(
+                "int64"
+            ),  # Random integers between 0 and num_classes
+            "num_classes": self.num_classes,
+        }
+        self.dynamic_shape_data = {
+            "x": lambda shape: np.random.randint(
+                0, self.num_classes, size=shape
+            )
+        }
+        self.program_config = {"feed_list": ["x"]}
+        self.min_shape = {"x": [1, 1]}
+        self.opt_shape = {"x": [3, 1]}
+        self.max_shape = {"x": [6, 1]}
+
+    def test_trt_result(self):
+        self.check_trt_result()
+
+
+if __name__ == '__main__':
+    unittest.main()