chore: import upstream snapshot with attribution

tests/python/relax/test_tvmscript_parser_op_set.py

@@ -0,0 +1,65 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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 tvm
+import tvm.script
+import tvm.testing
+from tvm import IRModule, relax
+from tvm.script import relax as R
+
+
+def _check(
+    parsed: relax.Function | IRModule,
+    expect: relax.Function | IRModule | None,
+):
+    test = parsed.script(show_meta=True)
+    roundtrip_mod = tvm.script.from_source(test)
+    tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
+    if expect:
+        tvm.ir.assert_structural_equal(parsed, expect)
+
+
+def test_unique():
+    @R.function
+    def foo(x: R.Tensor((2, 3, 4), dtype="float32")) -> R.Tuple(
+        R.Tensor(dtype="float32", ndim=3),
+        R.Tensor(dtype="int64", ndim=1),
+        R.Tensor(dtype="int64", ndim=1),
+    ):
+        gv: R.Tuple(
+            R.Tensor(dtype="float32", ndim=3),
+            R.Tensor(dtype="int64", ndim=1),
+            R.Tensor(dtype="int64", ndim=1),
+        ) = R.unique(
+            x, sorted=True, return_index=False, return_inverse=True, return_counts=True, axis=1
+        )
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x]):
+        gv = bb.emit(
+            relax.op.unique(x, sorted=True, return_inverse=True, return_counts=True, axis=1)
+        )
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+if __name__ == "__main__":
+    tvm.testing.main()