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""" Support level6 operator test cases.
"""
import numpy as np
import tvm
from tvm import te
from tvm import relay
import tvm.testing


@tvm.testing.uses_gpu
def test_dynamic_topk():
    def verify_topk(k, axis, ret_type, is_ascend, dtype):
        shape = (20, 100)
        x = relay.var("x", relay.TensorType(shape, "float32"))
        k_var = relay.var("x", relay.TensorType((1,), "float32"))
        out = relay.topk(x, k_var, axis, ret_type, is_ascend, dtype)
        if isinstance(out, relay.expr.TupleWrapper):
            out = out.astuple()
        func = relay.Function([x, k_var], out)

        np_data = np.random.uniform(size=shape).astype("float32")
        if is_ascend:
            np_indices = np.argsort(np_data, axis=axis)
        else:
            np_indices = np.argsort(-np_data, axis=axis)
        kk = k if k >= 1 else shape[axis]
        if axis == 0:
            np_indices = np_indices[:kk, :]
            np_values = np.zeros(np_indices.shape).astype("float32")
            for i in range(shape[1]):
                np_values[:, i] = np_data[np_indices[:, i], i]
        else:
            np_indices = np_indices[:, :kk]
            np_values = np.zeros(np_indices.shape).astype("float32")
            for i in range(shape[0]):
                np_values[i, :] = np_data[i, np_indices[i, :]]
        np_indices = np_indices.astype(dtype)

        for target, dev in tvm.testing.enabled_targets():
            for kind in ["vm", "debug"]:
                mod = tvm.ir.IRModule.from_expr(func)
                op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate()(
                    np_data, np.array([k]).astype("float32")
                )
                if ret_type == "both":
                    tvm.testing.assert_allclose(op_res[0].numpy(), np_values)
                    tvm.testing.assert_allclose(op_res[1].numpy(), np_indices)
                elif ret_type == "values":
                    tvm.testing.assert_allclose(op_res.numpy(), np_values)
                else:
                    tvm.testing.assert_allclose(op_res.numpy(), np_indices)

    np.random.seed(0)
    for k in [0, 1, 5]:
        for axis in [0, -1, 1]:
            for ret_type in ["both", "values", "indices"]:
                verify_topk(k, axis, ret_type, True, "int64")
                verify_topk(k, axis, ret_type, False, "float32")


if __name__ == "__main__":
    test_dynamic_topk()