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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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import numpy as np
import random
import tvm
import tvm.testing
import pickle
from tvm import te
from tvm import nd, relay
from tvm.runtime import container as _container


def test_adt_constructor():
    arr = nd.array([1, 2, 3])
    fields = [arr, arr]
    y = _container.ADT(0, [arr, arr])

    assert len(y) == 2
    assert isinstance(y, _container.ADT)
    y[0:1][-1] == arr
    assert y.tag == 0
    assert isinstance(arr, nd.NDArray)


def test_tuple_object():
    x = relay.var(
        "x",
        type_annotation=relay.ty.TupleType(
            [relay.ty.TensorType((), "int32"), relay.ty.TensorType((), "int32")]
        ),
    )

    fn = relay.Function([x], relay.expr.TupleGetItem(x, 0))
    mod = tvm.IRModule.from_expr(fn)

    f = relay.create_executor(kind="vm", mod=mod, device=nd.cpu(), target="llvm").evaluate()
    value_tuple = _container.tuple_object([nd.array(np.array(11)), nd.array(np.array(12))])
    # pass an ADT object to evaluate
    out = f(value_tuple)
    tvm.testing.assert_allclose(out.numpy(), np.array(11))


def test_string():
    s = tvm.runtime.String("xyz")

    assert isinstance(s, tvm.runtime.String)
    assert isinstance(s, str)
    assert s.startswith("xy")
    assert s + "1" == "xyz1"
    y = tvm.testing.echo(s)
    assert isinstance(y, tvm.runtime.String)
    assert s.__tvm_object__.same_as(y.__tvm_object__)
    assert s == y

    x = tvm.ir.load_json(tvm.ir.save_json(y))
    assert isinstance(x, tvm.runtime.String)
    assert x == y

    # test pickle
    z = pickle.loads(pickle.dumps(s))
    assert isinstance(z, tvm.runtime.String)
    assert s == z


def test_shape_tuple():
    shape = [random.randint(-10, 10) for _ in range(5)]
    stuple = _container.ShapeTuple(shape)
    len(stuple) == len(shape)
    for a, b in zip(stuple, shape):
        assert a == b


if __name__ == "__main__":
    test_string()
    test_adt_constructor()
    test_tuple_object()
    test_shape_tuple()