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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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# KIND, either express or implied.  See the License for the
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# under the License.
from typing import Callable
import pytest
import tvm
import tvm.testing
from tvm import relax, tir
from tvm import TVMError
from tvm.ir import Op, VDevice
from tvm.script import relax as R


def test_op_correctness():
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y = relax.Var("y", R.Tensor((2, 3), "float32"))
    assert relax.op.add(x, y).op == Op.get("relax.add")
    assert relax.op.divide(x, y).op == Op.get("relax.divide")
    assert relax.op.floor_divide(x, y).op == Op.get("relax.floor_divide")
    assert relax.op.multiply(x, y).op == Op.get("relax.multiply")
    assert relax.op.power(x, y).op == Op.get("relax.power")
    assert relax.op.subtract(x, y).op == Op.get("relax.subtract")

    assert relax.op.equal(x, y).op == Op.get("relax.equal")
    assert relax.op.greater(x, y).op == Op.get("relax.greater")
    assert relax.op.greater_equal(x, y).op == Op.get("relax.greater_equal")
    assert relax.op.less(x, y).op == Op.get("relax.less")
    assert relax.op.less_equal(x, y).op == Op.get("relax.less_equal")
    assert relax.op.not_equal(x, y).op == Op.get("relax.not_equal")

    x = relax.Var("x", R.Tensor((2, 3), "int32"))
    y = relax.Var("y", R.Tensor((2, 3), "int32"))
    assert relax.op.bitwise_and(x, y).op == Op.get("relax.bitwise_and")
    assert relax.op.bitwise_or(x, y).op == Op.get("relax.bitwise_or")
    assert relax.op.bitwise_xor(x, y).op == Op.get("relax.bitwise_xor")
    assert relax.op.left_shift(x, y).op == Op.get("relax.left_shift")
    assert relax.op.right_shift(x, y).op == Op.get("relax.right_shift")

    x = relax.Var("x", R.Tensor((2, 3), "bool"))
    y = relax.Var("y", R.Tensor((2, 3), "bool"))
    assert relax.op.logical_and(x, y).op == Op.get("relax.logical_and")
    assert relax.op.logical_or(x, y).op == Op.get("relax.logical_or")
    assert relax.op.logical_xor(x, y).op == Op.get("relax.logical_xor")


def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_sinfo: relax.StructInfo):
    ret = bb.normalize(call)
    tvm.ir.assert_structural_equal(ret.struct_info, expected_sinfo)


(binary_arith_op, tir_arith_op) = tvm.testing.parameters(
    (relax.op.add, tir.Add),
    (relax.op.divide, tir.Div),
    (relax.op.floor_divide, tir.FloorDiv),
    (relax.op.multiply, tir.Mul),
    (relax.op.power, tir.pow),
    (relax.op.subtract, tir.Sub),
    (relax.op.maximum, tir.Max),
    (relax.op.minimum, tir.Min),
)


def test_binary_arith_infer_struct_info(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    vdevice0 = VDevice("llvm")
    vdevice1 = VDevice("cuda", 0)
    x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
    x1 = relax.Var("x", R.Tensor((1, 3), "float32"))
    x2 = relax.Var("x", R.Tensor((3, 2, 3), "float32"))
    x3 = relax.Var("x", R.Tensor((3, 1, 3), "float32"))
    x4 = relax.Var("x", R.Tensor("float32", ndim=2))
    x5 = relax.Var("x", R.Tensor())
    x6 = relax.Var("x", R.Tensor("float32", ndim=2, vdevice=vdevice0))
    x7 = relax.Var("x", R.Tensor((2, 3), "float32", vdevice0))
    y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
    y1 = relax.Var("y", R.Tensor((4, 3, 2, 1), "float32"))
    y2 = relax.Var("y", R.Tensor("float32", ndim=2))
    y3 = relax.Var("y", R.Tensor("float32", ndim=-1))
    y4 = relax.Var("y", R.Tensor((2, 3), "float32", vdevice0))
    y5 = relax.Var("y", R.Tensor("float32", ndim=2, vdevice=vdevice0))

    _check_inference(bb, binary_arith_op(x0, y0), relax.TensorStructInfo((2, 3), "float32"))
    _check_inference(bb, binary_arith_op(x1, y0), relax.TensorStructInfo((2, 3), "float32"))
    _check_inference(bb, binary_arith_op(x1, y1), relax.TensorStructInfo((4, 3, 2, 3), "float32"))
    _check_inference(bb, binary_arith_op(x2, y2), relax.TensorStructInfo(dtype="float32", ndim=3))
    _check_inference(bb, binary_arith_op(x3, y2), relax.TensorStructInfo(dtype="float32", ndim=3))
    _check_inference(bb, binary_arith_op(x4, y0), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x4, y1), relax.TensorStructInfo(dtype="float32", ndim=4))
    _check_inference(bb, binary_arith_op(x4, y2), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x4, y3), relax.TensorStructInfo(dtype="float32", ndim=-1))
    _check_inference(bb, binary_arith_op(x5, y0), relax.TensorStructInfo(dtype="", ndim=-1))
    _check_inference(
        bb,
        binary_arith_op(x6, y5),
        relax.TensorStructInfo(dtype="float32", ndim=2, vdevice=vdevice0),
    )
    _check_inference(
        bb,
        binary_arith_op(x6, y2),
        relax.TensorStructInfo(dtype="float32", ndim=2, vdevice=vdevice0),
    )
    _check_inference(
        bb, binary_arith_op(x7, y4), relax.TensorStructInfo((2, 3), "float32", vdevice0)
    )


def test_infer_struct_info_binary_arith_prim_value_with_tensor(binary_arith_op: Callable):
    bb = relax.BlockBuilder()

    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y = relax.Var("y", R.Prim("float32"))

    _check_inference(bb, binary_arith_op(x, y), relax.TensorStructInfo((2, 3), "float32"))


def test_infer_struct_info_binary_arith_prim_value_with_prim_value(binary_arith_op: Callable):
    bb = relax.BlockBuilder()

    x = relax.Var("x", R.Prim("float32"))
    y = relax.Var("y", R.Prim("float32"))

    _check_inference(bb, binary_arith_op(x, y), relax.PrimStructInfo("float32"))


@pytest.mark.xfail(reason="Not yet implemented")
def test_infer_struct_info_binary_arith_known_prim_value_with_prim_value(
    binary_arith_op: Callable, tir_arith_op
):
    bb = relax.BlockBuilder()

    tir_x = tir.Var("tir_x", "float32")
    tir_y = tir.Var("tir_y", "float32")

    x = relax.Var("x", R.Prim(value=tir_x))
    y = relax.Var("y", R.Prim(value=tir_y))

    _check_inference(bb, binary_arith_op(x, y), relax.PrimStructInfo(value=tir_x + tir_y))
    _check_inference(bb, binary_arith_op(y, x), relax.PrimStructInfo(value=tir_y + tir_x))


(binary_cmp_op, tir_cmp_op) = tvm.testing.parameters(
    (relax.op.equal, tir.EQ),
    (relax.op.greater, tir.GT),
    (relax.op.greater_equal, tir.GE),
    (relax.op.less, tir.LT),
    (relax.op.less_equal, tir.LE),
    (relax.op.not_equal, tir.NE),
)


def test_binary_cmp_infer_struct_info(binary_cmp_op: Callable):
    bb = relax.BlockBuilder()
    vdev0 = VDevice("llvm")
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
    y1 = relax.Var("y", R.Tensor((2, 3), "int32"))
    y2 = relax.Var("y", R.Tensor((2, 3), "float32", vdev0))
    _check_inference(bb, binary_cmp_op(x, y0), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y1), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y0), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y1), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y0), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y1), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(x, y2), relax.TensorStructInfo((2, 3), "bool", vdev0))


def test_infer_struct_info_binary_cmp_prim_value_to_tensor(binary_cmp_op: Callable):
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y = relax.Var("y", R.Prim("float32"))
    _check_inference(bb, binary_cmp_op(x, y), relax.TensorStructInfo((2, 3), "bool"))
    _check_inference(bb, binary_cmp_op(y, x), relax.TensorStructInfo((2, 3), "bool"))


def test_infer_struct_info_binary_cmp_prim_value_to_prim_value(binary_cmp_op: Callable):
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Prim("float32"))
    y = relax.Var("y", R.Prim("float32"))
    _check_inference(bb, binary_cmp_op(x, y), relax.PrimStructInfo("bool"))
    _check_inference(bb, binary_cmp_op(y, x), relax.PrimStructInfo("bool"))


@pytest.mark.xfail(reason="Not yet implemented")
def test_infer_struct_info_binary_cmp_known_prim_value_to_prim_value(
    binary_cmp_op: Callable, tir_cmp_op
):
    bb = relax.BlockBuilder()

    tir_x = tir.Var("tir_x", "float32")
    tir_y = tir.Var("tir_y", "float32")

    x = relax.Var("x", R.Prim(value=tir_x))
    y = relax.Var("y", R.Prim(value=tir_y))

    _check_inference(bb, binary_cmp_op(x, y), relax.PrimStructInfo(value=tir_cmp_op(tir_x, tir_y)))
    _check_inference(bb, binary_cmp_op(y, x), relax.PrimStructInfo(value=tir_cmp_op(tir_y, tir_x)))


def test_binary_infer_struct_info_shape_symbolic(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    m = tir.Var("m", "int64")
    n = tir.Var("n", "int64")
    k = tir.Var("k", "int64")
    x0 = relax.Var("x", R.Tensor((m, n), "float32"))
    x1 = relax.Var("x", R.Tensor((1, n), "float32"))
    x2 = relax.Var("x", R.Tensor((k, n, m), "float32"))
    x3 = relax.Var("x", R.Tensor((3, 1, n), "float32"))
    x4 = relax.Var("x", R.Tensor("float32", ndim=2))
    y0 = relax.Var("y", R.Tensor((m, n), "float32"))
    y1 = relax.Var("y", R.Tensor((m, n + 2), "float32"))
    y2 = relax.Var("y", R.Tensor((4, k, m, 1), "float32"))
    y3 = relax.Var("y", R.Tensor("float32", ndim=2))
    y4 = relax.Var("y", R.Tensor("float32", ndim=-1))
    _check_inference(bb, binary_arith_op(x0, y0), relax.TensorStructInfo((m, n), "float32"))
    _check_inference(bb, binary_arith_op(x0, y1), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x1, y0), relax.TensorStructInfo((m, n), "float32"))
    _check_inference(bb, binary_arith_op(x1, y2), relax.TensorStructInfo((4, k, m, n), "float32"))
    _check_inference(bb, binary_arith_op(x2, y2), relax.TensorStructInfo(dtype="float32", ndim=4))
    _check_inference(bb, binary_arith_op(x2, y3), relax.TensorStructInfo(dtype="float32", ndim=3))
    _check_inference(bb, binary_arith_op(x3, y3), relax.TensorStructInfo(dtype="float32", ndim=3))
    _check_inference(bb, binary_arith_op(x4, y0), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x4, y2), relax.TensorStructInfo(dtype="float32", ndim=4))
    _check_inference(bb, binary_arith_op(x4, y3), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x4, y4), relax.TensorStructInfo(dtype="float32", ndim=-1))


def test_binary_infer_struct_info_shape_var(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    s0 = relax.Var("s0", relax.ShapeStructInfo(ndim=2))
    s1 = relax.Var("s1", relax.ShapeStructInfo(ndim=2))
    s2 = relax.Var("s2", relax.ShapeStructInfo(ndim=4))
    s3 = relax.Var("s3", relax.ShapeStructInfo(ndim=1))
    s4 = relax.Var("s4", relax.ShapeStructInfo())
    x = relax.Var("x", relax.TensorStructInfo(s0, "float32"))
    y0 = relax.Var("y", relax.TensorStructInfo(s0, "float32"))
    y1 = relax.Var("y", relax.TensorStructInfo(s1, "float32"))
    y2 = relax.Var("y", relax.TensorStructInfo(s2, "float32"))
    y3 = relax.Var("y", relax.TensorStructInfo(s3, "float32"))
    y4 = relax.Var("y", relax.TensorStructInfo(s4, "float32"))

    _check_inference(bb, binary_arith_op(x, y0), relax.TensorStructInfo(s0, "float32"))
    _check_inference(bb, binary_arith_op(x, y1), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x, y2), relax.TensorStructInfo(dtype="float32", ndim=4))
    _check_inference(bb, binary_arith_op(x, y3), relax.TensorStructInfo(dtype="float32", ndim=2))
    _check_inference(bb, binary_arith_op(x, y4), relax.TensorStructInfo(dtype="float32"))


def test_binary_arith_infer_struct_info_more_input_dtype(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
    y0 = relax.Var("y", R.Tensor((2, 3), "float64"))
    x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
    y1 = relax.Var("y", R.Tensor((2, 3), "int8"))
    x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
    y2 = relax.Var("y", R.Tensor((2, 3), "int64"))

    _check_inference(bb, binary_arith_op(x0, y0), relax.TensorStructInfo((2, 3), "float64"))
    _check_inference(bb, binary_arith_op(x1, y1), relax.TensorStructInfo((2, 3), "int8"))
    _check_inference(bb, binary_arith_op(x2, y2), relax.TensorStructInfo((2, 3), "int64"))


def test_binary_infer_struct_info_shape_unequal_const_int(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
    y0 = relax.Var("y", R.Tensor((2, 4), "float32"))
    with pytest.raises(TVMError):
        bb.normalize(binary_arith_op(x0, y0))


def test_binary_arith_infer_struct_info_dtype_mismatch(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32"))
    y = relax.Var("y", R.Tensor((2, 3), "int32"))
    with pytest.raises(TypeError):
        bb.normalize(binary_arith_op(x, y))


def test_binary_arith_infer_struct_info_vdevice_mismatch(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((2, 3), "float32", VDevice("llvm")))
    y = relax.Var("y", R.Tensor((2, 3), "int32", VDevice("cuda")))
    with pytest.raises(TypeError):
        bb.normalize(binary_arith_op(x, y))


def test_binary_wrong_input_number(binary_arith_op: Callable):
    x = relax.Var("x", R.Tensor((2, 3), "float32"))

    with pytest.raises(TypeError):
        binary_arith_op(x, x, x)
    with pytest.raises(TypeError):
        binary_arith_op(x)
    with pytest.raises(TypeError):
        binary_arith_op(x, x, x, x)


def test_binary_infer_struct_info_wrong_input_type(binary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x0 = relax.Var("x", relax.ShapeStructInfo((2, 3)))
    x1 = relax.Var("x", relax.FuncStructInfo([], R.Tensor((2, 3), "float32")))
    y = relax.Var("y", R.Tensor((2, 3), "float32"))

    with pytest.raises(TypeError):
        bb.normalize(binary_arith_op(x0, y))
    with pytest.raises(TypeError):
        bb.normalize(binary_arith_op(x1, y))


if __name__ == "__main__":
    tvm.testing.main()