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# to you under the Apache License, Version 2.0 (the
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#
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from collections.abc import Callable

import pytest

import tvm
import tvm.testing
from tvm import relax, tirx
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"))
    assert relax.op.abs(x).op == Op.get("relax.abs")
    assert relax.op.acos(x).op == Op.get("relax.acos")
    assert relax.op.acosh(x).op == Op.get("relax.acosh")
    assert relax.op.asin(x).op == Op.get("relax.asin")
    assert relax.op.asinh(x).op == Op.get("relax.asinh")
    assert relax.op.atan(x).op == Op.get("relax.atan")
    assert relax.op.atanh(x).op == Op.get("relax.atanh")
    assert relax.op.ceil(x).op == Op.get("relax.ceil")
    assert relax.op.cos(x).op == Op.get("relax.cos")
    assert relax.op.cosh(x).op == Op.get("relax.cosh")
    assert relax.op.exp(x).op == Op.get("relax.exp")
    assert relax.op.floor(x).op == Op.get("relax.floor")
    assert relax.op.isfinite(x).op == Op.get("relax.isfinite")
    assert relax.op.isinf(x).op == Op.get("relax.isinf")
    assert relax.op.isnan(x).op == Op.get("relax.isnan")
    assert relax.op.log(x).op == Op.get("relax.log")
    assert relax.op.negative(x).op == Op.get("relax.negative")
    assert relax.op.round(x).op == Op.get("relax.round")
    assert relax.op.rsqrt(x).op == Op.get("relax.rsqrt")
    assert relax.op.sigmoid(x).op == Op.get("relax.sigmoid")
    assert relax.op.sin(x).op == Op.get("relax.sin")
    assert relax.op.sinh(x).op == Op.get("relax.sinh")
    assert relax.op.square(x).op == Op.get("relax.square")
    assert relax.op.sqrt(x).op == Op.get("relax.sqrt")
    assert relax.op.tan(x).op == Op.get("relax.tan")
    assert relax.op.tanh(x).op == Op.get("relax.tanh")
    assert relax.op.clip(x, 0, 6).op == Op.get("relax.clip")
    assert relax.op.erf(x).op == Op.get("relax.erf")

    x = relax.Var("x", R.Tensor((2, 3), "int32"))
    assert relax.op.bitwise_not(x).op == Op.get("relax.bitwise_not")

    x = relax.Var("x", R.Tensor((2, 3), "bool"))
    assert relax.op.logical_not(x).op == Op.get("relax.logical_not")


def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
    ret = bb.normalize(call)
    tvm.ir.assert_structural_equal(ret.ty, expected_ty)


unary_arith_ops = [
    (relax.op.abs, False),
    (relax.op.acos, True),
    (relax.op.acosh, True),
    (relax.op.asin, True),
    (relax.op.asinh, True),
    (relax.op.atan, True),
    (relax.op.atanh, True),
    (relax.op.ceil, False),
    (relax.op.cos, True),
    (relax.op.cosh, True),
    (relax.op.exp, True),
    (relax.op.floor, False),
    (relax.op.log, True),
    (relax.op.negative, False),
    (relax.op.round, False),
    (relax.op.rsqrt, True),
    (relax.op.sigmoid, True),
    (relax.op.sign, False),
    (relax.op.sin, True),
    (relax.op.sinh, True),
    (relax.op.square, False),
    (relax.op.sqrt, True),
    (relax.op.tan, True),
    (relax.op.tanh, True),
]


@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
def test_unary_arith_infer_ty(unary_arith_op: Callable):
    bb = relax.BlockBuilder()
    vdev0 = VDevice("llvm")
    x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
    x1 = relax.Var("x", R.Tensor("float32", ndim=3))
    x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
    x3 = relax.Var("x", R.Tensor((2, 3)))
    x4 = relax.Var("x", R.Tensor())
    x5 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))

    _check_inference(bb, unary_arith_op(x0), relax.TensorType((2, 3), "float32"))
    _check_inference(bb, unary_arith_op(x5), relax.TensorType((2, 3), "float32", vdev0))
    _check_inference(bb, unary_arith_op(x1), relax.TensorType(dtype="float32", ndim=3))
    _check_inference(bb, unary_arith_op(x2), relax.TensorType(dtype="float32"))
    _check_inference(bb, unary_arith_op(x3), relax.TensorType((2, 3), dtype=""))
    _check_inference(bb, unary_arith_op(x4), relax.TensorType(dtype=""))


@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
def test_unary_arith_infer_ty_shape_symbolic(unary_arith_op: Callable):
    bb = relax.BlockBuilder()
    m = tirx.Var("m", "int64")
    n = tirx.Var("n", "int64")
    x0 = relax.Var("x", R.Tensor((m, n), "float32"))
    x1 = relax.Var("x", R.Tensor((4, n), "float32"))

    _check_inference(bb, unary_arith_op(x0), relax.TensorType((m, n), "float32"))
    _check_inference(bb, unary_arith_op(x1), relax.TensorType((4, n), "float32"))


@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
def test_unary_arith_infer_ty_shape_var(unary_arith_op: Callable):
    bb = relax.BlockBuilder()
    s0 = relax.Var("s", relax.ShapeType(ndim=2))
    s1 = relax.Var("s", relax.ShapeType())
    x0 = relax.Var("x", relax.TensorType(s0, "float32"))
    x1 = relax.Var("x", relax.TensorType(s1, "float32"))

    _check_inference(bb, unary_arith_op(x0), relax.TensorType(s0, "float32"))
    _check_inference(bb, unary_arith_op(x1), relax.TensorType(s1, "float32"))


@pytest.mark.parametrize("unary_arith_op,require_float_dtype", unary_arith_ops)
def test_unary_arith_infer_ty_more_input_dtype(unary_arith_op: Callable, require_float_dtype: bool):
    if require_float_dtype:
        return

    bb = relax.BlockBuilder()
    x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
    x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
    x2 = relax.Var("x", R.Tensor((2, 3), "int64"))

    _check_inference(bb, unary_arith_op(x0), relax.TensorType((2, 3), "float64"))
    _check_inference(bb, unary_arith_op(x1), relax.TensorType((2, 3), "int8"))
    _check_inference(bb, unary_arith_op(x2), relax.TensorType((2, 3), "int64"))


@pytest.mark.parametrize("unary_arith_op,require_float_dtype", unary_arith_ops)
def test_unary_arith_infer_ty_invalid_input_dtype(
    unary_arith_op: Callable, require_float_dtype: bool
):
    if not require_float_dtype:
        return

    bb = relax.BlockBuilder()
    x0 = relax.Var("x", R.Tensor((2, 3), "int8"))
    x1 = relax.Var("x", R.Tensor((2, 3), "int64"))

    with pytest.raises(TypeError):
        bb.normalize(unary_arith_op(x0))
    with pytest.raises(TypeError):
        bb.normalize(unary_arith_op(x1))


@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
def test_unary_arith_wrong_input_number(unary_arith_op: Callable):
    x = relax.Var("x", R.Tensor((2, 3), "float32"))

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


@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
def test_unary_arith_infer_ty_wrong_input_type(unary_arith_op: Callable):
    bb = relax.BlockBuilder()
    x0 = relax.Var("x", relax.ShapeType((2, 3)))
    x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))

    with pytest.raises(TypeError):
        bb.normalize(unary_arith_op(x0))
    with pytest.raises(TypeError):
        bb.normalize(unary_arith_op(x1))


def test_clip_infer_ty():
    bb = relax.BlockBuilder()
    vdev0 = VDevice("llvm")
    x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
    x1 = relax.Var("x", R.Tensor("float32", ndim=3))
    x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
    x3 = relax.Var("x", R.Tensor((2, 3)))
    x4 = relax.Var("x", R.Tensor())
    x5 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))

    _check_inference(bb, relax.op.clip(x0, 0, 6), relax.TensorType((2, 3), "float32"))
    _check_inference(bb, relax.op.clip(x5, 0, 6), relax.TensorType((2, 3), "float32", vdev0))
    _check_inference(bb, relax.op.clip(x1, 0, 6), relax.TensorType(dtype="float32", ndim=3))
    _check_inference(bb, relax.op.clip(x2, 0, 6), relax.TensorType(dtype="float32"))
    _check_inference(bb, relax.op.clip(x3, 0, 6), relax.TensorType((2, 3), dtype=""))
    _check_inference(bb, relax.op.clip(x4, 0, 6), relax.TensorType(dtype=""))

    # Symbolic
    m = tirx.Var("m", "int64")
    n = tirx.Var("n", "int64")
    x5 = relax.Var("x", R.Tensor((m, n), "float32"))
    x6 = relax.Var("x", R.Tensor((4, n), "float32"))

    _check_inference(bb, relax.op.clip(x5, 0, 6), relax.TensorType((m, n), "float32"))
    _check_inference(bb, relax.op.clip(x6, 0, 6), relax.TensorType((4, n), "float32"))


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