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import numpy as np
import tvm
import tvm.testing
from tvm import te
from tvm.topi.nn.pooling import pool2d


def test_tensor():
    m = te.size_var("m")
    n = te.size_var("n")
    l = te.size_var("l")
    A = te.placeholder((m, l), name="A")
    B = te.placeholder((n, l), name="B")
    T = te.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k])
    print(T)
    print(T.op.body)
    assert tuple(T.shape) == (m, n, l)
    assert isinstance(A.op, tvm.te.PlaceholderOp)
    assert A == A
    assert T.op.output(0) == T
    assert T.op.output(0).__hash__() == T.__hash__()
    d = {T.op.output(0): 1}
    assert d[T] == 1
    assert T[0][0][0].astype("float16").dtype == "float16"


def test_rank_zero():
    m = te.size_var("m")
    A = te.placeholder((m,), name="A")
    scale = te.placeholder((), name="s")
    k = te.reduce_axis((0, m), name="k")
    T = te.compute((), lambda: te.sum(A[k] * scale(), axis=k))
    print(T)
    print(T.op.body)
    assert tuple(T.shape) == ()


def test_conv1d():
    n = te.size_var("n")
    A = te.placeholder((n + 2), name="A")

    def computeB(ii):
        i = ii + 1
        return A[i - 1] + A[i] + A[i + 1]

    B = te.compute(n, computeB)


def test_tensor_slice():
    n = te.size_var("n")
    A = te.compute((n, n), lambda i, j: 1)
    B = te.compute((n,), lambda i: A[0][i] + A[0][i])


def test_tensor_reduce_multi_axis():
    m = te.size_var("m")
    n = te.size_var("n")
    A = te.placeholder((m, n), name="A")
    k1 = te.reduce_axis((0, n), "k")
    k2 = te.reduce_axis((0, m), "k")
    C = te.compute((1,), lambda _: te.sum(A[k1, k2], axis=(k1, k2)))
    C = te.compute((1,), lambda _: te.sum(A[k1, k2], axis=[k1, k2]))


def test_tensor_comm_reducer():
    m = te.size_var("m")
    n = te.size_var("n")
    A = te.placeholder((m, n), name="A")
    k = te.reduce_axis((0, n), "k")
    mysum = te.comm_reducer(lambda x, y: x + y, lambda t: tvm.tir.const(0, dtype=t))
    C = te.compute((m,), lambda i: mysum(A[i, k], axis=k))


def test_tensor_comm_reducer_overload():
    m = te.size_var("m")
    n = te.size_var("n")
    mysum = te.comm_reducer(lambda x, y: x + y, lambda t: tvm.tir.const(0, dtype=t))
    sum_res = mysum(m, n)


def test_tensor_reduce():
    m = te.size_var("m")
    n = te.size_var("n")
    l = te.size_var("l")
    A = te.placeholder((m, l), name="A")
    B = te.placeholder((n, l), name="B")
    T = te.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k])
    rv = te.reduce_axis((0, A.shape[1]), "k")
    C = te.compute((m, n), lambda i, j: te.sum(T(i, j, rv + 1), axis=rv))
    # json load save
    C_json = tvm.ir.save_json(C)
    C_loaded = tvm.ir.load_json(C_json)
    assert isinstance(C_loaded, te.tensor.Tensor)
    assert str(C_loaded) == str(C)


def test_tensor_reduce_multiout_with_cond():
    def fcombine(x, y):
        return x[0] + y[0], x[1] + y[1]

    def fidentity(t0, t1):
        return tvm.tir.const(0, t0), tvm.tir.const(1, t1)

    mysum = te.comm_reducer(fcombine, fidentity, name="mysum")

    m = te.var("m")
    n = te.var("n")
    idx = te.placeholder((m, n), name="idx", dtype="int32")
    val = te.placeholder((m, n), name="val", dtype="int32")
    k = te.reduce_axis((0, n), "k")
    cond = te.floormod(k, 2) == 0
    T0, T1 = te.compute((m,), lambda i: mysum((idx[i, k], val[i, k]), axis=k, where=cond), name="T")


def test_tensor_compute1():
    m = 1024
    factor = 16
    dtype = "float32"

    def intrin_vadd(n):
        x = te.placeholder((n,))
        y = te.placeholder((n,))
        z = te.compute(x.shape, lambda i: x[i] + y[i])

        def intrin_func(ins, outs):
            ib = tvm.tir.ir_builder.create()
            ib.emit(
                tvm.tir.call_extern(
                    outs[0].dtype,
                    "vadd",
                    ins[0].access_ptr("r"),
                    ins[1].access_ptr("r"),
                    outs[0].access_ptr("wr"),
                )
            )
            return ib.get()

        return te.decl_tensor_intrin(z.op, intrin_func, default_buffer_params={"offset_factor": n})

    vadd = intrin_vadd(factor)

    A = te.placeholder((m // factor, factor), name="A", dtype=dtype)
    B = te.placeholder((m // factor, factor), name="B", dtype=dtype)
    C = te.compute((m // factor, factor), lambda i: vadd(A[i, 0:factor], B[i, 0:factor]))

    s = te.create_schedule(C.op)
    # check lowering with the CSE pass disabled as otherwise it would do some commoning
    with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
        stmt = tvm.lower(s, [A, B, C])["main"].body
    assert isinstance(stmt.body, tvm.tir.Evaluate)


def test_tensor_compute2():
    M = 2048
    N = 1024
    L = 1024
    factor = 16
    factor1 = 32
    factor2 = 32
    dtype = "float32"

    def intrin_gemm(m, n, l):
        k = te.reduce_axis((0, l))
        x = te.placeholder((m, l))
        y = te.placeholder((n, l))
        # in theory, no relation
        z = te.compute((m, n), lambda i, j: te.sum(x[i][k] * y[j][k], axis=k))

        def intrin_func(ins, outs):
            x_ptr = ins[0].access_ptr("r")
            y_ptr = ins[1].access_ptr("r")
            z_ptr = outs[0].access_ptr("w")
            body = tvm.tir.call_packed("gemv", x_ptr, y_ptr, z_ptr, m, n, l)
            reset = tvm.tir.call_packed("fill_zero", z_ptr, m, n)
            update = tvm.tir.call_packed("gemv_add", x_ptr, y_ptr, z_ptr, m, n, l)
            return body, reset, update

        return te.decl_tensor_intrin(z.op, intrin_func, default_buffer_params={"offset_factor": n})

    vgemm = intrin_gemm(factor1, factor2, factor)

    A = te.placeholder((M // factor1, L // factor, factor1, factor), name="A", dtype=dtype)
    B = te.placeholder((N // factor2, L // factor, factor2, factor), name="B", dtype=dtype)
    k = te.reduce_axis((0, L // factor), name="k")
    C = te.compute(
        (M // factor1, N // factor2, factor1, factor2),
        lambda i, j: vgemm(
            A[i, k, 0:factor1, 0:factor], B[j, k, 0:factor2, 0:factor], reduce_axis=k
        ),
    )

    s = te.create_schedule(C.op)
    # check lowering with the CSE pass disabled as otherwise it would do some commoning
    with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
        stmt = tvm.lower(s, [A, B, C])["main"].body
    assert isinstance(stmt.body.body[0], tvm.tir.Evaluate)
    assert isinstance(stmt.body.body[1].body, tvm.tir.Evaluate)


def test_tensor_scan():
    m = te.size_var("m")
    n = te.size_var("n")
    x = te.placeholder((m, n))
    s = te.placeholder((m, n))
    res = tvm.te.scan(
        te.compute((1, n), lambda _, i: x[0, i]),
        te.compute((m, n), lambda t, i: s[t - 1, i] + x[t, i]),
        s,
    )
    assert tuple(res.shape) == (m, n)


def test_scan_multi_out():
    m = te.size_var("m")
    n = te.size_var("n")
    x1 = te.placeholder((m, n))
    s1 = te.placeholder((m, n))
    x2 = te.placeholder((m, n))
    s2 = te.placeholder((m, n))
    s1_init = te.compute((1, n), lambda _, i: x1[0, i])
    s2_init = te.compute((1, n), lambda _, i: x2[0, i])
    s1_update = te.compute((m, n), lambda t, i: s1[t - 1, i] + s2[t - 1, i] + x1[t, i])
    s2_update = te.compute((m, n), lambda t, i: x2[t, i] + s2[t - 1, i])

    r0, r1 = tvm.te.scan([s1_init, s2_init], [s1_update, s2_update], [s1, s2])
    assert r0.value_index == 0
    assert r1.value_index == 1
    json_str = tvm.ir.save_json(r0.op)
    zz = tvm.ir.load_json(json_str)
    assert isinstance(zz, tvm.te.ScanOp)


def test_extern():
    m = te.size_var("m")
    A = te.placeholder((m,), name="A")

    def extern_func(ins, outs):
        assert isinstance(ins[0], tvm.te.schedule.Buffer)
        return tvm.tir.call_packed("myadd", ins[0].data, outs[0].data, m)

    B = te.extern((m,), [A], extern_func)
    assert tuple(B.shape) == (m,)


def test_extern_multi_out():
    m = te.size_var("m")
    A = te.placeholder((m,), name="A")
    B = te.compute((m,), lambda i: A[i] * 10)

    def extern_func(ins, outs):
        assert isinstance(ins[0], tvm.te.schedule.Buffer)
        return tvm.tir.call_packed("myadd", ins[0].data, outs[0].data, outs[1].data, m)

    res = te.extern([A.shape, A.shape], [A, B], extern_func)
    assert len(res) == 2
    assert res[1].value_index == 1


def test_tuple_inputs():
    m = te.size_var("m")
    n = te.size_var("n")
    A0 = te.placeholder((m, n), name="A0")
    A1 = te.placeholder((m, n), name="A1")
    T0, T1 = te.compute((m, n), lambda i, j: (A0[i, j] * 2, A1[i, j] * 3), name="T")
    s = te.create_schedule(T0.op)

    for i in range(len(T0.shape)):
        assert T0.shape[i] == T1.shape[i]
    assert T0.op == T1.op
    assert T0.value_index == 0
    assert T1.value_index == 1


def test_tuple_with_different_deps():
    m = te.size_var("m")
    n = te.size_var("n")
    A0 = te.placeholder((m, n), name="A1")
    A1 = te.placeholder((m, n), name="A2")
    B0, B1 = te.compute((m, n), lambda i, j: (A0[i, j] * 2, A1[i, j] * 3), name="B")
    C = te.compute((m, n), lambda i, j: B0[i, j] + 4, name="C")

    s = te.create_schedule(C.op)
    xo, xi = s[C].split(C.op.axis[0], factor=10)
    s[B0.op].compute_at(s[C], xo)
    sch = s.normalize()
    bounds = tvm.te.schedule.InferBound(sch)
    stmt = tvm.te.schedule.ScheduleOps(sch, bounds)

    def get_B1_realize(x):
        if (
            isinstance(x, tvm.tir.ProducerRealize)
            and x.producer.op == B1.op
            and x.producer.value_index == 1
        ):
            ret.append(x)

    ret = []
    tvm.tir.stmt_functor.post_order_visit(stmt, get_B1_realize)

    assert stmt.producer == C and len(ret) == 1


def test_tensor_inputs():
    x = te.placeholder((1,), name="x")
    y = te.compute(x.shape, lambda i: x[i] + x[i])
    assert tuple(y.op.input_tensors) == (x,)


def test_tensor_pool():
    def intrin_pool():
        A = te.placeholder((64, 16, 16), name="A")
        kh = te.reduce_axis((0, 3), name="kh")
        kw = te.reduce_axis((0, 3), name="kw")
        P = te.compute(
            (64, 14, 14),
            lambda c, oh, ow: tvm.te.max(A[c, oh + kh, ow + kw], axis=[kh, kw]),
            name="p",
        )

        def intrin_func(ins, outs):
            dinp = ins[0]
            dout = outs[0]
            return tvm.tir.call_packed("op", dinp, dout)

        return te.decl_tensor_intrin(P.op, intrin_func, default_buffer_params={"offset_factor": 1})

    A = te.placeholder((1, 64, 16, 16), name="A")
    P = pool2d(
        data=A, kernel=(3, 3), stride=(1, 1), dilation=(1, 1), padding=(0, 0, 0, 0), pool_type="max"
    )
    s = te.create_schedule(P.op)
    _, oh, _, _ = P.op.axis
    intrin = intrin_pool()
    s[P].tensorize(oh, intrin)
    tvm.lower(s, [A, P])


def test_tensor_scalar_mixed():
    # test te with tensor and scalar
    a = np.array(np.random.uniform(size=(10,)), "float32")
    b = np.array(np.random.uniform(size=(1))[0], "float32")
    c = np.array(np.random.uniform(size=(10,)), "float32")

    @tvm.register_func("tvm.test_tensor_scalar_scale")
    def my_scale(tensor, scalar, out):
        out_np = tensor.numpy() * scalar.numpy()
        tvm.nd.array(out_np).copyto(out)

    A = te.placeholder(a.shape, name="A")
    B = te.placeholder(b.shape, name="B")
    C = te.extern(
        a.shape,
        [A, B],
        lambda ins, outs: tvm.tir.call_packed(
            "tvm.test_tensor_scalar_scale", ins[0], ins[1], outs[0]
        ),
        name="C",
    )
    s = te.create_schedule(C.op)
    f = tvm.build(s, [A, B, C], "llvm")

    ta = tvm.nd.array(a)
    tb = tvm.nd.array(b)
    tc = tvm.nd.array(c)
    f(ta, tb, tc)
    tvm.testing.assert_allclose(a * b, tc.numpy())


def test_tensor_scalar():
    # test te with scalar shape
    a = np.array(np.random.uniform(size=(1))[0], "float32")
    b = np.array(0.0, "float32")

    @tvm.register_func("tvm.test_tensor_scalar_copy")
    def mycopy(x, y):
        x.copyto(y)

    A = te.placeholder(a.shape, name="A")
    B = te.extern(
        a.shape,
        [A],
        lambda ins, outs: tvm.tir.call_packed("tvm.test_tensor_scalar_copy", ins[0], outs[0]),
        name="B",
    )
    s = te.create_schedule(B.op)
    f = tvm.build(s, [A, B], "llvm")

    ta = tvm.nd.array(a)
    tb = tvm.nd.array(b)
    f(ta, tb)
    tvm.testing.assert_allclose(ta.numpy(), tb.numpy())


if __name__ == "__main__":
    test_tensor()
    test_rank_zero()
    test_conv1d()
    test_tensor_slice()
    test_tensor_reduce_multi_axis()
    test_tensor_comm_reducer()
    test_tensor_comm_reducer_overload()
    test_tensor_reduce()
    test_tensor_reduce_multiout_with_cond()
    test_tensor_compute1()
    test_tensor_compute2()
    test_tensor_scan()
    test_scan_multi_out()
    test_extern()
    test_extern_multi_out()
    test_tuple_inputs()
    test_tuple_with_different_deps()
    test_tensor_inputs()
    test_tensor_pool()
    test_tensor_scalar_mixed()
    test_tensor_scalar()
