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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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# ruff: noqa: F401

import re
import subprocess
import tempfile

import numpy as np
import pytest

import tvm
from tvm.script import tirx as T
from tvm.target import codegen
from tvm.testing import env


@pytest.fixture(scope="session")
def sve_device_vector_length():
    c_code = r"""
    #include <stdio.h>
    #include <arm_sve.h>

    int main() {
        printf("%ld\n", svcntb() * 8);
    }
    """

    with tempfile.TemporaryDirectory() as tmp_dir:
        c_path = f"{tmp_dir}/vl.c"
        o_path = f"{tmp_dir}/out.o"
        with open(c_path, "w") as f:
            f.write(c_code)
        tvm.support.cc.create_executable(o_path, c_path, ["-march=native"])
        out = subprocess.check_output(o_path, shell=True).strip().decode()

    return int(out)


@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
def test_scalable_div(sve_device_vector_length):
    np.random.seed(0)
    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
    dev = tvm.cpu(0)

    @T.prim_func(s_tir=True)
    def my_func(a: T.handle):
        A = T.match_buffer(a, (1,), "int32")
        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
        A[0] = T.Div(10000, 4 * T.vscale())

    mod = tvm.compile(my_func, target=target)

    A_nd = tvm.runtime.tensor(np.empty((1,), dtype="int32"), device=dev)
    mod(A_nd)

    ref = 10000 // (sve_device_vector_length // 32)
    tvm.testing.assert_allclose(A_nd.numpy()[0], ref)


@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
def test_scalable_buffer_load_store(sve_device_vector_length):
    np.random.seed(0)
    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
    num_elements = sve_device_vector_length // 32
    dev = tvm.cpu(0)

    @T.prim_func(s_tir=True)
    def my_func(a: T.handle, b: T.handle):
        A = T.match_buffer(a, (num_elements,), "float32")
        B = T.match_buffer(b, (num_elements,), "float32")
        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
        B[T.ramp(0, 1, 4 * T.vscale())] = A[T.ramp(0, 1, 4 * T.vscale())]

    mod = tvm.compile(my_func, target=target)

    A_np = np.random.uniform(size=(num_elements,)).astype("float32")
    B_np = np.zeros((num_elements,)).astype("float32")
    A_nd = tvm.runtime.tensor(A_np, device=dev)
    B_nd = tvm.runtime.tensor(B_np, device=dev)
    mod(A_nd, B_nd)

    tvm.testing.assert_allclose(B_nd.numpy(), A_np)


@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
def test_scalable_loop_bound(sve_device_vector_length):
    np.random.seed(0)

    dtype = "float32"
    num_elements = sve_device_vector_length // 32
    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
    dev = tvm.cpu(0)

    @T.prim_func(s_tir=True)
    def my_func(a: T.handle, b: T.handle):
        A = T.match_buffer(a, (num_elements,), "float32")
        B = T.match_buffer(b, (num_elements,), "float32")
        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
        for i in T.serial(0, 4 * T.vscale()):
            B[i] = A[i]

    mod = tvm.compile(my_func, target=target)

    A_np = np.random.uniform(size=(num_elements,)).astype(dtype)
    B_np = np.zeros((num_elements,)).astype(dtype)
    A_nd = tvm.runtime.tensor(A_np, device=dev)
    B_nd = tvm.runtime.tensor(B_np, device=dev)
    mod(A_nd, B_nd)

    tvm.testing.assert_allclose(B_nd.numpy(), A_np)


@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
def test_scalable_broadcast(sve_device_vector_length):
    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
    num_elements = sve_device_vector_length // 32
    dev = tvm.cpu(0)

    @T.prim_func(s_tir=True)
    def my_func(a: T.handle):
        A = T.match_buffer(a, (num_elements,), "float32")
        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
        A[T.ramp(0, 1, 4 * T.vscale())] = T.broadcast(1, 4 * T.vscale())

    mod = tvm.compile(my_func, target=target)

    A_np = np.zeros((num_elements,)).astype("float32")
    A_nd = tvm.runtime.tensor(A_np, device=dev)
    mod(A_nd)

    ref = np.ones((num_elements,))
    tvm.testing.assert_allclose(A_nd.numpy(), ref)