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#
#   http://www.apache.org/licenses/LICENSE-2.0
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# ruff: noqa: F841
import numpy as np
import pytest

import tvm
import tvm.testing
from tvm.script import ir as I
from tvm.script import tirx as T


@pytest.mark.gpu
def test_add_pipeline():
    """Test extern-style add pipeline with vectorized operations."""
    nn = 64
    max_threads = 4

    # CPU version: serial loop with vectorized operations
    @I.ir_module
    class ModuleCPU:
        @T.prim_func(s_tir=True)
        def main(A: T.Buffer((64,), "float32"), C: T.Buffer((64,), "float32")):
            for i in T.serial((64 + 1) // 2):
                C[T.Ramp(i * 2, 1, 2)] = A[T.Ramp(i * 2, 1, 2)] + T.Broadcast(T.float32(1), 2)

    # GPU version: thread bindings with vectorized operations
    @I.ir_module
    class ModuleGPU:
        @T.prim_func(s_tir=True)
        def main(A: T.Buffer((64,), "float32"), C: T.Buffer((64,), "float32")):
            bx = T.launch_thread("blockIdx.x", (64 + 4 - 1) // 4)
            tx = T.launch_thread("threadIdx.x", 4)
            idx = bx * 4 + tx
            if T.likely(idx < 64):
                C[T.Ramp(idx * 2, 1, 2)] = A[T.Ramp(idx * 2, 1, 2)] + T.Broadcast(T.float32(1), 2)

    def check_target(target):
        if not tvm.testing.device_enabled(target):
            return
        mod = ModuleGPU if target in ["opencl", "cuda"] else ModuleCPU
        # build and invoke the kernel.
        f = tvm.compile(mod, target=target)
        n = nn

        def run_and_check():
            dev = tvm.device_from_target(target, 0)
            a = tvm.runtime.tensor(np.random.uniform(size=n).astype("float32"), dev)
            c = tvm.runtime.tensor(np.zeros(n, dtype="float32"), dev)
            f(a, c)
            tvm.testing.assert_allclose(c.numpy(), a.numpy() + 1)

        if target == "llvm":
            run_and_check()
        else:
            tvm.testing.run_with_gpu_lock(run_and_check)

    check_target("llvm")
    check_target("opencl")
    check_target("cuda")


def test_pack_buffer_simple():
    """Test call_packed with buffer arguments."""
    nn = 1024

    @I.ir_module
    class Module:
        @T.prim_func(s_tir=True)
        def main(A: T.Buffer((1024,), "float32"), C: T.Buffer((1024,), "float32")):
            T.evaluate(T.call_packed("my_extern_array_func1", A, C))

    @tvm.register_global_func
    def my_extern_array_func1(aa, bb):
        aa.copyto(bb)

    def check_target(target):
        if not tvm.testing.device_enabled(target):
            return
        # build and invoke the kernel.
        f = tvm.compile(Module, target=target)
        dev = tvm.cpu(0)
        # launch the kernel.
        n = nn
        a = tvm.runtime.tensor(np.random.uniform(size=n).astype("float32"), dev)
        c = tvm.runtime.tensor(np.zeros(n, dtype="float32"), dev)

        f(a, c)
        tvm.testing.assert_allclose(c.numpy(), a.numpy())

    check_target("llvm")


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