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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from functools import partial

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
from tvm.testing import env


@pytest.mark.gpu
@pytest.mark.skipif(not env.has_gpu(), reason="need gpu")
@pytest.mark.parametrize(
    "target",
    [
        pytest.param("cuda", marks=pytest.mark.gpu),
        pytest.param("metal", marks=pytest.mark.gpu),
        pytest.param({"kind": "vulkan", "supports_int64": True}, marks=pytest.mark.gpu),
        pytest.param("opencl", marks=pytest.mark.gpu),
    ],
)
@pytest.mark.parametrize("dtype", ["int32", "uint32", "int64", "uint64"])
def test_int_intrin(target, dtype):
    if not tvm.testing.device_enabled(target):
        pytest.skip(f"{target} not enabled")
    test_funcs = [
        (T.clz, lambda x, dtype: int(dtype[-2:]) - (len(bin(x)) - 2)),
    ]

    for tvm_intrin, np_func in test_funcs:
        n = 128

        @I.ir_module(s_tir=True)
        class Module:
            @T.prim_func(s_tir=True)
            def main(
                A: T.Buffer((n,), dtype),
                B: T.Buffer((n,), dtype),
            ):
                T.func_attr({"tirx.noalias": True})
                for i0 in T.thread_binding(n, thread="threadIdx.x"):
                    with T.sblock("B"):
                        v_i0 = T.axis.spatial(n, i0)
                        T.reads(A[v_i0])
                        T.writes(B[v_i0])
                        B[v_i0] = tvm_intrin(A[v_i0])

        f = tvm.compile(Module, target=target)

        def run_and_check():
            dev = tvm.device(target["kind"] if isinstance(target, dict) else target)
            a = tvm.runtime.tensor(np.random.randint(0, 100000, size=n).astype(dtype), dev)
            b = tvm.runtime.tensor(np.zeros(shape=(n,)).astype(dtype), dev)
            f(a, b)
            ref = np.vectorize(partial(np_func, dtype=dtype))(a.numpy())
            tvm.testing.assert_allclose(b.numpy(), ref)

        tvm.testing.run_with_gpu_lock(run_and_check)


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