import torch
import ctypes
from ctypes import c_uint64
from libinfiniop import (
    LIBINFINIOP,
    TestTensor,
    get_test_devices,
    check_error,
    test_operator,
    get_args,
    debug,
    get_tolerance,
    profile_operation,
    TestWorkspace,
    InfiniDtype,
    InfiniDtypeNames,
    InfiniDeviceEnum,
    InfiniDeviceNames,
    infiniopOperatorDescriptor_t,
)
from enum import Enum, auto

# ==============================================================================
#  Configuration (Internal Use Only)
# ==============================================================================
# These are not meant to be imported from other modules
_TEST_CASES_ = [
    # (shape, x_strides, y_strides)
    ((1, 32, 128), None, None),
    ((10, 32, 64), None, None),
    # 昇腾暂不满足这个用例，最后一维度 <=32 会有问题，可能与其核心
    # 接口 GatherMask 的内部实现相关，目前 48 64 128 都可以支持
    ((4, 1, 32), (64, 64, 1), None),
    ((11, 33, 128), None, (8000, 200, 1)),
    ((3, 32, 128), (8000, 200, 1), (7000, 128, 1)),
    ((8, 1, 32, 128), None, None),
    ((8, 10, 32, 64), None, None),
    ((8, 20, 32, 64), (40960, 64, 1280, 1), (40960, 64, 1280, 1)),
    ((8, 20, 4, 64), (1048576, 64, 262144, 1), (1048576, 64, 262144, 1)),
]

# Data types used for testing
_TENSOR_DTYPES = [InfiniDtype.F16, InfiniDtype.BF16, InfiniDtype.F32]

# Tolerance map for different data types
_TOLERANCE_MAP = {
    InfiniDtype.F16: {"atol": 1e-3, "rtol": 1e-2},
    InfiniDtype.BF16: {"atol": 5e-3, "rtol": 5e-2},
    InfiniDtype.F32: {"atol": 1e-4, "rtol": 1e-3},
}


class Inplace(Enum):
    OUT_OF_PLACE = auto()
    INPLACE_X = auto()


class Algorithm(Enum):
    GPT_J = 0
    GPT_NEOX = 1


_INPLACE = [
    Inplace.OUT_OF_PLACE,
    Inplace.INPLACE_X,
]


_ALGO = [
    Algorithm.GPT_J,
    Algorithm.GPT_NEOX,
]

_TEST_CASES = [
    test_case + (inplace_item, algo_item)
    for test_case in _TEST_CASES_
    for inplace_item in _INPLACE
    for algo_item in _ALGO
]

DEBUG = False
PROFILE = False
NUM_PRERUN = 10
NUM_ITERATIONS = 1000


def rotary_embedding(ans, t, sin, cos, device, algo):
    def _torch_rope(sin, cos, t1, t2):
        cos = cos.unsqueeze(1)  # [seq_len, 1, dh // 2]
        sin = sin.unsqueeze(1)  # [seq_len, 1, dh // 2]
        if device == InfiniDeviceEnum.CPU:
            (t1, t2, cos, sin) = (
                t1.float(),
                t2.float(),
                cos.float(),
                sin.float(),
            )

        t_out_1 = t1 * cos - t2 * sin
        t_out_2 = t1 * sin + t2 * cos

        return t_out_1, t_out_2

    dh = t.shape[-1]
    dt = t.dtype
    assert dh % 2 == 0, "Embedding dimension must be even."

    if algo == Algorithm.GPT_J:
        t_even = t[..., 0::2]  # [seq_len, n_head, dh // 2]
        t_odd = t[..., 1::2]  # [seq_len, n_head, dh // 2]

        t_out_even, t_out_odd = _torch_rope(sin, cos, t_even, t_odd)

        ans[..., 0::2] = t_out_even.to(dt)
        ans[..., 1::2] = t_out_odd.to(dt)
    else:
        half_dim = dh // 2
        t_first = t[..., :half_dim]
        t_second = t[..., half_dim:]

        t_out_first, t_out_second = _torch_rope(sin, cos, t_first, t_second)

        ans[..., :half_dim] = t_out_first.to(dt)
        ans[..., half_dim:] = t_out_second.to(dt)


def sin_cos_table(pos, dim, device, theta, dtype):
    assert dim % 2 == 0, "Embedding dimension must be even."
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    angles = torch.outer(pos.cpu(), freqs)
    return (
        TestTensor.from_torch(torch.sin(angles), dtype, device),
        TestTensor.from_torch(torch.cos(angles), dtype, device),
    )


def test(
    handle,
    device,
    shape,
    x_strides=None,
    y_strides=None,
    inplace=Inplace.OUT_OF_PLACE,
    algo=Algorithm.GPT_J,
    dtype=torch.float32,
    sync=None,
):
    x = TestTensor(shape, x_strides, dtype, device)
    if inplace == Inplace.INPLACE_X:
        if x_strides != y_strides:
            return
        y = x
    else:
        y = TestTensor(shape, y_strides, dtype, device)

    print(
        f"Testing Rotary Positional Embedding on {InfiniDeviceNames[device]} with shape:{shape} x_strides:{x_strides} y_strides:{y_strides} and dtype:{InfiniDtypeNames[dtype]} inplace:{inplace} algo:{algo}"
    )
    theta = 1e5
    pos = TestTensor.from_torch(torch.arange(0, x.shape[-3]), InfiniDtype.I32, device)
    sin_table, cos_table = sin_cos_table(
        pos.torch_tensor(), x.shape[-1], x.device, theta, dtype
    )

    rotary_embedding(
        y.torch_tensor(),
        x.torch_tensor(),
        sin_table.torch_tensor(),
        cos_table.torch_tensor(),
        device,
        algo,
    )

    descriptor = infiniopOperatorDescriptor_t()

    if sync is not None:
        sync()

    check_error(
        LIBINFINIOP.infiniopCreateRoPEDescriptor(
            handle,
            ctypes.byref(descriptor),
            y.descriptor,
            x.descriptor,
            pos.descriptor,
            sin_table.descriptor,
            cos_table.descriptor,
            algo.value,
        )
    )

    # Invalidate the shape and strides in the descriptor to prevent them from being directly used by the kernel
    for tensor in [y, x, pos, sin_table, cos_table]:
        tensor.destroy_desc()

    workspace_size = c_uint64(0)
    check_error(
        LIBINFINIOP.infiniopGetRoPEWorkspaceSize(
            descriptor, ctypes.byref(workspace_size)
        )
    )
    workspace = TestWorkspace(workspace_size.value, x.device)

    def lib_rope():
        check_error(
            LIBINFINIOP.infiniopRoPE(
                descriptor,
                workspace.data(),
                workspace_size.value,
                y.data(),
                x.data(),
                pos.data(),
                sin_table.data(),
                cos_table.data(),
                None,
            )
        )

    lib_rope()

    if sync is not None:
        sync()

    atol, rtol = get_tolerance(_TOLERANCE_MAP, dtype)
    if DEBUG:
        debug(y.actual_tensor(), y.torch_tensor(), atol=atol, rtol=rtol)
    assert torch.allclose(y.actual_tensor(), y.torch_tensor(), atol=atol, rtol=rtol)

    if PROFILE:
        profile_operation(
            "PyTorch",
            lambda: rotary_embedding(
                y.torch_tensor(),
                x.torch_tensor(),
                sin_table.torch_tensor(),
                cos_table.torch_tensor(),
                device,
                algo,
            ),
            device,
            NUM_PRERUN,
            NUM_ITERATIONS,
        )
        profile_operation(
            "    lib", lambda: lib_rope(), device, NUM_PRERUN, NUM_ITERATIONS
        )

    check_error(LIBINFINIOP.infiniopDestroyRoPEDescriptor(descriptor))


if __name__ == "__main__":
    args = get_args()

    # Configure testing options
    DEBUG = args.debug
    PROFILE = args.profile
    NUM_PRERUN = args.num_prerun
    NUM_ITERATIONS = args.num_iterations

    # Execute tests
    for device in get_test_devices(args):
        test_operator(device, test, _TEST_CASES, _TENSOR_DTYPES)

    print("\033[92mTest passed!\033[0m")
