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#
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"""Definition of generic operator strategy."""
# pylint: disable=invalid-name,unused-argument
import logging

import re
import topi
from topi.util import get_const_int, get_const_float, get_const_tuple, get_float_tuple
from .. import op as _op
from ....target import generic_func, override_native_generic_func

logger = logging.getLogger('strategy')

def wrap_topi_schedule(topi_schedule):
    """Wrap TOPI schedule which doesn't use attrs"""
    def wrapper(attrs, outs, target):
        with target:
            return topi_schedule(outs)
    return wrapper

def get_conv2d_in_channels(data_shape, data_layout):
    """Get conv2d input channels"""
    data_shape = get_const_tuple(data_shape)
    if len(data_shape) == 4:
        idx = data_layout.find("C")
        assert idx >= 0, "Invalid conv2d data layout {}".format(data_layout)
        return data_shape[idx]
    if re.match(r"NCHW\d*c", data_layout):
        # NCHW[8]c
        return data_shape[1] * data_shape[4]
    raise ValueError("Unknown conv2d data layout {}".format(data_layout))

def get_conv2d_out_channels(kernel_shape, kernel_layout):
    """Get conv2d output channels"""
    kernel_shape = get_const_tuple(kernel_shape)
    if len(kernel_shape) == 4:
        idx = kernel_layout.find("O")
        assert idx >= 0, "Invalid conv2d kernel layout {}".format(kernel_layout)
        return kernel_shape[idx]
    if re.match(r"OIHW\d*i\d*o", kernel_layout):
        return kernel_shape[0] * kernel_shape[5]
    if re.match(r"OIHW\d*o", kernel_layout):
        return kernel_shape[0] * kernel_shape[4]
    raise ValueError("Unknown conv2d kernel layout {}".format(kernel_layout))

def is_depthwise_conv2d(data_shape, data_layout, kernel_shape, kernel_layout, groups):
    ic = get_conv2d_in_channels(data_shape, data_layout)
    oc = get_conv2d_out_channels(kernel_shape, kernel_layout)
    return ic == oc == groups

@generic_func
def schedule_injective(attrs, outs, target):
    """Schedule injective ops"""
    with target:
        return topi.generic.schedule_injective(outs)

@generic_func
def schedule_reduce(attrs, outs, target):
    """Schedule reduction ops"""
    with target:
        return topi.generic.schedule_reduce(outs)

_op._schedule_injective = schedule_injective
_op._schedule_reduce = schedule_reduce

# concatenate
@generic_func
def schedule_concatenate(attrs, outs, target):
    """Schedule concatenate op"""
    with target:
        return topi.generic.schedule_injective(outs)

# pool
@generic_func
def schedule_pool(attrs, outs, target):
    """Schedule pooling ops"""
    with target:
        return topi.generic.schedule_pool(outs, attrs.layout)

# pool_grad
@generic_func
def schedule_pool_grad(attrs, outs, target):
    """Schedule pooling gradient ops"""
    with target:
        return topi.generic.schedule_pool_grad(outs)

# adaptive pool
@generic_func
def schedule_adaptive_pool(attrs, outs, target):
    """Schedule adaptive pooling ops"""
    with target:
        return topi.generic.schedule_adaptive_pool(outs)

# softmax
def wrap_compute_softmax(topi_compute):
    """Wrap softmax topi compute"""
    def _compute_softmax(attrs, inputs, out_type):
        axis = attrs.get_int("axis")
        return [topi_compute(inputs[0], axis)]
    return _compute_softmax

@override_native_generic_func("softmax_strategy")
def softmax_strategy(attrs, inputs, out_type, target):
    """softmax generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implemenation(
        wrap_compute_softmax(topi.nn.softmax),
        wrap_topi_schedule(topi.generic.schedule_softmax),
        name="softmax.generic")
    return strategy

# log_softmax
@generic_func
def schedule_log_softmax(attrs, outs, target):
    """Schedule log_softmax op"""
    with target:
        return topi.generic.schedule_softmax(outs)

# lrn
@generic_func
def schedule_lrn(attrs, outs, target):
    """Schedule LRN op"""
    with target:
        return topi.generic.schedule_lrn(outs)

# bitpack
@generic_func
def schedule_bitpack(attrs, outs, target):
    """Schedule bitpack"""
    with target:
        return topi.generic.schedule_bitpack(outs)

# conv2d
def wrap_compute_conv2d(topi_compute, need_data_layout=False, need_out_layout=False,
                        has_groups=False):
    """Wrap conv2d topi compute"""
    def _compute_conv2d(attrs, inputs, out_type):
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        dilation = get_const_tuple(attrs.dilation)
        data_layout = attrs.get_str("data_layout")
        out_layout = attrs.get_str("out_layout")
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                     else out_dtype)
        args = [inputs[0], inputs[1], strides, padding, dilation]
        if has_groups:
            args.append(attrs.groups)
        if need_data_layout:
            args.append(data_layout)
        if need_out_layout:
            args.append(out_layout)
        args.append(out_dtype)
        return [topi_compute(*args)]
    return _compute_conv2d

@override_native_generic_func("conv2d_strategy")
def conv2d_strategy(attrs, inputs, out_type, target):
    """conv2d generic strategy"""
    logger.warning("conv2d is not optimized for this platform.")
    strategy = _op.OpStrategy()
    data, kernel = inputs
    dilation = get_const_tuple(attrs.dilation)
    groups = attrs.groups
    layout = attrs.data_layout
    kernel_layout = attrs.kernel_layout
    (dilation_h, dilation_w) = dilation
    if dilation_h < 1 or dilation_w < 1:
        raise ValueError("dilation should be positive value")

    if groups == 1:
        if layout == "NCHW":
            assert kernel_layout == "OIHW"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.conv2d_nchw),
                wrap_topi_schedule(topi.generic.schedule_conv2d_nchw),
                name="conv2d_nchw.generic")
        elif layout == "NHWC":
            assert kernel_layout == "HWIO"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.conv2d_nhwc),
                wrap_topi_schedule(topi.generic.schedule_conv2d_nhwc),
                name="conv2d_nhwc.generic")
        elif layout == "HWCN":
            assert kernel_layout == "HWIO"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.conv2d_hwcn),
                wrap_topi_schedule(topi.generic.schedule_conv2d_hwcn),
                name="conv2d_hwcn.generic")
        else:
            raise RuntimeError("Unsupported conv2d layout {}".format(layout))
    elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout, groups):
        if layout == "NCHW":
            assert kernel_layout == "OIHW"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.depthwise_conv2d_nchw),
                wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nchw),
                name="depthwise_conv2d_nchw.generic")
        elif layout == "NHWC":
            assert kernel_layout == "HWOI"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.depthwise_conv2d_nhwc),
                wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nhwc),
                name="depthwise_conv2d_nhwc.generic")
        else:
            raise RuntimeError("Unsupported depthwise_conv2d layout {}".format(layout))
    else: # group_conv2d
        if layout == 'NCHW':
            assert kernel_layout == "OIHW"
            strategy.add_implementation(
                wrap_compute_conv2d(topi.nn.group_conv2d_nchw, has_groups=True),
                wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw),
                name="group_conv2d_nchw.generic")
        else:
            raise RuntimeError("Unsupported group_conv2d layout {}".format(layout))
    return strategy

# conv2d_NCHWc
@override_native_generic_func("conv2d_NCHWc_strategy")
def conv2d_NCHWc_strategy(attrs, inputs, out_type, target):
    """conv2d_NCHWc generic strategy"""
    logger.warning("conv2d_NCHWc is not optimized for this platform.")
    strategy = _op.OpStrategy()
    if inputs[0].dtype == "int8" or inputs[0].dtype == "uint8":
        strategy.add_implementation(
            wrap_compute_conv2d(topi.nn.conv2d_NCHWc_int8, True, True),
            wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc_int8),
            name="conv2d_NCHWc_int8.generic")
    else:
        strategy.add_implementation(
            wrap_compute_conv2d(topi.nn.conv2d_NCHWc, True, True),
            wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc),
            name="conv2d_NCHWc.generic")
    return strategy

# depthwise_conv2d_NCHWc
@override_native_generic_func("depthwise_conv2d_NCHWc_strategy")
def depthwise_conv2d_NCHWc_strategy(attrs, inputs, out_type, target):
    """depthwise_conv2d generic strategy"""
    logger.warning("depthwise_conv2d_NCHWc is not optimized for this platform.")
    strategy = _op.OpStrategy()
    strategy.add_implementation(
        wrap_compute_conv2d(topi.nn.depthwise_conv2d_NCHWc, True, True),
        wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_NCHWc),
        name="depthwise_conv2d_NCHWc.generic")
    return strategy

# conv2d_winograd_without_weight_transform
@override_native_generic_func("conv2d_winograd_without_weight_transform_strategy")
def conv2d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target):
    """conv2d_winograd_without_weight_transfrom generic strategy"""
    raise ValueError("No generic implemenation for conv2d_winograd_without_weight_transform")

# conv2d_winograd_weight_transform
@generic_func
def schedule_conv2d_winograd_weight_transform(attrs, outs, target):
    """Schedule conv2d_winograd_weight_transform"""
    with target:
        return topi.generic.schedule_conv2d_winograd_weight_transform(outs)

# conv2d_winograd_nnpack_weight_transform
@generic_func
def schedule_conv2d_winograd_nnpack_weight_transform(attrs, outs, target):
    """Schedule conv2d_winograd_nnpack_weight_transform"""
    with target:
        return topi.generic.schedule_conv2d_winograd_nnpack_weight_transform(outs)

# deformable_conv2d
def wrap_compute_deformable_conv2d(topi_compute):
    """wrap deformable_conv2d topi compute"""
    def _compute_deformable_conv2d(attrs, inputs, out_dtype):
        assert attrs.data_layout == "NCHW"
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        dilation = get_const_tuple(attrs.dilation)
        deformable_groups = attrs.deformable_groups
        groups = attrs.groups
        out_dtype = attrs.out_dtype
        out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype
        out = topi_compute(inputs[0], inputs[1], inputs[2], strides, padding,
                           dilation, deformable_groups, groups, out_dtype)
        return [out]
    return _compute_deformable_conv2d

@override_native_generic_func("deformable_conv2d_strategy")
def deformable_conv2d_strategy(attrs, inputs, out_type, target):
    """deformable_conv2d generic strategy"""
    logger.warning("deformable_conv2d is not optimized for this platform.")
    layout = attrs.data_layout
    assert layout == "NCHW"
    strategy = _op.OpStrategy()
    strategy.add_implementation(
        wrap_compute_deformable_conv2d(topi.nn.deformable_conv2d_nchw),
        wrap_topi_schedule(topi.generic.schedule_deformable_conv2d_nchw),
        name="deformable_conv2d.generic")
    return strategy

# conv2d_transpose
def wrap_compute_conv2d_transpose(topi_compute):
    """wrap conv2d_transpose topi compute"""
    def compute_conv2d_transpose(attrs, inputs, out_dtype):
        """Compute definition of conv2d_transpose"""
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                     else out_dtype)
        out = topi_compute(
            inputs[0], inputs[1], strides, padding, out_dtype)
        output_padding = get_const_tuple(attrs.output_padding)
        out = topi.nn.pad(out, [0, 0, 0, 0],
                          [0, 0, output_padding[0], output_padding[1]])
        return [out]
    return compute_conv2d_transpose

@override_native_generic_func("conv2d_transpose_strategy")
def conv2d_transpose_strategy(attrs, inputs, out_type, target):
    """conv2d_transpose generic strategy"""
    logger.warning("conv2d_transpose is not optimized for this platform.")
    layout = attrs.data_layout
    dilation = get_const_tuple(attrs.dilation)
    groups = attrs.groups
    assert layout == "NCHW", "only support nchw for now"
    assert dilation == (1, 1), "not support dilate now"
    assert groups == 1, "only support groups == 1 for now"
    strategy = _op.OpStrategy()
    strategy.add_implementation(
        wrap_compute_conv2d_transpose(topi.nn.conv2d_transpose_nchw),
        wrap_topi_schedule(topi.generic.schedule_conv2d_transpose_nchw),
        name="conv2d_transpose_nchw.generic")
    return strategy

# conv3d
def wrap_compute_conv3d(topi_compute, need_layout=False):
    """wrap conv3d topi compute"""
    def _compute_conv3d(attrs, inputs, out_type):
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        dilation = get_const_tuple(attrs.dilation)
        groups = attrs.groups
        layout = attrs.data_layout
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                     else out_dtype)

        (dilation_d, dilation_h, dilation_w) = dilation
        if dilation_d < 1 or dilation_h < 1 or dilation_w < 1:
            raise ValueError("Dilation should be positive value")
        if groups != 1:
            raise ValueError("Not support arbitrary group number for conv3d")
        if need_layout:
            out = topi_compute(inputs[0], inputs[1], strides, padding, dilation,
                               layout, out_dtype)
        else:
            out = topi_compute(inputs[0], inputs[1], strides, padding, dilation,
                               out_dtype)
        return [out]
    return _compute_conv3d

@override_native_generic_func("conv3d_strategy")
def conv3d_strategy(attrs, inputs, out_type, target):
    """conv3d generic strategy"""
    logger.warning("conv3d is not optimized for this platform.")
    strategy = _op.OpStrategy()
    layout = attrs.data_layout
    if layout == "NCDHW":
        strategy.add_implementation(
            wrap_compute_conv3d(topi.nn.conv3d_ncdhw),
            wrap_topi_schedule(topi.generic.schedule_conv3d_ncdhw),
            name="conv3d_ncdhw.generic")
    elif layout == "NDHWC":
        strategy.add_implementation(
            wrap_compute_conv3d(topi.nn.conv3d_ndhwc),
            wrap_topi_schedule(topi.generic.schedule_conv3d_ndhwc),
            name="conv3d_ndhwc.generic")
    else:
        raise ValueError("Not support this layout {} yet".format(layout))
    return strategy

# conv3d_winograd_without_weight_transform
@override_native_generic_func("conv3d_winograd_without_weight_transform_strategy")
def conv3d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target):
    """conv3d_winograd_without_weight_transfrom generic strategy"""
    raise ValueError("No generic implemenation for conv3d_winograd_without_weight_transform")

# conv3d_winograd_weight_transform
@generic_func
def schedule_conv3d_winograd_weight_transform(attrs, outs, target):
    """Schedule conv3d_winograd_weight_transform"""
    with target:
        return topi.generic.schedule_conv3d_winograd_weight_transform(outs)

# conv1d
def wrap_compute_conv1d(topi_compute):
    """wrap conv1d topi compute"""
    def _compute_conv1d(attrs, inputs, out_type):
        """Compute definition of conv1d"""
        strides = get_const_tuple(attrs.strides)
        padding = get_const_tuple(attrs.padding)
        dilation = get_const_tuple(attrs.dilation)
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                     else out_dtype)
        return [topi_compute(inputs[0], inputs[1], strides, padding, dilation,
                             out_dtype)]
    return _compute_conv1d

@override_native_generic_func("conv1d_strategy")
def conv1d_strategy(attrs, inputs, out_type, target):
    """conv1d generic strategy"""
    logger.warning("conv1d is not optimized for this platform.")
    layout = attrs.data_layout
    dilation = get_const_tuple(attrs.dilation)
    if dilation[0] < 1:
        raise ValueError("dilation should be a positive value")
    strategy = _op.OpStrategy()
    if layout == "NCW":
        strategy.add_implementation(
            wrap_compute_conv1d(topi.nn.conv1d_ncw),
            wrap_topi_schedule(topi.generic.schedule_conv1d_ncw),
            name="conv1d_ncw.generic")
    elif layout == "NWC":
        strategy.add_implementation(
            wrap_compute_conv1d(topi.nn.conv1d_nwc),
            wrap_topi_schedule(topi.generic.schedule_conv1d_nwc),
            name="conv1d_nwc.generic")
    else:
        raise ValueError("Unsupported conv1d layout {}".format(layout))
    return strategy

# conv1d_transpose
def wrap_compute_conv1d_transpose(topi_compute):
    """wrap conv1d_transpose topi compute"""
    def _compute_conv1d_tranpsoe(attrs, inputs, out_type):
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "") else out_dtype)
        out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype)
        output_padding = get_const_tuple(attrs.output_padding)
        out = topi.nn.pad(out, [0, 0, 0], [0, 0, output_padding[0]])
        return [out]
    return _compute_conv1d_tranpsoe

@override_native_generic_func("conv1d_transpose_strategy")
def conv1d_transpose_strategy(attrs, inputs, out_type, target):
    """conv1d_transpose generic strategy"""
    logger.warning("conv1d_transpose is not optimized for this platform.")
    strategy = _op.OpStrategy()
    layout = attrs.data_layout
    dilation = get_const_tuple(attrs.dilation)
    groups = attrs.groups
    assert layout == "NCW", "conv1d_transpose ncw only supported"
    assert dilation == (1,), "conv1d_transpose dilation is not supported"
    assert groups == 1, "conv1d_transpose groups == 1 only supported"
    strategy.add_implementation(wrap_compute_conv1d_transpose(topi.nn.conv1d_transpose_ncw),
                                wrap_topi_schedule(topi.generic.schedule_conv1d_transpose_ncw),
                                name="conv1d_transpose_ncw.generic")
    return strategy


# dilation2d
def wrap_compute_dilation2d(topi_compute, need_data_layout=False):
    """Wrap dilation2d topi compute"""
    def _compute_dilation2d(attrs, inputs, out_type):
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        dilations = get_const_tuple(attrs.dilations)
        data_layout = attrs.get_str("data_layout")
        out_dtype = attrs.out_dtype
        out_dtype = (inputs[0].dtype if out_dtype in ("same", "")
                     else out_dtype)
        args = [inputs[0], inputs[1], strides, padding, dilations]
        if need_data_layout:
            args.append(data_layout)
        args.append(out_dtype)
        return [topi_compute(*args)]
    return _compute_dilation2d


@override_native_generic_func("dilation2d_strategy")
def dilation2d_strategy(attrs, inputs, out_type, target):
    """dilation2d_strategy generic strategy"""
    logger.warning("dilation2d_strategy is not optimized for this platform.")
    strategy = _op.OpStrategy()
    dilations = get_const_tuple(attrs.dilations)
    layout = attrs.data_layout
    kernel_layout = attrs.kernel_layout

    assert layout in ["NCHW", "NHWC"]
    (dilation_h, dilation_w) = dilations
    if dilation_h < 1 or dilation_w < 1:
        raise ValueError("dilation should be positive value")

    if layout == "NCHW":
        assert kernel_layout == "IHW"
        strategy.add_implementation(
            wrap_compute_dilation2d(topi.image.dilation2d_nchw),
            wrap_topi_schedule(topi.generic.schedule_dilation2d_nchw),
            name="dilation2d_nchw.generic")
    elif layout == "NHWC":
        assert kernel_layout == "HWI"
        strategy.add_implementation(
            wrap_compute_dilation2d(topi.image.dilation2d_nhwc),
            wrap_topi_schedule(topi.generic.schedule_dilation2d_nhwc),
            name="dilation2d_nhwc.generic")
    else:
        raise RuntimeError("Unsupported dilation2d layout {}".format(layout))
    return strategy


# dense
def wrap_compute_dense(topi_compute):
    """wrap dense topi compute"""
    def _compute_dense(attrs, inputs, out_type):
        """Compute definition of dense"""
        out_dtype = attrs.out_dtype
        out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
        return [topi_compute(inputs[0], inputs[1], None, out_dtype)]
    return _compute_dense

@override_native_generic_func("dense_strategy")
def dense_strategy(attrs, inputs, out_type, target):
    """dense generic strategy"""
    logger.warning("dense is not optimized for this platform.")
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_dense(topi.nn.dense),
                                wrap_topi_schedule(topi.generic.schedule_dense),
                                name="dense.generic")
    return strategy

# batch_matmul
def wrap_compute_batch_matmul(topi_compute):
    """wrap batch_matmul topi compute"""
    def _compute_batch_matmul(attrs, inputs, out_type):
        return [topi_compute(inputs[0], inputs[1])]
    return _compute_batch_matmul

@override_native_generic_func("batch_matmul_strategy")
def batch_matmul_strategy(attrs, inputs, out_type, target):
    """batch_matmul generic strategy"""
    logger.warning("batch_matmul is not optimized for this platform.")
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_batch_matmul(topi.nn.batch_matmul),
                                wrap_topi_schedule(topi.generic.schedule_batch_matmul),
                                name="batch_matmul.generic")
    return strategy

# sparse_dense
@generic_func
def schedule_sparse_dense(attrs, outs, target):
    """schedule sparse_dense"""
    with target:
        return topi.generic.schedule_sparse_dense(outs)

# sparse_transpose
@generic_func
def schedule_sparse_transpose(attrs, outs, target):
    """schedule sparse_transpose"""
    with target:
        return topi.generic.schedule_sparse_transpose(outs)

# argsort
def wrap_compute_argsort(topi_compute):
    """Wrap argsort topi compute"""
    def _compute_argsort(attrs, inputs, _):
        axis = get_const_int(attrs.axis)
        is_ascend = bool(get_const_int(attrs.is_ascend))
        dtype = attrs.dtype
        return [topi_compute(inputs[0], axis=axis, is_ascend=is_ascend, dtype=dtype)]
    return _compute_argsort

@override_native_generic_func("argsort_strategy")
def argsort_strategy(attrs, inputs, out_type, target):
    """argsort generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_argsort(topi.argsort),
                                wrap_topi_schedule(topi.generic.schedule_argsort),
                                name="argsort.generic")
    return strategy

# topk
def wrap_compute_topk(topi_compute):
    """Wrap topk compute"""
    def _compute_topk(attrs, inputs, out_type):
        k = get_const_int(attrs.k)
        axis = get_const_int(attrs.axis)
        ret_type = attrs.ret_type
        is_ascend = bool(get_const_int(attrs.is_ascend))
        dtype = attrs.dtype
        out = topi_compute(inputs[0], k, axis, ret_type, is_ascend, dtype)
        out = out if isinstance(out, list) else [out]
        return out
    return _compute_topk

@override_native_generic_func("topk_strategy")
def topk_strategy(attrs, inputs, out_type, target):
    """topk generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_topk(topi.topk),
                                wrap_topi_schedule(topi.generic.schedule_topk),
                                name="topk.generic")
    return strategy

# multibox_prior
def wrap_compute_multibox_prior(topi_compute):
    """Wrap multibox_prior compute"""
    def _compute_multibox_prior(attrs, inputs, _):
        """Compute definition of multibox_prior"""
        sizes = get_float_tuple(attrs.sizes)
        ratios = get_float_tuple(attrs.ratios)
        steps = get_float_tuple(attrs.steps)
        offsets = get_float_tuple(attrs.offsets)
        clip = bool(get_const_int(attrs.clip))
        return [topi_compute(inputs[0], sizes, ratios, steps, offsets, clip)]
    return _compute_multibox_prior

@override_native_generic_func("multibox_prior_strategy")
def multibox_prior_strategy(attrs, inputs, out_type, target):
    """multibox_prior generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_multibox_prior(topi.vision.ssd.multibox_prior),
                                wrap_topi_schedule(topi.generic.schedule_multibox_prior),
                                name="multibox_prior.generic")
    return strategy

# multibox_transform_loc
def wrap_compute_multibox_transform_loc(topi_compute):
    """Wrap multibox_transform_loc compute"""
    def _compute_multibox_transform_loc(attrs, inputs, _):
        """Compute definition of multibox_detection"""
        clip = bool(get_const_int(attrs.clip))
        threshold = get_const_float(attrs.threshold)
        variances = get_float_tuple(attrs.variances)
        return topi_compute(
            inputs[0], inputs[1], inputs[2], clip, threshold, variances)
    return _compute_multibox_transform_loc

@override_native_generic_func("multibox_transform_loc_strategy")
def multibox_transform_loc_strategy(attrs, inputs, out_type, target):
    """schedule multibox_transform_loc"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(
        wrap_compute_multibox_transform_loc(topi.vision.ssd.multibox_transform_loc),
        wrap_topi_schedule(topi.generic.schedule_multibox_transform_loc),
        name="multibox_transform_loc.generic")
    return strategy

# get_valid_counts
def wrap_compute_get_valid_counts(topi_compute):
    """wrap get_valid_counts topi compute"""
    def _compute_get_valid_counts(attrs, inputs, out_type):
        score_threshold = get_const_float(attrs.score_threshold)
        id_index = get_const_int(attrs.id_index)
        score_index = get_const_int(attrs.score_index)
        return topi_compute(inputs[0], score_threshold, id_index, score_index)
    return _compute_get_valid_counts

@override_native_generic_func("get_valid_counts_strategy")
def get_valid_counts_strategy(attrs, inputs, out_type, target):
    """get_valid_counts generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_get_valid_counts(topi.vision.get_valid_counts),
                                wrap_topi_schedule(topi.generic.schedule_get_valid_counts),
                                name="get_valid_counts.generic")
    return strategy

# non-maximum suppression
def wrap_compute_nms(topi_compute):
    """wrap nms topi compute"""
    def _compute_nms(attrs, inputs, out_type):
        return_indices = bool(get_const_int(attrs.return_indices))
        max_output_size = get_const_int(attrs.max_output_size)
        iou_threshold = get_const_float(attrs.iou_threshold)
        force_suppress = bool(get_const_int(attrs.force_suppress))
        top_k = get_const_int(attrs.top_k)
        coord_start = get_const_int(attrs.coord_start)
        score_index = get_const_int(attrs.score_index)
        id_index = get_const_int(attrs.id_index)
        invalid_to_bottom = bool(get_const_int(attrs.invalid_to_bottom))
        return [topi_compute(inputs[0], inputs[1], max_output_size, iou_threshold,
                             force_suppress, top_k, coord_start, score_index,
                             id_index, return_indices, invalid_to_bottom)]
    return _compute_nms

@override_native_generic_func("non_max_suppression_strategy")
def nms_strategy(attrs, inputs, out_type, target):
    """nms generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_nms(topi.vision.non_max_suppression),
                                wrap_topi_schedule(topi.generic.schedule_nms),
                                name="nms.generic")
    return strategy

# roi_align
def wrap_compute_roi_align(topi_compute):
    """wrap roi_align topi compute"""
    def _compute_roi_align(attrs, inputs, out_type):
        assert attrs.layout == "NCHW"
        pooled_size = get_const_tuple(attrs.pooled_size)
        return [topi_compute(inputs[0], inputs[1],
                             pooled_size=pooled_size,
                             spatial_scale=attrs.spatial_scale,
                             sample_ratio=attrs.sample_ratio)]
    return _compute_roi_align

@override_native_generic_func("roi_align_strategy")
def roi_align_strategy(attrs, inputs, out_type, target):
    """roi_align generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
                                wrap_topi_schedule(topi.generic.schedule_roi_align),
                                name="roi_align.generic")
    return strategy

# roi_pool
@generic_func
def schedule_roi_pool(attrs, outs, target):
    """schedule roi_pool"""
    with target:
        return topi.generic.schedule_roi_pool(outs)

# proposal
def wrap_compute_proposal(topi_compute):
    """wrap proposal topi compute"""
    def _compute_proposal(attrs, inputs, out_type):
        scales = get_float_tuple(attrs.scales)
        ratios = get_float_tuple(attrs.ratios)
        feature_stride = attrs.feature_stride
        threshold = attrs.threshold
        rpn_pre_nms_top_n = attrs.rpn_pre_nms_top_n
        rpn_post_nms_top_n = attrs.rpn_post_nms_top_n
        rpn_min_size = attrs.rpn_min_size
        iou_loss = bool(get_const_int(attrs.iou_loss))
        return [topi_compute(inputs[0], inputs[1], inputs[2], scales, ratios,
                             feature_stride, threshold, rpn_pre_nms_top_n,
                             rpn_post_nms_top_n, rpn_min_size, iou_loss)]
    return _compute_proposal

@override_native_generic_func("proposal_strategy")
def proposal_strategy(attrs, inputs, out_type, target):
    """proposal generic strategy"""
    strategy = _op.OpStrategy()
    strategy.add_implementation(wrap_compute_proposal(topi.vision.rcnn.proposal),
                                wrap_topi_schedule(topi.generic.schedule_proposal),
                                name="proposal.generic")
    return strategy

# argwhere
@generic_func
def schedule_argwhere(attrs, outs, target):
    """schedule argwhere"""
    with target:
        return topi.generic.schedule_argwhere(outs)

# bitserial_conv2d
def wrap_compute_bitserial_conv2d(topi_compute):
    """wrap bitserial_conv2d topi compute"""
    def compute_bitserial_conv2d(attrs, inputs, out_dtype):
        """Compute definition for bitserial conv2d."""
        padding = get_const_tuple(attrs.padding)
        strides = get_const_tuple(attrs.strides)
        activation_bits = attrs.activation_bits
        weight_bits = attrs.weight_bits
        pack_dtype = attrs.pack_dtype
        out_dtype = attrs.out_dtype
        unipolar = attrs.unipolar
        return [topi_compute(inputs[0], inputs[1], strides, padding, activation_bits,
                             weight_bits, pack_dtype, out_dtype, unipolar)]
    return compute_bitserial_conv2d

@override_native_generic_func("bitserial_conv2d_strategy")
def bitserial_conv2d_strategy(attrs, inputs, out_type, target):
    """bitserial_conv2d generic strategy"""
    logger.warning("bitserial_conv2d is not optimized for this platform.")
    strategy = _op.OpStrategy()
    layout = attrs.data_layout
    if layout == "NCHW":
        strategy.add_implementation(
            wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nchw),
            wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nchw),
            name="bitserial_conv2d_nchw.generic")
    elif layout == "NHWC":
        strategy.add_implementation(
            wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nhwc),
            wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nhwc),
            name="bitserial_conv2d_nhwc.generic")
    else:
        raise ValueError("Data layout {} not supported.".format(layout))
    return strategy

# bitserial_dense
def wrap_compute_bitserial_dense(topi_compute):
    """wrap bitserial_dense topi compute"""
    def compute_bitserial_dense(attrs, inputs, out_type):
        """Compute definition of bitserial dense"""
        data_bits = attrs.data_bits
        weight_bits = attrs.weight_bits
        pack_dtype = attrs.pack_dtype
        out_dtype = attrs.out_dtype
        out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype
        unipolar = attrs.unipolar
        return [topi_compute(inputs[0], inputs[1], data_bits, weight_bits,
                             pack_dtype, out_dtype, unipolar)]
    return compute_bitserial_dense

@override_native_generic_func("bitserial_dense_strategy")
def bitserial_dense_strategy(attrs, inputs, out_type, target):
    """bitserial_dense generic strategy"""
    logger.warning("bitserial_dense is not optimized for this platform.")
    strategy = _op.OpStrategy()
    strategy.add_implementation(
        wrap_compute_bitserial_dense(topi.nn.bitserial_dense),
        wrap_topi_schedule(topi.generic.schedule_bitserial_dense),
        name="bitserial_dense.generic")
    return strategy
