import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 256
    x5 = xindex % 16
    x1 = xindex // 4 % 4
    x6 = xindex // 4 % 64
    x7 = xindex
    tmp0 = x3
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x5, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = 1.0
    tmp7 = tmp6 - tmp5
    tmp8 = tl.load(in_ptr0 + (64 + x5), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tl.load(in_ptr0 + (16 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (17 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp14 - tmp15
    tmp17 = tl.load(in_ptr1 + (x6 + 64 * x3), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp18 = tmp16 * tmp17
    tmp19 = tmp6 - tmp18
    tmp20 = triton_helpers.maximum(tmp19, tmp10)
    tmp21 = tmp20 * tmp12
    tmp22 = tmp13 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tmp0 >= tmp3
    tmp26 = tl.full([1], 8, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + x5, tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp30 = tmp6 - tmp29
    tmp31 = tl.load(in_ptr0 + (128 + x5), tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp32 = tmp30 + tmp31
    tmp33 = triton_helpers.maximum(tmp32, tmp10)
    tmp34 = tmp33 * tmp12
    tmp35 = tl.load(in_ptr0 + (16 + 4 * x1), tmp28 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp36 = tl.load(in_ptr0 + (17 + 4 * x1), tmp28 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp37 = tmp35 - tmp36
    tmp38 = tl.load(in_ptr1 + (x6 + 64 * (-4 + x3)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 * tmp38
    tmp40 = tmp6 - tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp10)
    tmp42 = tmp41 * tmp12
    tmp43 = tmp34 + tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp28, tmp43, tmp44)
    tmp46 = tmp0 >= tmp26
    tl.full([1], 12, tl.int64)
    tmp49 = tl.load(in_ptr0 + x5, tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp50 = tmp6 - tmp49
    tmp51 = tl.load(in_ptr0 + (192 + x5), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp52 = tmp50 + tmp51
    tmp53 = triton_helpers.maximum(tmp52, tmp10)
    tmp54 = tmp53 * tmp12
    tmp55 = tl.load(in_ptr0 + (16 + 4 * x1), tmp46 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp56 = tl.load(in_ptr0 + (17 + 4 * x1), tmp46 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp57 = tmp55 - tmp56
    tmp58 = tl.load(in_ptr1 + (x6 + 64 * (-8 + x3)), tmp46 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp59 = tmp57 * tmp58
    tmp60 = tmp6 - tmp59
    tmp61 = triton_helpers.maximum(tmp60, tmp10)
    tmp62 = tmp61 * tmp12
    tmp63 = tmp54 + tmp62
    tmp64 = tl.full(tmp63.shape, 0.0, tmp63.dtype)
    tmp65 = tl.where(tmp46, tmp63, tmp64)
    tmp66 = tl.where(tmp28, tmp45, tmp65)
    tmp67 = tl.where(tmp4, tmp24, tmp66)
    tl.store(out_ptr0 + x7, tmp67, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((12, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(3072)](arg0_1, arg1_1, buf0, 3072,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class HingeMarginLoss(nn.Module):
    """
    计算hinge loss 接口
    """

    def __init__(self):
        super(HingeMarginLoss, self).__init__()

    def forward(self, t, tr, delt=None, size_average=False):
        """
        计算hingle loss
        """
        if delt is None:
            loss = torch.clamp(1 - t + tr, min=0)
        else:
            loss = torch.clamp(1 - torch.mul(t - tr, torch.squeeze(delt)),
                min=0)
            loss = torch.unsqueeze(loss, dim=-1)
        return loss


class SSWELossNew(nn.Module):
    """
    SSWE 中考虑gram得分和情感得分的loss类
    """

    def __init__(self, alpha=0.5):
        super(SSWELossNew, self).__init__()
        self.alpha = alpha
        self.hingeLoss = HingeMarginLoss()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.015261