import torch
import torch.nn as nn
import torch.nn.functional as F


class HingeLoss(nn.Module):
    """Hinge loss function module for multi-label classification"""

    def __init__(self, margin=1.0, power=2, cost_weighted=False):
        """
        Args:
            margin (float, optional): margin for the hinge loss. Default 1.0
            power (int, optional): exponent for the hinge loss. Default to 2 for squared-hinge
            cost_weighted (bool, optional): whether to use label value as weight. Default False
        """
        super(HingeLoss, self).__init__()
        self.margin = margin
        self.power = power
        self.cost_weighted = cost_weighted

    def forward(self, z, y, C_pos=1.0, C_neg=1.0):
        """Compute the hinge loss

        Args:
            z (torch.tensor): predicted matrix of size: (batch_size * output_size)
            y (torch.tensor): 0/1 ground truth of size: (batch_size * output_size)
            C_pos (float, optional): positive penalty for the hinge loss. Default 1.0
            C_neg (float, optional): negative penalty for the hinge loss. Default 1.0

        Returns:
            loss (torch.tensor): the tensor of average loss
        """
        y_binary = (y > 0).float()
        y_new = 2.0 * y_binary - 1.0
        loss = F.relu(self.margin - y_new * z)
        loss = loss ** self.power
        if self.cost_weighted:
            loss = loss * (C_pos * y + C_neg * (1.0 - y_binary))
        else:
            loss = loss * (C_pos * y_binary + C_neg * (1.0 - y_binary))
        return loss.mean(1)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp24 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp35 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp40 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp56 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp5 - tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 - tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 * tmp6
    tmp15 = tmp6 - tmp3
    tmp16 = tmp15 * tmp6
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 * tmp17
    tmp20 = tmp19 > tmp1
    tmp21 = tmp20.to(tl.float32)
    tmp22 = tmp21 * tmp4
    tmp23 = tmp22 - tmp6
    tmp25 = tmp23 * tmp24
    tmp26 = tmp6 - tmp25
    tmp27 = triton_helpers.maximum(tmp11, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp21 * tmp6
    tmp30 = tmp6 - tmp21
    tmp31 = tmp30 * tmp6
    tmp32 = tmp29 + tmp31
    tmp33 = tmp28 * tmp32
    tmp34 = tmp18 + tmp33
    tmp36 = tmp35 > tmp1
    tmp37 = tmp36.to(tl.float32)
    tmp38 = tmp37 * tmp4
    tmp39 = tmp38 - tmp6
    tmp41 = tmp39 * tmp40
    tmp42 = tmp6 - tmp41
    tmp43 = triton_helpers.maximum(tmp11, tmp42)
    tmp44 = tmp43 * tmp43
    tmp45 = tmp37 * tmp6
    tmp46 = tmp6 - tmp37
    tmp47 = tmp46 * tmp6
    tmp48 = tmp45 + tmp47
    tmp49 = tmp44 * tmp48
    tmp50 = tmp34 + tmp49
    tmp52 = tmp51 > tmp1
    tmp53 = tmp52.to(tl.float32)
    tmp54 = tmp53 * tmp4
    tmp55 = tmp54 - tmp6
    tmp57 = tmp55 * tmp56
    tmp58 = tmp6 - tmp57
    tmp59 = triton_helpers.maximum(tmp11, tmp58)
    tmp60 = tmp59 * tmp59
    tmp61 = tmp53 * tmp6
    tmp62 = tmp6 - tmp53
    tmp63 = tmp62 * tmp6
    tmp64 = tmp61 + tmp63
    tmp65 = tmp60 * tmp64
    tmp66 = tmp50 + tmp65
    tmp67 = 4.0
    tmp68 = tmp66 / tmp67
    tl.store(out_ptr0 + x2, tmp68, 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((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0[grid(64)
            ](arg0_1, arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class HingeLossNew(nn.Module):
    """Hinge loss function module for multi-label classification"""

    def __init__(self, margin=1.0, power=2, cost_weighted=False):
        """
        Args:
            margin (float, optional): margin for the hinge loss. Default 1.0
            power (int, optional): exponent for the hinge loss. Default to 2 for squared-hinge
            cost_weighted (bool, optional): whether to use label value as weight. Default False
        """
        super(HingeLossNew, self).__init__()
        self.margin = margin
        self.power = power
        self.cost_weighted = cost_weighted

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