import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class HighLightLayer(nn.Module):

    def __init__(self, dim):
        super(HighLightLayer, self).__init__()
        self.conv1d = Conv1D(in_dim=dim, out_dim=1, kernel_size=1, stride=1,
            padding=0, bias=True)

    def forward(self, x, mask):
        logits = self.conv1d(x)
        logits = logits.squeeze(2)
        logits = mask_logits(logits, mask)
        scores = nn.Sigmoid()(logits)
        return scores

    @staticmethod
    def compute_loss(scores, labels, mask, epsilon=1e-12):
        labels = labels.type(torch.float32)
        weights = torch.where(labels == 0.0, labels + 1.0, 2.0 * labels)
        loss_per_location = nn.BCELoss(reduction='none')(scores, labels)
        loss_per_location = loss_per_location * weights
        mask = mask.type(torch.float32)
        loss = torch.sum(loss_per_location * mask) / (torch.sum(mask) + epsilon
            )
        return loss


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


def get_init_inputs():
    return [[], {'dim': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = -1e+30
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 + tmp8
    tmp10 = tl.sigmoid(tmp9)
    tl.store(in_out_ptr0 + x0, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4), (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_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4), (4, 4, 1))
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused_add_mul_rsub_sigmoid_1[grid(16)](buf2, primals_3,
            primals_4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        del primals_4
    return buf2, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1,
        4), 0), buf2


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class HighLightLayerNew(nn.Module):

    def __init__(self, dim):
        super(HighLightLayerNew, self).__init__()
        self.conv1d = Conv1D(in_dim=dim, out_dim=1, kernel_size=1, stride=1,
            padding=0, bias=True)

    @staticmethod
    def compute_loss(scores, labels, mask, epsilon=1e-12):
        labels = labels.type(torch.float32)
        weights = torch.where(labels == 0.0, labels + 1.0, 2.0 * labels)
        loss_per_location = nn.BCELoss(reduction='none')(scores, labels)
        loss_per_location = loss_per_location * weights
        mask = mask.type(torch.float32)
        loss = torch.sum(loss_per_location * mask) / (torch.sum(mask) + epsilon
            )
        return loss

    def forward(self, input_0, input_1):
        primals_2 = self.conv1d.conv1d.weight
        primals_3 = self.conv1d.conv1d.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
