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
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_mean_neg_pow_sigmoid_sub_0(in_out_ptr0, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex // 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + 5 * r1, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + r2, None)
    tmp2 = tmp0 - tmp1
    tmp3 = -tmp2
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tmp1 * tmp1
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = 16.0
    tmp14 = tmp7 / tmp13
    tmp15 = tmp12 / tmp13
    tmp16 = tmp14 + tmp15
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_mean_neg_pow_sigmoid_sub_0[grid(1)](buf2,
            arg0_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class BPRLoss(nn.Module):

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

    def forward(self, logit):
        """
        Args:
            logit (BxB): Variable that stores the logits for the items in the mini-batch
                         The first dimension corresponds to the batches, and the second
                         dimension corresponds to sampled number of items to evaluate
        """
        diff = logit.diag().view(-1, 1).expand_as(logit) - logit
        loss = -torch.mean(F.logsigmoid(diff))
        return loss


class BPR_max(nn.Module):

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

    def forward(self, logit):
        logit_softmax = F.softmax(logit, dim=1)
        diff = logit.diag().view(-1, 1).expand_as(logit) - logit
        loss = -torch.log(torch.mean(logit_softmax * torch.sigmoid(diff)))
        return loss


class SampledCrossEntropyLoss(nn.Module):
    """ CrossEntropyLoss with n_classes = batch_size = the number of samples in the session-parallel mini-batch """

    def __init__(self, use_cuda):
        """
        Args:
             use_cuda (bool): whether to use cuda or not
        """
        super(SampledCrossEntropyLoss, self).__init__()
        self.xe_loss = nn.CrossEntropyLoss()
        self.use_cuda = use_cuda

    def forward(self, logit):
        batch_size = logit.size(1)
        target = Variable(torch.arange(batch_size).long())
        if self.use_cuda:
            target = target
        return self.xe_loss(logit, target)


class TOP1Loss(nn.Module):

    def __init__(self):
        super(TOP1Loss, self).__init__()

    def forward(self, logit):
        """
        Args:
            logit (BxB): Variable that stores the logits for the items in the mini-batch
                         The first dimension corresponds to the batches, and the second
                         dimension corresponds to sampled number of items to evaluate
        """
        diff = -(logit.diag().view(-1, 1).expand_as(logit) - logit)
        loss = torch.sigmoid(diff).mean() + torch.sigmoid(logit ** 2).mean()
        return loss


class TOP1_max(nn.Module):

    def __init__(self):
        super(TOP1_max, self).__init__()

    def forward(self, logit):
        logit_softmax = F.softmax(logit, dim=1)
        diff = -(logit.diag().view(-1, 1).expand_as(logit) - logit)
        loss = torch.mean(logit_softmax * (torch.sigmoid(diff) + torch.
            sigmoid(logit ** 2)))
        return loss


class LossFunctionNew(nn.Module):

    def __init__(self, loss_type='TOP1', use_cuda=False):
        """ An abstract loss function that can supports custom loss functions compatible with PyTorch."""
        super(LossFunctionNew, self).__init__()
        self.loss_type = loss_type
        self.use_cuda = use_cuda
        if loss_type == 'CrossEntropy':
            self._loss_fn = SampledCrossEntropyLoss(use_cuda)
        elif loss_type == 'TOP1':
            self._loss_fn = TOP1Loss()
        elif loss_type == 'BPR':
            self._loss_fn = BPRLoss()
        elif loss_type == 'TOP1-max':
            self._loss_fn = TOP1_max()
        elif loss_type == 'BPR-max':
            self._loss_fn = BPR_max()
        else:
            raise NotImplementedError

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


Measured latency_ms: 0.014073