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
from torch import Tensor
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_add_neg_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -tmp0
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_sum_1(in_ptr0, 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)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_per_fused_all_ne_sum_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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)
    r0 = rindex % 16
    r1 = rindex // 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 != tmp7
    tmp9 = tmp8 == 0
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = triton_helpers.any(tmp10, 1)[:, None]
    tmp13 = tmp12 == 0
    tl.store(out_ptr0 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp6, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)


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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_neg_0[grid(256)](arg1_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_sum_1[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_2[grid(256)](arg0_1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((), (), torch.bool)
        buf5 = buf4
        del buf4
        triton_per_fused_all_ne_sum_3[grid(1)](buf5, arg1_1, buf3, 1, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
    return buf1, buf3, buf0, arg1_1, buf2, buf5


class BPMLLLossNew(torch.nn.Module):

    def __init__(self, bias=(1, 1)):
        super(BPMLLLossNew, self).__init__()
        self.bias = bias
        assert len(self.bias) == 2 and all(map(lambda x: isinstance(x, int) and
            x > 0, bias)), 'bias must be positive integers'

    def pairwise_sub_exp(self, y: 'Tensor', y_bar: 'Tensor', c: 'Tensor'
        ) ->Tensor:
        """
        compute \\sum_{(k, l) \\in Y_i \\times \\bar{Y}_i} \\exp{-c^i_k+c^i_l}
        """
        truth_matrix = y.unsqueeze(2).float() @ y_bar.unsqueeze(1).float()
        exp_matrix = torch.exp(c.unsqueeze(1) - c.unsqueeze(2))
        return torch.mul(truth_matrix, exp_matrix).sum(dim=(1, 2))

    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: 0.054524