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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
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_neg_0(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -tmp0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_per_fused_add_mean_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tl_math.log(tmp1)
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tl_math.log(tmp4)
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tl_math.log(tmp7)
    tmp9 = tmp5 + tmp8
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp9 + tmp12
    tmp15 = tl.sigmoid(tmp14)
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp13 + tmp16
    tmp18 = tmp2 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp22 = 4.0
    tmp23 = tmp21 / tmp22
    tmp24 = -tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp24, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 1, 4), (4, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 1), (4,
            1, 1), 0), out=buf0)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_neg_0[grid(64)](arg2_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg2_1
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf1, reinterpret_tensor(arg0_1, (4, 4, 1), (4, 
            1, 1), 0), out=buf2)
        del arg0_1
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_add_mean_neg_sum_1[grid(1)](buf4, buf0, buf2, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf2
    return buf4,


class NegativeSamplingLossNew(nn.Module):
    """
    loss function of negative-sampling.

    """

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.041705