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.triton_helpers import math as tl_math
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_per_fused_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + 64 * x0), xmask, other=0.0)
    tmp3 = tmp1 - tmp2
    tmp4 = tl_math.abs(tmp3)
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp9, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_per_fused_div_mean_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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = 4.0
    tmp7 = tmp5 / tmp6
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(4)](arg0_1, arg1_1, arg2_1, buf0, buf1,
            4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_div_mean_1[grid(1)](buf3, buf0, buf1, 1, 4, XBLOCK
            =1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


class DistanceMatrixLossNew(nn.Module):

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

    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: 0.030808