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
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing
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_mean_mul_pow_sub_0(in_ptr0, in_ptr1, in_ptr2, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 * tmp8
    tmp11 = tmp9 * tmp10
    tmp12 = tmp5 + tmp11
    tmp15 = tmp13 - tmp14
    tmp16 = tmp15 * tmp15
    tmp18 = tmp16 * tmp17
    tmp19 = tmp12 + tmp18
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp22
    tmp25 = tmp23 * tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)


@triton.jit
def triton_per_fused_mean_1(in_out_ptr0, in_ptr0, 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 + 16 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 16 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 16 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (4 + 16 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (5 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr0 + (6 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (7 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (8 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr0 + (9 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr0 + (10 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (11 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (12 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (13 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (14 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + 16 * r0), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.sum(tmp37, 1)[:, None]
    tmp40 = tmp39 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp40, 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, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_mul_pow_sub_0[grid(64)](arg0_1, arg1_1,
            arg2_1, buf0, 64, XBLOCK=64, num_warps=1, 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_mean_1[grid(1)](buf3, buf0, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
    return buf3,


class HeatmapLossNew(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: 0.028135