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_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp59 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 4
        r1 = rindex // 4 % 16
        r4 = rindex // 4
        tmp0 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + 4 * r4, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tl.load(in_ptr2 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp4 = tl.load(in_ptr3 + (r0 + 16 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp15 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + (1 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp19 = tl.load(in_ptr3 + (4 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp29 = tl.load(in_ptr0 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp30 = tl.load(in_ptr1 + (2 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp31 = tl.load(in_ptr2 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp33 = tl.load(in_ptr3 + (8 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp43 = tl.load(in_ptr0 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp44 = tl.load(in_ptr1 + (3 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp45 = tl.load(in_ptr2 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp47 = tl.load(in_ptr3 + (12 + r0 + 16 * r1), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 - tmp2
        tmp5 = tmp3 / tmp4
        tmp6 = tmp5 * tmp5
        tmp7 = -tmp6
        tmp8 = 0.5
        tmp9 = tmp7 * tmp8
        tmp10 = tl_math.exp(tmp9)
        tmp11 = 2.5066282746310002
        tmp12 = tmp4 * tmp11
        tmp13 = tmp10 / tmp12
        tmp14 = tmp0 * tmp13
        tmp18 = tmp16 - tmp17
        tmp20 = tmp18 / tmp19
        tmp21 = tmp20 * tmp20
        tmp22 = -tmp21
        tmp23 = tmp22 * tmp8
        tmp24 = tl_math.exp(tmp23)
        tmp25 = tmp19 * tmp11
        tmp26 = tmp24 / tmp25
        tmp27 = tmp15 * tmp26
        tmp28 = tmp14 + tmp27
        tmp32 = tmp30 - tmp31
        tmp34 = tmp32 / tmp33
        tmp35 = tmp34 * tmp34
        tmp36 = -tmp35
        tmp37 = tmp36 * tmp8
        tmp38 = tl_math.exp(tmp37)
        tmp39 = tmp33 * tmp11
        tmp40 = tmp38 / tmp39
        tmp41 = tmp29 * tmp40
        tmp42 = tmp28 + tmp41
        tmp46 = tmp44 - tmp45
        tmp48 = tmp46 / tmp47
        tmp49 = tmp48 * tmp48
        tmp50 = -tmp49
        tmp51 = tmp50 * tmp8
        tmp52 = tl_math.exp(tmp51)
        tmp53 = tmp47 * tmp11
        tmp54 = tmp52 / tmp53
        tmp55 = tmp43 * tmp54
        tmp56 = tmp42 + tmp55
        tmp57 = tl_math.log(tmp56)
        tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
        tmp60 = _tmp59 + tmp58
        _tmp59 = tl.where(rmask, tmp60, _tmp59)
    tmp59 = tl.sum(_tmp59, 1)[:, None]
    tmp61 = 256.0
    tmp62 = tmp59 / tmp61
    tmp63 = -tmp62
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp63, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_red_fused_div_exp_log_mean_mul_neg_pow_sqrt_sub_sum_0[grid(1)](
            buf2, arg3_1, arg0_1, arg1_1, arg2_1, 1, 256, XBLOCK=1, RBLOCK=
            256, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf2,


class GMMLossNew(nn.Module):

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

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]


Measured latency_ms: 0.017272