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.utils.data
import torch.nn.init
from torch.nn.modules.loss import _Loss
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_add_div_exp_mean_mul_pow_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp6 = tl.load(in_ptr3 + (r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp20 = tl.load(in_ptr3 + (16 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp31 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp34 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp35 = tl.load(in_ptr3 + (32 + r0 + 64 * r1), None)
    tmp45 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp46 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp49 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp50 = tl.load(in_ptr3 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tl_math.exp(tmp1)
    tmp10 = tmp8 / tmp9
    tmp11 = tmp4 - tmp10
    tmp12 = tl_math.exp(tmp0)
    tmp13 = tmp12 / tmp9
    tmp14 = tmp11 - tmp13
    tmp17 = tmp15 - tmp16
    tmp18 = tmp17 + tmp3
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tl_math.exp(tmp16)
    tmp24 = tmp22 / tmp23
    tmp25 = tmp18 - tmp24
    tmp26 = tl_math.exp(tmp15)
    tmp27 = tmp26 / tmp23
    tmp28 = tmp25 - tmp27
    tmp29 = tmp14 + tmp28
    tmp32 = tmp30 - tmp31
    tmp33 = tmp32 + tmp3
    tmp36 = tmp34 - tmp35
    tmp37 = tmp36 * tmp36
    tmp38 = tl_math.exp(tmp31)
    tmp39 = tmp37 / tmp38
    tmp40 = tmp33 - tmp39
    tmp41 = tl_math.exp(tmp30)
    tmp42 = tmp41 / tmp38
    tmp43 = tmp40 - tmp42
    tmp44 = tmp29 + tmp43
    tmp47 = tmp45 - tmp46
    tmp48 = tmp47 + tmp3
    tmp51 = tmp49 - tmp50
    tmp52 = tmp51 * tmp51
    tmp53 = tl_math.exp(tmp46)
    tmp54 = tmp52 / tmp53
    tmp55 = tmp48 - tmp54
    tmp56 = tl_math.exp(tmp45)
    tmp57 = tmp56 / tmp53
    tmp58 = tmp55 - tmp57
    tmp59 = tmp44 + tmp58
    tmp60 = -0.5
    tmp61 = tmp59 * tmp60
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = 64.0
    tmp66 = tmp64 / tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp66, 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_per_fused_add_div_exp_mean_mul_pow_sub_sum_0[grid(1)](buf2,
            arg0_1, arg1_1, arg2_1, arg3_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf2,


class NormKLLossNew(_Loss):

    def __init__(self, unit_average=False):
        super(NormKLLossNew, self).__init__()
        self.unit_average = unit_average

    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: 0.052035