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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
from torch.nn.parameter import Parameter
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_per_fused_add_mean_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    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.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_sqrt_sub_var_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + 2 * x2, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 2 * x2), xmask, eviction_policy='evict_last'
        )
    tmp39 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = 15.0
    tmp22 = tmp18 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp28 = triton_helpers.maximum(tmp26, tmp27)
    tmp29 = tmp26 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 - tmp28
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp30 / tmp33
    tmp35 = tmp0 - tmp20
    tmp36 = tmp35 / tmp25
    tmp37 = tmp34 * tmp36
    tmp38 = tmp32 / tmp33
    tmp40 = tmp0 - tmp39
    tmp42 = tmp40 / tmp41
    tmp43 = tmp38 * tmp42
    tmp44 = tmp37 + tmp43
    tmp46 = tmp44 * tmp45
    tmp48 = tmp46 + tmp47
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp48, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 2), (8, 2, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf7 = reinterpret_tensor(buf6, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf6
        buf11 = reinterpret_tensor(buf9, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf9
        get_raw_stream(0)
        triton_per_fused_add_mean_sqrt_var_0[grid(4)](buf7, buf11,
            primals_1, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        buf5 = reinterpret_tensor(buf3, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf3
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_add_div_mean_mul_sqrt_sub_var_1[grid(16)](buf1,
            buf5, primals_1, primals_2, buf7, buf11, primals_3, primals_4,
            buf12, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_4
    return buf12, primals_1, primals_2, primals_3, buf1, buf5, buf7, buf11


class ILNNew(nn.Module):

    def __init__(self, num_features, eps=1e-05, momentum=0.9,
        using_moving_average=True, using_bn=False):
        super(ILNNew, self).__init__()
        self.eps = eps
        self.momentum = momentum
        self.using_moving_average = using_moving_average
        self.using_bn = using_bn
        self.num_features = num_features
        if self.using_bn:
            self.rho = Parameter(torch.Tensor(1, num_features, 3))
            self.rho[:, :, 0].data.fill_(1)
            self.rho[:, :, 1].data.fill_(3)
            self.rho[:, :, 2].data.fill_(3)
            self.register_buffer('running_mean', torch.zeros(1,
                num_features, 1, 1))
            self.register_buffer('running_var', torch.zeros(1, num_features,
                1, 1))
            self.running_mean.zero_()
            self.running_var.zero_()
        else:
            self.rho = Parameter(torch.Tensor(1, num_features, 2))
            self.rho[:, :, 0].data.fill_(1)
            self.rho[:, :, 1].data.fill_(3.2)
        self.gamma = Parameter(torch.Tensor(1, num_features, 1, 1))
        self.beta = Parameter(torch.Tensor(1, num_features, 1, 1))
        self.gamma.data.fill_(1.0)
        self.beta.data.fill_(0.0)

    def forward(self, input_0):
        primals_2 = self.rho
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.051003