import torch
from torch._inductor.select_algorithm import extern_kernels
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 libdevice
import torch.nn
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
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_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 16
    x3 = xindex // 256
    x4 = xindex % 256
    x5 = xindex // 4 % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1 + 128 * x3), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x5, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (4 + x0 + 8 * x1 + 128 * x3), xmask,
        eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 - tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp2 * tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tl.store(out_ptr0 + x7, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(64)](primals_4, buf1,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_1[grid(1024)](buf0,
            primals_2, primals_4, buf1, buf2, buf3, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del primals_2
    return buf3, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class AffineLinear(nn.Module):

    def __init__(self, in_dim, out_dim):
        super(AffineLinear, self).__init__()
        affine = nn.Linear(in_dim, out_dim)
        self.affine = affine

    def forward(self, input):
        return self.affine(input)


class StyleAdaptiveLayerNormNew(nn.Module):

    def __init__(self, in_channel, style_dim):
        super(StyleAdaptiveLayerNormNew, self).__init__()
        self.in_channel = in_channel
        self.norm = nn.LayerNorm(in_channel, elementwise_affine=False)
        self.style = AffineLinear(style_dim, in_channel * 2)
        self.style.affine.bias.data[:in_channel] = 1
        self.style.affine.bias.data[in_channel:] = 0

    def forward(self, input_0, input_1):
        primals_1 = self.style.affine.weight
        primals_2 = self.style.affine.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.039691