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 as nn
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_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp2 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 - tmp10
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp10
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp10
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = 3.0
    tmp24 = tmp22 / tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = 1e-08
    tmp27 = tmp25 + tmp26
    tmp28 = tmp11 / tmp27
    tmp29 = tmp0 * tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x2, tmp31, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 4), (4, 1))
    assert_size_stride(primals_3, (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_2, (4, 8), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](buf0,
            primals_1, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
    return buf1, primals_1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class LinearNorm(nn.Module):
    """ LinearNorm Projection """

    def __init__(self, in_features, out_features, bias=False):
        super(LinearNorm, self).__init__()
        self.linear = nn.Linear(in_features, out_features, bias)
        nn.init.xavier_uniform_(self.linear.weight)
        if bias:
            nn.init.constant_(self.linear.bias, 0.0)

    def forward(self, x):
        x = self.linear(x)
        return x


class SCLNNew(nn.Module):
    """ Speaker Condition Layer Normalization """

    def __init__(self, s_size, hidden_size, eps=1e-08, bias=False):
        super(SCLNNew, self).__init__()
        self.hidden_size = hidden_size
        self.affine_layer = LinearNorm(s_size, 2 * hidden_size, bias)
        self.eps = eps

    def forward(self, input_0, input_1):
        primals_2 = self.affine_layer.linear.weight
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.022936