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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.cuda
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_mean_std_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tl.store(in_out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr0 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 0.001
    tmp8 = tmp6 + tmp7
    tmp9 = tmp4 / tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4,), (1,), 0)
        del buf3
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_add_mean_std_1[grid(4)](buf4, buf2, primals_1,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_div_mul_sub_2[grid(16)](buf2, primals_1, buf5,
            buf4, primals_6, primals_7, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf4
        del buf5
        del primals_7
    return buf6, primals_1, primals_6, buf1, buf2, primals_4


class Bottle(nn.Module):

    def forward(self, input):
        if len(input.size()) <= 2:
            return super(Bottle, self).forward(input)
        size = input.size()[:2]
        out = super(Bottle, self).forward(input.view(size[0] * size[1], -1))
        return out.contiguous().view(size[0], size[1], -1)


class BottleLinear(Bottle, nn.Linear):
    pass


class LayerNorm(nn.Module):
    """ Layer normalization module """

    def __init__(self, d_hid, eps=0.001):
        super(LayerNorm, self).__init__()
        self.eps = eps
        self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
        self.b_2 = nn.Parameter(torch.zeros(d_hid), requires_grad=True)

    def forward(self, z):
        if z.size(1) == 1:
            return z
        mu = torch.mean(z, dim=1)
        sigma = torch.std(z, dim=1)
        if mu.dim() == 1:
            mu = mu.unsqueeze(1)
            sigma = sigma.unsqueeze(1)
        ln_out = (z - mu.expand_as(z)) / (sigma.expand_as(z) + self.eps)
        ln_out = ln_out.mul(self.a_2.expand_as(ln_out)) + self.b_2.expand_as(
            ln_out)
        return ln_out


class BottleLayerNorm(Bottle, LayerNorm):
    pass


class PositionwiseFeedForwardNew(nn.Module):
    """ A two-layer Feed-Forward-Network."""

    def __init__(self, size, hidden_size, dropout=0.1):
        """
        Args:
            size(int): the size of input for the first-layer of the FFN.
            hidden_size(int): the hidden layer size of the second-layer
                              of the FNN.
            droput(float): dropout probability(0-1.0).
        """
        super(PositionwiseFeedForwardNew, self).__init__()
        self.w_1 = BottleLinear(size, hidden_size)
        self.w_2 = BottleLinear(hidden_size, size)
        self.layer_norm = BottleLayerNorm(size)
        self.dropout = nn.Dropout(dropout)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.w_1.weight
        primals_3 = self.w_1.bias
        primals_2 = self.w_2.weight
        primals_5 = self.w_2.bias
        primals_6 = self.layer_norm.a_2
        primals_7 = self.layer_norm.b_2
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.061813