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
import torch.nn.functional as F
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_elu_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_glu_1(in_ptr0, 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 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, 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_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 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(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, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 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, (8, 4), (4, 1))
    assert_size_stride(primals_7, (8,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(256)](buf0, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf2, reinterpret_tensor(primals_6,
            (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_glu_1[grid(256)](buf3, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_add_native_layer_norm_2[grid(64)](buf4, primals_1,
            buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_3[grid(256)](buf4, primals_1,
            buf5, buf6, primals_8, primals_9, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf5
        del buf6
        del primals_9
    return buf7, primals_1, primals_8, buf0, reinterpret_tensor(buf1, (64, 
        4), (4, 1), 0), buf2, reinterpret_tensor(buf3, (4, 4, 4, 8), (128, 
        32, 8, 1), 0), buf4, primals_6, primals_4


class _TimeDistributedInterpolation(nn.Module):

    def __init__(self, output_size: 'int', batch_first: 'bool'=False,
        trainable: 'bool'=False):
        super().__init__()
        self.output_size = output_size
        self.batch_first = batch_first
        self.trainable = trainable
        if self.trainable:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float32))
            self.gate = nn.Sigmoid()

    def interpolate(self, x):
        upsampled = F.interpolate(x.unsqueeze(1), self.output_size, mode=
            'linear', align_corners=True).squeeze(1)
        if self.trainable:
            upsampled = upsampled * self.gate(self.mask.unsqueeze(0)) * 2.0
        return upsampled

    def forward(self, x):
        if len(x.size()) <= 2:
            return self.interpolate(x)
        x_reshape = x.contiguous().view(-1, x.size(-1))
        y = self.interpolate(x_reshape)
        if self.batch_first:
            y = y.contiguous().view(x.size(0), -1, y.size(-1))
        else:
            y = y.view(-1, x.size(1), y.size(-1))
        return y


class _AddNorm(nn.Module):

    def __init__(self, input_size: 'int', skip_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.skip_size = skip_size or input_size
        if self.input_size != self.skip_size:
            self.resample = _TimeDistributedInterpolation(self.input_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.input_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.input_size)

    def forward(self, x: 'torch.Tensor', skip: 'torch.Tensor'):
        if self.input_size != self.skip_size:
            skip = self.resample(skip)
        if self.trainable_add:
            skip = skip * self.gate(self.mask) * 2.0
        output = self.norm(x + skip)
        return output


class _GatedLinearUnit(nn.Module):
    """Gated Linear Unit"""

    def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
        'float'=None):
        super().__init__()
        if dropout is not None:
            self.dropout = nn.Dropout(dropout)
        else:
            self.dropout = dropout
        self.hidden_size = hidden_size or input_size
        self.fc = nn.Linear(input_size, self.hidden_size * 2)
        self.init_weights()

    def init_weights(self):
        for n, p in self.named_parameters():
            if 'bias' in n:
                torch.nn.init.zeros_(p)
            elif 'fc' in n:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, x):
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.fc(x)
        x = F.glu(x, dim=-1)
        return x


class _GateAddNorm(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int'=None,
        skip_size: 'int'=None, trainable_add: 'bool'=False, dropout:
        'float'=None):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size or input_size
        self.skip_size = skip_size or self.hidden_size
        self.dropout = dropout
        self.glu = _GatedLinearUnit(self.input_size, hidden_size=self.
            hidden_size, dropout=self.dropout)
        self.add_norm = _AddNorm(self.hidden_size, skip_size=self.skip_size,
            trainable_add=trainable_add)

    def forward(self, x, skip):
        output = self.glu(x)
        output = self.add_norm(output, skip)
        return output


class _ResampleNorm(nn.Module):

    def __init__(self, input_size: 'int', output_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.output_size = output_size or input_size
        if self.input_size != self.output_size:
            self.resample = _TimeDistributedInterpolation(self.output_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.output_size)

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        if self.input_size != self.output_size:
            x = self.resample(x)
        if self.trainable_add:
            x = x * self.gate(self.mask) * 2.0
        output = self.norm(x)
        return output


class _GatedResidualNetworkNew(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
        'int', dropout: 'float'=0.1, context_size: 'int'=None, residual:
        'bool'=False):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.context_size = context_size
        self.hidden_size = hidden_size
        self.dropout = dropout
        self.residual = residual
        if self.input_size != self.output_size and not self.residual:
            residual_size = self.input_size
        else:
            residual_size = self.output_size
        if self.output_size != residual_size:
            self.resample_norm = _ResampleNorm(residual_size, self.output_size)
        self.fc1 = nn.Linear(self.input_size, self.hidden_size)
        self.elu = nn.ELU()
        if self.context_size is not None:
            self.context = nn.Linear(self.context_size, self.hidden_size,
                bias=False)
        self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
        self.init_weights()
        self.gate_norm = _GateAddNorm(input_size=self.hidden_size,
            skip_size=self.output_size, hidden_size=self.output_size,
            dropout=self.dropout, trainable_add=False)

    def init_weights(self):
        for name, p in self.named_parameters():
            if 'bias' in name:
                torch.nn.init.zeros_(p)
            elif 'fc1' in name or 'fc2' in name:
                torch.nn.init.kaiming_normal_(p, a=0, mode='fan_in',
                    nonlinearity='leaky_relu')
            elif 'context' in name:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.gate_norm.glu.fc.weight
        primals_7 = self.gate_norm.glu.fc.bias
        primals_8 = self.gate_norm.add_norm.norm.weight
        primals_9 = self.gate_norm.add_norm.norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.032979