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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 28
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_1(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp6 * tmp2
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp5, xmask)
    tl.store(out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(28)](primals_1, buf0, 28,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_4, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_1[grid(16)](buf2, buf4,
            primals_3, primals_5, buf5, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_3
        del primals_5
        buf6 = empty_strided_cuda((4, 1, 2), (2, 2, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 1, 2), (2, 2, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_2[grid(8)](buf5, buf6,
            buf7, 8, XBLOCK=8, num_warps=1, num_stages=1)
    return reinterpret_tensor(buf7, (4, 2), (2, 1), 0
        ), primals_2, primals_4, reinterpret_tensor(buf0, (1, 4, 7), (28, 7,
        1), 0), buf2, buf4, reinterpret_tensor(buf5, (4, 1, 4), (4, 4, 1), 0
        ), buf6


class _GLUBlockNew(nn.Module):

    def __init__(self, n_c_in, n_c_out):
        super(_GLUBlockNew, self).__init__()
        self.pad = nn.ConstantPad1d((1, 2), 0)
        self.conv_data = nn.Conv1d(n_c_in, n_c_out, 4, stride=1, bias=True)
        self.conv_gate = nn.Conv1d(n_c_in, n_c_out, 4, stride=1, bias=True)
        self.sigmoid = nn.Sigmoid()
        self.pool = nn.MaxPool1d(2)

    def forward(self, input_0):
        primals_2 = self.conv_data.weight
        primals_3 = self.conv_data.bias
        primals_4 = self.conv_gate.weight
        primals_5 = self.conv_gate.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.066115