import torch
from torch import nn
import torch.nn.init as init


class GatedConv(nn.Module):
    """GatedConv."""

    def __init__(self, input_size, width=3, dropout=0.2, nopad=False):
        """init."""
        super(GatedConv, self).__init__()
        self.conv = nn.Conv2d(in_channels=input_size, out_channels=2 *
            input_size, kernel_size=(width, 1), stride=(1, 1), padding=(
            width // 2 * (1 - nopad), 0))
        init.xavier_uniform_(self.conv.weight, gain=(4 * (1 - dropout)) ** 0.5)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x_var):
        """forward."""
        x_var = self.dropout(x_var)
        x_var = self.conv(x_var)
        out, gate = x_var.split(int(x_var.size(1) / 2), 1)
        out = out * torch.sigmoid(gate)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'input_size': 4}]

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 import nn
import torch.nn.init as init
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask)
    tmp2 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp2 * tmp1
    tl.store(out_ptr0 + x2, tmp1, xmask)
    tl.store(out_ptr1 + x2, tmp3, 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, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_3, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(512)](buf1, primals_3, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_1[grid(256)](buf1, buf2, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf3, primals_1, primals_2, reinterpret_tensor(buf1, (4, 4, 4, 4
        ), (128, 16, 4, 1), 0), buf2


class GatedConvNew(nn.Module):
    """GatedConv."""

    def __init__(self, input_size, width=3, dropout=0.2, nopad=False):
        """init."""
        super(GatedConvNew, self).__init__()
        self.conv = nn.Conv2d(in_channels=input_size, out_channels=2 *
            input_size, kernel_size=(width, 1), stride=(1, 1), padding=(
            width // 2 * (1 - nopad), 0))
        init.xavier_uniform_(self.conv.weight, gain=(4 * (1 - dropout)) ** 0.5)
        self.dropout = nn.Dropout(dropout)

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