import torch
import torch.nn as nn
import torch.utils.data


class GatedConv2d(nn.Module):

    def __init__(self, input_channels, output_channels, kernel_size, stride,
        padding, dilation=1, activation=None):
        super(GatedConv2d, self).__init__()
        self.activation = activation
        self.sigmoid = nn.Sigmoid()
        self.h = nn.Conv2d(input_channels, output_channels, kernel_size,
            stride, padding, dilation)
        self.g = nn.Conv2d(input_channels, output_channels, kernel_size,
            stride, padding, dilation)

    def forward(self, x):
        if self.activation is None:
            h = self.h(x)
        else:
            h = self.activation(self.h(x))
        g = self.sigmoid(self.g(x))
        return h * g


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


def get_init_inputs():
    return [[], {'input_channels': 4, 'output_channels': 4, 'kernel_size': 
        4, 'stride': 1, 'padding': 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp2 * tmp6
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp5, xmask)
    tl.store(out_ptr0 + x3, tmp7, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = buf0
        del buf0
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_mul_sigmoid_0[grid(1296)](buf1, buf3,
            primals_2, primals_5, buf4, 1296, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_5
    return buf4, primals_1, primals_3, primals_4, buf1, buf3


class GatedConv2dNew(nn.Module):

    def __init__(self, input_channels, output_channels, kernel_size, stride,
        padding, dilation=1, activation=None):
        super(GatedConv2dNew, self).__init__()
        self.activation = activation
        self.sigmoid = nn.Sigmoid()
        self.h = nn.Conv2d(input_channels, output_channels, kernel_size,
            stride, padding, dilation)
        self.g = nn.Conv2d(input_channels, output_channels, kernel_size,
            stride, padding, dilation)

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