import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel


class Conv2D(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', stride=1, dilation=1, groups=1):
        super(Conv2D, self).__init__()
        assert type(kernel_size) in [int, tuple
            ], 'Allowed kernel type [int or tuple], not {}'.format(type(
            kernel_size))
        assert padding == 'same', 'Allowed padding type {}, not {}'.format(
            'same', padding)
        self.kernel_size = kernel_size
        if isinstance(kernel_size, tuple):
            self.h_kernel = kernel_size[0]
            self.w_kernel = kernel_size[1]
        else:
            self.h_kernel = kernel_size
            self.w_kernel = kernel_size
        self.padding = padding
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=self.stride,
            dilation=self.dilation, groups=self.groups)

    def forward(self, x):
        if self.padding == 'same':
            height, width = x.shape[2:]
            h_pad_need = max(0, (height - 1) * self.stride + self.h_kernel -
                height)
            w_pad_need = max(0, (width - 1) * self.stride + self.w_kernel -
                width)
            pad_left = w_pad_need // 2
            pad_right = w_pad_need - pad_left
            pad_top = h_pad_need // 2
            pad_bottom = h_pad_need - pad_top
            padding = pad_left, pad_right, pad_top, pad_bottom
            x = F.pad(x, padding, 'constant', 0)
        x = self.conv(x)
        return x


class GCN(nn.Module):
    """
        Large Kernel Matters -- https://arxiv.org/abs/1703.02719
    """

    def __init__(self, in_channels, out_channels, k=3):
        super(GCN, self).__init__()
        self.conv_l1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')
        self.conv_l2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')

    def forward(self, x):
        x1 = self.conv_l1(x)
        x1 = self.conv_l2(x1)
        x2 = self.conv_r1(x)
        x2 = self.conv_r2(x2)
        out = x1 + x2
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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.functional as F
import torch.nn.parallel
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x2 = xindex // 24
    x3 = xindex % 24
    x4 = xindex
    tmp0 = -1 + x1
    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 + (-4 + x3 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x4 = xindex // 6
    x2 = xindex // 24 % 4
    x5 = 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 * x4), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x2, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    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_constant_pad_nd_convolution_3(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 6
    x4 = xindex // 24
    x5 = xindex % 24
    x2 = xindex // 24 % 4
    x6 = xindex
    tmp0 = -1 + x1
    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 + (-4 + x5 + 16 * x4), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x2, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x6, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, 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, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3, 1), (12, 3, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(384)](primals_1, buf0, 384,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_1[grid(384)](buf1,
            primals_3, buf2, 384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 6), (96, 24, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(384)](primals_1, buf4, 384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 6, 4), (96, 24, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_3[grid(384)](buf5,
            primals_7, buf6, 384, XBLOCK=128, num_warps=4, num_stages=1)
        del buf5
        del primals_7
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf3
        del buf3
        triton_poi_fused_add_convolution_4[grid(256)](buf8, primals_5, buf7,
            primals_9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        del primals_5
        del primals_9
    return (buf8, primals_2, primals_4, primals_6, primals_8, buf0, buf2,
        buf4, buf6)


class Conv2D(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', stride=1, dilation=1, groups=1):
        super(Conv2D, self).__init__()
        assert type(kernel_size) in [int, tuple
            ], 'Allowed kernel type [int or tuple], not {}'.format(type(
            kernel_size))
        assert padding == 'same', 'Allowed padding type {}, not {}'.format(
            'same', padding)
        self.kernel_size = kernel_size
        if isinstance(kernel_size, tuple):
            self.h_kernel = kernel_size[0]
            self.w_kernel = kernel_size[1]
        else:
            self.h_kernel = kernel_size
            self.w_kernel = kernel_size
        self.padding = padding
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=self.stride,
            dilation=self.dilation, groups=self.groups)

    def forward(self, x):
        if self.padding == 'same':
            height, width = x.shape[2:]
            h_pad_need = max(0, (height - 1) * self.stride + self.h_kernel -
                height)
            w_pad_need = max(0, (width - 1) * self.stride + self.w_kernel -
                width)
            pad_left = w_pad_need // 2
            pad_right = w_pad_need - pad_left
            pad_top = h_pad_need // 2
            pad_bottom = h_pad_need - pad_top
            padding = pad_left, pad_right, pad_top, pad_bottom
            x = F.pad(x, padding, 'constant', 0)
        x = self.conv(x)
        return x


class GCNNew(nn.Module):
    """
        Large Kernel Matters -- https://arxiv.org/abs/1703.02719
    """

    def __init__(self, in_channels, out_channels, k=3):
        super(GCNNew, self).__init__()
        self.conv_l1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')
        self.conv_l2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r1 = Conv2D(in_channels=in_channels, out_channels=
            out_channels, kernel_size=(1, k), padding='same')
        self.conv_r2 = Conv2D(in_channels=out_channels, out_channels=
            out_channels, kernel_size=(k, 1), padding='same')

    def forward(self, input_0):
        primals_2 = self.conv_l1.conv.weight
        primals_3 = self.conv_l1.conv.bias
        primals_4 = self.conv_l2.conv.weight
        primals_5 = self.conv_l2.conv.bias
        primals_6 = self.conv_r1.conv.weight
        primals_7 = self.conv_r1.conv.bias
        primals_8 = self.conv_r2.conv.weight
        primals_9 = self.conv_r2.conv.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]
