import torch
import torch.nn as nn


class CDCM(nn.Module):
    """
    Compact Dilation Convolution based Module
    """

    def __init__(self, in_channels, out_channels):
        super(CDCM, self).__init__()
        self.relu1 = nn.ReLU()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1,
            padding=0)
        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=5, padding=5, bias=False)
        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=7, padding=7, bias=False)
        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=9, padding=9, bias=False)
        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=11, padding=11, bias=False)
        nn.init.constant_(self.conv1.bias, 0)

    def forward(self, x):
        x = self.relu1(x)
        x = self.conv1(x)
        x1 = self.conv2_1(x)
        x2 = self.conv2_2(x)
        x3 = self.conv2_3(x)
        x4 = self.conv2_4(x)
        return x1 + x2 + x3 + x4


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._inductor.runtime import triton_helpers
import torch.nn as nn
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_relu_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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, 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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_2(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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_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 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(5, 5), dilation=(5, 5), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(7, 7), dilation=(7, 7), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1),
            padding=(9, 9), dilation=(9, 9), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
            padding=(11, 11), dilation=(11, 11), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf3
        del buf3
        triton_poi_fused_add_2[grid(256)](buf7, buf4, buf5, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del buf5
        del buf6
    return (buf7, primals_2, primals_4, primals_5, primals_6, primals_7,
        buf0, buf2)


class CDCMNew(nn.Module):
    """
    Compact Dilation Convolution based Module
    """

    def __init__(self, in_channels, out_channels):
        super(CDCMNew, self).__init__()
        self.relu1 = nn.ReLU()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1,
            padding=0)
        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=5, padding=5, bias=False)
        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=7, padding=7, bias=False)
        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=9, padding=9, bias=False)
        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            dilation=11, padding=11, bias=False)
        nn.init.constant_(self.conv1.bias, 0)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2_1.weight
        primals_5 = self.conv2_2.weight
        primals_6 = self.conv2_3.weight
        primals_7 = self.conv2_4.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
