import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.nn.init


class conv23DUnit(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, groups=1, bias=True, dilation=1, nd=2):
        super(conv23DUnit, self).__init__()
        assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}'
        if nd == 2:
            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        elif nd == 3:
            self.conv = nn.Conv3d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        else:
            self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0))
        init.constant(self.conv.bias, 0)

    def forward(self, x):
        return self.conv(x)


class residualUnit(nn.Module):

    def __init__(self, in_size, out_size, kernel_size=3, stride=1, padding=
        1, activation=F.relu, nd=2):
        super(residualUnit, self).__init__()
        self.conv1 = conv23DUnit(in_size, out_size, kernel_size, stride,
            padding, nd=nd)
        self.conv2 = conv23DUnit(out_size, out_size, kernel_size, stride,
            padding, nd=nd)

    def forward(self, x):
        return F.relu(self.conv2(F.elu(self.conv1(x))) + x)


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


def get_init_inputs():
    return [[], {'in_size': 4, 'out_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.nn.init
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_elu_0(in_out_ptr0, 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
    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
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_out_ptr0,
    in_ptr0, in_ptr1, out_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')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = 0.0
    tmp8 = tmp6 <= tmp7
    tl.store(in_out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 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, 3, 3), (36, 9, 3, 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=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_elu_0[grid(256)](buf1, primals_2, buf2,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), 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 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
            buf4, primals_5, primals_3, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_5
    return buf4, primals_1, primals_3, primals_4, buf1, buf2, buf5


class conv23DUnit(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, groups=1, bias=True, dilation=1, nd=2):
        super(conv23DUnit, self).__init__()
        assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}'
        if nd == 2:
            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        elif nd == 3:
            self.conv = nn.Conv3d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        else:
            self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
                stride=stride, padding=padding, groups=groups, bias=bias,
                dilation=dilation)
        init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0))
        init.constant(self.conv.bias, 0)

    def forward(self, x):
        return self.conv(x)


class residualUnitNew(nn.Module):

    def __init__(self, in_size, out_size, kernel_size=3, stride=1, padding=
        1, activation=F.relu, nd=2):
        super(residualUnitNew, self).__init__()
        self.conv1 = conv23DUnit(in_size, out_size, kernel_size, stride,
            padding, nd=nd)
        self.conv2 = conv23DUnit(out_size, out_size, kernel_size, stride,
            padding, nd=nd)

    def forward(self, input_0):
        primals_1 = self.conv1.conv.weight
        primals_2 = self.conv1.conv.bias
        primals_4 = self.conv2.conv.weight
        primals_5 = self.conv2.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
