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
from typing import Union
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_replication_pad3d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x2 = xindex // 64 % 8
    x3 = xindex // 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -2 + x1) + (-2 + x1) *
        (-2 + x1 > 0)) + (0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0)) *
        (0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0) < 3)) + 16 * (3 * (
        3 <= 0 * (0 >= -2 + x2) + (-2 + x2) * (-2 + x2 > 0)) + (0 * (0 >= -
        2 + x2) + (-2 + x2) * (-2 + x2 > 0)) * (0 * (0 >= -2 + x2) + (-2 +
        x2) * (-2 + x2 > 0) < 3)) + 64 * x3 + (3 * (3 <= 0 * (0 >= -2 + x0) +
        (-2 + x0) * (-2 + x0 > 0)) + (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 +
        x0 > 0)) * (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0) < 3))),
        None, eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp0, None)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 500
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 125
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 8, 8), (512, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad3d_0[grid(2048)](primals_3, buf0, 
            2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 8,
            8, 8), (0, 512, 64, 8, 1), 0), primals_1, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 5, 5, 5), (500, 125, 25, 5, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(500)](buf2, primals_2, 500,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf2, (4, 5, 5, 5), (125, 25, 5, 1), 0
        ), primals_1, reinterpret_tensor(buf0, (1, 4, 8, 8, 8), (2048, 512,
        64, 8, 1), 0)


def act_layer(act):
    if act == 'relu':
        return nn.ReLU()
    elif act == 'lrelu':
        return nn.LeakyReLU(LRELU_SLOPE)
    elif act == 'elu':
        return nn.ELU()
    elif act == 'tanh':
        return nn.Tanh()
    elif act == 'prelu':
        return nn.PReLU()
    else:
        raise ValueError('%s not recognized.' % act)


class Conv3DBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_sizes:
        'Union[int, list]', strides, norm=None, activation=None,
        padding_mode='replicate', padding=None):
        super(Conv3DBlockNew, self).__init__()
        padding = kernel_sizes // 2 if padding is None else padding
        self.conv3d = nn.Conv3d(in_channels, out_channels, kernel_sizes,
            strides, padding=padding, padding_mode=padding_mode)
        if activation is None:
            nn.init.xavier_uniform_(self.conv3d.weight, gain=nn.init.
                calculate_gain('linear'))
            nn.init.zeros_(self.conv3d.bias)
        elif activation == 'tanh':
            nn.init.xavier_uniform_(self.conv3d.weight, gain=nn.init.
                calculate_gain('tanh'))
            nn.init.zeros_(self.conv3d.bias)
        elif activation == 'lrelu':
            nn.init.kaiming_uniform_(self.conv3d.weight, a=LRELU_SLOPE,
                nonlinearity='leaky_relu')
            nn.init.zeros_(self.conv3d.bias)
        elif activation == 'relu':
            nn.init.kaiming_uniform_(self.conv3d.weight, nonlinearity='relu')
            nn.init.zeros_(self.conv3d.bias)
        else:
            raise ValueError()
        self.activation = None
        self.norm = None
        if norm is not None:
            self.norm = norm_layer3d(norm, out_channels)
        if activation is not None:
            self.activation = act_layer(activation)

    def forward(self, input_0):
        primals_1 = self.conv3d.weight
        primals_2 = self.conv3d.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.033387