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.nn import Module
import math
from torch.nn import functional as F
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
from torch.nn.modules.utils import _triple
import torch.nn.parallel
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 500
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 25 % 5
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x3 = xindex // 125
    x4 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x1
    tmp4 = tmp3 < tmp1
    tmp5 = x0
    tmp6 = tmp5 < tmp1
    tmp7 = tmp2 & tmp4
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2 + 64 * x3), tmp8 &
        xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp9, 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
    x2 = xindex
    x1 = xindex // 64
    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, 5, 5, 5), (125, 25, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(500)](primals_3, buf0, 500,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 5,
            5, 5), (0, 125, 25, 5, 1), 0), primals_1, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, reinterpret_tensor(buf0, (1, 4, 5, 5, 5), (500, 125, 
        25, 5, 1), 0)


def conv3d_same_padding(input, weight, bias=None, stride=1, padding=1,
    dilation=1, groups=1):
    input_rows = input.size(3)
    filter_rows = weight.size(3)
    out_rows = (input_rows + stride[0] - 1) // stride[0]
    padding_rows = max(0, (out_rows - 1) * stride[0] + (filter_rows - 1) *
        dilation[0] + 1 - input_rows)
    rows_odd = padding_rows % 2 != 0
    padding_cols = max(0, (out_rows - 1) * stride[0] + (filter_rows - 1) *
        dilation[0] + 1 - input_rows)
    cols_odd = padding_rows % 2 != 0
    padding_d = max(0, (out_rows - 1) * stride[0] + (filter_rows - 1) *
        dilation[0] + 1 - input_rows)
    d_odd = padding_rows % 2 != 0
    if rows_odd or cols_odd or d_odd:
        input = pad(input, [0, int(d_odd), 0, int(cols_odd), 0, int(rows_odd)])
    return F.conv3d(input, weight, bias, stride, padding=(padding_d // 2, 
        padding_rows // 2, padding_cols // 2), dilation=dilation, groups=groups
        )


class _ConvNd(Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, dilation, transposed, output_padding, groups, bias):
        super(_ConvNd, self).__init__()
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        if transposed:
            self.weight = Parameter(torch.Tensor(in_channels, out_channels //
                groups, *kernel_size))
        else:
            self.weight = Parameter(torch.Tensor(out_channels, in_channels //
                groups, *kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1.0 / math.sqrt(n)
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def __repr__(self):
        s = (
            '{name}({in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}'
            )
        if self.padding != (0,) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1,) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0,) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias is None:
            s += ', bias=False'
        s += ')'
        return s.format(name=self.__class__.__name__, **self.__dict__)


class Conv3dNew(_ConvNd):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True):
        kernel_size = _triple(kernel_size)
        stride = _triple(stride)
        padding = _triple(padding)
        dilation = _triple(dilation)
        super(Conv3dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, dilation, False, _triple(0),
            groups, bias)

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


Measured latency_ms: 0.035789