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
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 = 1372
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 49 % 7
    x1 = xindex // 7 % 7
    x0 = xindex % 7
    x3 = xindex // 343
    x7 = xindex
    tmp0 = -2 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = -2 + x0
    tmp9 = tmp8 >= tmp1
    tmp10 = tmp8 < tmp3
    tmp11 = tmp2 & tmp4
    tmp12 = tmp11 & tmp6
    tmp13 = tmp12 & tmp7
    tmp14 = tmp13 & tmp9
    tmp15 = tmp14 & tmp10
    tmp16 = tl.load(in_ptr0 + (-42 + x0 + 4 * x1 + 16 * x2 + 64 * x3), 
        tmp15 & xmask, other=0.0)
    tl.store(out_ptr0 + x7, tmp16, 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), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7, 7, 7), (343, 49, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(1372)](primals_1, buf0, 
            1372, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7,
            7, 7), (0, 343, 49, 7, 1), 0), primals_2, 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, 4, 4, 4), (256, 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
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_2, reinterpret_tensor(buf0, (1, 4, 7, 7, 7), (1372, 343,
        49, 7, 1), 0)


class SamePadConv3dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        bias=True):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3
        total_pad = tuple([(k - s) for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]:
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        self.pad_input = pad_input
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size,
            stride=stride, padding=0, bias=bias)

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


Measured latency_ms: 0.014264