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.triton_helpers import libdevice
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_mul_pow_1(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tmp11 * tmp11
    tmp13 = 1.0
    tmp14 = tmp12 + tmp13
    tmp15 = tmp12 / tmp14
    tmp16 = 1e-08
    tmp17 = tmp11 + tmp16
    tmp18 = tmp15 / tmp17
    tmp20 = tmp18 * tmp19
    tl.store(out_ptr0 + x2, tmp20, 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,), (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 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16)](buf1, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_add_div_linalg_vector_norm_mul_pow_1[grid(16)](buf1,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf2, primals_1, primals_3, buf1


def squash(inputs, axis=-1):
    """capsule输出的激活函数"""
    norm = torch.norm(inputs, dim=axis, keepdim=True)
    scale = norm ** 2 / (1 + norm ** 2) / (norm + 1e-08)
    return scale * inputs


class PrimaryCapsNew(nn.Module):
    """计算第一层capsules的输入，转换成32*6*6个8维的capsule vector
    in_channels：原文中256
    out_channels：卷积后的通道数，原文中256
    dim_caps: PrimaryCaps输出的每个capsule的维度
    kernel_size：原文中9 * 9
    stride：2
    """

    def __init__(self, in_channels, out_channels, dim_caps, kernel_size,
        stride=1, padding=0):
        super(PrimaryCapsNew, self).__init__()
        self.dim_caps = dim_caps
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding)

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_2 = self.conv2d.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.033558