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 = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 784 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_pow_sqrt_sum_1(in_ptr0, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 12544
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 3136
    y1 = yindex // 3136
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (784 * x2 + 3136 * (y0 // 784) + 12544 * y1 + 
        y0 % 784), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (3136 * (y0 // 784) + 12544 * y1 + y0 % 784),
        ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (784 + 3136 * (y0 // 784) + 12544 * y1 + y0 % 
        784), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1568 + 3136 * (y0 // 784) + 12544 * y1 + y0 %
        784), ymask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2352 + 3136 * (y0 // 784) + 12544 * y1 + y0 %
        784), ymask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 1.0
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 / tmp13
    tmp15 = libdevice.sqrt(tmp11)
    tmp16 = tmp14 / tmp15
    tmp17 = tmp0 * tmp16
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp17, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (16, 4, 9, 9), (324, 81, 9, 1))
    assert_size_stride(primals_3, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 28, 28), (12544, 784, 28, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(50176)](buf1, primals_3, 50176,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 3136, 4), (12544, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_pow_sqrt_sum_1[grid(12544, 4)](buf1,
            buf2, 12544, 4, XBLOCK=4, YBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_2, buf1


def squash(x, dim=2):
    v_length_sq = x.pow(2).sum(dim=dim, keepdim=True)
    v_length = torch.sqrt(v_length_sq)
    scaling_factor = v_length_sq / (1 + v_length_sq) / v_length
    return x * scaling_factor


class PrimaryCapsNew(nn.Module):
    """
    PrimaryCaps layers.
    """

    def __init__(self, in_channels, out_capsules, out_capsule_dim,
        kernel_size=9, stride=2):
        super(PrimaryCapsNew, self).__init__()
        self.in_channels = in_channels
        self.out_capsules = out_capsules
        self.out_capsule_dim = out_capsule_dim
        self.capsules = nn.Conv2d(in_channels=in_channels, out_channels=
            out_capsules * out_capsule_dim, kernel_size=kernel_size, stride
            =stride, bias=True)

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


Measured latency_ms: 0.042543