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 import nn as nn
import torch.nn.init
from torchvision import models as models
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.load(in_ptr0 + 4 * x1, tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 - tmp10
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tl.load(in_ptr0 + (1 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp14 = tmp12 - tmp13
    tmp15 = tmp11 * tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp6, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp5, tmp17)
    tl.store(out_ptr0 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_add_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2 + 16 * x1), xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 5), (5, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(80)](primals_2, buf0, 80, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 5), (5, 1), 0),
            reinterpret_tensor(primals_3, (5, 4), (1, 5), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_1[grid(64)](primals_1, buf1, primals_4, buf2, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_1
        del primals_4
    return buf2, reinterpret_tensor(buf0, (16, 5), (5, 1), 0)


class PositionalEncodingImageBoxesNew(nn.Module):

    def __init__(self, d_model, mode='project-and-sum'):
        super().__init__()
        self.mode = mode
        if mode == 'project-and-sum':
            self.map = nn.Linear(5, d_model)
        elif mode == 'concat-and-process':
            self.map = nn.Sequential(nn.Linear(d_model + 5, d_model), nn.
                ReLU(), nn.Linear(d_model, d_model))

    def forward(self, input_0, input_1):
        primals_3 = self.map.weight
        primals_4 = self.map.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.036006