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 import triton_helpers
import torch.utils.data
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_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x0, tmp15, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 30
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (30, 64), (64, 1))
    assert_size_stride(primals_5, (30,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0[grid(64)](primals_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 2, 2), (256, 4, 2, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(1024)](buf2, primals_3, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 1, 1), torch.int8)
        buf4 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_2[grid(256)](buf2, buf3,
            buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 30), (30, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (4, 64), (64, 1), 0),
            reinterpret_tensor(primals_4, (64, 30), (1, 64), 0), out=buf5)
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 30), (30, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(120)](buf6,
            primals_5, buf7, 120, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
    return buf6, primals_2, buf0, buf2, buf3, reinterpret_tensor(buf4, (4, 
        64), (64, 1), 0), buf7, primals_4


class LocalizationNetNew(nn.Module):

    def __init__(self, inplanes, inputsize, nheads=1, use_bn=False):
        super(LocalizationNetNew, self).__init__()
        inputH, inputW = inputsize
        self.use_bn = use_bn
        if self.use_bn:
            None
        self.pool = nn.MaxPool2d(2, stride=2)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(inplanes, 64, kernel_size=3, stride=1, padding=1
            )
        if self.use_bn:
            self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = None
        self.conv3 = None
        self.factor = 4
        self.channels = 64
        if inputH >= 8:
            self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
            self.channels = 64
            self.factor = 8
            if self.use_bn:
                self.bn2 = nn.BatchNorm2d(64)
        if inputH >= 16:
            self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
            self.channels = 128
            self.factor = 16
            if self.use_bn:
                self.bn3 = nn.BatchNorm2d(128)
        self.nheads = nheads
        if self.nheads > 1:
            self.nlinear = []
            if self.nheads >= 5:
                self.conv3 = None
                self.factor = 8
                self.channels = 64
            fw = inputW // self.factor
            self.fw_strip = [(fw // nheads) for i in range(nheads)]
            if fw % nheads != 0:
                self.fw_strip[-1] += 1
            for i in range(nheads):
                self.nlinear.append(nn.Linear(self.channels * (inputH //
                    self.factor) * self.fw_strip[i], 30))
            self.nlinear = nn.ModuleList(self.nlinear)
        else:
            self.inplanes = self.channels * (inputH // self.factor) * (inputW
                 // self.factor)
            self.linear = nn.Linear(self.inplanes, 30)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.linear.weight
        primals_5 = self.linear.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.071234