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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 960
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 15
    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 = args
    args.clear()
    assert_size_stride(primals_1, (15, 4), (4, 1))
    assert_size_stride(primals_2, (15,), (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 = empty_strided_cuda((64, 15), (15, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 15), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 15), (240, 60, 15, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 15), (240, 60, 15, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(960)](buf1,
            primals_2, buf2, 960, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2


class FeatureExtractFFNew(nn.Module):

    def __init__(self, input_dim, hidden_sizes=(15,), activation_fn=nn.ReLU,
        **activation_args):
        super(FeatureExtractFFNew, self).__init__()
        self._in = input_dim
        self._hidden_sizes = hidden_sizes
        self._activation_fn = activation_fn
        self._activation_args = activation_args
        self.feature = nn.Sequential()
        hin = self._in
        for i, h in enumerate(self._hidden_sizes):
            self.feature.add_module(f'f_fc{i}', nn.Linear(hin, h))
            self.feature.add_module(f'f_{activation_fn.__name__}{i}',
                activation_fn(**activation_args))
            hin = h
        self._out_features = hin

    def extra_repr(self):
        return f'FC: {self.hidden_sizes}x{self._activation_fn.__name__}'

    def hidden_layer(self, index=0):
        return self.feature[index * 2]

    def output_size(self):
        return self._out_features

    def forward(self, input_0):
        primals_1 = self.feature.f_fc0.weight
        primals_2 = self.feature.f_fc0.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.024129