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.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 = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    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 + x0, tmp4, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, 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
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    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 + x0, tmp4, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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 + x2, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (32, 4, 9, 9), (324, 81, 9, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (512, 16), (16, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (4, 512), (512, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4, 4), (64, 16, 4, 1), 0), primals_2, stride=(4, 4), padding
            =(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 32, 1, 1), (32, 1, 1, 1))
        buf1 = reinterpret_tensor(buf0, (32, 1, 1), (1, 1, 1), 0)
        del buf0
        buf11 = empty_strided_cuda((32, 1, 1), (1, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(32)](buf1,
            primals_3, buf11, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 32, 
            1, 1), (0, 1, 0, 0), 0), primals_4, stride=(2, 2), padding=(2, 
            2), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (1, 64, 1, 1), (64, 1, 1, 1))
        buf3 = reinterpret_tensor(buf2, (64, 1, 1), (1, 1, 1), 0)
        del buf2
        buf10 = empty_strided_cuda((64, 1, 1), (1, 1, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(64)](buf3,
            primals_5, buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (1, 64, 
            1, 1), (0, 1, 0, 0), 0), primals_6, stride=(1, 1), padding=(1, 
            1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf4, (1, 64, 1, 1), (64, 1, 1, 1))
        buf5 = reinterpret_tensor(buf4, (64, 1, 1), (1, 1, 1), 0)
        del buf4
        buf9 = empty_strided_cuda((64, 1, 1), (1, 1, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(64)](buf5,
            primals_7, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 16), (16, 1), 0),
            reinterpret_tensor(primals_8, (16, 512), (1, 16), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_2[grid(2048)](buf7, primals_9, 2048, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
            primals_10, (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf8)
        del primals_11
    return buf8, primals_2, primals_4, primals_6, reinterpret_tensor(primals_1,
        (1, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf1, (1, 32, 
        1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf3, (1, 64, 1, 1), (
        64, 1, 1, 1), 0), reinterpret_tensor(buf5, (4, 16), (16, 1), 0
        ), buf7, primals_10, primals_8, buf9, buf10, buf11


class DeepQNetworkNew(nn.Module):

    def __init__(self, imagesize, num_input_frames, num_actions, **kwargs):
        super(DeepQNetworkNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=num_input_frames, out_channels=
            32, kernel_size=9, stride=4, padding=4)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =5, stride=2, padding=2)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding=1)
        in_features = 64 * imagesize[0] * imagesize[1] // 64
        self.fc1 = nn.Linear(in_features=in_features, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=num_actions)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.127315