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
from torch import 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_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 23104
    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.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5184
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 81 % 16
    x2 = xindex // 1296
    x3 = xindex % 1296
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x1, 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(out_ptr0 + (x3 + 1312 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1408 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_2(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
    x2 = xindex
    x0 = xindex % 256
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 6, 6), (72, 36, 6, 1))
    assert_size_stride(primals_2, (4, 2, 81, 81), (13122, 6561, 81, 1))
    assert_size_stride(primals_3, (16, 4, 6, 6), (144, 36, 6, 1))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (256, 1296), (1296, 1))
    assert_size_stride(primals_6, (256,), (1,))
    assert_size_stride(primals_7, (1, 256), (256, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 38, 38), (5776, 1444, 38, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(23104)](buf1, 23104, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(4, 4),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 9, 9), (1296, 81, 9, 1))
        buf3 = empty_strided_cuda((4, 16, 9, 9), (1312, 81, 9, 1), torch.
            float32)
        buf8 = empty_strided_cuda((4, 16, 9, 9), (1408, 81, 9, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(5184)](buf2
            , primals_4, buf3, buf8, 5184, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf2
        del primals_4
        buf4 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (4, 1296), (1312, 1), 0),
            reinterpret_tensor(primals_5, (1296, 256), (1, 1296), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(1024)](buf5, primals_6, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_7, (256, 1), (1,
            256), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_sigmoid_3[grid(4)](buf7, primals_8, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_8
    return buf7, primals_1, primals_2, primals_3, buf1, reinterpret_tensor(buf3
        , (4, 1296), (1312, 1), 0), buf5, buf7, primals_7, primals_5, buf8


class PolicyNew(nn.Module):

    def __init__(self):
        super(PolicyNew, self).__init__()
        self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
        self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
        self.size = 9 * 9 * 16
        self.fc1 = nn.Linear(self.size, 256)
        self.fc2 = nn.Linear(256, 1)
        self.sig = nn.Sigmoid()

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


Measured latency_ms: 0.090031