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 = 16
    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_sigmoid_2(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 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (32, 64), (64, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (16, 32), (32, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (1, 64), (64, 1), 0
            ), reinterpret_tensor(primals_2, (64, 32), (1, 64), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (32,), (1,), 0)
        del buf0
        buf7 = empty_strided_cuda((32,), (1,), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(32)](buf1,
            primals_3, buf7, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((1, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (1, 32), (0, 1), 0),
            reinterpret_tensor(primals_4, (32, 16), (1, 32), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (16,), (1,), 0)
        del buf2
        buf6 = empty_strided_cuda((16,), (1,), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16)](buf3,
            primals_5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 16), (0, 1), 0),
            reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4,), (1,), 0)
        del buf4
        triton_poi_fused_sigmoid_2[grid(4)](buf5, primals_7, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_7
    return buf5, reinterpret_tensor(primals_1, (1, 64), (64, 1), 0
        ), reinterpret_tensor(buf1, (1, 32), (32, 1), 0), reinterpret_tensor(
        buf3, (1, 16), (16, 1), 0), buf5, primals_6, buf6, primals_4, buf7


class PolicyNetNew(nn.Module):

    def __init__(self):
        super(PolicyNetNew, self).__init__()
        self.fc1 = nn.Linear(64, 32)
        self.fc2 = nn.Linear(32, 16)
        self.fc3 = nn.Linear(16, 4)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.06643