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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_add_mean_squeeze_sub_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x5 = xindex
    x3 = xindex // 64
    x6 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + x5, xmask)
    tmp5 = tl.load(in_ptr2 + (x6 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr2 + (16 + x6 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr2 + (32 + x6 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr2 + (48 + x6 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp0 + tmp2
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tmp4 - tmp13
    tmp15 = tmp3 + tmp14
    tl.store(out_ptr0 + x5, tmp15, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (256, 4), (4, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (1, 256), (256, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 256), (256, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 256), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf1,
            primals_3, buf5, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_4, (256, 1), (1, 256), 0), out=buf2)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_6, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mean_squeeze_sub_1[grid(256)](buf2, primals_5,
            buf3, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        del buf3
        del primals_5
    return buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 256), (256, 1), 0
        ), primals_6, primals_4, buf5


class DuelingMLPNew(nn.Module):

    def __init__(self, state_size, num_actions):
        super().__init__()
        self.linear = nn.Linear(state_size, 256)
        self.value_head = nn.Linear(256, 1)
        self.advantage_head = nn.Linear(256, num_actions)

    def forward(self, input_0):
        primals_2 = self.linear.weight
        primals_3 = self.linear.bias
        primals_4 = self.value_head.weight
        primals_5 = self.value_head.bias
        primals_6 = self.advantage_head.weight
        primals_7 = self.advantage_head.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: 0.058334