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_div_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.00392156862745098
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(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_relu_threshold_backward_2(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 % 128
    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_relu_threshold_backward_3(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 % 64
    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_sub_4(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, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = 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, (256, 256), (256, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (128, 256), (256, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (64, 128), (128, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (1, 64), (64, 1))
    assert_size_stride(primals_11, (1,), (1,))
    assert_size_stride(primals_12, (4, 64), (64, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(256)](primals_1, buf0, primals_1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 256), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf1
        buf15 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16384)](buf2,
            primals_3, buf15, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_4, (256, 256), (1, 256), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf3
        buf14 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16384)](buf4,
            primals_5, buf14, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_6, (256, 128), (1, 256), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf5
        buf13 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(8192)](buf6,
            primals_7, buf13, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_8, (128, 64), (1, 128), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf7
        buf12 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_3[grid(4096)](buf8,
            primals_9, buf12, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_10, (64, 1), (1, 64), 0), out=buf9)
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf8, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_12, (64, 4), (1, 64), 0
            ), alpha=1, beta=1, out=buf10)
        del primals_13
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mean_sub_4[grid(256)](buf9, primals_11, buf10,
            buf11, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf10
        del buf9
        del primals_11
    return (buf11, reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf2, (64, 256), (256, 1), 0),
        reinterpret_tensor(buf4, (64, 256), (256, 1), 0),
        reinterpret_tensor(buf6, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf8, (64, 64), (64, 1), 0), primals_12,
        primals_10, buf12, primals_8, buf13, primals_6, buf14, primals_4, buf15
        )


class BigRamDuelNew(nn.Module):
    """
    Definition: DuelDQNet(obs_size, act_size)
    """

    def __init__(self, obs_size, act_size):
        super().__init__()
        self.base = nn.Linear(obs_size, 256)
        self.fc1 = nn.Linear(256, 256)
        self.drop1 = nn.Dropout()
        self.fc2 = nn.Linear(256, 128)
        self.drop2 = nn.Dropout()
        self.fc3 = nn.Linear(128, 64)
        self.val = nn.Linear(64, 1)
        self.adv = nn.Linear(64, act_size)

    def forward(self, input_0):
        primals_2 = self.base.weight
        primals_3 = self.base.bias
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_8 = self.fc3.weight
        primals_9 = self.fc3.bias
        primals_10 = self.val.weight
        primals_11 = self.val.bias
        primals_12 = self.adv.weight
        primals_13 = self.adv.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, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.054542