import torch
import torch.nn.functional as F
import torch.nn as nn


class DuelingQNetwork(nn.Module):

    def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128):
        super(DuelingQNetwork, self).__init__()
        self.fc1_val = nn.Linear(state_size, hidsize1)
        self.fc2_val = nn.Linear(hidsize1, hidsize2)
        self.fc4_val = nn.Linear(hidsize2, 1)
        self.fc1_adv = nn.Linear(state_size, hidsize1)
        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
        self.fc4_adv = nn.Linear(hidsize2, action_size)

    def forward(self, x):
        val = F.relu(self.fc1_val(x))
        val = F.relu(self.fc2_val(val))
        val = self.fc4_val(val)
        adv = F.relu(self.fc1_adv(x))
        adv = F.relu(self.fc2_adv(adv))
        adv = self.fc4_adv(adv)
        return val + adv - adv.mean()


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'state_size': 4, 'action_size': 4}]

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 % 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_per_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    r2 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp4 = tl.load(in_ptr1 + r2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + 0)
    tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp7 = tmp4 + tmp6
    tmp8 = tmp7 + tmp0
    tmp9 = 256.0
    tmp10 = tmp3 / tmp9
    tmp11 = tmp8 - tmp10
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp11, 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, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (128, 128), (128, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (1, 128), (128, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (128, 4), (4, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (128, 128), (128, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (4, 128), (128, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        buf15 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
            primals_2, buf15, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_4, (128, 128), (1, 128), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf2
        buf14 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf3,
            primals_5, buf14, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_6, (128, 1), (1, 128), 0), out=buf4)
        buf5 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 128), (1, 4), 0), out=buf5)
        del primals_8
        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_0[grid(8192)](buf6,
            primals_9, buf13, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf7 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_10, (128, 128), (1, 128), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf7
        buf12 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf8,
            primals_11, buf12, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf8, (64, 128),
            (128, 1), 0), reinterpret_tensor(primals_12, (128, 4), (1, 128),
            0), alpha=1, beta=1, out=buf9)
        del primals_13
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_add_mean_sub_1[grid(1)](buf9, buf4, primals_7,
            buf11, 1, 256, num_warps=2, num_stages=1)
        del buf4
        del buf9
        del primals_7
    return buf11, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 128), (128, 1), 0
        ), reinterpret_tensor(buf3, (64, 128), (128, 1), 0
        ), reinterpret_tensor(buf6, (64, 128), (128, 1), 0
        ), reinterpret_tensor(buf8, (64, 128), (128, 1), 0
        ), primals_12, buf12, primals_10, buf13, primals_6, buf14, primals_4, buf15


class DuelingQNetworkNew(nn.Module):

    def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128):
        super(DuelingQNetworkNew, self).__init__()
        self.fc1_val = nn.Linear(state_size, hidsize1)
        self.fc2_val = nn.Linear(hidsize1, hidsize2)
        self.fc4_val = nn.Linear(hidsize2, 1)
        self.fc1_adv = nn.Linear(state_size, hidsize1)
        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
        self.fc4_adv = nn.Linear(hidsize2, action_size)

    def forward(self, input_0):
        primals_1 = self.fc1_val.weight
        primals_2 = self.fc1_val.bias
        primals_4 = self.fc2_val.weight
        primals_5 = self.fc2_val.bias
        primals_6 = self.fc4_val.weight
        primals_7 = self.fc4_val.bias
        primals_8 = self.fc1_adv.weight
        primals_9 = self.fc1_adv.bias
        primals_10 = self.fc2_adv.weight
        primals_11 = self.fc2_adv.bias
        primals_12 = self.fc4_adv.weight
        primals_13 = self.fc4_adv.bias
        primals_3 = 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]
