# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/w3/cw3egt7ajdde7mbqzrdxs4mdcaxj75b4l3brz5gbsf4yd73gbids.py
# Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   a => relu
# Graph fragment:
#   %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_2), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_2,), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    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)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/t5/ct5armnj6ripy7coupdufbsa57m2pv5ldwavtst3ppznb5iwhf35.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   x => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%addmm_2, %expand), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %expand_1), kwargs = {})
triton_poi_fused_add_sub_1 = async_compile.triton('triton_poi_fused_add_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp5 - tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (512, 4), (4, 1))
    assert_size_stride(primals_2, (512, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (512, 4), (4, 1))
    assert_size_stride(primals_5, (512, ), (1, ))
    assert_size_stride(primals_6, (4, 512), (512, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (1, 512), (512, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 512), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_0.run(buf1, primals_2, 2048, grid=grid(2048), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_4, (4, 512), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.relu]
        triton_poi_fused_relu_0.run(buf3, primals_5, 2048, grid=grid(2048), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6, (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (512, 1), (1, 512), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.sub]
        triton_poi_fused_add_sub_1.run(buf4, buf5, primals_9, buf6, 16, grid=grid(16), stream=stream0)
        del buf4
        del buf5
        del primals_9
    return (buf6, primals_3, buf1, buf3, primals_8, primals_6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((512, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((512, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.autograd


class duelingdqnNet(nn.Module):

    def __init__(self, STATE_NUM, ACTION_NUM):
        super(duelingdqnNet, self).__init__()
        self.ACTION_NUM = ACTION_NUM
        self.fc1_a = nn.Linear(in_features=STATE_NUM, out_features=512)
        self.fc1_v = nn.Linear(in_features=STATE_NUM, out_features=512)
        self.fc2_a = nn.Linear(in_features=512, out_features=ACTION_NUM)
        self.fc2_v = nn.Linear(in_features=512, out_features=1)

    def forward(self, x):
        a = F.relu(self.fc1_a(x))
        v = F.relu(self.fc1_v(x))
        a = self.fc2_a(a)
        v = self.fc2_v(v).expand(x.size(0), self.ACTION_NUM)
        x = a + v - a.mean(1).unsqueeze(1).expand(x.size(0), self.ACTION_NUM)
        return x


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


def get_init_inputs():
    return [[], {'STATE_NUM': 4, 'ACTION_NUM': 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
import torch.optim
import torch.autograd
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, in_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 % 512
    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)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_add_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp5 - tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (512, 4), (4, 1))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (512, 4), (4, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (4, 512), (512, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 512), (512, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 512),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(2048)](buf1, primals_2, 2048, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_4, (4, 512),
            (1, 4), 0), out=buf2)
        del primals_4
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(2048)](buf3, primals_5, 2048, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
            (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (512, 1), (1,
            512), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_sub_1[grid(16)](buf4, buf5, primals_9, buf6, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
        del buf5
        del primals_9
    return buf6, primals_3, buf1, buf3, primals_8, primals_6


class duelingdqnNetNew(nn.Module):

    def __init__(self, STATE_NUM, ACTION_NUM):
        super(duelingdqnNetNew, self).__init__()
        self.ACTION_NUM = ACTION_NUM
        self.fc1_a = nn.Linear(in_features=STATE_NUM, out_features=512)
        self.fc1_v = nn.Linear(in_features=STATE_NUM, out_features=512)
        self.fc2_a = nn.Linear(in_features=512, out_features=ACTION_NUM)
        self.fc2_v = nn.Linear(in_features=512, out_features=1)

    def forward(self, input_0):
        primals_1 = self.fc1_a.weight
        primals_2 = self.fc1_a.bias
        primals_4 = self.fc1_v.weight
        primals_5 = self.fc1_v.bias
        primals_6 = self.fc2_a.weight
        primals_7 = self.fc2_a.bias
        primals_8 = self.fc2_v.weight
        primals_9 = self.fc2_v.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])
        return output[0]
