# 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_7/inductor_cache/jo/cjoedcmy7gkzotfopp7atueg5hlb65rgyaj7o2wkgwedzdx5r26r.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x => gt, mul, where
# Graph fragment:
#   %add_tensor_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_2), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_1, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_1, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_tensor_1, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_leaky_relu_0', 'mutated_arg_names': [], '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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tj/ctjbdubzq27k64ogf43asej6474qk3zscfyidqrh5oszfb7rplqn.py
# Topologically Sorted Source Nodes: [a_mean], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   a_mean => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1]), kwargs = {})
triton_poi_fused_mean_1 = async_compile.triton('triton_poi_fused_mean_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_mean_1(in_ptr0, out_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.full([1], 0, tl.int64)
    tmp1 = tmp0 >= tmp0
    tmp2 = tl.full([1], 1, tl.int64)
    tmp3 = tmp0 < tmp2
    tmp4 = tl.load(in_ptr0 + (4*x0), tmp3 & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tmp0 >= tmp2
    tmp6 = tl.full([1], 2, tl.int64)
    tmp7 = tmp0 < tmp6
    tmp8 = tmp5 & tmp7
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), tmp8 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tmp0 >= tmp6
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (2 + (4*x0)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp0 >= tmp11
    tmp16 = tl.full([1], 4, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr0 + (3 + (4*x0)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.where(tmp13, tmp14, tmp18)
    tmp20 = tl.where(tmp8, tmp9, tmp19)
    tmp21 = tl.where(tmp3, tmp4, tmp20)
    tmp22 = tmp2 >= tmp0
    tmp23 = tmp2 < tmp2
    tmp24 = tl.load(in_ptr0 + (4*x0), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp2 >= tmp2
    tmp26 = tmp2 < tmp6
    tmp27 = tmp25 & tmp26
    tmp28 = tl.load(in_ptr0 + (1 + (4*x0)), tmp27 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tmp2 >= tmp6
    tmp30 = tmp2 < tmp11
    tmp31 = tmp29 & tmp30
    tmp32 = tl.load(in_ptr0 + (2 + (4*x0)), tmp31 & xmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tmp2 >= tmp11
    tmp34 = tmp2 < tmp16
    tmp35 = tl.load(in_ptr0 + (3 + (4*x0)), tmp33 & xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = tl.where(tmp31, tmp32, tmp35)
    tmp37 = tl.where(tmp27, tmp28, tmp36)
    tmp38 = tl.where(tmp23, tmp24, tmp37)
    tmp39 = tmp21 + tmp38
    tmp40 = tmp6 >= tmp0
    tmp41 = tmp6 < tmp2
    tmp42 = tl.load(in_ptr0 + (4*x0), tmp41 & xmask, eviction_policy='evict_last', other=0.0)
    tmp43 = tmp6 >= tmp2
    tmp44 = tmp6 < tmp6
    tmp45 = tmp43 & tmp44
    tmp46 = tl.load(in_ptr0 + (1 + (4*x0)), tmp45 & xmask, eviction_policy='evict_last', other=0.0)
    tmp47 = tmp6 >= tmp6
    tmp48 = tmp6 < tmp11
    tmp49 = tmp47 & tmp48
    tmp50 = tl.load(in_ptr0 + (2 + (4*x0)), tmp49 & xmask, eviction_policy='evict_last', other=0.0)
    tmp51 = tmp6 >= tmp11
    tmp52 = tmp6 < tmp16
    tmp53 = tl.load(in_ptr0 + (3 + (4*x0)), tmp51 & xmask, eviction_policy='evict_last', other=0.0)
    tmp54 = tl.where(tmp49, tmp50, tmp53)
    tmp55 = tl.where(tmp45, tmp46, tmp54)
    tmp56 = tl.where(tmp41, tmp42, tmp55)
    tmp57 = tmp39 + tmp56
    tmp58 = tmp11 >= tmp0
    tmp59 = tmp11 < tmp2
    tmp60 = tl.load(in_ptr0 + (4*x0), tmp59 & xmask, eviction_policy='evict_last', other=0.0)
    tmp61 = tmp11 >= tmp2
    tmp62 = tmp11 < tmp6
    tmp63 = tmp61 & tmp62
    tmp64 = tl.load(in_ptr0 + (1 + (4*x0)), tmp63 & xmask, eviction_policy='evict_last', other=0.0)
    tmp65 = tmp11 >= tmp6
    tmp66 = tmp11 < tmp11
    tmp67 = tmp65 & tmp66
    tmp68 = tl.load(in_ptr0 + (2 + (4*x0)), tmp67 & xmask, eviction_policy='evict_last', other=0.0)
    tmp69 = tmp11 >= tmp11
    tmp70 = tmp11 < tmp16
    tmp71 = tl.load(in_ptr0 + (3 + (4*x0)), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp72 = tl.where(tmp67, tmp68, tmp71)
    tmp73 = tl.where(tmp63, tmp64, tmp72)
    tmp74 = tl.where(tmp59, tmp60, tmp73)
    tmp75 = tmp57 + tmp74
    tmp76 = 4.0
    tmp77 = tmp75 / tmp76
    tl.store(out_ptr0 + (x0), tmp77, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pb/cpbcy5pyu4zhpzfxf7a6xu2ndk6ilijodflhglzty5licqmyy3a3.py
# Topologically Sorted Source Nodes: [repeat, add, repeat_1, x_2], Original ATen: [aten.repeat, aten.add, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   repeat => repeat
#   repeat_1 => repeat_1
#   x_2 => sub
# Graph fragment:
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%addmm_2, [1, 4]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%repeat, %addmm_3), kwargs = {})
#   %repeat_1 : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%mean, [1, 4]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %repeat_1), kwargs = {})
triton_poi_fused_add_repeat_sub_2 = async_compile.triton('triton_poi_fused_add_repeat_sub_2', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_repeat_sub_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_repeat_sub_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tl.store(in_out_ptr0 + (x2), tmp4, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_0.run(buf3, primals_5, buf4, buf5, 16, grid=grid(16), stream=stream0)
        del primals_5
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_7
        buf8 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_9
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [a_mean], Original ATen: [aten.mean]
        triton_poi_fused_mean_1.run(buf8, buf9, 4, grid=grid(4), stream=stream0)
        buf10 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [repeat, add, repeat_1, x_2], Original ATen: [aten.repeat, aten.add, aten.sub]
        triton_poi_fused_add_repeat_sub_2.run(buf10, buf7, buf9, 16, grid=grid(16), stream=stream0)
        del buf7
        del buf9
    return (buf10, primals_3, buf1, buf2, buf4, buf5, primals_8, primals_6, primals_4, )


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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F


class Dueling_DQN(nn.Module):

    def __init__(self, args):
        super().__init__()
        self.state_space = args.state_space
        self.fc1 = nn.Linear(self.state_space, args.hidden_size)
        self.action_space = args.action_space
        self.fc_h = nn.Linear(args.hidden_size, args.hidden_size)
        self.fc_z_v = nn.Linear(args.hidden_size, 1)
        self.fc_z_a = nn.Linear(args.hidden_size, self.action_space)

    def forward(self, x):
        x = F.leaky_relu(self.fc1(x))
        x = F.leaky_relu(self.fc_h(x))
        v, a = self.fc_z_v(x), self.fc_z_a(x)
        a_mean = torch.stack(a.chunk(self.action_space, 1), 1).mean(1)
        x = v.repeat(1, self.action_space) + a - a_mean.repeat(1, self.
            action_space)
        return x

    def parameter_update(self, source):
        self.load_state_dict(source.state_dict())


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


def get_init_inputs():
    return [[], {'args': _mock_config(state_space=4, hidden_size=4,
        action_space=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
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_mean_1(in_ptr0, out_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.full([1], 0, tl.int64)
    tmp2 = tl.full([1], 1, tl.int64)
    tmp3 = tmp0 < tmp2
    tmp4 = tl.load(in_ptr0 + 4 * x0, tmp3 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp5 = tmp0 >= tmp2
    tmp6 = tl.full([1], 2, tl.int64)
    tmp7 = tmp0 < tmp6
    tmp8 = tmp5 & tmp7
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), tmp8 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tmp0 >= tmp6
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x0), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp0 >= tmp11
    tl.full([1], 4, tl.int64)
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), tmp15 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tl.where(tmp13, tmp14, tmp18)
    tmp20 = tl.where(tmp8, tmp9, tmp19)
    tmp21 = tl.where(tmp3, tmp4, tmp20)
    tmp23 = tmp2 < tmp2
    tmp24 = tl.load(in_ptr0 + 4 * x0, tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp2 >= tmp2
    tmp26 = tmp2 < tmp6
    tmp27 = tmp25 & tmp26
    tmp28 = tl.load(in_ptr0 + (1 + 4 * x0), tmp27 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp29 = tmp2 >= tmp6
    tmp30 = tmp2 < tmp11
    tmp31 = tmp29 & tmp30
    tmp32 = tl.load(in_ptr0 + (2 + 4 * x0), tmp31 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp33 = tmp2 >= tmp11
    tmp35 = tl.load(in_ptr0 + (3 + 4 * x0), tmp33 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp36 = tl.where(tmp31, tmp32, tmp35)
    tmp37 = tl.where(tmp27, tmp28, tmp36)
    tmp38 = tl.where(tmp23, tmp24, tmp37)
    tmp39 = tmp21 + tmp38
    tmp41 = tmp6 < tmp2
    tmp42 = tl.load(in_ptr0 + 4 * x0, tmp41 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp43 = tmp6 >= tmp2
    tmp44 = tmp6 < tmp6
    tmp45 = tmp43 & tmp44
    tmp46 = tl.load(in_ptr0 + (1 + 4 * x0), tmp45 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp47 = tmp6 >= tmp6
    tmp48 = tmp6 < tmp11
    tmp49 = tmp47 & tmp48
    tmp50 = tl.load(in_ptr0 + (2 + 4 * x0), tmp49 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp51 = tmp6 >= tmp11
    tmp53 = tl.load(in_ptr0 + (3 + 4 * x0), tmp51 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp54 = tl.where(tmp49, tmp50, tmp53)
    tmp55 = tl.where(tmp45, tmp46, tmp54)
    tmp56 = tl.where(tmp41, tmp42, tmp55)
    tmp57 = tmp39 + tmp56
    tmp59 = tmp11 < tmp2
    tmp60 = tl.load(in_ptr0 + 4 * x0, tmp59 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp61 = tmp11 >= tmp2
    tmp62 = tmp11 < tmp6
    tmp63 = tmp61 & tmp62
    tmp64 = tl.load(in_ptr0 + (1 + 4 * x0), tmp63 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp65 = tmp11 >= tmp6
    tmp66 = tmp11 < tmp11
    tmp67 = tmp65 & tmp66
    tmp68 = tl.load(in_ptr0 + (2 + 4 * x0), tmp67 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp69 = tmp11 >= tmp11
    tmp71 = tl.load(in_ptr0 + (3 + 4 * x0), tmp69 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp72 = tl.where(tmp67, tmp68, tmp71)
    tmp73 = tl.where(tmp63, tmp64, tmp72)
    tmp74 = tl.where(tmp59, tmp60, tmp73)
    tmp75 = tmp57 + tmp74
    tmp76 = 4.0
    tmp77 = tmp75 / tmp76
    tl.store(out_ptr0 + x0, tmp77, xmask)


@triton.jit
def triton_poi_fused_add_repeat_sub_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tl.store(in_out_ptr0 + x2, tmp4, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(16)](buf0, primals_2, buf1, buf2,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_leaky_relu_0[grid(16)](buf3, primals_5, buf4, buf5,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_7
        buf8 = buf3
        del buf3
        extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_9
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_mean_1[grid(4)](buf8, buf9, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf10 = buf8
        del buf8
        triton_poi_fused_add_repeat_sub_2[grid(16)](buf10, buf7, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf7
        del buf9
    return (buf10, primals_3, buf1, buf2, buf4, buf5, primals_8, primals_6,
        primals_4)


class Dueling_DQNNew(nn.Module):

    def __init__(self, args):
        super().__init__()
        self.state_space = args.state_space
        self.fc1 = nn.Linear(self.state_space, args.hidden_size)
        self.action_space = args.action_space
        self.fc_h = nn.Linear(args.hidden_size, args.hidden_size)
        self.fc_z_v = nn.Linear(args.hidden_size, 1)
        self.fc_z_a = nn.Linear(args.hidden_size, self.action_space)

    def parameter_update(self, source):
        self.load_state_dict(source.state_dict())

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_3 = self.fc_h.weight
        primals_5 = self.fc_h.bias
        primals_6 = self.fc_z_v.weight
        primals_7 = self.fc_z_v.bias
        primals_4 = self.fc_z_a.weight
        primals_9 = self.fc_z_a.bias
        primals_8 = 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]
