# 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_8/inductor_cache/ms/cmsuzohbg5nq52jnvirovzkvykrzzko5xomu7zyu5e5u2lhegppw.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    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_cat_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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vj/cvjomvh7yyetlbksqnc7tbtgp4psipw45myrk747m24qh2klfjfx.py
# Topologically Sorted Source Nodes: [relu, x_1], Original ATen: [aten.relu, aten.native_layer_norm]
# Source node to ATen node mapping:
#   relu => relu
#   x_1 => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%addmm,), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%relu, [1]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_5), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_6), kwargs = {})
triton_per_fused_native_layer_norm_relu_1 = async_compile.triton('triton_per_fused_native_layer_norm_relu_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.persistent_reduction(
    size_hints=[4, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 5, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_native_layer_norm_relu_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 400
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (400*x0)), rmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 400, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 400.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp23, None)
    tl.store(out_ptr1 + (r1 + (400*x0)), tmp29, rmask)
    tl.store(out_ptr0 + (x0), tmp12, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/p5/cp5lv2ndoetibz6kpv46gauxejrvyysltbws6gl6rlice6vmcpqo.py
# Topologically Sorted Source Nodes: [relu_1, x_2], Original ATen: [aten.relu, aten.native_layer_norm]
# Source node to ATen node mapping:
#   relu_1 => relu_1
#   x_2 => add_2, add_3, mul_2, mul_3, rsqrt_1, sub_1, var_mean_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%addmm_1,), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%relu_1, [1]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu_1, %getitem_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_9), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_10), kwargs = {})
triton_per_fused_native_layer_norm_relu_2 = async_compile.triton('triton_per_fused_native_layer_norm_relu_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.persistent_reduction(
    size_hints=[4, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_native_layer_norm_relu_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 300
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (300*x0)), rmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 300, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 300.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp23, None)
    tl.store(out_ptr1 + (r1 + (300*x0)), tmp29, rmask)
    tl.store(out_ptr0 + (x0), tmp12, None)
''', 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, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (400, 8), (8, 1))
    assert_size_stride(primals_4, (400, ), (1, ))
    assert_size_stride(primals_5, (400, ), (1, ))
    assert_size_stride(primals_6, (400, ), (1, ))
    assert_size_stride(primals_7, (300, 400), (400, 1))
    assert_size_stride(primals_8, (300, ), (1, ))
    assert_size_stride(primals_9, (300, ), (1, ))
    assert_size_stride(primals_10, (300, ), (1, ))
    assert_size_stride(primals_11, (1, 300), (300, 1))
    assert_size_stride(primals_12, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf5 = reinterpret_tensor(buf3, (4, 1), (1, 1), 0); del buf3  # reuse
        buf6 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [relu, x_1], Original ATen: [aten.relu, aten.native_layer_norm]
        triton_per_fused_native_layer_norm_relu_1.run(buf5, buf1, primals_5, primals_6, buf2, buf6, 4, 400, grid=grid(4), stream=stream0)
        del primals_6
        buf7 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, buf6, reinterpret_tensor(primals_7, (400, 300), (1, 400), 0), alpha=1, beta=1, out=buf7)
        del primals_8
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf11 = reinterpret_tensor(buf9, (4, 1), (1, 1), 0); del buf9  # reuse
        buf12 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [relu_1, x_2], Original ATen: [aten.relu, aten.native_layer_norm]
        triton_per_fused_native_layer_norm_relu_2.run(buf11, buf7, primals_9, primals_10, buf8, buf12, 4, 300, grid=grid(4), stream=stream0)
        del primals_10
        buf14 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, buf12, reinterpret_tensor(primals_11, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf14)
        del primals_12
    return (buf14, primals_5, primals_9, buf0, buf1, buf2, buf5, buf6, buf7, buf8, buf11, buf12, primals_11, primals_7, )


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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((400, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, 300), (300, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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, primals_10, primals_11, primals_12])
    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.functional as F
import torch.nn as nn


class SoftQNetwork(nn.Module):

    def __init__(self, num_inputs, num_actions, hidden_size=[400, 300],
        init_w=0.003):
        super(SoftQNetwork, self).__init__()
        self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size[0])
        self.linear2 = nn.Linear(hidden_size[0], hidden_size[1])
        self.linear3 = nn.Linear(hidden_size[1], 1)
        self.ln1 = nn.LayerNorm(hidden_size[0])
        self.ln2 = nn.LayerNorm(hidden_size[1])
        self.linear3.weight.data.uniform_(-init_w, init_w)
        self.linear3.bias.data.uniform_(-init_w, init_w)

    def forward(self, state, action):
        x = torch.cat([state, action], 1)
        x = self.ln1(F.relu(self.linear1(x)))
        x = self.ln2(F.relu(self.linear2(x)))
        x = self.linear3(x)
        return x


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


def get_init_inputs():
    return [[], {'num_inputs': 4, 'num_actions': 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
from torch._inductor.runtime.triton_helpers import libdevice
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_native_layer_norm_relu_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    rnumel = 400
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 400 * x0), rmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp28 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 400, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 400.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp23, None)
    tl.store(out_ptr1 + (r1 + 400 * x0), tmp29, rmask)
    tl.store(out_ptr0 + x0, tmp12, None)


@triton.jit
def triton_per_fused_native_layer_norm_relu_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    rnumel = 300
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 300 * x0), rmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp28 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 300, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 300.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp23, None)
    tl.store(out_ptr1 + (r1 + 300 * x0), tmp29, rmask)
    tl.store(out_ptr0 + x0, tmp12, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (400, 8), (8, 1))
    assert_size_stride(primals_4, (400,), (1,))
    assert_size_stride(primals_5, (400,), (1,))
    assert_size_stride(primals_6, (400,), (1,))
    assert_size_stride(primals_7, (300, 400), (400, 1))
    assert_size_stride(primals_8, (300,), (1,))
    assert_size_stride(primals_9, (300,), (1,))
    assert_size_stride(primals_10, (300,), (1,))
    assert_size_stride(primals_11, (1, 300), (300, 1))
    assert_size_stride(primals_12, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
            (8, 400), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf5 = reinterpret_tensor(buf3, (4, 1), (1, 1), 0)
        del buf3
        buf6 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        triton_per_fused_native_layer_norm_relu_1[grid(4)](buf5, buf1,
            primals_5, primals_6, buf2, buf6, 4, 400, num_warps=4, num_stages=1
            )
        del primals_6
        buf7 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf6, reinterpret_tensor(primals_7,
            (400, 300), (1, 400), 0), alpha=1, beta=1, out=buf7)
        del primals_8
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf11 = reinterpret_tensor(buf9, (4, 1), (1, 1), 0)
        del buf9
        buf12 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        triton_per_fused_native_layer_norm_relu_2[grid(4)](buf11, buf7,
            primals_9, primals_10, buf8, buf12, 4, 300, num_warps=4,
            num_stages=1)
        del primals_10
        buf14 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_12, buf12, reinterpret_tensor(
            primals_11, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf14)
        del primals_12
    return (buf14, primals_5, primals_9, buf0, buf1, buf2, buf5, buf6, buf7,
        buf8, buf11, buf12, primals_11, primals_7)


class SoftQNetworkNew(nn.Module):

    def __init__(self, num_inputs, num_actions, hidden_size=[400, 300],
        init_w=0.003):
        super(SoftQNetworkNew, self).__init__()
        self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size[0])
        self.linear2 = nn.Linear(hidden_size[0], hidden_size[1])
        self.linear3 = nn.Linear(hidden_size[1], 1)
        self.ln1 = nn.LayerNorm(hidden_size[0])
        self.ln2 = nn.LayerNorm(hidden_size[1])
        self.linear3.weight.data.uniform_(-init_w, init_w)
        self.linear3.bias.data.uniform_(-init_w, init_w)

    def forward(self, input_0, input_1):
        primals_3 = self.linear1.weight
        primals_4 = self.linear1.bias
        primals_7 = self.linear2.weight
        primals_8 = self.linear2.bias
        primals_11 = self.linear3.weight
        primals_12 = self.linear3.bias
        primals_5 = self.ln1.weight
        primals_6 = self.ln1.bias
        primals_9 = self.ln2.weight
        primals_10 = self.ln2.bias
        primals_1 = input_0
        primals_2 = input_1
        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])
        return output[0]
