# 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/fv/cfvetaedgv74kp75lxerv5neciwqnjmm4hos5n22v7vev4p4zw2o.py
# Topologically Sorted Source Nodes: [common_res_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   common_res_1 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%addmm, %primals_4], 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=[16], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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, 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
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tl.store(out_ptr0 + (x0 + (260*x1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mk/cmk77m3r3ze2mrmpvia72szqtikcdfkcrmgskh6rpvudyzl5gsrn.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_2 => gt, mul, where
# Graph fragment:
#   %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_6), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_2, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_2, 0.22916666666666666), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_tensor_2, %mul), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_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.pointwise(
    size_hints=[1024], 
    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_1', '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_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    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 = 0.22916666666666666
    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/nl/cnlcz5j7iizcao57byyw72hysqynwd3ekkpet6aq7h37a2on7dgv.py
# Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_4 => gt_1, mul_1, where_1
# Graph fragment:
#   %add_tensor_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_8), kwargs = {})
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_1, 0.22916666666666666), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %add_tensor_1, %mul_1), kwargs = {})
triton_poi_fused_leaky_relu_2 = async_compile.triton('triton_poi_fused_leaky_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.pointwise(
    size_hints=[512], 
    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_2', '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_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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 = 0.22916666666666666
    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/wu/cwuygupncxbbcn43yrnkgdl4n4buyhccmnpzavr4e2cuitvgzow6.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_6 => gt_2, mul_2, where_2
# Graph fragment:
#   %add_tensor : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_10), kwargs = {})
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor, 0.22916666666666666), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add_tensor, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_leaky_relu_3', '''
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=[256], 
    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_3', '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_3(in_ptr0, in_ptr1, 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
    x2 = xindex
    x0 = xindex % 64
    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 = 0.22916666666666666
    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')


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, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (256, 260), (260, 1))
    assert_size_stride(primals_6, (256, ), (1, ))
    assert_size_stride(primals_7, (128, 256), (256, 1))
    assert_size_stride(primals_8, (128, ), (1, ))
    assert_size_stride(primals_9, (64, 128), (128, 1))
    assert_size_stride(primals_10, (64, ), (1, ))
    assert_size_stride(primals_11, (1, 64), (64, 1))
    assert_size_stride(primals_12, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 260), (260, 1), torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 256), (260, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [common_res], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(primals_1, (4, 256), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = reinterpret_tensor(buf2, (4, 4), (260, 1), 256)  # alias
        # Topologically Sorted Source Nodes: [common_res_1], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_4, buf1, 16, grid=grid(16), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (260, 256), (1, 260), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf3, primals_6, buf4, buf5, 1024, grid=grid(1024), stream=stream0)
        del buf3
        del primals_6
        buf6 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_7, (256, 128), (1, 256), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 128), (128, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, primals_8, buf7, buf8, 512, grid=grid(512), stream=stream0)
        del buf6
        del primals_8
        buf9 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf8, reinterpret_tensor(primals_9, (128, 64), (1, 128), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 64), (64, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf9, primals_10, buf10, buf11, 256, grid=grid(256), stream=stream0)
        del buf9
        del primals_10
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, buf11, reinterpret_tensor(primals_11, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf13)
        del primals_12
    return (buf13, primals_3, buf2, buf4, buf5, buf7, buf8, buf10, buf11, primals_11, primals_9, primals_7, primals_5, )


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((256, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, ), (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((256, 260), (260, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, 64), (64, 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 as nn
from collections import OrderedDict


class Critic(nn.Module):

    def __init__(self, state_size, action_size, critic_fc_sizes=[256, 128, 64]
        ):
        super(Critic, self).__init__()
        sequence_dict_critic = OrderedDict()
        self.critic_first_layer = nn.Linear(state_size, critic_fc_sizes[0])
        sequence_dict_critic['fc0'] = nn.Linear(critic_fc_sizes[0] +
            action_size, critic_fc_sizes[0])
        sequence_dict_critic['fc_rrelu0'] = nn.RReLU()
        for i, critic_fc_size in enumerate(critic_fc_sizes):
            if i == len(critic_fc_sizes) - 1:
                break
            sequence_dict_critic['fc{}'.format(i + 1)] = nn.Linear(
                critic_fc_size, critic_fc_sizes[i + 1])
            sequence_dict_critic['fc_rrelu{}'.format(i + 1)] = nn.RReLU()
        sequence_dict_critic['logit'] = nn.Linear(critic_fc_sizes[-1], 1)
        self.fc_critic = nn.Sequential(sequence_dict_critic)

    def forward(self, common_res, action):
        common_res = self.critic_first_layer(common_res)
        common_res = nn.RReLU(common_res)
        common_res = torch.cat((common_res.lower, action), dim=1)
        value = self.fc_critic(common_res)
        return value


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([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
import torch.nn as nn
from collections import OrderedDict
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, 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
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 260 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    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 = 0.22916666666666666
    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_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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 = 0.22916666666666666
    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_leaky_relu_3(in_ptr0, in_ptr1, 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
    x2 = xindex
    x0 = xindex % 64
    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 = 0.22916666666666666
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (256, 260), (260, 1))
    assert_size_stride(primals_6, (256,), (1,))
    assert_size_stride(primals_7, (128, 256), (256, 1))
    assert_size_stride(primals_8, (128,), (1,))
    assert_size_stride(primals_9, (64, 128), (128, 1))
    assert_size_stride(primals_10, (64,), (1,))
    assert_size_stride(primals_11, (1, 64), (64, 1))
    assert_size_stride(primals_12, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 260), (260, 1), torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 256), (260, 1), 0)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 256), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = reinterpret_tensor(buf2, (4, 4), (260, 1), 256)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(16)](primals_4, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (260, 256), (
            1, 260), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(1024)](buf3, primals_6, buf4,
            buf5, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_6
        buf6 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_7, (256, 128), (
            1, 256), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 128), (128, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        triton_poi_fused_leaky_relu_2[grid(512)](buf6, primals_8, buf7,
            buf8, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del buf6
        del primals_8
        buf9 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_9, (128, 64), (1,
            128), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 64), (64, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        triton_poi_fused_leaky_relu_3[grid(256)](buf9, primals_10, buf10,
            buf11, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf9
        del primals_10
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_12, buf11, reinterpret_tensor(
            primals_11, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf13)
        del primals_12
    return (buf13, primals_3, buf2, buf4, buf5, buf7, buf8, buf10, buf11,
        primals_11, primals_9, primals_7, primals_5)


class CriticNew(nn.Module):

    def __init__(self, state_size, action_size, critic_fc_sizes=[256, 128, 64]
        ):
        super(CriticNew, self).__init__()
        sequence_dict_critic = OrderedDict()
        self.critic_first_layer = nn.Linear(state_size, critic_fc_sizes[0])
        sequence_dict_critic['fc0'] = nn.Linear(critic_fc_sizes[0] +
            action_size, critic_fc_sizes[0])
        sequence_dict_critic['fc_rrelu0'] = nn.RReLU()
        for i, critic_fc_size in enumerate(critic_fc_sizes):
            if i == len(critic_fc_sizes) - 1:
                break
            sequence_dict_critic['fc{}'.format(i + 1)] = nn.Linear(
                critic_fc_size, critic_fc_sizes[i + 1])
            sequence_dict_critic['fc_rrelu{}'.format(i + 1)] = nn.RReLU()
        sequence_dict_critic['logit'] = nn.Linear(critic_fc_sizes[-1], 1)
        self.fc_critic = nn.Sequential(sequence_dict_critic)

    def forward(self, input_0, input_1):
        primals_1 = self.critic_first_layer.weight
        primals_2 = self.critic_first_layer.bias
        primals_5 = self.fc_critic.fc0.weight
        primals_6 = self.fc_critic.fc0.bias
        primals_7 = self.fc_critic.fc1.weight
        primals_8 = self.fc_critic.fc1.bias
        primals_9 = self.fc_critic.fc2.weight
        primals_10 = self.fc_critic.fc2.bias
        primals_11 = self.fc_critic.logit.weight
        primals_12 = self.fc_critic.logit.bias
        primals_3 = input_0
        primals_4 = 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]
