# 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/yw/cywuzkgetyg6nbayjbju6on5wsp3yuwvuwlhny5nf5swuvme2wpi.py
# Topologically Sorted Source Nodes: [s1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   s1 => relu
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_2), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 800
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/st/cstrm6actnb47mfx7dzjhc6mdjyuviugte2zzw4tf2emczpuzl7g.py
# Topologically Sorted Source Nodes: [s1, add, add_1, s1_1], Original ATen: [aten.relu, aten.add]
# Source node to ATen node mapping:
#   add => mm_plus_mm_1
#   add_1 => add_1
#   s1 => relu
#   s1_1 => relu_1
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_2), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
#   %mm_plus_mm_1 : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.mm_plus_mm](args = (), kwargs = {mat1: %relu, mat2: %permute_3, mat3: %primals_8, mat4: %permute_4})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_plus_mm_1, %primals_7), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_relu_1 = async_compile.triton('triton_poi_fused_add_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=[4096], 
    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_relu_1', '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_relu_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 600
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ol/coltw5bfxsc6xavfnmctcn6noqzail6tyuwko6ijho36ltiubynd.py
# Topologically Sorted Source Nodes: [s2, add_2, add_3, s2_1], Original ATen: [aten.relu, aten.add]
# Source node to ATen node mapping:
#   add_2 => mm_plus_mm
#   add_3 => add_3
#   s2 => relu_2
#   s2_1 => relu_3
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_12), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %mm_plus_mm : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.mm_plus_mm](args = (), kwargs = {mat1: %relu_2, mat2: %permute_9, mat3: %primals_8, mat4: %permute_10})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_plus_mm, %primals_16), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_3,), kwargs = {})
triton_poi_fused_add_relu_2 = async_compile.triton('triton_poi_fused_add_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=[4096], 
    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_relu_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_relu_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 600
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + (x2), tmp6, 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, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18 = args
    args.clear()
    assert_size_stride(primals_1, (800, 4), (4, 1))
    assert_size_stride(primals_2, (800, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (600, 800), (800, 1))
    assert_size_stride(primals_5, (600, ), (1, ))
    assert_size_stride(primals_6, (600, 4), (4, 1))
    assert_size_stride(primals_7, (600, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 600), (600, 1))
    assert_size_stride(primals_10, (1, ), (1, ))
    assert_size_stride(primals_11, (800, 4), (4, 1))
    assert_size_stride(primals_12, (800, ), (1, ))
    assert_size_stride(primals_13, (600, 800), (800, 1))
    assert_size_stride(primals_14, (600, ), (1, ))
    assert_size_stride(primals_15, (600, 4), (4, 1))
    assert_size_stride(primals_16, (600, ), (1, ))
    assert_size_stride(primals_17, (1, 600), (600, 1))
    assert_size_stride(primals_18, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 800), (800, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 800), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0; del buf0  # reuse
        buf15 = empty_strided_cuda((4, 800), (800, 1), torch.bool)
        # Topologically Sorted Source Nodes: [s1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf15, 3200, grid=grid(3200), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [s1, add], Original ATen: [aten.relu, aten.add]
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (800, 600), (1, 800), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [s1, add], Original ATen: [aten.relu, aten.add]
        extern_kernels.mm(primals_8, reinterpret_tensor(primals_6, (4, 600), (1, 4), 0), out=buf3)
        del primals_6
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [s1, add, add_1, s1_1], Original ATen: [aten.relu, aten.add]
        triton_poi_fused_add_relu_1.run(buf4, buf3, primals_7, 2400, grid=grid(2400), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9, (600, 1), (1, 600), 0), alpha=1, beta=1, out=buf6)
        del primals_10
        buf7 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_11, (4, 800), (1, 4), 0), out=buf7)
        del primals_11
        buf8 = buf7; del buf7  # reuse
        buf14 = empty_strided_cuda((4, 800), (800, 1), torch.bool)
        # Topologically Sorted Source Nodes: [s2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf8, primals_12, buf14, 3200, grid=grid(3200), stream=stream0)
        del primals_12
        buf9 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [s2, add_2], Original ATen: [aten.relu, aten.add]
        extern_kernels.mm(buf8, reinterpret_tensor(primals_13, (800, 600), (1, 800), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [s2, add_2], Original ATen: [aten.relu, aten.add]
        extern_kernels.mm(primals_8, reinterpret_tensor(primals_15, (4, 600), (1, 4), 0), out=buf10)
        del primals_15
        del primals_8
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [s2, add_2, add_3, s2_1], Original ATen: [aten.relu, aten.add]
        triton_poi_fused_add_relu_2.run(buf11, buf9, primals_16, 2400, grid=grid(2400), stream=stream0)
        del buf9
        del primals_16
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_18, buf11, reinterpret_tensor(primals_17, (600, 1), (1, 600), 0), alpha=1, beta=1, out=buf13)
        del primals_18
    return (buf6, buf13, primals_3, buf4, buf11, primals_17, primals_13, buf14, primals_9, primals_4, buf15, )


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((800, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((800, ), (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((600, 800), (800, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((600, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((600, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((600, ), (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((1, 600), (600, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((800, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((800, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((600, 800), (800, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((600, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((600, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((600, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((1, 600), (600, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = 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, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18])
    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


class Critic(nn.Module):

    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.layer_1 = nn.Linear(state_dim, 800)
        self.layer_2_s = nn.Linear(800, 600)
        self.layer_2_a = nn.Linear(action_dim, 600)
        self.layer_3 = nn.Linear(600, 1)
        self.layer_4 = nn.Linear(state_dim, 800)
        self.layer_5_s = nn.Linear(800, 600)
        self.layer_5_a = nn.Linear(action_dim, 600)
        self.layer_6 = nn.Linear(600, 1)

    def forward(self, s, a):
        s1 = F.relu(self.layer_1(s))
        self.layer_2_s(s1)
        self.layer_2_a(a)
        s11 = torch.mm(s1, self.layer_2_s.weight.data.t())
        s12 = torch.mm(a, self.layer_2_a.weight.data.t())
        s1 = F.relu(s11 + s12 + self.layer_2_a.bias.data)
        q1 = self.layer_3(s1)
        s2 = F.relu(self.layer_4(s))
        self.layer_5_s(s2)
        self.layer_5_a(a)
        s21 = torch.mm(s2, self.layer_5_s.weight.data.t())
        s22 = torch.mm(a, self.layer_5_a.weight.data.t())
        s2 = F.relu(s21 + s22 + self.layer_5_a.bias.data)
        q2 = self.layer_6(s2)
        return q1, q2


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


def get_init_inputs():
    return [[], {'state_dim': 4, 'action_dim': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 800
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_relu_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 600
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_relu_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 600
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + x2, tmp6, 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,
        primals_13, primals_14, primals_15, primals_16, primals_17, primals_18
        ) = args
    args.clear()
    assert_size_stride(primals_1, (800, 4), (4, 1))
    assert_size_stride(primals_2, (800,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (600, 800), (800, 1))
    assert_size_stride(primals_5, (600,), (1,))
    assert_size_stride(primals_6, (600, 4), (4, 1))
    assert_size_stride(primals_7, (600,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 600), (600, 1))
    assert_size_stride(primals_10, (1,), (1,))
    assert_size_stride(primals_11, (800, 4), (4, 1))
    assert_size_stride(primals_12, (800,), (1,))
    assert_size_stride(primals_13, (600, 800), (800, 1))
    assert_size_stride(primals_14, (600,), (1,))
    assert_size_stride(primals_15, (600, 4), (4, 1))
    assert_size_stride(primals_16, (600,), (1,))
    assert_size_stride(primals_17, (1, 600), (600, 1))
    assert_size_stride(primals_18, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 800), (800, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 800),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        buf15 = empty_strided_cuda((4, 800), (800, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(3200)](buf1,
            primals_2, buf15, 3200, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (800, 600), (
            1, 800), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        extern_kernels.mm(primals_8, reinterpret_tensor(primals_6, (4, 600),
            (1, 4), 0), out=buf3)
        del primals_6
        buf4 = buf2
        del buf2
        triton_poi_fused_add_relu_1[grid(2400)](buf4, buf3, primals_7, 2400,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9,
            (600, 1), (1, 600), 0), alpha=1, beta=1, out=buf6)
        del primals_10
        buf7 = buf1
        del buf1
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_11, (4, 800
            ), (1, 4), 0), out=buf7)
        del primals_11
        buf8 = buf7
        del buf7
        buf14 = empty_strided_cuda((4, 800), (800, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(3200)](buf8,
            primals_12, buf14, 3200, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_12
        buf9 = buf3
        del buf3
        extern_kernels.mm(buf8, reinterpret_tensor(primals_13, (800, 600),
            (1, 800), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 600), (600, 1), torch.float32)
        extern_kernels.mm(primals_8, reinterpret_tensor(primals_15, (4, 600
            ), (1, 4), 0), out=buf10)
        del primals_15
        del primals_8
        buf11 = buf10
        del buf10
        triton_poi_fused_add_relu_2[grid(2400)](buf11, buf9, primals_16, 
            2400, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
        del primals_16
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_18, buf11, reinterpret_tensor(
            primals_17, (600, 1), (1, 600), 0), alpha=1, beta=1, out=buf13)
        del primals_18
    return (buf6, buf13, primals_3, buf4, buf11, primals_17, primals_13,
        buf14, primals_9, primals_4, buf15)


class CriticNew(nn.Module):

    def __init__(self, state_dim, action_dim):
        super(CriticNew, self).__init__()
        self.layer_1 = nn.Linear(state_dim, 800)
        self.layer_2_s = nn.Linear(800, 600)
        self.layer_2_a = nn.Linear(action_dim, 600)
        self.layer_3 = nn.Linear(600, 1)
        self.layer_4 = nn.Linear(state_dim, 800)
        self.layer_5_s = nn.Linear(800, 600)
        self.layer_5_a = nn.Linear(action_dim, 600)
        self.layer_6 = nn.Linear(600, 1)

    def forward(self, input_0, input_1):
        primals_1 = self.layer_1.weight
        primals_2 = self.layer_1.bias
        primals_4 = self.layer_2_s.weight
        primals_5 = self.layer_2_s.bias
        primals_6 = self.layer_2_a.weight
        primals_7 = self.layer_2_a.bias
        primals_9 = self.layer_3.weight
        primals_10 = self.layer_3.bias
        primals_11 = self.layer_4.weight
        primals_12 = self.layer_4.bias
        primals_13 = self.layer_5_s.weight
        primals_14 = self.layer_5_s.bias
        primals_15 = self.layer_5_a.weight
        primals_16 = self.layer_5_a.bias
        primals_17 = self.layer_6.weight
        primals_18 = self.layer_6.bias
        primals_3 = input_0
        primals_8 = 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, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18])
        return output[0], output[1]
