# 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_4/inductor_cache/v7/cv7zazascu4rpkkwoxbiwk6c2le2e6wshdhae73bmaoapelvwguv.py
# Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   a => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_1 : [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=[1024], 
    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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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_4/inductor_cache/oz/coz2vvm7j43dgp2scg63a7xyihnzn7xcwkbiwytoo3i3naiahfwp.py
# Topologically Sorted Source Nodes: [tanh, mu], Original ATen: [aten.tanh, aten.mul]
# Source node to ATen node mapping:
#   mu => mul
#   tanh => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, 2.0), kwargs = {})
triton_poi_fused_mul_tanh_1 = async_compile.triton('triton_poi_fused_mul_tanh_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=[256], 
    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_mul_tanh_1', '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_mul_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vn/cvnduo4s466rgfr5qur2ih4vzvlqu5mjrir76fxacpwhzg7t533r.py
# Topologically Sorted Source Nodes: [sigma, log, action_entropy], Original ATen: [aten.softplus, aten.log, aten.add]
# Source node to ATen node mapping:
#   action_entropy => add
#   log => log
#   sigma => exp, gt, log1p, where
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_7,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 20), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_7, %log1p), kwargs = {})
#   %log : [num_users=2] = call_function[target=torch.ops.aten.log.default](args = (%where,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, 1.4189385332046727), kwargs = {})
triton_poi_fused_add_log_softplus_2 = async_compile.triton('triton_poi_fused_add_log_softplus_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=[256], 
    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_log_softplus_2', '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_add_log_softplus_2(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tl_math.log(tmp5)
    tmp7 = 1.4189385332046727
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr1 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5x/c5xxopo7kvrfl7zh4iho33rpirl2hnwrmhemgvjglcq7m3e6yekh.py
# Topologically Sorted Source Nodes: [log, action_1, var, sub, pow_2, neg, mul_1, truediv, sub_1, action_log_prob], Original ATen: [aten.log, aten.clamp, aten.pow, aten.sub, aten.neg, aten.mul, aten.div]
# Source node to ATen node mapping:
#   action_1 => clamp_max, clamp_min
#   action_log_prob => sub_2
#   log => log
#   mul_1 => mul_1
#   neg => neg
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   truediv => div
#   var => pow_1
# Graph fragment:
#   %log : [num_users=2] = call_function[target=torch.ops.aten.log.default](args = (%where,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%normal, -4), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 4), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where, 2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_max, %mul), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_2,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %mul_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %log), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, 0.9189385332046727), kwargs = {})
triton_poi_fused_clamp_div_log_mul_neg_pow_sub_3 = async_compile.triton('triton_poi_fused_clamp_div_log_mul_neg_pow_sub_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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_poi_fused_clamp_div_log_mul_neg_pow_sub_3', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_clamp_div_log_mul_neg_pow_sub_3(in_out_ptr0, in_out_ptr1, in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp5 = tl.load(in_out_ptr1 + (x0), xmask)
    tmp7 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = -4.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 4.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp6 = tmp4 - tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp6 * tmp6
    tmp12 = -tmp11
    tmp13 = tmp12 / tmp10
    tmp14 = tl_math.log(tmp7)
    tmp15 = tmp13 - tmp14
    tmp16 = 0.9189385332046727
    tmp17 = tmp15 - tmp16
    tl.store(in_out_ptr0 + (x0), tmp4, xmask)
    tl.store(in_out_ptr1 + (x0), tmp6, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr1 + (x0), tmp17, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 16), (16, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 16), (256, 64, 16, 1), 0); del buf0  # reuse
        buf16 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf16, 1024, grid=grid(1024), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 16), (16, 1), 0), reinterpret_tensor(primals_4, (16, 16), (1, 16), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 16), (256, 64, 16, 1), 0); del buf2  # reuse
        buf15 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [a_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf15, 1024, grid=grid(1024), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 16), (16, 1), 0), reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh, mu], Original ATen: [aten.tanh, aten.mul]
        triton_poi_fused_mul_tanh_1.run(buf4, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigma, log, action_entropy], Original ATen: [aten.softplus, aten.log, aten.add]
        triton_poi_fused_add_log_softplus_2.run(buf5, buf7, buf10, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [tanh, mu, sigma, action], Original ATen: [aten.tanh, aten.mul, aten.softplus, aten.normal]
        buf8 = torch.ops.aten.normal.Tensor_Tensor(buf6, buf7)
        buf9 = buf8
        del buf8
        buf11 = buf9; del buf9  # reuse
        buf12 = buf6; del buf6  # reuse
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log, action_1, var, sub, pow_2, neg, mul_1, truediv, sub_1, action_log_prob], Original ATen: [aten.log, aten.clamp, aten.pow, aten.sub, aten.neg, aten.mul, aten.div]
        triton_poi_fused_clamp_div_log_mul_neg_pow_sub_3.run(buf11, buf12, buf7, buf13, buf14, 256, grid=grid(256), stream=stream0)
        del buf7
    return (buf11, buf14, buf10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 16), (16, 1), 0), reinterpret_tensor(buf3, (64, 16), (16, 1), 0), buf4, buf5, buf12, buf13, primals_8, primals_6, buf15, primals_4, buf16, )


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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 16), (16, 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)

import torch
import torch as t
import torch.nn as nn
from torch.distributions import Normal
import torch.nn.functional as F


class A2CActorCont(nn.Module):

    def __init__(self, state_dim, action_dim, action_range):
        super().__init__()
        self.fc1 = nn.Linear(state_dim, 16)
        self.fc2 = nn.Linear(16, 16)
        self.mu_head = nn.Linear(16, action_dim)
        self.sigma_head = nn.Linear(16, action_dim)
        self.action_range = action_range

    def forward(self, state, action=None):
        a = t.relu(self.fc1(state))
        a = t.relu(self.fc2(a))
        mu = 2.0 * t.tanh(self.mu_head(a))
        sigma = F.softplus(self.sigma_head(a))
        dist = Normal(mu, sigma)
        action = action if action is not None else dist.sample()
        action_entropy = dist.entropy()
        action = action.clamp(-self.action_range, self.action_range)
        action_log_prob = dist.log_prob(action)
        return action, action_log_prob, action_entropy


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


def get_init_inputs():
    return [[], {'state_dim': 4, 'action_dim': 4, 'action_range': 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, math as tl_math
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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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_mul_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_log_softplus_2(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tl_math.log(tmp5)
    tmp7 = 1.4189385332046727
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_clamp_div_log_mul_neg_pow_sub_3(in_out_ptr0,
    in_out_ptr1, in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp5 = tl.load(in_out_ptr1 + x0, xmask)
    tmp7 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -4.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 4.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp6 = tmp4 - tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp6 * tmp6
    tmp12 = -tmp11
    tmp13 = tmp12 / tmp10
    tmp14 = tl_math.log(tmp7)
    tmp15 = tmp13 - tmp14
    tmp16 = 0.9189385332046727
    tmp17 = tmp15 - tmp16
    tl.store(in_out_ptr0 + x0, tmp4, xmask)
    tl.store(in_out_ptr1 + x0, tmp6, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp17, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 16), (16, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 16), (256, 64, 16, 1), 0)
        del buf0
        buf16 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(1024)](buf1,
            primals_2, buf16, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 16), (1, 16), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 16), (256, 64, 16, 1), 0)
        del buf2
        buf15 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(1024)](buf3,
            primals_5, buf15, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_6, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_tanh_1[grid(256)](buf4, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_log_softplus_2[grid(256)](buf5, buf7, buf10, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = torch.ops.aten.normal.Tensor_Tensor(buf6, buf7)
        buf9 = buf8
        del buf8
        buf11 = buf9
        del buf9
        buf12 = buf6
        del buf6
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_div_log_mul_neg_pow_sub_3[grid(256)](buf11,
            buf12, buf7, buf13, buf14, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf7
    return buf11, buf14, buf10, reinterpret_tensor(primals_3, (64, 4), (4, 
        1), 0), reinterpret_tensor(buf1, (64, 16), (16, 1), 0
        ), reinterpret_tensor(buf3, (64, 16), (16, 1), 0
        ), buf4, buf5, buf12, buf13, primals_8, primals_6, buf15, primals_4, buf16


class A2CActorContNew(nn.Module):

    def __init__(self, state_dim, action_dim, action_range):
        super().__init__()
        self.fc1 = nn.Linear(state_dim, 16)
        self.fc2 = nn.Linear(16, 16)
        self.mu_head = nn.Linear(16, action_dim)
        self.sigma_head = nn.Linear(16, action_dim)
        self.action_range = action_range

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.mu_head.weight
        primals_7 = self.mu_head.bias
        primals_8 = self.sigma_head.weight
        primals_9 = self.sigma_head.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1], output[2]
