# 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/md/cmd3ewacyhu5w5hausgbjbmtnt5rr66cgczh4ibdypq7dz6p4v7g.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [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=[8192], 
    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 = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, 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, None)
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/nz/cnzt4vke3cfy65kpwt7xgkuqbxcqudeqtxuabin636dlu5ix4yh4.py
# Topologically Sorted Source Nodes: [std, mul, action, var, log_scale, sub, pow_2, neg, mul_1, truediv, sub_1, log_prob, real_action, tanh_1, pow_3, sub_3, add_1, log_1, real_log_prob], Original ATen: [aten.softplus, aten.mul, aten.add, aten.pow, aten.log, aten.sub, aten.neg, aten.div, aten.tanh, aten.rsub]
# Source node to ATen node mapping:
#   action => add
#   add_1 => add_1
#   log_1 => log_1
#   log_prob => sub_2
#   log_scale => log
#   mul => mul
#   mul_1 => mul_1
#   neg => neg
#   pow_2 => pow_2
#   pow_3 => pow_3
#   real_action => tanh
#   real_log_prob => sub_4
#   std => exp, gt, log1p, where
#   sub => sub
#   sub_1 => sub_1
#   sub_3 => sub_3
#   tanh_1 => tanh_1
#   truediv => div
#   var => pow_1
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_5,), 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_5, 20), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_5, %log1p), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %where), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %mul), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where, 2), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%where,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %view_3), 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=1] = 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 = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%tanh_1, 2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %pow_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, 1e-07), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %log_1), kwargs = {})
triton_poi_fused_add_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1 = async_compile.triton('triton_poi_fused_add_div_log_mul_neg_pow_rsub_softplus_sub_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=[64], 
    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_add_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1', 'mutated_arg_names': [], '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_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tl.load(in_ptr2 + (x0), xmask)
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp1 * tmp7
    tmp9 = tmp0 + tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = tmp9 - tmp0
    tmp12 = tmp11 * tmp11
    tmp13 = -tmp12
    tmp14 = tmp7 * tmp7
    tmp15 = 2.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 / tmp16
    tmp18 = tl_math.log(tmp7)
    tmp19 = tmp17 - tmp18
    tmp20 = 0.9189385332046727
    tmp21 = tmp19 - tmp20
    tmp22 = tmp10 * tmp10
    tmp23 = 1.0
    tmp24 = tmp23 - tmp22
    tmp25 = 1e-07
    tmp26 = tmp24 + tmp25
    tmp27 = tl_math.log(tmp26)
    tmp28 = tmp21 - tmp27
    tl.store(out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr1 + (x0), tmp28, 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 = args
    args.clear()
    assert_size_stride(primals_1, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 128), (128, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (1, 128), (128, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 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, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0); del buf0  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf11, 8192, grid=grid(8192), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 128), (128, 1), 0), reinterpret_tensor(primals_4, (128, 1), (1, 128), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 128), (128, 1), 0), reinterpret_tensor(primals_6, (128, 1), (1, 128), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf7 = torch.ops.aten.normal_functional.default(buf6)
        buf8 = buf7
        del buf7
        buf9 = buf6; del buf6  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [std, mul, action, var, log_scale, sub, pow_2, neg, mul_1, truediv, sub_1, log_prob, real_action, tanh_1, pow_3, sub_3, add_1, log_1, real_log_prob], Original ATen: [aten.softplus, aten.mul, aten.add, aten.pow, aten.log, aten.sub, aten.neg, aten.div, aten.tanh, aten.rsub]
        triton_poi_fused_add_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1.run(buf3, buf8, buf5, buf9, buf10, 64, grid=grid(64), stream=stream0)
    return (buf9, buf10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 128), (128, 1), 0), buf3, buf5, buf8, buf9, primals_6, primals_4, buf11, )


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((128, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((128, ), (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((1, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = 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])
    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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim
import torch.autograd
from torch.distributions import Normal


class PolicyNet(nn.Module):

    def __init__(self, learning_rate, lr_alpha, init_alpha, target_entropy,
        in_dim):
        self.target_entropy = target_entropy
        super(PolicyNet, self).__init__()
        self.fc1 = nn.Linear(in_dim, 128)
        self.fc_mu = nn.Linear(128, 1)
        self.fc_std = nn.Linear(128, 1)
        self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)
        self.log_alpha = torch.tensor(np.log(init_alpha))
        self.log_alpha.requires_grad = True
        self.log_alpha_optimizer = optim.Adam([self.log_alpha], lr=lr_alpha)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        mu = self.fc_mu(x)
        std = F.softplus(self.fc_std(x))
        dist = Normal(mu, std)
        action = dist.rsample()
        log_prob = dist.log_prob(action)
        real_action = torch.tanh(action)
        real_log_prob = log_prob - torch.log(1 - torch.tanh(action).pow(2) +
            1e-07)
        return real_action, real_log_prob

    def train_net(self, q1, q2, mini_batch):
        s, _, _, _, _ = mini_batch
        a, log_prob = self.forward(s)
        entropy = -self.log_alpha.exp() * log_prob
        q1_val, q2_val = q1(s, a), q2(s, a)
        q1_q2 = torch.cat([q1_val, q2_val], dim=1)
        min_q = torch.min(q1_q2, 1, keepdim=True)[0]
        loss = -min_q - entropy
        self.optimizer.zero_grad()
        loss.mean().backward()
        self.optimizer.step()
        self.log_alpha_optimizer.zero_grad()
        alpha_loss = -(self.log_alpha.exp() * (log_prob + self.
            target_entropy).detach()).mean()
        alpha_loss.backward()
        self.log_alpha_optimizer.step()


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


def get_init_inputs():
    return [[], {'learning_rate': 4, 'lr_alpha': 4, 'init_alpha': 4,
        'target_entropy': 4, 'in_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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.optim
import torch.autograd
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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_add_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp1 * tmp7
    tmp9 = tmp0 + tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = tmp9 - tmp0
    tmp12 = tmp11 * tmp11
    tmp13 = -tmp12
    tmp14 = tmp7 * tmp7
    tmp15 = 2.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 / tmp16
    tmp18 = tl_math.log(tmp7)
    tmp19 = tmp17 - tmp18
    tmp20 = 0.9189385332046727
    tmp21 = tmp19 - tmp20
    tmp22 = tmp10 * tmp10
    tmp23 = 1.0
    tmp24 = tmp23 - tmp22
    tmp25 = 1e-07
    tmp26 = tmp24 + tmp25
    tmp27 = tl_math.log(tmp26)
    tmp28 = tmp21 - tmp27
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 128), (128, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1, 128), (128, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
            primals_2, buf11, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 128),
            (128, 1), 0), reinterpret_tensor(primals_4, (128, 1), (1, 128),
            0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 128),
            (128, 1), 0), reinterpret_tensor(primals_6, (128, 1), (1, 128),
            0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf7 = torch.ops.aten.normal_functional.default(buf6)
        buf8 = buf7
        del buf7
        buf9 = buf6
        del buf6
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_add_div_log_mul_neg_pow_rsub_softplus_sub_tanh_1[grid
            (64)](buf3, buf8, buf5, buf9, buf10, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
    return buf9, buf10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 128), (128, 1), 0
        ), buf3, buf5, buf8, buf9, primals_6, primals_4, buf11


class PolicyNetNew(nn.Module):

    def __init__(self, learning_rate, lr_alpha, init_alpha, target_entropy,
        in_dim):
        self.target_entropy = target_entropy
        super(PolicyNetNew, self).__init__()
        self.fc1 = nn.Linear(in_dim, 128)
        self.fc_mu = nn.Linear(128, 1)
        self.fc_std = nn.Linear(128, 1)
        self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)
        self.log_alpha = torch.tensor(np.log(init_alpha))
        self.log_alpha.requires_grad = True
        self.log_alpha_optimizer = optim.Adam([self.log_alpha], lr=lr_alpha)

    def train_net(self, q1, q2, mini_batch):
        s, _, _, _, _ = mini_batch
        a, log_prob = self.forward(s)
        entropy = -self.log_alpha.exp() * log_prob
        q1_val, q2_val = q1(s, a), q2(s, a)
        q1_q2 = torch.cat([q1_val, q2_val], dim=1)
        min_q = torch.min(q1_q2, 1, keepdim=True)[0]
        loss = -min_q - entropy
        self.optimizer.zero_grad()
        loss.mean().backward()
        self.optimizer.step()
        self.log_alpha_optimizer.zero_grad()
        alpha_loss = -(self.log_alpha.exp() * (log_prob + self.
            target_entropy).detach()).mean()
        alpha_loss.backward()
        self.log_alpha_optimizer.step()

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc_mu.weight
        primals_5 = self.fc_mu.bias
        primals_6 = self.fc_std.weight
        primals_7 = self.fc_std.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
