# 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/wb/cwban6vrsf5bwkkkcwxjqgr3j3df4rtyzenlfm7ijyld6jqf76er.py
# Topologically Sorted Source Nodes: [pi1], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   pi1 => tanh
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_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=[64], 
    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_tanh_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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (16, 4), (4, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (1, 16), (16, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [pi1], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf1, primals_3, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf3)
        del primals_6
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [v1], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_0.run(buf4, primals_7, 64, grid=grid(64), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [values], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf4, reinterpret_tensor(primals_8, (16, 1), (1, 16), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return (buf2, buf6, primals_1, buf1, buf4, primals_8, primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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


def set_init(layers):
    for layer in layers:
        nn.init.normal_(layer.weight, mean=0.0, std=0.1)
        nn.init.constant_(layer.bias, 0.0)


class Net(nn.Module):

    def __init__(self, s_dim, a_dim, hidden=16):
        super(Net, self).__init__()
        self.s_dim = s_dim
        self.a_dim = a_dim
        self.pi1 = nn.Linear(s_dim, hidden)
        self.pi2 = nn.Linear(hidden, a_dim)
        self.v1 = nn.Linear(s_dim, hidden)
        self.v2 = nn.Linear(hidden, 1)
        set_init([self.pi1, self.pi2, self.v1, self.v2])
        self.distribution = torch.distributions.Categorical
        self.initialized = True

    def forward(self, x):
        x = torch.flatten(x, start_dim=1)
        pi1 = torch.tanh(self.pi1(x))
        logits = self.pi2(pi1)
        v1 = torch.tanh(self.v1(x))
        values = self.v2(v1)
        return logits, values

    def choose_action(self, s):
        self.eval()
        logits, _ = self.forward(s)
        prob = F.softmax(logits, dim=1).data
        m = self.distribution(prob)
        return m.sample().numpy()[0]

    def loss_func(self, s, a, v_t):
        self.train()
        logits, values = self.forward(s)
        td = v_t - values
        c_loss = td.pow(2)
        probs = F.softmax(logits, dim=1)
        m = self.distribution(probs)
        exp_v = m.log_prob(a) * td.detach().squeeze()
        a_loss = -exp_v
        total_loss = (c_loss + a_loss).mean()
        return total_loss


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


def get_init_inputs():
    return [[], {'s_dim': 4, 'a_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.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (16, 4), (4, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (1, 16), (16, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_6, (4, 16),
            (1, 4), 0), out=buf3)
        del primals_6
        buf4 = buf3
        del buf3
        triton_poi_fused_tanh_0[grid(64)](buf4, primals_7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf4, reinterpret_tensor(primals_8,
            (16, 1), (1, 16), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return buf2, buf6, primals_1, buf1, buf4, primals_8, primals_4


def set_init(layers):
    for layer in layers:
        nn.init.normal_(layer.weight, mean=0.0, std=0.1)
        nn.init.constant_(layer.bias, 0.0)


class NetNew(nn.Module):

    def __init__(self, s_dim, a_dim, hidden=16):
        super(NetNew, self).__init__()
        self.s_dim = s_dim
        self.a_dim = a_dim
        self.pi1 = nn.Linear(s_dim, hidden)
        self.pi2 = nn.Linear(hidden, a_dim)
        self.v1 = nn.Linear(s_dim, hidden)
        self.v2 = nn.Linear(hidden, 1)
        set_init([self.pi1, self.pi2, self.v1, self.v2])
        self.distribution = torch.distributions.Categorical
        self.initialized = True

    def choose_action(self, s):
        self.eval()
        logits, _ = self.forward(s)
        prob = F.softmax(logits, dim=1).data
        m = self.distribution(prob)
        return m.sample().numpy()[0]

    def loss_func(self, s, a, v_t):
        self.train()
        logits, values = self.forward(s)
        td = v_t - values
        c_loss = td.pow(2)
        probs = F.softmax(logits, dim=1)
        m = self.distribution(probs)
        exp_v = m.log_prob(a) * td.detach().squeeze()
        a_loss = -exp_v
        total_loss = (c_loss + a_loss).mean()
        return total_loss

    def forward(self, input_0):
        primals_2 = self.pi1.weight
        primals_3 = self.pi1.bias
        primals_4 = self.pi2.weight
        primals_5 = self.pi2.bias
        primals_6 = self.v1.weight
        primals_7 = self.v1.bias
        primals_8 = self.v2.weight
        primals_9 = self.v2.bias
        primals_1 = 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]
