# 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/nc/cnc6a3vkphurm472zdavmn3qnff4lmaezxs63jlllw2kks2e62a4.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %primals_3], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', '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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = (xindex // 12)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 12), (12, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_3, buf0, 48, grid=grid(48), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Q], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4, (12, 1), (1, 12), 0), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
    return (buf2, buf0, )


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((4, 4), (4, 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((1, 12), (12, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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_size, action_size, action_parameter_size,
        hidden_layers=None, action_input_layer=0, init_type='normal',
        activation='leaky_relu', init_std=0.01):
        super(Critic, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.action_parameter_size = action_parameter_size
        self.activation = activation
        self.layers = nn.ModuleList()
        input_size = self.state_size + action_size + action_parameter_size
        last_hidden_layer_size = input_size
        if hidden_layers is not None:
            nh = len(hidden_layers)
            self.layers.append(nn.Linear(input_size, hidden_layers[0]))
            for i in range(1, nh):
                self.layers.append(nn.Linear(hidden_layers[i - 1],
                    hidden_layers[i]))
            last_hidden_layer_size = hidden_layers[nh - 1]
        self.output_layer = nn.Linear(last_hidden_layer_size, 1)
        for i in range(0, len(self.layers)):
            if init_type == 'kaiming':
                nn.init.kaiming_normal_(self.layers[i].weight.data,
                    nonlinearity=self.activation)
            elif init_type == 'normal':
                nn.init.normal_(self.layers[i].weight.data, std=init_std)
            else:
                raise ValueError('Unknown init_type ' + str(init_type))
            nn.init.zeros_(self.layers[i].bias.data)
        nn.init.normal_(self.output_layer.weight, std=init_std)
        nn.init.zeros_(self.output_layer.bias)

    def forward(self, state, actions, action_parameters):
        x = torch.cat((state, actions, action_parameters), dim=1)
        negative_slope = 0.01
        num_hidden_layers = len(self.layers)
        for i in range(0, num_hidden_layers):
            if self.activation == 'relu':
                x = F.relu(self.layers[i](x))
            elif self.activation == 'leaky_relu':
                x = F.leaky_relu(self.layers[i](x), negative_slope)
            else:
                raise ValueError('Unknown activation function ' + str(self.
                    activation))
        Q = self.output_layer(x)
        return Q


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


def get_init_inputs():
    return [[], {'state_size': 4, 'action_size': 4, 'action_parameter_size': 4}
        ]

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


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 12), (12, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](primals_1, primals_2, primals_3,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4,
            (12, 1), (1, 12), 0), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
    return buf2, buf0


class CriticNew(nn.Module):

    def __init__(self, state_size, action_size, action_parameter_size,
        hidden_layers=None, action_input_layer=0, init_type='normal',
        activation='leaky_relu', init_std=0.01):
        super(CriticNew, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.action_parameter_size = action_parameter_size
        self.activation = activation
        self.layers = nn.ModuleList()
        input_size = self.state_size + action_size + action_parameter_size
        last_hidden_layer_size = input_size
        if hidden_layers is not None:
            nh = len(hidden_layers)
            self.layers.append(nn.Linear(input_size, hidden_layers[0]))
            for i in range(1, nh):
                self.layers.append(nn.Linear(hidden_layers[i - 1],
                    hidden_layers[i]))
            last_hidden_layer_size = hidden_layers[nh - 1]
        self.output_layer = nn.Linear(last_hidden_layer_size, 1)
        for i in range(0, len(self.layers)):
            if init_type == 'kaiming':
                nn.init.kaiming_normal_(self.layers[i].weight.data,
                    nonlinearity=self.activation)
            elif init_type == 'normal':
                nn.init.normal_(self.layers[i].weight.data, std=init_std)
            else:
                raise ValueError('Unknown init_type ' + str(init_type))
            nn.init.zeros_(self.layers[i].bias.data)
        nn.init.normal_(self.output_layer.weight, std=init_std)
        nn.init.zeros_(self.output_layer.bias)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.output_layer.weight
        primals_5 = self.output_layer.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
