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]


Measured latency_ms: 0.040588