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_add_view_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x4, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x4, tmp6, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_poi_fused_add_view_0[grid(256)](buf4, primals_5, buf2,
            primals_7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        del primals_5
        del primals_7
    return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0)


class ActorNew(nn.Module):

    def __init__(self, state_size, action_size, action_parameter_size,
        hidden_layers=None, init_std=0.01, init_type='normal', activation=
        'leaky_relu', squashing_function=False):
        super(ActorNew, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.action_parameter_size = action_parameter_size
        self.squashing_function = squashing_function
        assert self.squashing_function is False
        self.activation = activation
        self.layers = nn.ModuleList()
        last_hidden_layer_size = self.state_size
        if hidden_layers is not None:
            nh = len(hidden_layers)
            self.layers.append(nn.Linear(self.state_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.action_output_layer = nn.Linear(last_hidden_layer_size, self.
            action_size)
        self.action_parameters_output_layer = nn.Linear(last_hidden_layer_size,
            self.action_parameter_size)
        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.action_output_layer.weight, std=init_std)
        nn.init.zeros_(self.action_output_layer.bias)
        nn.init.normal_(self.action_parameters_output_layer.weight, std=
            init_std)
        nn.init.zeros_(self.action_parameters_output_layer.bias)
        self.action_parameters_passthrough_layer = nn.Linear(self.
            state_size, self.action_parameter_size)
        nn.init.zeros_(self.action_parameters_passthrough_layer.weight)
        nn.init.zeros_(self.action_parameters_passthrough_layer.bias)
        self.action_parameters_passthrough_layer.weight.requires_grad = False
        self.action_parameters_passthrough_layer.bias.requires_grad = False

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


Measured latency_ms: 0.040014