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
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_relu_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 % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_zeros_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12, 4), (4, 1))
    assert_size_stride(primals_6, (12,), (1,))
    assert_size_stride(primals_7, (12,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused_zeros_1[grid(64)](buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 12), (1, 
            4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (4, 12), (1, 
            4), 0), out=buf4)
        del primals_5
        buf5 = torch.ops.aten._thnn_fused_gru_cell.default(buf3, buf4, buf2,
            primals_6, primals_7)
        del buf3
        del buf4
        del primals_6
        del primals_7
        buf6 = buf5[0]
        buf7 = buf5[1]
        del buf5
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf6, reinterpret_tensor(primals_8,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_9
    return reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf1, buf2, buf6, buf7, primals_8, primals_4


class RNNAgentNew(nn.Module):

    def __init__(self, input_shape, args):
        super(RNNAgentNew, self).__init__()
        self.args = args
        self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
        self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim)
        self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions)

    def init_hidden(self):
        return self.fc1.weight.new(1, self.args.rnn_hidden_dim).zero_()

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.rnn.weight_ih
        primals_5 = self.rnn.weight_hh
        primals_6 = self.rnn.bias_ih
        primals_7 = self.rnn.bias_hh
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.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]


Measured latency_ms: 0.12174