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
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 = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    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)


@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 960
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 15
    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)


@triton.jit
def triton_poi_fused_tanh_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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 = 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (20, 4), (4, 1))
    assert_size_stride(primals_3, (20,), (1,))
    assert_size_stride(primals_4, (15, 20), (20, 1))
    assert_size_stride(primals_5, (15,), (1,))
    assert_size_stride(primals_6, (4, 15), (15, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 20), (320, 80, 20, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(1280)](buf1, primals_3, 1280, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 15), (15, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 20), (20, 1), 0),
            reinterpret_tensor(primals_4, (20, 15), (1, 20), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 15), (240, 60, 15, 1), 0)
        del buf2
        triton_poi_fused_tanh_1[grid(960)](buf3, primals_5, 960, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 15), (15, 1), 0),
            reinterpret_tensor(primals_6, (15, 4), (1, 15), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_tanh_2[grid(256)](buf5, primals_7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
    return buf5, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, buf3, buf5, primals_6, primals_4


class AgentNNNew(torch.nn.Module):
    """ Simple network. """

    def __init__(self, D_in, D_out):
        super(AgentNNNew, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, 20)
        self.h1 = torch.nn.Linear(20, 15)
        self.linear2 = torch.nn.Linear(15, D_out)
        self.activation = torch.nn.Tanh()

    def save_model(self, path: 'str'=None) ->None:
        if not path:
            path = 'agent_model.pt'
        torch.save(self.state_dict(), path)

    def forward(self, input_0):
        primals_2 = self.linear1.weight
        primals_3 = self.linear1.bias
        primals_4 = self.h1.weight
        primals_5 = self.h1.bias
        primals_6 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.061978