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
import numpy as np
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    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, 1))
    assert_size_stride(primals_2, (256, 4), (4, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256, 256), (256, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (4, 256), (256, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 256),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(1024)](buf1, primals_3, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (256, 256), (
            1, 256), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_tanh_0[grid(1024)](buf3, primals_5, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf4)
        del primals_7
    return buf4, primals_1, buf1, buf3, primals_6, primals_4


class TorchDenseNew(nn.Module):

    def __init__(self, state_shape, action_size: 'int'):
        super(TorchDenseNew, self).__init__()
        input_size_flatten = self.num_flat_features(state_shape)
        self.flatten = nn.Flatten(start_dim=1, end_dim=-1)
        self.h1 = nn.Linear(input_size_flatten, 256)
        self.h2 = nn.Linear(256, 256)
        self.out = nn.Linear(256, action_size)

    def num_flat_features(self, x):
        return np.prod(x)

    def forward(self, input_0):
        primals_2 = self.h1.weight
        primals_3 = self.h1.bias
        primals_4 = self.h2.weight
        primals_5 = self.h2.bias
        primals_6 = self.out.weight
        primals_7 = self.out.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: 0.045322