import torch
import torch.nn as nn


class A2CNetwork(nn.Module):

    def __init__(self, input_shape, output_shape, n_features, **kwargs):
        super(A2CNetwork, self).__init__()
        n_input = input_shape[-1]
        n_output = output_shape[0]
        self._h1 = nn.Linear(n_input, n_features)
        self._h2 = nn.Linear(n_features, n_features)
        self._h3 = nn.Linear(n_features, n_output)
        nn.init.xavier_uniform_(self._h1.weight, gain=nn.init.
            calculate_gain('tanh'))
        nn.init.xavier_uniform_(self._h2.weight, gain=nn.init.
            calculate_gain('tanh'))
        nn.init.xavier_uniform_(self._h3.weight, gain=nn.init.
            calculate_gain('linear'))

    def forward(self, state, **kwargs):
        features1 = torch.tanh(self._h1(torch.squeeze(state, 1).float()))
        features2 = torch.tanh(self._h2(features1))
        a = self._h3(features2)
        return a


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'input_shape': [4, 4], 'output_shape': [4, 4],
        'n_features': 4}]

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 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 = 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, (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.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_tanh_0[grid(256)](buf3, primals_5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, buf3, primals_6, primals_4


class A2CNetworkNew(nn.Module):

    def __init__(self, input_shape, output_shape, n_features, **kwargs):
        super(A2CNetworkNew, self).__init__()
        n_input = input_shape[-1]
        n_output = output_shape[0]
        self._h1 = nn.Linear(n_input, n_features)
        self._h2 = nn.Linear(n_features, n_features)
        self._h3 = nn.Linear(n_features, n_output)
        nn.init.xavier_uniform_(self._h1.weight, gain=nn.init.
            calculate_gain('tanh'))
        nn.init.xavier_uniform_(self._h2.weight, gain=nn.init.
            calculate_gain('tanh'))
        nn.init.xavier_uniform_(self._h3.weight, gain=nn.init.
            calculate_gain('linear'))

    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._h3.weight
        primals_7 = self._h3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
