import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.parameter import Parameter


class NeuralAccumulatorCell(nn.Module):
    """A Neural Accumulator (NAC) cell [1].

    Attributes:
        in_dim: size of the input sample.
        out_dim: size of the output sample.

    Sources:
        [1]: https://arxiv.org/abs/1808.00508
    """

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.W_hat = Parameter(torch.Tensor(out_dim, in_dim))
        self.M_hat = Parameter(torch.Tensor(out_dim, in_dim))
        self.register_parameter('W_hat', self.W_hat)
        self.register_parameter('M_hat', self.M_hat)
        self.register_parameter('bias', None)
        self._reset_params()

    def _reset_params(self):
        init.kaiming_uniform_(self.W_hat)
        init.kaiming_uniform_(self.M_hat)

    def forward(self, input):
        W = torch.tanh(self.W_hat) * torch.sigmoid(self.M_hat)
        return F.linear(input, W, self.bias)

    def extra_repr(self):
        return 'in_dim={}, out_dim={}'.format(self.in_dim, self.out_dim)


class NALUCell(nn.Module):
    """A Neural Arithmetic Logic Unit (NALU) cell [1].

    Attributes:
        in_dim: size of the input sample.
        out_dim: size of the output sample.

    Sources:
        [1]: https://arxiv.org/abs/1808.00508
    """

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.eps = 1e-10
        self.G = Parameter(torch.Tensor(out_dim, in_dim))
        self.nac = NeuralAccumulatorCell(in_dim, out_dim)
        self.register_parameter('bias', None)
        init.kaiming_uniform_(self.G, a=math.sqrt(5))

    def forward(self, input: 'torch.Tensor'):
        a = self.nac(input)
        g = F.linear(input, self.G, self.bias).sigmoid()
        add_sub = g * a
        log_input = (input.abs() + self.eps).log()
        m = self.nac(log_input).exp()
        mul_div = (1 - g) * m
        y = add_sub + mul_div
        return y

    def extra_repr(self):
        return 'in_dim={}, out_dim={}'.format(self.in_dim, self.out_dim)


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


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 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, math as tl_math
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.parameter import Parameter
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_mul_sigmoid_tanh_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_abs_add_log_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = 1e-10
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_rsub_sigmoid_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp3 + tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_tanh_0[grid(16)](primals_1, primals_2,
            buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_abs_add_log_1[grid(256)](primals_3, buf3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf4)
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_rsub_sigmoid_2[grid(256)](buf2, buf1,
            buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf5, primals_1, primals_2, reinterpret_tensor(primals_3, (64, 4
        ), (4, 1), 0), buf1, buf2, reinterpret_tensor(buf3, (64, 4), (4, 1), 0
        ), buf4


class NeuralAccumulatorCell(nn.Module):
    """A Neural Accumulator (NAC) cell [1].

    Attributes:
        in_dim: size of the input sample.
        out_dim: size of the output sample.

    Sources:
        [1]: https://arxiv.org/abs/1808.00508
    """

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.W_hat = Parameter(torch.Tensor(out_dim, in_dim))
        self.M_hat = Parameter(torch.Tensor(out_dim, in_dim))
        self.register_parameter('W_hat', self.W_hat)
        self.register_parameter('M_hat', self.M_hat)
        self.register_parameter('bias', None)
        self._reset_params()

    def _reset_params(self):
        init.kaiming_uniform_(self.W_hat)
        init.kaiming_uniform_(self.M_hat)

    def forward(self, input):
        W = torch.tanh(self.W_hat) * torch.sigmoid(self.M_hat)
        return F.linear(input, W, self.bias)

    def extra_repr(self):
        return 'in_dim={}, out_dim={}'.format(self.in_dim, self.out_dim)


class NALUCellNew(nn.Module):
    """A Neural Arithmetic Logic Unit (NALU) cell [1].

    Attributes:
        in_dim: size of the input sample.
        out_dim: size of the output sample.

    Sources:
        [1]: https://arxiv.org/abs/1808.00508
    """

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.eps = 1e-10
        self.G = Parameter(torch.Tensor(out_dim, in_dim))
        self.nac = NeuralAccumulatorCell(in_dim, out_dim)
        self.register_parameter('bias', None)
        init.kaiming_uniform_(self.G, a=math.sqrt(5))

    def extra_repr(self):
        return 'in_dim={}, out_dim={}'.format(self.in_dim, self.out_dim)

    def forward(self, input_0):
        primals_1 = self.G
        primals_2 = self.nac.W_hat
        primals_4 = self.nac.M_hat
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
