from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd


class LSTMCell(Module):
    """
    ## Long Short-Term Memory Cell

    LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
    and $h$ is like the short term memory.
    We use the input $x$ and $h$ to update the long term memory.
    In the update, some features of $c$ are cleared with a forget gate $f$,
    and some features $i$ are added through a gate $g$.

    The new short term memory is the $	anh$ of the long-term memory
    multiplied by the output gate $o$.

    Note that the cell doesn't look at long term memory $c$ when doing the update. It only modifies it.
    Also $c$ never goes through a linear transformation.
    This is what solves vanishing and exploding gradients.

    Here's the update rule.

    egin{align}
    c_t &= \\sigma(f_t) \\odot c_{t-1} + \\sigma(i_t) \\odot 	anh(g_t) \\
    h_t &= \\sigma(o_t) \\odot 	anh(c_t)
    \\end{align}

    $\\odot$ stands for element-wise multiplication.

    Intermediate values and gates are computed as linear transformations of the hidden
    state and input.

    egin{align}
    i_t &= lin_x^i(x_t) + lin_h^i(h_{t-1}) \\
    f_t &= lin_x^f(x_t) + lin_h^f(h_{t-1}) \\
    g_t &= lin_x^g(x_t) + lin_h^g(h_{t-1}) \\
    o_t &= lin_x^o(x_t) + lin_h^o(h_{t-1})
    \\end{align}

    """

    def __init__(self, input_size: 'int', hidden_size: 'int', layer_norm:
        'bool'=False):
        super().__init__()
        self.hidden_lin = nn.Linear(hidden_size, 4 * hidden_size)
        self.input_lin = nn.Linear(input_size, 4 * hidden_size, bias=False)
        if layer_norm:
            self.layer_norm = nn.ModuleList([nn.LayerNorm(hidden_size) for
                _ in range(4)])
            self.layer_norm_c = nn.LayerNorm(hidden_size)
        else:
            self.layer_norm = nn.ModuleList([nn.Identity() for _ in range(4)])
            self.layer_norm_c = nn.Identity()

    def forward(self, x: 'torch.Tensor', h: 'torch.Tensor', c: 'torch.Tensor'):
        ifgo = self.hidden_lin(h) + self.input_lin(x)
        ifgo = ifgo.chunk(4, dim=-1)
        ifgo = [self.layer_norm[i](ifgo[i]) for i in range(4)]
        i, f, g, o = ifgo
        c_next = torch.sigmoid(f) * c + torch.sigmoid(i) * torch.tanh(g)
        h_next = torch.sigmoid(o) * torch.tanh(self.layer_norm_c(c_next))
        return h_next, c_next


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 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
from torch.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_add_mul_sigmoid_sigmoid_backward_tanh_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3,
    out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp18 = tl.load(in_ptr3 + x2, xmask)
    tmp25 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp26 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tl.sigmoid(tmp16)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.sigmoid(tmp29)
    tmp31 = libdevice.tanh(tmp21)
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp21, xmask)
    tl.store(out_ptr3 + x2, tmp24, xmask)
    tl.store(out_ptr4 + x2, tmp30, xmask)
    tl.store(out_ptr5 + x2, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0[grid(256)](
            buf0, primals_2, buf1, primals_6, buf2, buf3, buf4, buf7, buf5,
            buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_2
    return buf6, buf4, primals_6, reinterpret_tensor(primals_3, (64, 4), (4,
        1), 0), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0
        ), buf2, buf3, buf4, buf5, buf7


class LSTMCellNew(Module):
    """
    ## Long Short-Term Memory Cell

    LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
    and $h$ is like the short term memory.
    We use the input $x$ and $h$ to update the long term memory.
    In the update, some features of $c$ are cleared with a forget gate $f$,
    and some features $i$ are added through a gate $g$.

    The new short term memory is the $	anh$ of the long-term memory
    multiplied by the output gate $o$.

    Note that the cell doesn't look at long term memory $c$ when doing the update. It only modifies it.
    Also $c$ never goes through a linear transformation.
    This is what solves vanishing and exploding gradients.

    Here's the update rule.

    egin{align}
    c_t &= \\sigma(f_t) \\odot c_{t-1} + \\sigma(i_t) \\odot 	anh(g_t) \\
    h_t &= \\sigma(o_t) \\odot 	anh(c_t)
    \\end{align}

    $\\odot$ stands for element-wise multiplication.

    Intermediate values and gates are computed as linear transformations of the hidden
    state and input.

    egin{align}
    i_t &= lin_x^i(x_t) + lin_h^i(h_{t-1}) \\
    f_t &= lin_x^f(x_t) + lin_h^f(h_{t-1}) \\
    g_t &= lin_x^g(x_t) + lin_h^g(h_{t-1}) \\
    o_t &= lin_x^o(x_t) + lin_h^o(h_{t-1})
    \\end{align}

    """

    def __init__(self, input_size: 'int', hidden_size: 'int', layer_norm:
        'bool'=False):
        super().__init__()
        self.hidden_lin = nn.Linear(hidden_size, 4 * hidden_size)
        self.input_lin = nn.Linear(input_size, 4 * hidden_size, bias=False)
        if layer_norm:
            self.layer_norm = nn.ModuleList([nn.LayerNorm(hidden_size) for
                _ in range(4)])
            self.layer_norm_c = nn.LayerNorm(hidden_size)
        else:
            self.layer_norm = nn.ModuleList([nn.Identity() for _ in range(4)])
            self.layer_norm_c = nn.Identity()

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.hidden_lin.weight
        primals_2 = self.hidden_lin.bias
        primals_4 = self.input_lin.weight
        primals_3 = input_0
        primals_5 = input_1
        primals_6 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
