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
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
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_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 36
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 3
    x2 = xindex // 12
    x3 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr0 + (6 + 4 * x1 + (-2 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = -tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tl.where(tmp4, tmp5, tmp12)
    tmp14 = tl.load(in_ptr0 + (4 * x2 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (6 + 4 * x2 + (-2 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = -tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp6, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp14, tmp18)
    tmp20 = tmp13 - tmp19
    tl.store(out_ptr0 + x3, tmp20, xmask)


@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 36
    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 = 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,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((3, 3, 4), (12, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sub_0[grid(36)](primals_1, buf0, 36, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((9, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (9, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = buf1
        del buf1
        triton_poi_fused_tanh_1[grid(36)](buf2, primals_3, 36, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((9, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf2, primals_4, alpha=1, beta=1,
            out=buf3)
        del primals_5
    return reinterpret_tensor(buf3, (3, 3, 4), (12, 4, 1), 0
        ), reinterpret_tensor(buf0, (9, 4), (4, 1), 0
        ), buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0)


class FencepostModuleNew(nn.Module):

    def __init__(self, input_dim, repr_dim, n_labels, disentangle=False,
        label_bias=True, span_bias=False, activation='tanh'):
        super(FencepostModuleNew, self).__init__()
        self.disentangle = disentangle
        self.activation_name = activation
        if activation == 'tanh':
            self.activation = torch.tanh
        elif activation == 'relu':
            self.activation = torch.relu
        elif activation == 'leaky_relu':
            self.activation = torch.nn.LeakyReLU()
        else:
            raise RuntimeError('Unknown activation function: %s' % self.
                activation_name)
        self.label_bias = label_bias
        self.span_bias = span_bias
        self.label_output_mlp = nn.Linear(input_dim, repr_dim, bias=True)
        self.label_output_projection = torch.nn.Parameter(data=torch.Tensor
            (repr_dim, n_labels))
        if self.span_bias:
            self.span_output_mlp = nn.Linear(input_dim, repr_dim, bias=True)
            self.span_output_projection = torch.nn.Parameter(data=torch.
                Tensor(repr_dim, 1))
        if self.label_bias:
            self.output_bias = torch.nn.Parameter(data=torch.Tensor(1,
                n_labels))
        self.initialize_parameters()

    def initialize_parameters(self):
        with torch.no_grad():
            if self.activation_name == 'tanh':
                torch.nn.init.xavier_uniform_(self.label_output_mlp.weight)
            else:
                torch.nn.init.kaiming_normal_(self.label_output_mlp.weight,
                    nonlinearity=self.activation_name)
            torch.nn.init.zeros_(self.label_output_mlp.bias)
            torch.nn.init.xavier_uniform_(self.label_output_projection)
            if self.span_bias:
                if self.activation_name == 'tanh':
                    torch.nn.init.xavier_uniform_(self.span_output_mlp.weight)
                else:
                    torch.nn.init.kaiming_uniform_(self.span_output_mlp.
                        weight, nonlinearity=self.activation_name)
                torch.nn.init.zeros_(self.span_output_mlp.bias)
                torch.nn.init.xavier_uniform_(self.span_output_projection)
            if self.label_bias:
                self.output_bias.fill_(0.0)
    """
        Input dimension is: (n_words + 2,  input_repr)
    """

    def forward(self, input_0):
        primals_1 = self.label_output_projection
        primals_5 = self.output_bias
        primals_2 = self.label_output_mlp.weight
        primals_3 = self.label_output_mlp.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.027876