yzhangcs

# -*- coding: utf-8 -*-

import functools
import signal


class timeout(object):
    r"""Context-manager that prevents function execution from timeout.

    Also functions as a decorator.

yzhangcs

Windows Registry Editor Version 5.00

[HKEY_CLASSES_ROOT\Directory\Background\shell\Bash]
@="Bash here"
"Icon"="C:\\Program Files\\WindowsApps\\CanonicalGroupLimited.UbuntuonWindows_2004.2020.424.0_x64__79rhkp1fndgsc\\ubuntu.exe"

[HKEY_CLASSES_ROOT\Directory\Background\shell\Bash\command]
@="C:\\Program Files\\WindowsApps\\CanonicalGroupLimited.UbuntuonWindows_2004.2020.424.0_x64__79rhkp1fndgsc\\ubuntu.exe -c bash"

yzhangcs

# -*- coding: utf-8 -*-

from parser.modules.dropout import SharedDropout

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import PackedSequence

yzhangcs

# -*- coding: utf-8 -*-

import torch
import torch.nn as nn


class Transformer(nn.Module):

    def __init__(self, n_layers, n_heads, n_model, n_embed, n_inner,
                 input_dropout=0.1, attn_dropout=0.1, ffn_dropout=0.1):

yzhangcs

# -*- coding: utf-8 -*-

import torch
from torch.nn.utils.rnn import pad_sequence


@torch.enable_grad()
def crf(scores, mask, target=None, partial=False):
    lens = mask.sum(1)
    total = lens.sum()

yzhangcs

# -*- coding: utf-8 -*-

import argparse
import unicodedata

# FF00-FF5F -> 0020-007E
MAP = {'　': ' ', '！': '!', '＂': '"', '＃': '#', '＄': '$', '％': '%', '＆': '&', 
       '＇': "'", '（': '(', '）': ')', '＊': '*', '＋': '+', '，': ',', '－': '-', 
       '．': '.', '／': '/', 
       '０': '0', '１': '1', '２': '2', '３': '3', '４': '4', '５': '5', '６': '6', 

yzhangcs

# -*- coding: utf-8 -*-

import argparse
import os
import unicodedata

import torch


def ispunct(token):

yzhangcs

def tarjan(tree):
    tree[0] = -1
    # record the search order, i.e., the timestep
    dfn = [-1] * len(tree)
    # record the the smallest timestep in a SCC
    low = [-1] * len(tree)
    # push the visited into the stack
    stack, onstack = [], [False] * len(tree)

    def connect(i, timestep):

yzhangcs

# -*- coding: utf-8 -*-

import torch
import torch.autograd as autograd
import torch.nn as nn


class MatrixTree(nn.Module):

    def __init__(self, *args, **kwargs):

yzhangcs

# -*- coding: utf-8 -*-

import torch
import torch.nn as nn


class GAT(nn.Module):

    def __init__(self, n_input, n_inner, n_layers, alpha=0.1, dropout=0.5):
        super(GAT, self).__init__()