Data Processing & Result Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from random import random\n",
    "from nltk import word_tokenize,sent_tokenize\n",
    "from torch import nn\n",
    "from torch.autograd import Variable\n",
    "import pandas as pd\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence,pad_packed_sequence\n",
    "import re\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import os\n",
    "# from utils import *\n",
    "from torch.utils.data import DataLoader, Dataset, IterableDataset\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence,pad_packed_sequence\n",
    "# from simple_optimizer import Optimizer\n",
    "import sys\n",
    "# from models import *\n",
    "import argparse\n",
    "from transformers import BertModel, BertConfig\n",
    "# from run import *\n",
    "import matplotlib.pyplot as plt\n",
    "# import allennlp.nn\n",
    "from build_gt_tree import *\n",
    "from transformers import BertTokenizer, BertConfig\n",
    "from evaluation import *\n",
    "import matplotlib.pyplot as plt\n",
    "# from torchsummary import summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RSTDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = []\n",
    "# dir_path = '/ubc/cs/research/nlp/corpora/RST-DT/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0/TRAINING/'\n",
    "dir_path = '/scratch/wenxiao/rst-dt_revised/TRAINING/'\n",
    "files = [fname.split('.')[0] for fname in os.listdir(dir_path) if fname.endswith('.edus')]\n",
    "for file in files:\n",
    "    edu_file = dir_path+'/'+file+'.out.edus'\n",
    "    sent_file = dir_path+'/'+file+'.out'\n",
    "    if not os.path.exists(edu_file):\n",
    "        edu_file = dir_path+'/'+file+'.edus'\n",
    "        sent_file = dir_path+'/'+file\n",
    "    with open(edu_file,'r') as of:\n",
    "        edu_text = of.readlines()\n",
    "    with open(sent_file,'r') as of:\n",
    "        sent_text = of.readlines()\n",
    "    \n",
    "    all_data.append(build_dataset(edu_text,sent_text,file.split('.')[0]))\n",
    "torch.save(all_data,'./train/train.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(edu_text,sent_text,fid):\n",
    "#     all_sents = ' '.join([line.strip() for line in text])\n",
    "    print(fid)\n",
    "    sentences = [s.strip().lower() for s in sent_text if s.strip()!='']\n",
    "    edus=[edu.strip().lower() for edu in edu_text]\n",
    "    edu_sent_mapping=map_edu_sent(edus,sentences)\n",
    "    data={}\n",
    "    data['disco_txt'] = [word_tokenize(edu) for edu in edus]\n",
    "    data['sent_txt'] = [word_tokenize(s) for s in sentences]\n",
    "    data['tgt_list_str']=['Place Holder']\n",
    "    data['tgt_tok_list_list_str'] = [['Place','Holder']]\n",
    "    data['d_labels'] = [[1]+[0]*(len(data['disco_txt'])-1)]\n",
    "    data['labels'] = [[1]+[0]*(len(data['sent_txt'])-1)]\n",
    "    data['disco_dep']=[]\n",
    "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir='/scratch/wenxiao/pretrained_bert/',do_lower_case=True)\n",
    "    src,lengths=get_bert_idxs(tokenizer,edus,edu_sent_mapping)\n",
    "    data['d_span']=get_dspan(lengths, src)\n",
    "#     print(data['d_span'])\n",
    "    data['doc_id']=fid\n",
    "    data['src']=src\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bert_idxs(tokenizer,edus,edu_sent_mapping):\n",
    "\n",
    "    sep_vid = tokenizer.vocab['[SEP]']\n",
    "    cls_vid = tokenizer.vocab['[CLS]']\n",
    "    pad_vid = tokenizer.vocab['[PAD]']\n",
    "    sent_to_edu={}\n",
    "    for edu,sent in enumerate(edu_sent_mapping):\n",
    "        sent_to_edu[sent]=sent_to_edu.get(sent,[])+[edu]\n",
    "    src_subtokens = []\n",
    "    lengths = []\n",
    "    for sent in sent_to_edu.keys():\n",
    "        sent_subtokens = ['[CLS]']\n",
    "        for edu_idx in sent_to_edu[sent]:\n",
    "            tokens = tokenizer.tokenize(edus[edu_idx])\n",
    "            sent_subtokens.extend(tokens)\n",
    "            lengths.append(len(tokens))\n",
    "        sent_subtokens.append('[SEP]')\n",
    "        src_subtokens.append(sent_subtokens)\n",
    "#     src_subtokens = [['[CLS]']+tokenizer.tokenize(' '.join(sent))+['[SEP]'] for sent in src]\n",
    "#     print(src_subtokens)\n",
    "#     tokenizer.tokenize(' '.join(sent)) for sent in src]\n",
    "#     src_subtokens = [['[CLS]']+sent+['[SEP]'] for sent in src]\n",
    "    src_subtoken_idxs = [tokenizer.convert_tokens_to_ids(sent) for sent in src_subtokens]\n",
    "    segs=[]\n",
    "    cur_seg=0\n",
    "    for sent in src_subtoken_idxs:\n",
    "        segs.extend([cur_seg]*len(sent))\n",
    "        cur_seg=1-cur_seg\n",
    "        cur_clss+=(len(sent)-1)\n",
    "    src_subtoken_idxs = [token for sent in src_subtoken_idxs for token in sent]\n",
    "    return src_subtoken_idxs,lengths,segs\n",
    "\n",
    "def get_dspan(edu_lengths, src):\n",
    "    d_span=[]\n",
    "    start_idx =1\n",
    "    for edu_l in edu_lengths:\n",
    "        if src[start_idx]==102:\n",
    "            start_idx+=2\n",
    "        d_span.append((start_idx,start_idx+edu_l))\n",
    "        start_idx = start_idx+edu_l\n",
    "    return d_span  \n",
    "\n",
    "def map_edu_sent(edu_text,sent_text):\n",
    "    edu_sent_mapping = []\n",
    "    cur_sent = ''\n",
    "    cur_sent_idx=0\n",
    "    for edu in edu_text:\n",
    "        cur_sent+=edu\n",
    "        edu_sent_mapping.append(cur_sent_idx)\n",
    "        if cur_sent==sent_text[cur_sent_idx]:\n",
    "            cur_sent=''\n",
    "            cur_sent_idx+=1\n",
    "        else:\n",
    "            cur_sent+=' '\n",
    "\n",
    "    if len(sent_text)-1!=edu_sent_mapping[-1]:\n",
    "        print(cur_sent)\n",
    "        print(sent_text[cur_sent_idx])\n",
    "    return edu_sent_mapping\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###### BUILD DATASET FROM VERTIBI EM PAPER\n",
    "def build_dataset_with_boundaries(edu_text,sentence_boundary,paragraph_boundary,fid):\n",
    "#     all_sents = ' '.join([line.strip() for line in text])\n",
    "    print(fid)\n",
    "    edus=[edu.strip().lower() for edu in edu_text]\n",
    "    assert len(edus)==sentence_boundary[-1][-1]+1\n",
    "    assert len(sentence_boundary)==paragraph_boundary[-1][-1]+1\n",
    "    edu_sent_mapping=[]\n",
    "    for i, (s_start,s_end) in enumerate(sentence_boundary):\n",
    "        edu_sent_mapping+=[i]*(s_end-s_start+1)\n",
    "    data={}\n",
    "    data['disco_txt'] = [word_tokenize(edu) for edu in edus]\n",
    "    data['sent_txt'] = [[token for edu in data['disco_txt'][s_start:s_end+1] for token in edu] for (s_start,s_end) in sentence_boundary]\n",
    "    data['tgt_list_str']=['Place Holder']\n",
    "    data['tgt_tok_list_list_str'] = [['Place','Holder']]\n",
    "    data['d_labels'] = [[1]+[0]*(len(data['disco_txt'])-1)]\n",
    "    data['labels'] = [[1]+[0]*(len(data['sent_txt'])-1)]\n",
    "    data['disco_dep']=[]\n",
    "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir='/scratch/wenxiao/pretrained_bert/',do_lower_case=True)\n",
    "    src,lengths,segs=get_bert_idxs(tokenizer,edus,edu_sent_mapping)\n",
    "    data['d_span']=get_dspan(lengths, src)\n",
    "    data['segs']=segs\n",
    "#     print(data['d_span'])\n",
    "    data['sentence_boundary']= sentence_boundary\n",
    "    data['paragraph_boundary'] = paragraph_boundary\n",
    "    data['doc_id']=fid\n",
    "    data['src']=src\n",
    "    \n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = []\n",
    "# dir_path = '/ubc/cs/research/nlp/corpora/RST-DT/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0/TRAINING/'\n",
    "dir_path = '/ubc/cs/research/nlp/wenxiao/discourse_Constituency_EM/data/rstdt/wsj/test/'\n",
    "files = [fname.split('.')[0] for fname in os.listdir(dir_path) if fname.endswith('.edus.tokens')]\n",
    "for file in files:\n",
    "    edu_file = dir_path+'/'+file+'.edus.tokens'\n",
    "    with open(edu_file,'r') as of:\n",
    "        edu_text = of.readlines()\n",
    "        \n",
    "    s_bndry_file = dir_path+'/'+file+'.sbnds'\n",
    "    with open(s_bndry_file,'r') as of:\n",
    "        lines = of.readlines()\n",
    "        s_bndry=[]\n",
    "        for l in lines:\n",
    "            s=l.split()\n",
    "            s_bndry.append((int(s[0]),int(s[1])))\n",
    "            \n",
    "    p_bndry_file = dir_path+'/'+file+'.pbnds'\n",
    "    with open(p_bndry_file,'r') as of:\n",
    "        lines = of.readlines()\n",
    "        p_bndry=[]\n",
    "        for l in lines:\n",
    "            p=l.split()\n",
    "            p_bndry.append((int(p[0]),int(p[1])))\n",
    "    \n",
    "    all_data.append(build_dataset_with_boundaries(edu_text,s_bndry,p_bndry,file))\n",
    "dir_path = '/ubc/cs/research/nlp/wenxiao/discourse_Constituency_EM/data/rstdt/wsj/train/'\n",
    "files = [fname.split('.')[0] for fname in os.listdir(dir_path) if fname.endswith('.edus.tokens')]\n",
    "for file in files:\n",
    "    edu_file = dir_path+'/'+file+'.edus.tokens'\n",
    "    with open(edu_file,'r') as of:\n",
    "        edu_text = of.readlines()\n",
    "        \n",
    "    s_bndry_file = dir_path+'/'+file+'.sbnds'\n",
    "    with open(s_bndry_file,'r') as of:\n",
    "        lines = of.readlines()\n",
    "        s_bndry=[]\n",
    "        for l in lines:\n",
    "            s=l.split()\n",
    "            s_bndry.append((int(s[0]),int(s[1])))\n",
    "            \n",
    "    p_bndry_file = dir_path+'/'+file+'.pbnds'\n",
    "    with open(p_bndry_file,'r') as of:\n",
    "        lines = of.readlines()\n",
    "        p_bndry=[]\n",
    "        for l in lines:\n",
    "            p=l.split()\n",
    "            p_bndry.append((int(p[0]),int(p[1])))\n",
    "    \n",
    "    all_data.append(build_dataset_with_boundaries(edu_text,s_bndry,p_bndry,file))\n",
    "torch.save(all_data,'./em_dataset/all/all.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(dis_text,fid):\n",
    "#     all_sents = ' '.join([line.strip() for line in text])\n",
    "    edu_sent_mapping,edus,sentences,sentence_boundary,paragraph_boundary = map_edu_sent_para(dis_text)\n",
    "    data={}\n",
    "    data['disco_txt'] = [word_tokenize(edu) for edu in edus]\n",
    "    data['sent_txt'] = [word_tokenize(s) for s in sentences]\n",
    "    data['tgt_list_str']=['Place Holder']\n",
    "    data['tgt_tok_list_list_str'] = [['Place','Holder']]\n",
    "    data['d_labels'] = [[1]+[0]*(len(data['disco_txt'])-1)]\n",
    "    data['labels'] = [[1]+[0]*(len(data['sent_txt'])-1)]\n",
    "    data['disco_dep']=[]\n",
    "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir='/scratch/wenxiao/pretrained_bert/',do_lower_case=True)\n",
    "    src,lengths=get_bert_idxs(tokenizer,edus,edu_sent_mapping)\n",
    "    data['d_span']=get_dspan(lengths, src)\n",
    "#     print(data['d_span'])\n",
    "    data['doc_id']=fid\n",
    "    data['src']=src\n",
    "    data['sentence_boundary']= sentence_boundary\n",
    "    data['paragraph_boundary'] = paragraph_boundary\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bert_idxs(tokenizer,edus,edu_sent_mapping):\n",
    "\n",
    "    sep_vid = tokenizer.vocab['[SEP]']\n",
    "    cls_vid = tokenizer.vocab['[CLS]']\n",
    "    pad_vid = tokenizer.vocab['[PAD]']\n",
    "    sent_to_edu={}\n",
    "    for edu,sent in enumerate(edu_sent_mapping):\n",
    "        sent_to_edu[sent]=sent_to_edu.get(sent,[])+[edu]\n",
    "    src_subtokens = []\n",
    "    lengths = []\n",
    "    for sent in sent_to_edu.keys():\n",
    "        sent_subtokens = ['[CLS]']\n",
    "        for edu_idx in sent_to_edu[sent]:\n",
    "            tokens = tokenizer.tokenize(edus[edu_idx])\n",
    "            sent_subtokens.extend(tokens)\n",
    "            lengths.append(len(tokens))\n",
    "        sent_subtokens.append('[SEP]')\n",
    "        src_subtokens.append(sent_subtokens)\n",
    "#     src_subtokens = [['[CLS]']+tokenizer.tokenize(' '.join(sent))+['[SEP]'] for sent in src]\n",
    "#     print(src_subtokens)\n",
    "#     tokenizer.tokenize(' '.join(sent)) for sent in src]\n",
    "#     src_subtokens = [['[CLS]']+sent+['[SEP]'] for sent in src]\n",
    "    src_subtoken_idxs = [tokenizer.convert_tokens_to_ids(sent) for sent in src_subtokens]\n",
    "    segs=[]\n",
    "    cur_seg=0\n",
    "    for sent in src_subtoken_idxs:\n",
    "        segs.extend([cur_seg]*len(sent))\n",
    "        cur_seg=1-cur_seg\n",
    "#         cur_clss+=(len(sent)-1)\n",
    "    src_subtoken_idxs = [token for sent in src_subtoken_idxs for token in sent]\n",
    "    return src_subtoken_idxs,lengths\n",
    "\n",
    "def get_dspan(edu_lengths, src):\n",
    "    d_span=[]\n",
    "    start_idx =1\n",
    "    for edu_l in edu_lengths:\n",
    "        if src[start_idx]==102:\n",
    "            start_idx+=2\n",
    "        d_span.append((start_idx,start_idx+edu_l))\n",
    "        start_idx = start_idx+edu_l\n",
    "    return d_span  \n",
    "\n",
    "def map_edu_sent_para(dis_text):\n",
    "    edu_sent_mapping = []\n",
    "    edu_text=re.findall(r'\\(text (.*?)\\) \\)', dis_text)\n",
    "    disco_txt=[]\n",
    "    sent_txt=[]\n",
    "    sentence_boundary=[]\n",
    "    cur_sent_start=0\n",
    "    cur_sent_idx=0\n",
    "    cur_sent=''\n",
    "    for i_edu,edu in enumerate(edu_text):\n",
    "        edu_sent_mapping.append(cur_sent_idx)\n",
    "        revised_edu = edu.replace('<EDU>','').replace('</EDU>','').replace('<s>','').replace('</s>','')\n",
    "        disco_txt.append(revised_edu)\n",
    "        cur_sent=cur_sent + ' ' +revised_edu\n",
    "        if '</s>' in edu:\n",
    "            sent_txt.append(cur_sent)\n",
    "            sentence_boundary.append((cur_sent_start,i_edu))\n",
    "            cur_sent_idx+=1\n",
    "            cur_sent=''\n",
    "            cur_sent_start=i_edu+1\n",
    "    all_wrong_leaf = re.findall(r'Root \\(leaf (.*?)\\)', dis_text)\n",
    "    if len(all_wrong_leaf)!=0:\n",
    "        for l in all_wrong_leaf:\n",
    "#             print(l)\n",
    "            dis_text = dis_text.replace('Root (leaf %s)'%(l),'Root (span %s %s)'%(l,l))\n",
    "#             print(dis_text)\n",
    "    all_root = re.findall(r'Root \\(span (.*?)\\)', dis_text)\n",
    "    cur_para_start=0\n",
    "    paragraph_boundary = []\n",
    "    for span in all_root:\n",
    "        span = span.split()\n",
    "        first_id = int(span[0])-1\n",
    "        last_id= int(span[1])-1\n",
    "        if sentence_boundary[cur_para_start][0]!=first_id:\n",
    "            raise 'error on para boundary!!'\n",
    "        end = cur_para_start\n",
    "        while sentence_boundary[end][1]!=last_id:\n",
    "            end+=1\n",
    "            if end>=len(sentence_boundary):\n",
    "                print(sentence_boundary)\n",
    "                print(disco_txt)\n",
    "                print(sent_txt)\n",
    "                raise 'error on para boundary!!'\n",
    "        paragraph_boundary.append((cur_para_start,end))\n",
    "        cur_para_start = end+1\n",
    "    if cur_para_start!=len(sentence_boundary):\n",
    "        raise 'error again.....'\n",
    "    return edu_sent_mapping,disco_txt,sent_txt,sentence_boundary,paragraph_boundary\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sunset2-3-ceramic_21\n",
      "sunset1-paint_22\n",
      "sunset1-prep_2\n",
      "sunset2-3-ceramic_36\n",
      "sunset1-fabric_19\n",
      "sunset2-3-ceramic_15\n",
      "sunset2-5-parquet_11\n",
      "sunset2-4-resil_6\n",
      "sunset1-paint_5\n",
      "sunset2-3-ceramic_6\n",
      "sunset1-panel_8\n",
      "sunset2-3-ceramic_24\n",
      "sunset2-5-parquet_10\n",
      "sunset2-4-resil_3\n",
      "sunset1-paint_11\n",
      "sunset2-3-ceramic_26\n",
      "sunset1-panel_4\n",
      "sunset1-fabric_4\n"
     ]
    }
   ],
   "source": [
    "all_data = []\n",
    "dir_path = '/scratch/wenxiao/instruction_discourse_annotation/'\n",
    "# files = [fname for fname in os.listdir(dir_path) if fname.endswith('.dis')]\n",
    "files = ['sunset2-3-ceramic.21.out.dis',\n",
    "'sunset1-paint.22.out.dis',\n",
    "'sunset1-prep.2.out.dis',\n",
    "'sunset2-3-ceramic.36.out.dis',\n",
    "'sunset1-fabric.19.out.dis',\n",
    "'sunset2-3-ceramic.15.out.dis',\n",
    "'sunset2-5-parquet.11.out.dis',\n",
    "'sunset2-4-resil.6.out.dis',\n",
    "'sunset1-paint.5.out.dis',\n",
    "'sunset2-3-ceramic.6.out.dis',\n",
    "'sunset1-panel.8.out.dis',\n",
    "'sunset2-3-ceramic.24.out.dis',\n",
    "'sunset2-5-parquet.10.out.dis',\n",
    "'sunset2-4-resil.3.out.dis',\n",
    "'sunset1-paint.11.out.dis',\n",
    "'sunset2-3-ceramic.26.out.dis',\n",
    "'sunset1-panel.4.out.dis',\n",
    "'sunset1-fabric.4.out.dis']\n",
    "for file in files:\n",
    "    with open(dir_path+file,'r') as of:\n",
    "        dis_text = of.read()\n",
    "    fid = '%s_%s'%(file.split('.')[0],file.split('.')[1])\n",
    "    print(fid)\n",
    "    all_data.append(build_dataset(dis_text,fid))\n",
    "torch.save(all_data,'./instruction_dataset/test.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GUM dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(edus,sentences,fid):\n",
    "    edus = [edu.strip('\\n') for edu in edus]\n",
    "    sentences = [sent.strip('\\n') for sent in sentences]\n",
    "#     all_sents = ' '.join([line.strip() for line in text])\n",
    "    edu_sent_mapping,sentence_boundary,paragraph_boundary = map_edu_sent_para(edus,sentences)\n",
    "    data={}\n",
    "    data['disco_txt'] = [word_tokenize(edu) for edu in edus]\n",
    "    data['sent_txt'] = [word_tokenize(s) for s in sentences]\n",
    "    data['tgt_list_str']=['Place Holder']\n",
    "    data['tgt_tok_list_list_str'] = [['Place','Holder']]\n",
    "    data['d_labels'] = [[1]+[0]*(len(data['disco_txt'])-1)]\n",
    "    data['labels'] = [[1]+[0]*(len(data['sent_txt'])-1)]\n",
    "    data['disco_dep']=[]\n",
    "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir='/scratch/wenxiao/pretrained_bert/',do_lower_case=True)\n",
    "    src,lengths=get_bert_idxs(tokenizer,edus,edu_sent_mapping)\n",
    "    data['d_span']=get_dspan(lengths, src)\n",
    "#     print(data['d_span'])\n",
    "    data['doc_id']=fid\n",
    "    data['src']=src\n",
    "    data['sentence_boundary']= sentence_boundary\n",
    "    data['paragraph_boundary'] = paragraph_boundary\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bert_idxs(tokenizer,edus,edu_sent_mapping):\n",
    "\n",
    "    sep_vid = tokenizer.vocab['[SEP]']\n",
    "    cls_vid = tokenizer.vocab['[CLS]']\n",
    "    pad_vid = tokenizer.vocab['[PAD]']\n",
    "    sent_to_edu={}\n",
    "    for edu,sent in enumerate(edu_sent_mapping):\n",
    "        sent_to_edu[sent]=sent_to_edu.get(sent,[])+[edu]\n",
    "    src_subtokens = []\n",
    "    lengths = []\n",
    "    for sent in sent_to_edu.keys():\n",
    "        sent_subtokens = ['[CLS]']\n",
    "        for edu_idx in sent_to_edu[sent]:\n",
    "            tokens = tokenizer.tokenize(edus[edu_idx])\n",
    "            sent_subtokens.extend(tokens)\n",
    "            lengths.append(len(tokens))\n",
    "        sent_subtokens.append('[SEP]')\n",
    "        src_subtokens.append(sent_subtokens)\n",
    "#     src_subtokens = [['[CLS]']+tokenizer.tokenize(' '.join(sent))+['[SEP]'] for sent in src]\n",
    "#     print(src_subtokens)\n",
    "#     tokenizer.tokenize(' '.join(sent)) for sent in src]\n",
    "#     src_subtokens = [['[CLS]']+sent+['[SEP]'] for sent in src]\n",
    "    src_subtoken_idxs = [tokenizer.convert_tokens_to_ids(sent) for sent in src_subtokens]\n",
    "    segs=[]\n",
    "    cur_seg=0\n",
    "    for sent in src_subtoken_idxs:\n",
    "        segs.extend([cur_seg]*len(sent))\n",
    "        cur_seg=1-cur_seg\n",
    "#         cur_clss+=(len(sent)-1)\n",
    "    src_subtoken_idxs = [token for sent in src_subtoken_idxs for token in sent]\n",
    "    return src_subtoken_idxs,lengths\n",
    "\n",
    "def get_dspan(edu_lengths, src):\n",
    "    d_span=[]\n",
    "    start_idx =1\n",
    "    for edu_l in edu_lengths:\n",
    "        if src[start_idx]==102:\n",
    "            start_idx+=2\n",
    "        d_span.append((start_idx,start_idx+edu_l))\n",
    "        start_idx = start_idx+edu_l\n",
    "    return d_span  \n",
    "\n",
    "def map_edu_sent_para(edu_text,sent_text):\n",
    "    edu_sent_mapping = []\n",
    "    cur_sent = ''\n",
    "    cur_sent_idx=0\n",
    "    cur_sent_start_idx=0\n",
    "    sentence_boundary=[]\n",
    "    for i_edu,edu in enumerate(edu_text):\n",
    "        cur_sent+=edu\n",
    "        edu_sent_mapping.append(cur_sent_idx)\n",
    "        if cur_sent==sent_text[cur_sent_idx]:\n",
    "            sentence_boundary.append((cur_sent_start_idx,i_edu))\n",
    "            cur_sent=''\n",
    "            cur_sent_idx+=1\n",
    "            cur_sent_start_idx=i_edu+1\n",
    "        else:\n",
    "            cur_sent+=' '\n",
    "\n",
    "    if len(sent_text)-1!=edu_sent_mapping[-1]:\n",
    "        print(cur_sent)\n",
    "        print(sent_text[cur_sent_idx])\n",
    "        raise 'errorrrrr'\n",
    "    paragraph_boundary=[(0,len(sent_text)-1)]\n",
    "    \n",
    "    return edu_sent_mapping,sentence_boundary,paragraph_boundary\n",
    "        \n",
    "    edu_sent_mapping = []\n",
    "    edu_text=re.findall(r'\\(text _\\!(.*?)\\)_\\! \\)', dis_text)\n",
    "    disco_txt=[]\n",
    "    sent_txt=[]\n",
    "    sentence_boundary=[]\n",
    "    cur_sent_start=0\n",
    "    cur_sent_idx=0\n",
    "    cur_sent=''\n",
    "    for i_edu,edu in enumerate(edu_text):\n",
    "        edu_sent_mapping.append(cur_sent_idx)\n",
    "        revised_edu = edu.replace('_!','').replace('<s>','').replace('</s>','')\n",
    "        disco_txt.append(revised_edu)\n",
    "        cur_sent=cur_sent + ' ' +revised_edu\n",
    "        if '</s>' in edu:\n",
    "            sent_txt.append(cur_sent)\n",
    "            sentence_boundary.append((cur_sent_start,i_edu))\n",
    "            cur_sent_idx+=1\n",
    "            cur_sent=''\n",
    "            cur_sent_start=i_edu+1\n",
    "    all_wrong_leaf = re.findall(r'Root \\(leaf (.*?)\\)', dis_text)\n",
    "    if len(all_wrong_leaf)!=0:\n",
    "        for l in all_wrong_leaf:\n",
    "#             print(l)\n",
    "            dis_text = dis_text.replace('Root (leaf %s)'%(l),'Root (span %s %s)'%(l,l))\n",
    "#             print(dis_text)\n",
    "    all_root = re.findall(r'Root \\(span (.*?)\\)', dis_text)\n",
    "    cur_para_start=0\n",
    "    paragraph_boundary = []\n",
    "    for span in all_root:\n",
    "        span = span.split()\n",
    "        first_id = int(span[0])-1\n",
    "        last_id= int(span[1])-1\n",
    "        if sentence_boundary[cur_para_start][0]!=first_id:\n",
    "            raise 'error on para boundary!!'\n",
    "        end = cur_para_start\n",
    "        while sentence_boundary[end][1]!=last_id:\n",
    "            end+=1\n",
    "            if end>=len(sentence_boundary):\n",
    "                print(sentence_boundary)\n",
    "                print(disco_txt)\n",
    "                print(sent_txt)\n",
    "                raise 'error on para boundary!!'\n",
    "        paragraph_boundary.append((cur_para_start,end))\n",
    "        cur_para_start = end+1\n",
    "    if cur_para_start!=len(sentence_boundary):\n",
    "        raise 'error again.....'\n",
    "    return edu_sent_mapping,disco_txt,sent_txt,sentence_boundary,paragraph_boundary\n",
    "\n",
    "def revise_sentence_file(sent_text):\n",
    "    sent_text=sent_text.replace('-LRB-','(')\n",
    "    sent_text=sent_text.replace('-RRB-',')')\n",
    "    sent_text=sent_text.replace('&amp;','&')\n",
    "    return sent_text.split('\\n')\n",
    "\n",
    "def revise_edu_file(edu_text):\n",
    "    edu_text=edu_text.replace('&amp;','&')\n",
    "    edu_text=edu_text.replace('&lt;','<')\n",
    "    return edu_text.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = []\n",
    "dir_path = '/scratch/wenxiao/GUM_binary/'\n",
    "files = [fname for fname in os.listdir(dir_path) if fname.endswith('.out')]\n",
    "for file in files:\n",
    "    with open(dir_path+file,'r') as of:\n",
    "        sent_txt = of.read()\n",
    "    with open(dir_path+file+'.edus','r') as of:\n",
    "        edu_txt = of.read()\n",
    "    fid = file.split('.')[0]\n",
    "    if fid=='GUM_voyage_fortlee':\n",
    "        continue\n",
    "    print(fid)\n",
    "    sent_txt=revise_sentence_file(sent_txt)\n",
    "    edu_txt=revise_edu_file(edu_txt)\n",
    "    all_data.append(build_dataset(edu_txt,sent_txt,fid))\n",
    "torch.save(all_data,'./GUM_binary/all.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build ground-truth trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def const_to_dep_tree_li14(node):\n",
    "    const_leaves = get_leaf_nodes(node)\n",
    "    dep_tree={}\n",
    "    for leaf in const_leaves:\n",
    "        print(leaf.start_idx)\n",
    "        print(leaf.end_idx)\n",
    "        p = find_my_top_node(leaf)\n",
    "        print(p)\n",
    "        if p.nuclearity=='Root':\n",
    "            root = leaf.start_idx\n",
    "        else:\n",
    "            \n",
    "            head = find_head_edu(p.parent)\n",
    "\n",
    "            if head not in dep_tree:\n",
    "                dep_tree[head]=[]\n",
    "            dep_tree[head].append(leaf.start_idx)\n",
    "#     print(dep_tree)\n",
    "    return dep_tree,root\n",
    "\n",
    "def find_my_top_node(e):\n",
    "    C=e\n",
    "    p = C.parent\n",
    "    nucleus_child = [p.children[i] for i in range(len(p.children)) if p.children[i].nuclearity=='Nucleus']\n",
    "    print(nucleus_child)\n",
    "    while nucleus_child[0]==C and not p.nuclearity=='Root':\n",
    "        C = p\n",
    "        p = C.parent\n",
    "        # print(p.idx)\n",
    "        nucleus_child = [p.children[i] for i in range(len(p.children)) if p.children[i].nuclearity=='Nucleus']\n",
    "    if p.nuclearity=='Root'and nucleus_child[0]==C:\n",
    "        C=p\n",
    "    return C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_preorder(node_list,start,end,num_nonbinary=0):\n",
    "    if start>end:\n",
    "        return\n",
    "\n",
    "    node = node_list[start]\n",
    "    \n",
    "    node_index_tupel = node[0]\n",
    "    node_index_str = str(node[0])\n",
    "    \n",
    "    node_nuclearity = node[1]\n",
    "    tmp_node = Node(idx = node_index_str, nuclearity = node_nuclearity,start=node_index_tupel[0],end=node_index_tupel[1])\n",
    "    if start==end:\n",
    "        return tmp_node,num_nonbinary\n",
    "    i=start+1\n",
    "    splits = []\n",
    "    splits.append(i)\n",
    "    while i<=end:\n",
    "        if node_list[i][0][0]>node_list[splits[-1]][0][1]:\n",
    "            splits.append(i)\n",
    "            if node_index_tupel[1]==node_list[i][0][1]:\n",
    "                break\n",
    "        i+=1\n",
    "    end_idx = node_index_tupel[1]\n",
    "    splits.append(end)   \n",
    "#     print(splits)\n",
    "    right,num_nonbinary=construct_preorder(node_list,splits[-2],splits[-1],num_nonbinary)\n",
    "    if len(splits)>3:\n",
    "        while len(splits)>3:\n",
    "            print('rb')\n",
    "            num_nonbinary +=1\n",
    "#             node_index_tupel = [node_list[splits[-3]][0][0],node_list[splits[-1]][0][1]]\n",
    "            node_index_tupel = [node_list[splits[-3]][0][0],end_idx]\n",
    "            node_index_str = str(node_index_tupel)\n",
    "            if 'Nucleus' in [node_list[splits[-3]][1],node_list[splits[-2]][1]]:\n",
    "                node_nuclearity = 'Nucleus'\n",
    "            else:\n",
    "                node_nuclearity = 'Satellite'\n",
    "            # print(node_index_tupel)\n",
    "            tmp_tmp_node = Node(idx = node_index_str, nuclearity = node_nuclearity,start=node_index_tupel[0],end=node_index_tupel[1])\n",
    "            \n",
    "            left,num_nonbinary = construct_preorder(node_list,splits[-3],splits[-2]-1,num_nonbinary)\n",
    "            if left!=None:\n",
    "                tmp_tmp_node.add_child(left,branch = 0)\n",
    "            if right!=None:\n",
    "                tmp_tmp_node.add_child(right,branch = 1)\n",
    "            right = tmp_tmp_node\n",
    "            del splits[-1]            \n",
    "    left,num_nonbinary = construct_preorder(node_list,splits[-3],splits[-2]-1,num_nonbinary)\n",
    "    if left!=None:\n",
    "        tmp_node.add_child(left,branch = 0)\n",
    "    if right!=None:\n",
    "        tmp_node.add_child(right,branch = 1)\n",
    "    return tmp_node,num_nonbinary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_dir_list=['/scratch/wenxiao/rst-dt_revised/ALL/', \\\n",
    "                '/scratch/wenxiao/instruction_discourse_annotation/',\\\n",
    "                '/scratch/wenxiao/GUM_binary/']\n",
    "\n",
    "# tree_dir_list=['/scratch/wenxiao/rst-dt_revised/ALL/']\n",
    "\n",
    "dataset_list=['rstdt-full','instructional','GUM']\n",
    "# dataset_list=['rstdt-full']\n",
    "for i_data in range(len(tree_dir_list[:1])):\n",
    "    savedtrees = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset_list[i_data]))\n",
    "    dir_path = tree_dir_list[i_data]\n",
    "    gt_tree_dict = {}\n",
    "    files = [f for f in os.listdir(dir_path) if f.endswith('.dis')]\n",
    "    num_data = len(files)\n",
    "    num_edus_total = 0\n",
    "    num_sentences_total = 0\n",
    "    length_doc_total = 0\n",
    "    for f in files:\n",
    "        if f=='GUM_voyage_fortlee.out.dis':\n",
    "            gt_tree_dict['GUM_voyage_fortlee']=[]\n",
    "            continue\n",
    "        file = dir_path+f\n",
    "        with open(file,'r') as of:\n",
    "            text = of.readlines()\n",
    "        text=revise_instruction(file)\n",
    "        list_nodes=convert_gt(text)\n",
    "        root,num_nonbinary=construct_preorder(list_nodes,0,len(list_nodes)-1)\n",
    "        const_tree_gt=return_tree_postorder(root)\n",
    "        dep_tree_gt,dep_root=const_to_dep_tree_li14(root)\n",
    "        \n",
    "        if i_data ==1:\n",
    "            fid = '%s_%s'%(f.split('.')[0],f.split('.')[1])\n",
    "        else:\n",
    "            if f == \"file1.dis\":\n",
    "                fid= \"wsj_0764\"\n",
    "            elif f == \"file2.dis\":\n",
    "                fid =  \"wsj_0430\"\n",
    "            elif f == \"file3.dis\":\n",
    "                fid =  \"wsj_0766\"\n",
    "            elif f == \"file4.dis\":\n",
    "                fid =  \"wsj_0778\"\n",
    "            elif f == \"file5.dis\":\n",
    "                fid =  \"wsj_2172\"\n",
    "            else:\n",
    "                fid =f.split('.')[0]\n",
    "        gt_tree_dict[fid]={'const_tree':const_tree_gt,'dep_tree':dep_tree_gt,'dep_root':dep_root}\n",
    "        print(dep_tree_gt)\n",
    "#     save_path='/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset_list[i_data])\n",
    "#     torch.save(gt_tree_dict,save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list):\n",
    "\tcorrect_all=0\n",
    "\ttotal_all=0\n",
    "\ttotal_macro=0\n",
    "\tfor i in range(len(dep_tree_gt_list)):\n",
    "\t\tcorrect,total=unlabeled_attachment_score(dep_tree_gt_list[i],dep_tree_cand_list[i])\n",
    "\t\tcorrect_all+=correct\n",
    "\t\ttotal_all+=total\n",
    "\t\ttotal_macro+=(correct/total)\n",
    "\tmicro_f1 = correct_all/total_all\n",
    "\tmacro_f1 = total_macro/len(dep_tree_gt_list)\n",
    "# \tprint('correct num: ',correct_all)\n",
    "# \tprint('total_num: ',total_all)\n",
    "# \tprint('micro f1: ',micro_f1)\n",
    "# \tprint('macro f1: ', macro_f1)\n",
    "\treturn micro_f1,macro_f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct num:  689\n",
      "total_num:  21404\n",
      "micro f1:  3.219024481405345\n",
      "macro f1:  6.1604377101101875\n",
      "correct num:  1545\n",
      "total_num:  21404\n",
      "micro f1:  7.218276957578023\n",
      "macro f1:  13.202230225019282\n",
      "correct num:  1052\n",
      "total_num:  21404\n",
      "micro f1:  4.914969164642123\n",
      "macro f1:  8.108697713090239\n",
      "correct num:  820\n",
      "total_num:  21404\n",
      "micro f1:  3.831059615025229\n",
      "macro f1:  9.57917793286025\n",
      "correct num:  539\n",
      "total_num:  21404\n",
      "micro f1:  2.5182208932909735\n",
      "macro f1:  6.183961831763682\n",
      "correct num:  1065\n",
      "total_num:  21404\n",
      "micro f1:  4.975705475612035\n",
      "macro f1:  12.216071830595096\n",
      "correct num:  1337\n",
      "total_num:  21404\n",
      "micro f1:  6.246495982059428\n",
      "macro f1:  11.279922862254251\n",
      "correct num:  705\n",
      "total_num:  21404\n",
      "micro f1:  3.2937768641375444\n",
      "macro f1:  5.601182306842524\n",
      "correct num:  1275\n",
      "total_num:  21404\n",
      "micro f1:  5.956830498972155\n",
      "macro f1:  10.20923773185731\n",
      "correct num:  6102\n",
      "total_num:  21404\n",
      "micro f1:  28.508689964492618\n",
      "macro f1:  36.256614625319976\n",
      "correct num:  3107\n",
      "total_num:  21404\n",
      "micro f1:  14.515978321809008\n",
      "macro f1:  14.300761896510597\n",
      "correct num:  185\n",
      "total_num:  21404\n",
      "micro f1:  0.8643244253410577\n",
      "macro f1:  3.163196004254308\n",
      "correct num:  564\n",
      "total_num:  21404\n",
      "micro f1:  2.6350214913100354\n",
      "macro f1:  4.964578718238342\n",
      "correct num:  516\n",
      "total_num:  21404\n",
      "micro f1:  2.4107643431134367\n",
      "macro f1:  5.533660753416248\n",
      "correct num:  332\n",
      "total_num:  21404\n",
      "micro f1:  1.5511119416931416\n",
      "macro f1:  6.122931533082222\n",
      "correct num:  256\n",
      "total_num:  21404\n",
      "micro f1:  1.1960381237151934\n",
      "macro f1:  3.3699361497903273\n",
      "correct num:  198\n",
      "total_num:  21404\n",
      "micro f1:  0.92506073631097\n",
      "macro f1:  3.5296676168605585\n",
      "correct num:  211\n",
      "total_num:  21404\n",
      "micro f1:  0.985797047280882\n",
      "macro f1:  2.534954716486198\n",
      "correct num:  232\n",
      "total_num:  21404\n",
      "micro f1:  1.083909549616894\n",
      "macro f1:  2.8718399882649153\n",
      "correct num:  505\n",
      "total_num:  21404\n",
      "micro f1:  2.3593720799850497\n",
      "macro f1:  6.222147036530911\n",
      "correct num:  286\n",
      "total_num:  21404\n",
      "micro f1:  1.3361988413380677\n",
      "macro f1:  3.804826856433121\n",
      "correct num:  1012\n",
      "total_num:  21404\n",
      "micro f1:  4.7280882078116235\n",
      "macro f1:  10.986828785844406\n",
      "correct num:  348\n",
      "total_num:  21404\n",
      "micro f1:  1.6258643244253408\n",
      "macro f1:  3.250405370525728\n",
      "correct num:  371\n",
      "total_num:  21404\n",
      "micro f1:  1.7333208746028779\n",
      "macro f1:  5.741185867888998\n",
      "correct num:  550\n",
      "total_num:  21404\n",
      "micro f1:  2.569613156419361\n",
      "macro f1:  7.65186051566185\n",
      "correct num:  179\n",
      "total_num:  21404\n",
      "micro f1:  0.836292281816483\n",
      "macro f1:  1.6986687041086277\n",
      "correct num:  339\n",
      "total_num:  21404\n",
      "micro f1:  1.5838161091384788\n",
      "macro f1:  2.311569393317962\n",
      "correct num:  380\n",
      "total_num:  21404\n",
      "micro f1:  1.7753690898897403\n",
      "macro f1:  3.1918141336125827\n",
      "correct num:  568\n",
      "total_num:  21404\n",
      "micro f1:  2.6537095869930853\n",
      "macro f1:  5.305656461287372\n",
      "correct num:  279\n",
      "total_num:  21404\n",
      "micro f1:  1.3034946738927304\n",
      "macro f1:  3.4814719928624247\n",
      "correct num:  116\n",
      "total_num:  21404\n",
      "micro f1:  0.541954774808447\n",
      "macro f1:  1.4440643087457992\n",
      "correct num:  433\n",
      "total_num:  21404\n",
      "micro f1:  2.0229863576901512\n",
      "macro f1:  5.536755661876533\n",
      "correct num:  944\n",
      "total_num:  21404\n",
      "micro f1:  4.410390581199776\n",
      "macro f1:  12.138401371765022\n",
      "correct num:  965\n",
      "total_num:  21404\n",
      "micro f1:  4.508503083535788\n",
      "macro f1:  12.394940349420336\n",
      "correct num:  414\n",
      "total_num:  21404\n",
      "micro f1:  1.9342179031956643\n",
      "macro f1:  8.18756573390802\n",
      "correct num:  727\n",
      "total_num:  21404\n",
      "micro f1:  3.396561390394319\n",
      "macro f1:  7.236383736841162\n",
      "correct num:  772\n",
      "total_num:  21404\n",
      "micro f1:  3.60680246682863\n",
      "macro f1:  6.065227848045144\n",
      "correct num:  210\n",
      "total_num:  21404\n",
      "micro f1:  0.9811250233601196\n",
      "macro f1:  3.9064900632142368\n",
      "correct num:  976\n",
      "total_num:  21404\n",
      "micro f1:  4.559895346664175\n",
      "macro f1:  12.073806530910963\n",
      "correct num:  935\n",
      "total_num:  21404\n",
      "micro f1:  4.3683423659129135\n",
      "macro f1:  11.094933681996896\n",
      "correct num:  186\n",
      "total_num:  21404\n",
      "micro f1:  0.8689964492618203\n",
      "macro f1:  4.96629721853147\n",
      "correct num:  863\n",
      "total_num:  21404\n",
      "micro f1:  4.031956643618016\n",
      "macro f1:  8.95310482129002\n",
      "correct num:  854\n",
      "total_num:  21404\n",
      "micro f1:  3.989908428331153\n",
      "macro f1:  10.62244087200713\n",
      "correct num:  963\n",
      "total_num:  21404\n",
      "micro f1:  4.499159035694262\n",
      "macro f1:  9.963775878858705\n",
      "correct num:  595\n",
      "total_num:  21404\n",
      "micro f1:  2.779854232853672\n",
      "macro f1:  5.870509069427599\n",
      "correct num:  201\n",
      "total_num:  21404\n",
      "micro f1:  0.9390768080732574\n",
      "macro f1:  1.5811923605284166\n",
      "correct num:  345\n",
      "total_num:  21404\n",
      "micro f1:  1.6118482526630538\n",
      "macro f1:  4.763773869727688\n",
      "correct num:  341\n",
      "total_num:  21404\n",
      "micro f1:  1.5931601569800038\n",
      "macro f1:  4.646154653653729\n",
      "max: 28.508690, min: 0.541955, avg:3.541686\n",
      "correct num:  4342\n",
      "total_num:  21404\n",
      "micro f1:  20.285927863950665\n",
      "macro f1:  21.739207825202026\n",
      "correct num:  5263\n",
      "total_num:  21404\n",
      "micro f1:  24.5888618949729\n",
      "macro f1:  29.19425376243735\n",
      "correct num:  3612\n",
      "total_num:  21404\n",
      "micro f1:  16.875350401794055\n",
      "macro f1:  19.383414124177744\n",
      "correct num:  3990\n",
      "total_num:  21404\n",
      "micro f1:  18.64137544384227\n",
      "macro f1:  21.516801130934628\n",
      "correct num:  3737\n",
      "total_num:  21404\n",
      "micro f1:  17.45935339188937\n",
      "macro f1:  18.80954482306899\n",
      "correct num:  4916\n",
      "total_num:  21404\n",
      "micro f1:  22.967669594468322\n",
      "macro f1:  27.263634778642277\n",
      "correct num:  3816\n",
      "total_num:  21404\n",
      "micro f1:  17.828443281629603\n",
      "macro f1:  19.653664166647182\n",
      "correct num:  3628\n",
      "total_num:  21404\n",
      "micro f1:  16.95010278452626\n",
      "macro f1:  17.92537722109625\n",
      "correct num:  3170\n",
      "total_num:  21404\n",
      "micro f1:  14.810315828817044\n",
      "macro f1:  15.573675547841015\n",
      "correct num:  7575\n",
      "total_num:  21404\n",
      "micro f1:  35.390581199775745\n",
      "macro f1:  39.085997112787425\n",
      "correct num:  5521\n",
      "total_num:  21404\n",
      "micro f1:  25.79424406652962\n",
      "macro f1:  24.26088873854069\n",
      "correct num:  3957\n",
      "total_num:  21404\n",
      "micro f1:  18.48719865445711\n",
      "macro f1:  19.0135551098224\n",
      "correct num:  4236\n",
      "total_num:  21404\n",
      "micro f1:  19.79069332834984\n",
      "macro f1:  20.092567850590225\n",
      "correct num:  4571\n",
      "total_num:  21404\n",
      "micro f1:  21.35582134180527\n",
      "macro f1:  22.590102635220703\n",
      "correct num:  4187\n",
      "total_num:  21404\n",
      "micro f1:  19.56176415623248\n",
      "macro f1:  22.492138459529663\n",
      "correct num:  3685\n",
      "total_num:  21404\n",
      "micro f1:  17.21640814800972\n",
      "macro f1:  17.277497714239743\n",
      "correct num:  4140\n",
      "total_num:  21404\n",
      "micro f1:  19.342179031956643\n",
      "macro f1:  20.177941123671623\n",
      "correct num:  3711\n",
      "total_num:  21404\n",
      "micro f1:  17.337880769949543\n",
      "macro f1:  17.68258221082888\n",
      "correct num:  3908\n",
      "total_num:  21404\n",
      "micro f1:  18.25826948233975\n",
      "macro f1:  18.68511725544921\n",
      "correct num:  5108\n",
      "total_num:  21404\n",
      "micro f1:  23.864698187254717\n",
      "macro f1:  24.109298305134896\n",
      "correct num:  4988\n",
      "total_num:  21404\n",
      "micro f1:  23.304055316763222\n",
      "macro f1:  23.610812472542825\n",
      "correct num:  4557\n",
      "total_num:  21404\n",
      "micro f1:  21.290413006914598\n",
      "macro f1:  25.26032084770431\n",
      "correct num:  4391\n",
      "total_num:  21404\n",
      "micro f1:  20.514857036068022\n",
      "macro f1:  21.068067452861648\n",
      "correct num:  4220\n",
      "total_num:  21404\n",
      "micro f1:  19.715940945617643\n",
      "macro f1:  21.64737357521147\n",
      "correct num:  3525\n",
      "total_num:  21404\n",
      "micro f1:  16.46888432068772\n",
      "macro f1:  21.22148563970318\n",
      "correct num:  3035\n",
      "total_num:  21404\n",
      "micro f1:  14.179592599514109\n",
      "macro f1:  14.243091060499463\n",
      "correct num:  4600\n",
      "total_num:  21404\n",
      "micro f1:  21.491310035507382\n",
      "macro f1:  18.32333516566596\n",
      "correct num:  4064\n",
      "total_num:  21404\n",
      "micro f1:  18.987105213978694\n",
      "macro f1:  18.595706485080125\n",
      "correct num:  4710\n",
      "total_num:  21404\n",
      "micro f1:  22.005232666791255\n",
      "macro f1:  21.584757437760942\n",
      "correct num:  4483\n",
      "total_num:  21404\n",
      "micro f1:  20.94468323677817\n",
      "macro f1:  19.675281453978226\n",
      "correct num:  3110\n",
      "total_num:  21404\n",
      "micro f1:  14.529994393571293\n",
      "macro f1:  12.971656527934494\n",
      "correct num:  4204\n",
      "total_num:  21404\n",
      "micro f1:  19.641188562885443\n",
      "macro f1:  20.257916508341328\n",
      "correct num:  3652\n",
      "total_num:  21404\n",
      "micro f1:  17.062231358624555\n",
      "macro f1:  23.757033012830338\n",
      "correct num:  3643\n",
      "total_num:  21404\n",
      "micro f1:  17.020183143337693\n",
      "macro f1:  22.793928164359688\n",
      "correct num:  2751\n",
      "total_num:  21404\n",
      "micro f1:  12.852737806017567\n",
      "macro f1:  19.005337938877627\n",
      "correct num:  4064\n",
      "total_num:  21404\n",
      "micro f1:  18.987105213978694\n",
      "macro f1:  21.00507829529622\n",
      "correct num:  3965\n",
      "total_num:  21404\n",
      "micro f1:  18.52457484582321\n",
      "macro f1:  20.94507371590054\n",
      "correct num:  3142\n",
      "total_num:  21404\n",
      "micro f1:  14.679499159035695\n",
      "macro f1:  17.051767752854477\n",
      "correct num:  3950\n",
      "total_num:  21404\n",
      "micro f1:  18.454494487011775\n",
      "macro f1:  24.932229429806778\n",
      "correct num:  3697\n",
      "total_num:  21404\n",
      "micro f1:  17.272472435058866\n",
      "macro f1:  22.342051545595375\n",
      "correct num:  3300\n",
      "total_num:  21404\n",
      "micro f1:  15.417678938516167\n",
      "macro f1:  18.55525823609396\n",
      "correct num:  4045\n",
      "total_num:  21404\n",
      "micro f1:  18.89833675948421\n",
      "macro f1:  23.145612641557182\n",
      "correct num:  4997\n",
      "total_num:  21404\n",
      "micro f1:  23.346103532050083\n",
      "macro f1:  27.873275769958205\n",
      "correct num:  3980\n",
      "total_num:  21404\n",
      "micro f1:  18.59465520463465\n",
      "macro f1:  22.457866223014435\n",
      "correct num:  5482\n",
      "total_num:  21404\n",
      "micro f1:  25.61203513361988\n",
      "macro f1:  26.40366926998081\n",
      "correct num:  2874\n",
      "total_num:  21404\n",
      "micro f1:  13.42739674827135\n",
      "macro f1:  12.046428363410577\n",
      "correct num:  3520\n",
      "total_num:  21404\n",
      "micro f1:  16.44552420108391\n",
      "macro f1:  17.8751494380399\n",
      "correct num:  3053\n",
      "total_num:  21404\n",
      "micro f1:  14.263689030087834\n",
      "macro f1:  18.166505460104567\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max: 35.390581, min: 12.852738, avg:19.182065\n",
      "correct num:  201\n",
      "total_num:  5578\n",
      "micro f1:  3.60344209394048\n",
      "macro f1:  5.058347831384409\n",
      "correct num:  419\n",
      "total_num:  5578\n",
      "micro f1:  7.511652922194336\n",
      "macro f1:  12.785652912441863\n",
      "correct num:  221\n",
      "total_num:  5578\n",
      "micro f1:  3.961993546073862\n",
      "macro f1:  6.929914248639002\n",
      "correct num:  317\n",
      "total_num:  5578\n",
      "micro f1:  5.683040516314091\n",
      "macro f1:  13.265576536355567\n",
      "correct num:  156\n",
      "total_num:  5578\n",
      "micro f1:  2.7967013266403726\n",
      "macro f1:  5.073238006984666\n",
      "correct num:  391\n",
      "total_num:  5578\n",
      "micro f1:  7.0096808892076\n",
      "macro f1:  15.519291087216272\n",
      "correct num:  369\n",
      "total_num:  5578\n",
      "micro f1:  6.615274291860882\n",
      "macro f1:  11.751372713055506\n",
      "correct num:  250\n",
      "total_num:  5578\n",
      "micro f1:  4.481893151667264\n",
      "macro f1:  7.632351157843428\n",
      "correct num:  231\n",
      "total_num:  5578\n",
      "micro f1:  4.141269272140552\n",
      "macro f1:  6.701002612452371\n",
      "correct num:  1926\n",
      "total_num:  5578\n",
      "micro f1:  34.528504840444604\n",
      "macro f1:  40.554679333713594\n",
      "correct num:  832\n",
      "total_num:  5578\n",
      "micro f1:  14.915740408748658\n",
      "macro f1:  14.844119720297485\n",
      "correct num:  73\n",
      "total_num:  5578\n",
      "micro f1:  1.3087128002868411\n",
      "macro f1:  3.6544606445180436\n",
      "correct num:  169\n",
      "total_num:  5578\n",
      "micro f1:  3.0297597705270705\n",
      "macro f1:  5.824715105866931\n",
      "correct num:  261\n",
      "total_num:  5578\n",
      "micro f1:  4.679096450340624\n",
      "macro f1:  9.794310865493165\n",
      "correct num:  162\n",
      "total_num:  5578\n",
      "micro f1:  2.904266762280387\n",
      "macro f1:  8.469311239405924\n",
      "correct num:  195\n",
      "total_num:  5578\n",
      "micro f1:  3.495876658300466\n",
      "macro f1:  8.684852180276485\n",
      "correct num:  95\n",
      "total_num:  5578\n",
      "micro f1:  1.7031193976335604\n",
      "macro f1:  4.3760055729489435\n",
      "correct num:  101\n",
      "total_num:  5578\n",
      "micro f1:  1.8106848332735748\n",
      "macro f1:  4.594902787094117\n",
      "correct num:  105\n",
      "total_num:  5578\n",
      "micro f1:  1.882395123700251\n",
      "macro f1:  4.235883346433786\n",
      "correct num:  156\n",
      "total_num:  5578\n",
      "micro f1:  2.7967013266403726\n",
      "macro f1:  5.736102115056436\n",
      "correct num:  80\n",
      "total_num:  5578\n",
      "micro f1:  1.4342058085335245\n",
      "macro f1:  4.757266532978976\n",
      "correct num:  351\n",
      "total_num:  5578\n",
      "micro f1:  6.29257798494084\n",
      "macro f1:  11.926022835753432\n",
      "correct num:  184\n",
      "total_num:  5578\n",
      "micro f1:  3.2986733596271063\n",
      "macro f1:  9.296629293572582\n",
      "correct num:  125\n",
      "total_num:  5578\n",
      "micro f1:  2.240946575833632\n",
      "macro f1:  7.517276833828378\n",
      "correct num:  216\n",
      "total_num:  5578\n",
      "micro f1:  3.872355683040516\n",
      "macro f1:  8.676076975792858\n",
      "correct num:  110\n",
      "total_num:  5578\n",
      "micro f1:  1.9720329867335962\n",
      "macro f1:  4.917044685516206\n",
      "correct num:  183\n",
      "total_num:  5578\n",
      "micro f1:  3.2807457870204373\n",
      "macro f1:  6.33808957368725\n",
      "correct num:  165\n",
      "total_num:  5578\n",
      "micro f1:  2.958049480100394\n",
      "macro f1:  6.465169548148185\n",
      "correct num:  183\n",
      "total_num:  5578\n",
      "micro f1:  3.2807457870204373\n",
      "macro f1:  6.729721508183243\n",
      "correct num:  127\n",
      "total_num:  5578\n",
      "micro f1:  2.2768017210469704\n",
      "macro f1:  5.105855144357523\n",
      "correct num:  129\n",
      "total_num:  5578\n",
      "micro f1:  2.3126568662603084\n",
      "macro f1:  6.015137478642351\n",
      "correct num:  206\n",
      "total_num:  5578\n",
      "micro f1:  3.6930799569738255\n",
      "macro f1:  7.795405540754236\n",
      "correct num:  219\n",
      "total_num:  5578\n",
      "micro f1:  3.926138400860524\n",
      "macro f1:  9.106128850933365\n",
      "correct num:  230\n",
      "total_num:  5578\n",
      "micro f1:  4.123341699533883\n",
      "macro f1:  9.688116429495002\n",
      "correct num:  141\n",
      "total_num:  5578\n",
      "micro f1:  2.527787737540337\n",
      "macro f1:  7.617964577535958\n",
      "correct num:  249\n",
      "total_num:  5578\n",
      "micro f1:  4.463965579060595\n",
      "macro f1:  9.598675130572907\n",
      "correct num:  184\n",
      "total_num:  5578\n",
      "micro f1:  3.2986733596271063\n",
      "macro f1:  6.9235654589793025\n",
      "correct num:  114\n",
      "total_num:  5578\n",
      "micro f1:  2.0437432771602726\n",
      "macro f1:  5.826781070196039\n",
      "correct num:  224\n",
      "total_num:  5578\n",
      "micro f1:  4.015776263893868\n",
      "macro f1:  9.90878740327983\n",
      "correct num:  224\n",
      "total_num:  5578\n",
      "micro f1:  4.015776263893868\n",
      "macro f1:  8.954299148888744\n",
      "correct num:  122\n",
      "total_num:  5578\n",
      "micro f1:  2.187163858013625\n",
      "macro f1:  6.793270689151175\n",
      "correct num:  223\n",
      "total_num:  5578\n",
      "micro f1:  3.9978486912871993\n",
      "macro f1:  8.10471290355509\n",
      "correct num:  202\n",
      "total_num:  5578\n",
      "micro f1:  3.6213696665471495\n",
      "macro f1:  9.015518171200657\n",
      "correct num:  209\n",
      "total_num:  5578\n",
      "micro f1:  3.7468626747938325\n",
      "macro f1:  7.858867288437918\n",
      "correct num:  173\n",
      "total_num:  5578\n",
      "micro f1:  3.101470060953747\n",
      "macro f1:  7.824888344298435\n",
      "correct num:  134\n",
      "total_num:  5578\n",
      "micro f1:  2.4022947292936534\n",
      "macro f1:  6.31383306783217\n",
      "correct num:  166\n",
      "total_num:  5578\n",
      "micro f1:  2.9759770527070635\n",
      "macro f1:  7.6453173616387415\n",
      "correct num:  203\n",
      "total_num:  5578\n",
      "micro f1:  3.6392972391538185\n",
      "macro f1:  7.646821467281619\n",
      "max: 34.528505, min: 1.308713, avg:4.372087\n",
      "correct num:  987\n",
      "total_num:  5578\n",
      "micro f1:  17.69451416278236\n",
      "macro f1:  17.968609872874737\n",
      "correct num:  1287\n",
      "total_num:  5578\n",
      "micro f1:  23.072785944783075\n",
      "macro f1:  28.148508026198005\n",
      "correct num:  837\n",
      "total_num:  5578\n",
      "micro f1:  15.005378271782\n",
      "macro f1:  16.20743107475986\n",
      "correct num:  963\n",
      "total_num:  5578\n",
      "micro f1:  17.264252420222302\n",
      "macro f1:  21.914464193241876\n",
      "correct num:  939\n",
      "total_num:  5578\n",
      "micro f1:  16.833990677662243\n",
      "macro f1:  16.902007345483014\n",
      "correct num:  1134\n",
      "total_num:  5578\n",
      "micro f1:  20.32986733596271\n",
      "macro f1:  26.690739342959947\n",
      "correct num:  937\n",
      "total_num:  5578\n",
      "micro f1:  16.798135532448907\n",
      "macro f1:  20.21446865537459\n",
      "correct num:  922\n",
      "total_num:  5578\n",
      "micro f1:  16.529221943348873\n",
      "macro f1:  18.01527740360706\n",
      "correct num:  569\n",
      "total_num:  5578\n",
      "micro f1:  10.200788813194693\n",
      "macro f1:  10.110228815090073\n",
      "correct num:  2322\n",
      "total_num:  5578\n",
      "micro f1:  41.627823592685544\n",
      "macro f1:  45.85864925017411\n",
      "correct num:  1398\n",
      "total_num:  5578\n",
      "micro f1:  25.06274650412334\n",
      "macro f1:  24.64689809044183\n",
      "correct num:  895\n",
      "total_num:  5578\n",
      "micro f1:  16.045177482968807\n",
      "macro f1:  16.97935629426735\n",
      "correct num:  954\n",
      "total_num:  5578\n",
      "micro f1:  17.10290426676228\n",
      "macro f1:  18.032828042749216\n",
      "correct num:  1049\n",
      "total_num:  5578\n",
      "micro f1:  18.80602366439584\n",
      "macro f1:  21.296990961129318\n",
      "correct num:  972\n",
      "total_num:  5578\n",
      "micro f1:  17.425600573682324\n",
      "macro f1:  20.509963253624306\n",
      "correct num:  1082\n",
      "total_num:  5578\n",
      "micro f1:  19.39763356041592\n",
      "macro f1:  23.375875132175185\n",
      "correct num:  1048\n",
      "total_num:  5578\n",
      "micro f1:  18.78809609178917\n",
      "macro f1:  20.231701769361262\n",
      "correct num:  890\n",
      "total_num:  5578\n",
      "micro f1:  15.955539619935461\n",
      "macro f1:  17.128708797144252\n",
      "correct num:  906\n",
      "total_num:  5578\n",
      "micro f1:  16.242380781642165\n",
      "macro f1:  17.26435618881722\n",
      "correct num:  962\n",
      "total_num:  5578\n",
      "micro f1:  17.246324847615632\n",
      "macro f1:  17.115804509721\n",
      "correct num:  1040\n",
      "total_num:  5578\n",
      "micro f1:  18.64467551093582\n",
      "macro f1:  19.629935265581892\n",
      "correct num:  1191\n",
      "total_num:  5578\n",
      "micro f1:  21.351738974542847\n",
      "macro f1:  24.828422895826996\n",
      "correct num:  1055\n",
      "total_num:  5578\n",
      "micro f1:  18.913589100035853\n",
      "macro f1:  22.644782861752425\n",
      "correct num:  1043\n",
      "total_num:  5578\n",
      "micro f1:  18.698458228755825\n",
      "macro f1:  21.533492887148615\n",
      "correct num:  910\n",
      "total_num:  5578\n",
      "micro f1:  16.31409107206884\n",
      "macro f1:  19.534974417283628\n",
      "correct num:  854\n",
      "total_num:  5578\n",
      "micro f1:  15.310147006095375\n",
      "macro f1:  17.177278061249492\n",
      "correct num:  1009\n",
      "total_num:  5578\n",
      "micro f1:  18.088920760129078\n",
      "macro f1:  19.12128274382656\n",
      "correct num:  986\n",
      "total_num:  5578\n",
      "micro f1:  17.676586590175692\n",
      "macro f1:  20.702533915030052\n",
      "correct num:  1030\n",
      "total_num:  5578\n",
      "micro f1:  18.46539978486913\n",
      "macro f1:  19.819303720945765\n",
      "correct num:  1040\n",
      "total_num:  5578\n",
      "micro f1:  18.64467551093582\n",
      "macro f1:  18.117800622188014\n",
      "correct num:  897\n",
      "total_num:  5578\n",
      "micro f1:  16.081032628182143\n",
      "macro f1:  18.868747619941068\n",
      "correct num:  1041\n",
      "total_num:  5578\n",
      "micro f1:  18.66260308354249\n",
      "macro f1:  20.07026130976943\n",
      "correct num:  1039\n",
      "total_num:  5578\n",
      "micro f1:  18.62674793832915\n",
      "macro f1:  22.68767568770463\n",
      "correct num:  968\n",
      "total_num:  5578\n",
      "micro f1:  17.353890283255645\n",
      "macro f1:  22.298191115911216\n",
      "correct num:  828\n",
      "total_num:  5578\n",
      "micro f1:  14.84403011832198\n",
      "macro f1:  19.900778217775226\n",
      "correct num:  1006\n",
      "total_num:  5578\n",
      "micro f1:  18.03513804230907\n",
      "macro f1:  21.735072958856545\n",
      "correct num:  1055\n",
      "total_num:  5578\n",
      "micro f1:  18.913589100035853\n",
      "macro f1:  20.60376176271435\n",
      "correct num:  1003\n",
      "total_num:  5578\n",
      "micro f1:  17.981355324489066\n",
      "macro f1:  19.487412670743932\n",
      "correct num:  990\n",
      "total_num:  5578\n",
      "micro f1:  17.748296880602364\n",
      "macro f1:  22.894819652077246\n",
      "correct num:  1016\n",
      "total_num:  5578\n",
      "micro f1:  18.214413768375763\n",
      "macro f1:  22.55179731649953\n",
      "correct num:  949\n",
      "total_num:  5578\n",
      "micro f1:  17.013266403728935\n",
      "macro f1:  19.208287876586443\n",
      "correct num:  1057\n",
      "total_num:  5578\n",
      "micro f1:  18.949444245249193\n",
      "macro f1:  21.29671829374375\n",
      "correct num:  1079\n",
      "total_num:  5578\n",
      "micro f1:  19.343850842595913\n",
      "macro f1:  23.499937946601097\n",
      "correct num:  999\n",
      "total_num:  5578\n",
      "micro f1:  17.90964503406239\n",
      "macro f1:  21.07267311108269\n",
      "correct num:  1242\n",
      "total_num:  5578\n",
      "micro f1:  22.26604517748297\n",
      "macro f1:  24.927982931425916\n",
      "correct num:  875\n",
      "total_num:  5578\n",
      "micro f1:  15.686626030835424\n",
      "macro f1:  18.306450442207613\n",
      "correct num:  874\n",
      "total_num:  5578\n",
      "micro f1:  15.668698458228755\n",
      "macro f1:  18.99552685584269\n",
      "correct num:  901\n",
      "total_num:  5578\n",
      "micro f1:  16.15274291860882\n",
      "macro f1:  19.337884413992974\n",
      "max: 41.627824, min: 10.200789, avg:18.312268\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct num:  262\n",
      "total_num:  13443\n",
      "micro f1:  1.948969724019936\n",
      "macro f1:  2.325555584961286\n",
      "correct num:  612\n",
      "total_num:  13443\n",
      "micro f1:  4.552555233206874\n",
      "macro f1:  5.421160029557177\n",
      "correct num:  463\n",
      "total_num:  13443\n",
      "micro f1:  3.4441716878672914\n",
      "macro f1:  3.869723615705785\n",
      "correct num:  291\n",
      "total_num:  13443\n",
      "micro f1:  2.1646953804954254\n",
      "macro f1:  2.5111240133987667\n",
      "correct num:  194\n",
      "total_num:  13443\n",
      "micro f1:  1.4431302536636168\n",
      "macro f1:  1.6338739762946424\n",
      "correct num:  395\n",
      "total_num:  13443\n",
      "micro f1:  2.9383322175109723\n",
      "macro f1:  3.3503502193073924\n",
      "correct num:  540\n",
      "total_num:  13443\n",
      "micro f1:  4.016960499888418\n",
      "macro f1:  4.656928985173921\n",
      "correct num:  299\n",
      "total_num:  13443\n",
      "micro f1:  2.224205906419698\n",
      "macro f1:  2.54350694182956\n",
      "correct num:  527\n",
      "total_num:  13443\n",
      "micro f1:  3.9202558952614743\n",
      "macro f1:  4.492767515735944\n",
      "correct num:  3164\n",
      "total_num:  13443\n",
      "micro f1:  23.536413003049912\n",
      "macro f1:  26.336995418736453\n",
      "correct num:  1827\n",
      "total_num:  13443\n",
      "micro f1:  13.590716357955813\n",
      "macro f1:  14.892667403867154\n",
      "correct num:  60\n",
      "total_num:  13443\n",
      "micro f1:  0.44632894443204646\n",
      "macro f1:  0.43385139264917416\n",
      "correct num:  265\n",
      "total_num:  13443\n",
      "micro f1:  1.9712861712415382\n",
      "macro f1:  2.2047030620742465\n",
      "correct num:  140\n",
      "total_num:  13443\n",
      "micro f1:  1.0414342036747748\n",
      "macro f1:  1.3885161126373573\n",
      "correct num:  130\n",
      "total_num:  13443\n",
      "micro f1:  0.9670460462694338\n",
      "macro f1:  1.0812877823375286\n",
      "correct num:  91\n",
      "total_num:  13443\n",
      "micro f1:  0.6769322323886038\n",
      "macro f1:  0.7515182150418744\n",
      "correct num:  88\n",
      "total_num:  13443\n",
      "micro f1:  0.6546157851670015\n",
      "macro f1:  0.8377710789794395\n",
      "correct num:  96\n",
      "total_num:  13443\n",
      "micro f1:  0.7141263110912742\n",
      "macro f1:  1.119230406425956\n",
      "correct num:  91\n",
      "total_num:  13443\n",
      "micro f1:  0.6769322323886038\n",
      "macro f1:  0.8602813043590921\n",
      "correct num:  142\n",
      "total_num:  13443\n",
      "micro f1:  1.0563118351558431\n",
      "macro f1:  1.0910258803248034\n",
      "correct num:  68\n",
      "total_num:  13443\n",
      "micro f1:  0.5058394703563193\n",
      "macro f1:  0.7223805411127538\n",
      "correct num:  245\n",
      "total_num:  13443\n",
      "micro f1:  1.8225098564308564\n",
      "macro f1:  2.2758234391976364\n",
      "correct num:  152\n",
      "total_num:  13443\n",
      "micro f1:  1.1306999925611843\n",
      "macro f1:  1.4763256711880346\n",
      "correct num:  197\n",
      "total_num:  13443\n",
      "micro f1:  1.465446700885219\n",
      "macro f1:  1.650244550879013\n",
      "correct num:  168\n",
      "total_num:  13443\n",
      "micro f1:  1.24972104440973\n",
      "macro f1:  1.4425021447378368\n",
      "correct num:  82\n",
      "total_num:  13443\n",
      "micro f1:  0.6099828907237967\n",
      "macro f1:  0.7201796984617539\n",
      "correct num:  133\n",
      "total_num:  13443\n",
      "micro f1:  0.9893624934910363\n",
      "macro f1:  1.0593160024528514\n",
      "correct num:  148\n",
      "total_num:  13443\n",
      "micro f1:  1.100944729599048\n",
      "macro f1:  1.2371925134372246\n",
      "correct num:  198\n",
      "total_num:  13443\n",
      "micro f1:  1.472885516625753\n",
      "macro f1:  1.7680721334330602\n",
      "correct num:  132\n",
      "total_num:  13443\n",
      "micro f1:  0.981923677750502\n",
      "macro f1:  1.1321814925266085\n",
      "correct num:  65\n",
      "total_num:  13443\n",
      "micro f1:  0.4835230231347169\n",
      "macro f1:  0.6473452451696761\n",
      "correct num:  149\n",
      "total_num:  13443\n",
      "micro f1:  1.1083835453395818\n",
      "macro f1:  1.3465267677211907\n",
      "correct num:  194\n",
      "total_num:  13443\n",
      "micro f1:  1.4431302536636168\n",
      "macro f1:  2.2233479130350586\n",
      "correct num:  212\n",
      "total_num:  13443\n",
      "micro f1:  1.5770289369932307\n",
      "macro f1:  1.9506104170599863\n",
      "correct num:  57\n",
      "total_num:  13443\n",
      "micro f1:  0.4240124972104441\n",
      "macro f1:  0.5612278004106611\n",
      "correct num:  215\n",
      "total_num:  13443\n",
      "micro f1:  1.599345384214833\n",
      "macro f1:  2.108591084207786\n",
      "correct num:  200\n",
      "total_num:  13443\n",
      "micro f1:  1.4877631481068214\n",
      "macro f1:  2.1047402606620293\n",
      "correct num:  85\n",
      "total_num:  13443\n",
      "micro f1:  0.632299337945399\n",
      "macro f1:  0.7902883708221617\n",
      "correct num:  231\n",
      "total_num:  13443\n",
      "micro f1:  1.7183664360633788\n",
      "macro f1:  2.5438492038995624\n",
      "correct num:  178\n",
      "total_num:  13443\n",
      "micro f1:  1.3241092018150709\n",
      "macro f1:  2.0407036455782523\n",
      "correct num:  28\n",
      "total_num:  13443\n",
      "micro f1:  0.208286840734955\n",
      "macro f1:  0.28351707786216956\n",
      "correct num:  219\n",
      "total_num:  13443\n",
      "micro f1:  1.6291006471769693\n",
      "macro f1:  1.9676555072076278\n",
      "correct num:  207\n",
      "total_num:  13443\n",
      "micro f1:  1.53983485829056\n",
      "macro f1:  1.777418224277786\n",
      "correct num:  203\n",
      "total_num:  13443\n",
      "micro f1:  1.5100795953284236\n",
      "macro f1:  2.1710261216309488\n",
      "correct num:  189\n",
      "total_num:  13443\n",
      "micro f1:  1.4059361749609462\n",
      "macro f1:  1.7848992343019998\n",
      "correct num:  98\n",
      "total_num:  13443\n",
      "micro f1:  0.7290039425723425\n",
      "macro f1:  0.8626781545497364\n",
      "correct num:  144\n",
      "total_num:  13443\n",
      "micro f1:  1.0711894666369115\n",
      "macro f1:  1.179606563224334\n",
      "correct num:  177\n",
      "total_num:  13443\n",
      "micro f1:  1.316670386074537\n",
      "macro f1:  1.4702363987605973\n",
      "max: 23.536413, min: 0.208287, avg:2.177558\n",
      "correct num:  2534\n",
      "total_num:  13443\n",
      "micro f1:  18.849959086513426\n",
      "macro f1:  18.94265668711012\n",
      "correct num:  2967\n",
      "total_num:  13443\n",
      "micro f1:  22.070966302164695\n",
      "macro f1:  22.59640140287294\n",
      "correct num:  1952\n",
      "total_num:  13443\n",
      "micro f1:  14.520568325522575\n",
      "macro f1:  14.660230520327545\n",
      "correct num:  2185\n",
      "total_num:  13443\n",
      "micro f1:  16.253812393067022\n",
      "macro f1:  16.321657280658926\n",
      "correct num:  2257\n",
      "total_num:  13443\n",
      "micro f1:  16.78940712638548\n",
      "macro f1:  16.841671299426604\n",
      "correct num:  2716\n",
      "total_num:  13443\n",
      "micro f1:  20.203823551290633\n",
      "macro f1:  20.738075308207183\n",
      "correct num:  2178\n",
      "total_num:  13443\n",
      "micro f1:  16.201740682883283\n",
      "macro f1:  16.31533569012588\n",
      "correct num:  1937\n",
      "total_num:  13443\n",
      "micro f1:  14.408986089414565\n",
      "macro f1:  14.595339984376304\n",
      "correct num:  1971\n",
      "total_num:  13443\n",
      "micro f1:  14.661905824592726\n",
      "macro f1:  14.397245267640283\n",
      "correct num:  4322\n",
      "total_num:  13443\n",
      "micro f1:  32.150561630588406\n",
      "macro f1:  33.77311624039929\n",
      "correct num:  3194\n",
      "total_num:  13443\n",
      "micro f1:  23.75957747526594\n",
      "macro f1:  24.302138487278643\n",
      "correct num:  2334\n",
      "total_num:  13443\n",
      "micro f1:  17.362195938406604\n",
      "macro f1:  17.422826453284944\n",
      "correct num:  2555\n",
      "total_num:  13443\n",
      "micro f1:  19.006174217064643\n",
      "macro f1:  18.87609092898624\n",
      "correct num:  2420\n",
      "total_num:  13443\n",
      "micro f1:  18.00193409209254\n",
      "macro f1:  18.634628790905268\n",
      "correct num:  2363\n",
      "total_num:  13443\n",
      "micro f1:  17.577921594882095\n",
      "macro f1:  17.812023653059697\n",
      "correct num:  2257\n",
      "total_num:  13443\n",
      "micro f1:  16.78940712638548\n",
      "macro f1:  16.88374704204029\n",
      "correct num:  2348\n",
      "total_num:  13443\n",
      "micro f1:  17.466339358774082\n",
      "macro f1:  17.58740591914817\n",
      "correct num:  2255\n",
      "total_num:  13443\n",
      "micro f1:  16.77452949490441\n",
      "macro f1:  16.96823137922721\n",
      "correct num:  2188\n",
      "total_num:  13443\n",
      "micro f1:  16.276128840288624\n",
      "macro f1:  16.51287893570876\n",
      "correct num:  2847\n",
      "total_num:  13443\n",
      "micro f1:  21.178308413300602\n",
      "macro f1:  21.123147961811135\n",
      "correct num:  2720\n",
      "total_num:  13443\n",
      "micro f1:  20.23357881425277\n",
      "macro f1:  20.673743860755927\n",
      "correct num:  2417\n",
      "total_num:  13443\n",
      "micro f1:  17.979617644870938\n",
      "macro f1:  18.467929372616297\n",
      "correct num:  2480\n",
      "total_num:  13443\n",
      "micro f1:  18.448263036524583\n",
      "macro f1:  18.833043724362838\n",
      "correct num:  2597\n",
      "total_num:  13443\n",
      "micro f1:  19.318604478167074\n",
      "macro f1:  19.29839318714673\n",
      "correct num:  2015\n",
      "total_num:  13443\n",
      "micro f1:  14.989213717176225\n",
      "macro f1:  14.949523742893092\n",
      "correct num:  1880\n",
      "total_num:  13443\n",
      "micro f1:  13.984973592204122\n",
      "macro f1:  13.687791320433243\n",
      "correct num:  2796\n",
      "total_num:  13443\n",
      "micro f1:  20.79892881053336\n",
      "macro f1:  20.922874654212148\n",
      "correct num:  2494\n",
      "total_num:  13443\n",
      "micro f1:  18.55240645689206\n",
      "macro f1:  18.917143197459723\n",
      "correct num:  2796\n",
      "total_num:  13443\n",
      "micro f1:  20.79892881053336\n",
      "macro f1:  20.845154940975863\n",
      "correct num:  2775\n",
      "total_num:  13443\n",
      "micro f1:  20.642713679982148\n",
      "macro f1:  20.785186197060373\n",
      "correct num:  2065\n",
      "total_num:  13443\n",
      "micro f1:  15.361154504202931\n",
      "macro f1:  15.25994136271195\n",
      "correct num:  2570\n",
      "total_num:  13443\n",
      "micro f1:  19.117756453172653\n",
      "macro f1:  19.169035128926996\n",
      "correct num:  1874\n",
      "total_num:  13443\n",
      "micro f1:  13.940340697760917\n",
      "macro f1:  14.508741538000935\n",
      "correct num:  1937\n",
      "total_num:  13443\n",
      "micro f1:  14.408986089414565\n",
      "macro f1:  14.78407606217539\n",
      "correct num:  1501\n",
      "total_num:  13443\n",
      "micro f1:  11.165662426541695\n",
      "macro f1:  10.819182711557888\n",
      "correct num:  2324\n",
      "total_num:  13443\n",
      "micro f1:  17.287807781001266\n",
      "macro f1:  17.696140179108383\n",
      "correct num:  1974\n",
      "total_num:  13443\n",
      "micro f1:  14.684222271814326\n",
      "macro f1:  15.311214420695277\n",
      "correct num:  1925\n",
      "total_num:  13443\n",
      "micro f1:  14.319720300528155\n",
      "macro f1:  14.142876540686359\n",
      "correct num:  1964\n",
      "total_num:  13443\n",
      "micro f1:  14.609834114408986\n",
      "macro f1:  15.11424116866399\n",
      "correct num:  1780\n",
      "total_num:  13443\n",
      "micro f1:  13.24109201815071\n",
      "macro f1:  13.942543630194246\n",
      "correct num:  1941\n",
      "total_num:  13443\n",
      "micro f1:  14.438741352376702\n",
      "macro f1:  14.19706789846268\n",
      "correct num:  2070\n",
      "total_num:  13443\n",
      "micro f1:  15.398348582905601\n",
      "macro f1:  15.857604467767858\n",
      "correct num:  2489\n",
      "total_num:  13443\n",
      "micro f1:  18.515212378189393\n",
      "macro f1:  18.558445830455224\n",
      "correct num:  2029\n",
      "total_num:  13443\n",
      "micro f1:  15.093357137543704\n",
      "macro f1:  15.305282565345543\n",
      "correct num:  3131\n",
      "total_num:  13443\n",
      "micro f1:  23.29093208361229\n",
      "macro f1:  23.659698723029493\n",
      "correct num:  1760\n",
      "total_num:  13443\n",
      "micro f1:  13.092315703340029\n",
      "macro f1:  13.136950048601287\n",
      "correct num:  2202\n",
      "total_num:  13443\n",
      "micro f1:  16.380272260656103\n",
      "macro f1:  16.205974244268013\n",
      "correct num:  1897\n",
      "total_num:  13443\n",
      "micro f1:  14.111433459793203\n",
      "macro f1:  14.079133390101251\n",
      "max: 32.150562, min: 11.165662, avg:17.385597\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_list=['rstdt-full','instructional','GUM']\n",
    "dataset=dataset_list[2]\n",
    "# figure=plt.figure(figsize=(13,5.5))\n",
    "figure=plt.figure(figsize=(10,6))\n",
    "# figure=plt.figure()\n",
    "plt.subplots_adjust(wspace=1,hspace=0.1)\n",
    "plt.subplots_adjust(wspace=0.3,hspace=0.1)\n",
    "for idata,dataset in enumerate(dataset_list):\n",
    "    f = '/scratch/wenxiao/summ_guided_dp_result/random_init/%s-cnndm-6-8-3-dep_perhead_cle_full.pt'%(dataset)\n",
    "    all_trees= torch.load(f)\n",
    "    ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "    dep_tree_gt_list = []\n",
    "    all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "    for i in ids:\n",
    "    #     if all_gt[i]==[]:\n",
    "    #         continue\n",
    "    #     ids.append(i)\n",
    "    #     print(all_gt[i])\n",
    "        dep_tree_gt=all_gt[i]['dep_tree']\n",
    "        dep_tree_gt_list.append(dep_tree_gt)\n",
    "    \n",
    "    ax=plt.subplot(2, 3, idata+1)\n",
    "    acc_matrix = np.zeros((6,8))\n",
    "    for l in range(6):\n",
    "        for h in range(8):\n",
    "            dep_tree_cand_list=[]\n",
    "            for i in ids:\n",
    "                dep_tree_cand_list.append(all_trees['%d-%d-nocons-cle'%(l,h)][i])\n",
    "            acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list)[0]\n",
    "            acc_matrix[l,h]=acc\n",
    "    print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower')\n",
    "#     plt.colorbar()\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.xlabel('Head',fontsize=14)\n",
    "    plt.ylabel('Layer',fontsize=14) \n",
    "\n",
    "\n",
    "    ax=plt.subplot(2, 3, idata+4)        \n",
    "    acc_matrix = np.zeros((6,8))\n",
    "    for l in range(6):\n",
    "        for h in range(8):\n",
    "            dep_tree_cand_list=[]\n",
    "            for i in ids:\n",
    "                dep_tree_cand_list.append(all_trees['%d-%d-sent_cons-cle'%(l,h)][i])\n",
    "            acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list)[0]\n",
    "            acc_matrix[l,h]=acc\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower')\n",
    "#     plt.colorbar()\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.xlabel('Head',fontsize=14)\n",
    "    plt.ylabel('Layer',fontsize=14) \n",
    "    print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max: 16.894038, min: 0.635395, avg:3.473844\n",
      "max: 26.368903, min: 14.637451, avg:20.282716\n",
      "max: 22.552886, min: 1.272858, avg:4.402340\n",
      "max: 28.217999, min: 15.346002, avg:18.619278\n",
      "max: 14.647028, min: 0.238042, avg:2.074810\n",
      "max: 23.625679, min: 13.769248, avg:17.854398\n",
      "max: 29.209494, min: 0.616707, avg:3.459731\n",
      "max: 35.927864, min: 11.824893, avg:20.268310\n",
      "max: 35.424883, min: 1.487989, avg:4.567796\n",
      "max: 41.430620, min: 9.394048, avg:18.880348\n",
      "max: 23.335565, min: 0.141337, avg:2.252101\n",
      "max: 31.406680, min: 11.173101, avg:18.118631\n",
      "max: 28.854420, min: 0.672771, avg:3.133371\n",
      "max: 35.213044, min: 12.264063, avg:18.892302\n",
      "max: 34.725708, min: 1.147365, avg:3.851440\n",
      "max: 41.179634, min: 12.047329, avg:17.719912\n",
      "max: 23.871160, min: 0.290114, avg:1.940756\n",
      "max: 31.748866, min: 11.254928, avg:17.046511\n",
      "max: 28.508690, min: 0.541955, avg:3.541686\n",
      "max: 35.390581, min: 12.852738, avg:19.182065\n",
      "max: 34.528505, min: 1.308713, avg:4.372087\n",
      "max: 41.627824, min: 10.200789, avg:18.312268\n",
      "max: 23.536413, min: 0.208287, avg:2.177558\n",
      "max: 32.150562, min: 11.165662, avg:17.385597\n",
      "max: 27.564941, min: 0.266305, avg:3.040028\n",
      "max: 34.133807, min: 10.026163, avg:18.593682\n",
      "max: 34.528505, min: 1.003944, avg:4.136787\n",
      "max: 41.448548, min: 9.304410, avg:17.990319\n",
      "max: 23.015696, min: 0.215726, avg:1.868383\n",
      "max: 30.543777, min: 10.622629, avg:16.727417\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rstdt_nocons=[]\n",
    "instruction_nocons=[]\n",
    "gum_nocons=[]\n",
    "\n",
    "rstdt_sentcons=[]\n",
    "instruction_sentcons=[]\n",
    "gum_sentcons=[]\n",
    "\n",
    "for i_run in range(5):\n",
    "    dataset_list=['rstdt-full','instructional','GUM']\n",
    "    dataset=dataset_list[2]\n",
    "    # figure=plt.figure(figsize=(13,5.5))\n",
    "    figure=plt.figure(figsize=(10,6))\n",
    "    # figure=plt.figure()\n",
    "    plt.subplots_adjust(wspace=1,hspace=0.1)\n",
    "    plt.subplots_adjust(wspace=0.3,hspace=0.1)\n",
    "    for idata,dataset in enumerate(dataset_list):\n",
    "        f = '/scratch/wenxiao/summ_guided_dp_result/random_init/%s-cnndm-6-8-%d-dep_perhead_cle_full.pt'%(dataset,i_run)\n",
    "        all_trees= torch.load(f)\n",
    "        ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "        dep_tree_gt_list = []\n",
    "        all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "        for i in ids:\n",
    "        #     if all_gt[i]==[]:\n",
    "        #         continue\n",
    "        #     ids.append(i)\n",
    "        #     print(all_gt[i])\n",
    "            dep_tree_gt=all_gt[i]['dep_tree']\n",
    "            dep_tree_gt_list.append(dep_tree_gt)\n",
    "\n",
    "        ax=plt.subplot(2, 4, idata+1)\n",
    "        acc_matrix = np.zeros((6,8))\n",
    "        for l in range(6):\n",
    "            for h in range(8):\n",
    "                dep_tree_cand_list=[]\n",
    "                for i in ids:\n",
    "                    dep_tree_cand_list.append(all_trees['%d-%d-nocons-cle'%(l,h)][i])\n",
    "                acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list,silent=True)[0]\n",
    "                acc_matrix[l,h]=acc\n",
    "        if dataset=='rstdt-full':\n",
    "            rstdt_nocons.append(acc_matrix)\n",
    "        elif dataset=='instructional':\n",
    "            instruction_nocons.append(acc_matrix)\n",
    "        else:\n",
    "            gum_nocons.append(acc_matrix)\n",
    "        print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n",
    "        im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower')\n",
    "    #     plt.colorbar()\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "    #     plt.xlabel('Head',fontsize=14)\n",
    "    #     plt.ylabel('Layer',fontsize=14) \n",
    "\n",
    "\n",
    "        ax=plt.subplot(2, 4, idata+5)        \n",
    "        acc_matrix = np.zeros((6,8))\n",
    "        for l in range(6):\n",
    "            for h in range(8):\n",
    "                dep_tree_cand_list=[]\n",
    "                for i in ids:\n",
    "                    dep_tree_cand_list.append(all_trees['%d-%d-sent_cons-cle'%(l,h)][i])\n",
    "                acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list,silent=True)[0]\n",
    "                acc_matrix[l,h]=acc\n",
    "        if dataset=='rstdt-full':\n",
    "            rstdt_sentcons.append(acc_matrix)\n",
    "        elif dataset=='instructional':\n",
    "            instruction_sentcons.append(acc_matrix)\n",
    "        else:\n",
    "            gum_sentcons.append(acc_matrix)\n",
    "        im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower')\n",
    "    #     plt.colorbar()\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "    #     plt.xlabel('Head',fontsize=14)\n",
    "    #     plt.ylabel('Layer',fontsize=14) \n",
    "        print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d8c9vXLTyP3rux638cx/5w+zso5/xro0sKWVHl2cvNP/6b3F2esnKu/svSc/+7B9YeWd2mySVxjwrqbl5VlVd6aYx2+3Tj/2Ktf73n37Eyu9Pblv53hHuszdHr1n5+6a8z1e7zJ9lVjb4G0GraFkzEf/syd+11q9TbeU3zNlkC1P+fbbTzZ/FJkm3qz0rvzW8YeVz8csQAAAIjTIEAABCowwBAIDQKEMAACA0yhAAAAiNMgQAAEKjDAEAgNAoQwAAIDTKEAAACI0yBAAAQqMMAQCA0IpkzDMqiuINSWvHdzg4RD+ldLaJhdjLu66xvZTYz7cBrs17B3t5b8naT6sMAQAA3Gv4bzIAABAaZQgAAIRGGQIAAKFRhgAAQGiUIQAAEBplCAAAhEYZAgAAoVGGAABAaJQhAAAQ2n8CzoLTrWlP5E4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x216 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=plt.figure(figsize=(10,3))\n",
    "for i in range(len(rstdt_sentcons)):\n",
    "    ax=plt.subplot(2, 5, i+1) \n",
    "    acc_matrix = (rstdt_nocons[i]+instruction_nocons[i]+gum_nocons[i])/3\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=0, vmax=35)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "#     plt.xlabel('Head',fontsize=14)\n",
    "#     plt.ylabel('Layer',fontsize=14) \n",
    "    \n",
    "    ax=plt.subplot(2, 5, i+6) \n",
    "    acc_matrix = (rstdt_sentcons[i]+instruction_sentcons[i]+gum_sentcons[i])/3\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=15, vmax=50)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "#     plt.xlabel('Head',fontsize=14)\n",
    "#     plt.ylabel('Layer',fontsize=14) \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max: 3.275089, min: 0.901701, avg:1.769042\n",
      "max: 22.103345, min: 15.585872, avg:18.425781\n",
      "max: 4.266762, min: 1.738975, avg:3.053663\n",
      "max: 19.881678, min: 15.614916, avg:17.731116\n",
      "max: 1.859704, min: 0.438890, avg:0.909085\n",
      "max: 19.169828, min: 13.888269, avg:16.604367\n",
      "max: 5.256027, min: 0.331714, avg:1.736144\n",
      "max: 26.210054, min: 11.021304, avg:18.482040\n",
      "max: 7.995697, min: 1.523844, avg:2.970001\n",
      "max: 21.764073, min: 12.979563, avg:17.579479\n",
      "max: 2.291155, min: 0.312430, avg:0.925513\n",
      "max: 22.063527, min: 11.552481, avg:16.481936\n",
      "max: 19.015137, min: 0.415810, avg:2.633269\n",
      "max: 30.569053, min: 10.054195, avg:19.518451\n",
      "max: 23.915382, min: 1.774830, avg:3.950415\n",
      "max: 32.054500, min: 13.033345, avg:18.123282\n",
      "max: 18.039128, min: 0.238042, avg:1.783611\n",
      "max: 26.757420, min: 10.935059, avg:17.943508\n",
      "max: 22.794805, min: 0.397122, avg:3.003430\n",
      "max: 30.910110, min: 10.451318, avg:18.987008\n",
      "max: 27.447114, min: 1.452133, avg:4.025861\n",
      "max: 35.389028, min: 13.624955, avg:17.819634\n",
      "max: 18.425947, min: 0.290114, avg:1.930838\n",
      "max: 28.498103, min: 9.945697, avg:17.143061\n",
      "max: 25.434498, min: 0.509251, avg:3.173862\n",
      "max: 32.409830, min: 12.483648, avg:19.406225\n",
      "max: 33.578343, min: 1.900323, avg:4.258172\n",
      "max: 40.731445, min: 14.019362, avg:18.355593\n",
      "max: 21.550249, min: 0.550472, avg:2.032966\n",
      "max: 30.179275, min: 11.106152, avg:17.594659\n",
      "max: 26.607176, min: 0.401794, avg:3.145635\n",
      "max: 33.292842, min: 12.820034, avg:19.492365\n",
      "max: 33.201864, min: 1.523844, avg:4.038559\n",
      "max: 40.910721, min: 14.126927, avg:18.358955\n",
      "max: 21.914751, min: 0.468645, avg:2.044744\n",
      "max: 30.209031, min: 12.385628, avg:17.718484\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rstdt_nocons=[]\n",
    "instruction_nocons=[]\n",
    "gum_nocons=[]\n",
    "\n",
    "rstdt_sentcons=[]\n",
    "instruction_sentcons=[]\n",
    "gum_sentcons=[]\n",
    "\n",
    "for runtime in [0,1,5,10,20,23]:\n",
    "    dataset_list=['rstdt-full','instructional','GUM']\n",
    "    dataset=dataset_list[2]\n",
    "    # figure=plt.figure(figsize=(13,5.5))\n",
    "    figure=plt.figure(figsize=(10,6))\n",
    "    # figure=plt.figure()\n",
    "    plt.subplots_adjust(wspace=1,hspace=0.1)\n",
    "    plt.subplots_adjust(wspace=0.3,hspace=0.1)\n",
    "    for idata,dataset in enumerate(dataset_list):\n",
    "        f = '/scratch/wenxiao/summ_guided_dp_result/error_pg/%s-cnndm-6-8-%d-dep_perhead_cle.pt'%(dataset,runtime)\n",
    "        all_trees= torch.load(f)\n",
    "        ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "        dep_tree_gt_list = []\n",
    "        all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "        for i in ids:\n",
    "        #     if all_gt[i]==[]:\n",
    "        #         continue\n",
    "        #     ids.append(i)\n",
    "        #     print(all_gt[i])\n",
    "            dep_tree_gt=all_gt[i]['dep_tree']\n",
    "            dep_tree_gt_list.append(dep_tree_gt)\n",
    "        \n",
    "        \n",
    "        ax=plt.subplot(2, 3, idata+1)\n",
    "        acc_matrix = np.zeros((6,8))\n",
    "        for l in range(6):\n",
    "            for h in range(8):\n",
    "                dep_tree_cand_list=[]\n",
    "                for i in ids:\n",
    "                    dep_tree_cand_list.append(all_trees['%d-%d-nocons-cle'%(l,h)][i])\n",
    "                acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list,silent=True)[0]\n",
    "                acc_matrix[l,h]=acc\n",
    "        print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n",
    "        if dataset=='rstdt-full':\n",
    "            rstdt_nocons.append(acc_matrix)\n",
    "        elif dataset=='instructional':\n",
    "            instruction_nocons.append(acc_matrix)\n",
    "        else:\n",
    "            gum_nocons.append(acc_matrix)\n",
    "        im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=0, vmax=35)\n",
    "    #     plt.colorbar()\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.xlabel('Head',fontsize=14)\n",
    "        plt.ylabel('Layer',fontsize=14) \n",
    "\n",
    "\n",
    "        ax=plt.subplot(2, 3, idata+4)        \n",
    "        acc_matrix = np.zeros((6,8))\n",
    "        for l in range(6):\n",
    "            for h in range(8):\n",
    "                dep_tree_cand_list=[]\n",
    "                for i in ids:\n",
    "                    dep_tree_cand_list.append(all_trees['%d-%d-sent_cons-cle'%(l,h)][i])\n",
    "                acc = evaluate_dep_tree_list(dep_tree_gt_list,dep_tree_cand_list,silent=True)[0]\n",
    "                acc_matrix[l,h]=acc\n",
    "        if dataset=='rstdt-full':\n",
    "            rstdt_sentcons.append(acc_matrix)\n",
    "        elif dataset=='instructional':\n",
    "            instruction_sentcons.append(acc_matrix)\n",
    "        else:\n",
    "            gum_sentcons.append(acc_matrix)\n",
    "        im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=15, vmax=50)\n",
    "    #     plt.colorbar()\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.xlabel('Head',fontsize=14)\n",
    "        plt.ylabel('Layer',fontsize=14) \n",
    "        print('max: %f, min: %f, avg:%f'%(acc_matrix.max(),acc_matrix.min(),acc_matrix.mean()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sTufK8X3Dtn5Sebly3wdrmfXtZi4e3Yc/nzsi4DOu+VpjeiUNBmbjTMepIrIGmKqq3/s6uFCVW1DCO99vP3DmvzFnPztyC9BKxcC7to+jX3xbzhzZnb7xbejv/vHv1amVTXwfwlT1exEZA9yIM9/ynTizro3FqfH1nXW0cMpt11dR1RtTxiYx47WVfLNhJ78Y1PCG7GArK1f++NaPvLl8G785ri93TBwaESd3jblieBp4BqdqZBzOnLUvApHVglSHP7zxAwvWZtGmRTT9EtpyVFInJiX0ol9CW/rFO1cD1g4Qvtz2tMdEZB7OPCGXAdeo6pbgRhYaCkvKSN+VzxmHNb1M9OmHdeP+D1owe3F62CWG4tJybnxtJR+uymDGyQO54aSBEZEUwIvEICJzgBtUdZe7qDsw1x3HkC8ir9GMLq237cnn83VZXH18P247bUjE/BDMQSIyHBiCUxvpdBG5FPhCRB4H/ulOU9tsbczZT1m5NrrhubK4mGh+fXRvnvryZ7bvLaBnx1Y+iND/CkvKuG7uCv77UzZ3nTE07NtIqvPmnsYS4DsRmeK+fhlYKiIPicjfcKYnfNFfAYaal5c4J42XjUu2pBCBROQmnNHMt+CUgv+Nqs4FjsapDrxYRJrN1XFN1me7s7Y1YnBbTS4Z40zV+/KSdJ/sz9/yikq54oXvWJiazYPnHRZxSQG8SAyq+i+cgW2TRORTnPaFP+JcbQhOd9ab/BlkqCgqLeO177Zy0tCuYXNmYxrsVuAMVR0LHIn721bVXap6OU6bw2ve7EhEnheRbBFZXWnZ4SKySERWicj7InJIiU0R6S0iC0VkrYisEZEa50EPltRMDzFRQnKXNj7ZX69OrZkwpCuvLt1KUWmZT/bpL7n5JUx+dglLN+/mH78edSCpRRqvWkFVdauqng08CywAhgO3qOqNqvqWN5fWItJSRJaKyA/uj/1ed3lfEVkiIhtE5DURCdlx4x+vzmTX/mKmjPWu/rwJS8LBLqllVK2uiqp+Rg3zitRiFs70tZU9C9ymqocBb+NcmVRXCtysqsNwGr2vF5FhXn6m36VleeiX0IYWMb7rRDFlXBK79hfz8erQHVSe4yni1zMXsXbHPv5z6ZGcM6pnsEPyG6//z4pIF1V9HecsagDO7aUjG/BZRcAEVT0cGAWcJiJjcUZW/11VBwB7gKsasM+AemlROsldWnPsgPhgh2L85xFgvoh8C6wE/lZ9A2/nZFDVr4Dd1RYPAr5yny8AflXD+zJUdYX73AOswylFExLSsvJ80r5Q2XED4knu0pqXFoXm7aSM3AJ+/fQi0nfl89zlKZw6vFuwQ/KrehODiJwkIllAjjtv7TBVnY5ziT1bRB5z53Cukzry3Jex7kOBCcCb7vIXgXMb8T38bs2OXJan72Hy2KSw7W9t6qeqj+Kcpf8dOFZVZ/r4I9YA57jPJwG969rYnR/iCJy2vqDLLy5ly+58nyeGqChh8tgklqfvYc2OXJ/uu6nSd+1n0lOLyPEU8dJVozluYHj1nmoMb64YngQeBloDvwX+AaCqX+L8YAtwzqzqJSLRIrISp4TxAuBnYK+qVows3UYtZ0YiMl1ElonIspycwJdjmrN4Cy1jo5h0VJ3HsYkAqrpKVd9Q1Z/8sPsrgetEZDlODbLi2jYUkbbAW8CM2gaSBvq4WJ/VtFIYdZl0VG9axkYxZ3Ho9Apen+Vh0lOL2F9Uysu/GcvRyZ2DHVJAeJMYugMfupfPHwMH0qWqFqvqXcD53nyYqpap6iigFzAap0ugV1R1pqqmqGpKQkJgM/a+QmdA29mH94iYmi7mUCJylzui35ttx4vIWQ39DFX9SVVPVdWjgFdwTo5q2n8sTlKYq6rz6thfQI+LtCzf9kiqrEPrWM4+vAfvfL+9SrWAYFm9PZcLn14EOFNxHtYr8qrA1sabxPAe8KaIPIgzs9UhdehVdU1DPlRV9wILgXFARxGpGE/RC9jekH0FwlvLt1FQUsaUscnBDsX4V39gi4jMFJGzROTACC6388SRIvJ7EVmK0ztvT0M/QEQS3f9GAXcBT9WwjQDPAetU9ZA2jmBKy/IQFxNFHz/NQjZlbDIFJWXMW7HNL/v31rLNu7l45mJat4jhjWvG+eUKKZR5kxiuwhnt3AGnouSMxnyQiCSISEf3eSvgFJxGtYXABe5mU4F3G7N/f1FVZi9OZ1Tvjs3qjKE5UtUrcEb0K/ASsE1ESkWkANiPUyLjMpyeRUNV9Zu69icirwCLgMEisk1ErgIudusu/YRTwv4Fd9seIlJx0jUemAJMEJGV7mOij79uo6Rm5TEgsa3fagEd1qsDo3p3ZPbidII1jvCb9TuZ8txSEtrH8ea140jyUbfccFLvyGdVLQb+5YPP6g68KCLROAnpdVX9QETWAq+KyJ+B73HOlELGop93sTFnP49NOjzYoZgAUNVVwNUici1OmZckoBWwE1hZy7Sfte3r4lpWPV7DtjuAie7zb6jWTTZUrM/yMK5fF79+xpSxSdz8xg98+/Muxge4B+CnazL57cvf0y+hDXOmjSG+bVxAPz9U1DdRz7H1nRVV2rYt0Nc9sA6hqj9SQ/9vVd2I094Qkl5alE6n1rGcMbLpdWFM+HDrJa3Ey44VzUFuQQkZuYUM9PNtlTNGdufPH67lpUWbA5oY3l25nZte/4HDenZg1hVH07F1yA6p8rv6biU9KyKfi8jFNY3QBBCRkSLyMLABiKjT6ozcAhasy+LCo3vbfAjNmHsb9G4ReVhEjg12PMGy/kDDc9OrqtalZWw0Fx7dmwVrs8jILfDrZ1V4ZekWZry2kqOTOzFn2phmnRSg/sQwApgH3APsFpFUd6j+RyKyWET24PSv7okzeC2iZrV6ZelWylW5dLSNdG4u3IbnZyq9boNTO+kuYDqwUESqj2ZuFtLcrqoDE/3fEDt5TBIKvLLE/11Xn/16I7fPW8UJgxKYdcVomy6XehKDqpaq6pOqOgRn0M9/cC6t04HPgGlAT1W9tGLqw0hRXFrOK0u3cMKgBPp08U8PDBOSjgPeqfR6MtAeGAh0wumAUVMZi4iXluWhTYvogNQJ6925NScOTuTlpVspLvXPpHmqyj8+S+PPH67jjMO68/SUFLsz4PI6NarqMpxeGc3Cp2szyfEUcdm45GCHYgKrF06PoQonA2+qajqAW3r742AEFmypmR4Gdm0XsJH/U8YmccWs7/hkTSZnHd7Dp/tWVR6cv45nvt7EpKN68ddfjQz7Wdd8qdFVsETkFyIysaILaqSZvSid3p1bhd3kIabJSoHKp41jgMWVXu/FuYJodtZn+2bWNm8dPyiB3p1bMdvH9ZPKy5U731nNM19v4vJjknnIksIhvKmV9FsRubPasg+AL4APgDUiMtQ/4QVHWpaHJZt2c+mYJPvBND/rgPPA6ViB0362sNL6JCArCHEF1c68InbmFQd0oFdUlDB5TBJLN+/mp0zfTC1fWlbOTa+v5OUlW7juhP7cc9Ywq31WA2+uGKbitCkAICLnAb/EGYCT4q77k1+iC5LZi9JpERPFhSlWF6kZehi4X0S+wmlHm6+qmyqtnwgsDUpkQeTPUhh1uTClNy1ionxy1VBU6sy69s7KHdzyy8HcajMw1sqbxNAfZ+BZhYnAe6o61y0NfAfOSM2IkFdUyrwV2zhzZHc6t2neXdaaI1V9BzgdWA48Bvy62ib5OJ0wmhV/Fs+rS6c2LThrZA/e/n47nsLG10/KLy5l2ovL+HRtFveePZzrTxzgwygjjzeJIQ6nHECFcRysJw+wEUj0ZVDB9Pb329lfXGaT8TRjqvq5OwnVQ+7c5pXX3YtTBbhZSc3y0KFVLIntAj8S+LJxSeQXl/H2940ro7avsISpzy/lfxt28sgFI5l6TLJvA4xA3iSGdJz5bisKgA0FKo+G7obTIBf2VJXZizYzomd7RvWOyDZ100huEb3JIvIFkBrseAItLdPD4K7tgnLr5fDeHRnZqwMvLWp4/aTd+4u59JklrNy6lycuOZJJdnvYK94khheBJ0XkPpwJddap6vJK648BaiyDEW6WbtpNWlYel41NtnuPBgC3ouqTOAXvHgHW4nRhbTZUlbQsDwMD2COpusljk9iQncfijdUnxKtd9r5CLpq5iLQsDzOnpDDxMCtr4y1vEsMjOKWBz8K5pTSp2vrxwOs+jisoZi9Op33LGJ/3mTbhRUQ6iMh1IrIC+BKnsnA74CRVvU5VF9a9h8iSta+IfYWlAW94ruzsw3vQoVUssxdv9mr7bXvymfT0IrbvKWDWFaM5cUjE3O0OiHoTg6qWq+rdqnqEqp6uquuqrZ+kqs/6L8TAyN5XyMerM5mU0ptWLWz0Y3MlIrNx2hDOx6mC2k1VJwc3quCq6JEUzDkJWsZGc2FKLz5Zk0XWvrqn3N6Yk8ekpxaxZ38xc6aNYVx//1aDjUSNHuAGICJ9ROT/RCQ0Z/BugFe/20ppuTLZGp2bu4uBfwKTVPVFVd1f3xsiXSgkBnBuJ5WVKy/XUT9pXcY+Lnx6ESVl5bx29TiO6NMpgBFGjgYnBhGJFZFJIvIJTo+kC4FZvg4skErLynl5yRaOGxhP3/jmNymHqeJCYBTOJD1vi8h57jSbzVZqpof4tnFB776d1KUNxw9K4JWlWygpO7R+0sqte7lo5mJio6N47epxDO3eLAeo+4TXiUFEhonI33Cm3nwUOAk4S1WHqeo9Xry/t1uZda2IrBGRG9zlo9xKrSvdSc0DPjfDZ+uyydxXaF1UDao6T1XPAAYDK3B+65k4x8oR0oBeCSLyvIhki8jqSssOF5FFIrJKRN6vo5z9aW414w0iclvTvlXTpGXn+b3UtrcuG5dEtqeIBWurDj5fvHEXlz6zmI6tY3n96nH0TwiNeMOVNyUxrhKRxTj1YjrhDPjpizP9YUNuIZUCN6vqMJxKrdeLyDCckab3quoo4G73dUDNXryZnh1bcdLQroH+aBOiVHWbqt6vqv1xfvNv4EzpmSEi3g5wmwVUL9H9LHCbqh4GvE0NlVrdWQ6fxBloNwxnOtBhjfoiTVRerqzP8gSk1LY3ThicSM+OrXhp0eYDyxamZjP1+aX07NSKN64eR28/zUfdnHhzxfA0sABIVNUrVHWhO7tVg6hqhjtSGlX14NSk6YmTYCrOmjrgdAsMmA3Zefxvwy4uGdPH6iKZGqnqZ6p6EdAD+AtOF21v3vcVUL1/5SAODhBdAPyqhreOBjao6kZ3at1XgXMaE3tTbd9bQH5xWVB7JFUWHSVcOrYPizfuZn2Wh/mrMpj+0jIGdW3Hq9PHkdi+ZbBDjAjeJIZ/A9cCX4vI70WkyeVGRSQZZ5rPJcAM4BER2Ypz2X57Le+Z7t5qWpaTk9PUEA6YuySd2GixukimXqq6R1UfV9WmzFS4hoN/5CcBNf3wegJbK73e5i47hL+Oiwqh0vBc2a9TetMiOoqbXv+B3768glG9OzL3N2OC3gYSSbzprvp7nDOlvwFnA1tF5EOcycob3Cjnzg39FjBDVffhJJ0bVbU3cCPwXC1xzFTVFFVNSUjwTSns/OJS3ly+jYmHdSchCEP9TegRkfdqecwWkbvc0f9NcSVwnYgsxxkbUdyUnfnjuKgs1U0MwRzcVl2XtnGcMbI7q7bnMn5APC9eOZr2LZt1/wCf82qiHvdy9hXgFRHpC1wFjAS+FZGPgDdU9bX69uP27ngLmKuq89zFU4Eb3OcV93ED4t2VO/AUllqjs6lsVy3LO+L8Ub9ZRI5T1dW1bFcnVf0JOBVARAYBZ9Sw2XaqXkn0cpcFXFqmhx4dWobcH95bTxvMkG7tuHx8MnExNu7I1xo8ualbgvguEbkbp3FsGvASUGdicHtzPIdTUuNvlVbtAI7Hmd9hArC+oTE1hlMXKZ0h3dpxVJL1dTYOVb2itnUiEgU8g9POcFZj9i8iiaqa7e7rLpyqAtV9Bwx0T8K2AxcBlzTm85oqLSuPQSHSvlBZ9w6tuPr4/sEOI2I1eoCbOyL6Q1U9j5rvk1Y3HmcOhwlu19SVIjIR+A3wmIj8ADyIM+G6363Ysoe1GfuYMi7J6iIZr7idLv4JHOXN9iLyCrAIGCwi20TkKpweRmk404fuAF6BNHmeAAAgAElEQVRwt+0hIvPdzykFfgt8gtNJ43VVXePr71Of0rJyNuTkhVT7ggmMeq8YROSmWlblAqmq+o2qZte3H1X9BqddoiZeHWi+NHtROu3iYjh3VI1tesbUJh/wqj+kql5cy6rHa9h2B85cJxWv5wPzGxOgr6Tvzqe4tNwSQzPkza2k39WyvCPQQUS+Bc5WVe/LHgbZzrwi5q/K5JIxfWgT1+C7aaZ5OwVIC3YQgbC+YtY2SwzNTr1/FVW1b23rRKQfMAd4AKd3UVh4fdlWisvKmTy2T7BDMSFGRM6vZVUHnCvbK4Fa2yEiSWpmHiIwIDF0eiSZwGjS6bKqbnSH67/go3j8rqxcmbt4C8f078KAEBnNaULKm7Us9+BM0HOlNz3wIkFaloc+nVtbteFmyBf3UbYAYVNLYuFP2WzfW8BdZwwNdigmBKlqkyoOR5K0LI+1LzRTvjgIRtKwmklBNXtxOl3bx3HysLDJZcYEXFFpGZt27mdQCA1sM4HjTa+kzrWsqrjn+gjwvC+D8pfNO/fzZVoON548iNhoOzE0pjabdu6ntFztiqGZ8uZW0k6cQnc1UZyRyg/5LCI/mrsknZgo4aLRVhfJmLqkZeUBhEzxPBNY3iSGE2tZvg9Yr6p5PozHbwpLynh92TZ+ObwbXa0CozF1Ssv0EB0lNnFVM+VNd9UvAxGIv733ww5yC0qYMs7qIhlTn9QsD33j21gdomaq2YzumrM4nYGJbRnTt7YmE2NqJiIdqdZRI5wGdDbG+iwPw3rY1JjNVbNogf1h615+3JZrdZGM10QkSUQ+EpECnIqrOe5jp/vfiFVQXEb67nxreG7GmsUVw+zF6bRpEc15R1hdJOO1F3DKvlyFU+yutg4YEWdDdh6qVgqjOYv4xLBnfzHv/7CDSSm9aBdiNeVNSBsNjG3svAvhLO3A5DyWGJorr28ludNx1rbOqzlwg+GN5VspKi1nsk3GYxpmE9Asp/VLy/LQIjqK5C5eFZE1EaghbQw/iMjkygtEJEpE7gMW1vdmEektIgtFZK2IrBGRGyqt+52I/OQuf7gBMdWpvFyZs3gLo5M7M6SbNaSZBrkB+IuIDAh2IIGWluWhf2JbYmwQaLPVkFtJtwJPuZPrXAMkAHNxph2saXrC6kqBm1V1hYi0A5aLyAKcOkvnAIerapEP5tQ9YG3GPrbszueGkwb6apem+XgX54ohVUSKcH6/B6hqvWcaIvI8cCaQraoj3GWjcGZta+nu8zpVXVrDex/GOa6igAXADaoakHaOtKw8UpJtVsPmzOvEoKpPi8iXOMlgNU7D3AJgojdd91Q1A8hwn3tEZB3QE2cGt7+qapG7rt5Jf7y1fW8BYKM3TaP81gf7mAU8gTP1bYWHgXtV9SP3JOth4ITKb3JvzY7HqUMG8A0Hp7/1K09hCdv3FnBJVytJ35w1tPE5A9gMjMA5k/m4Mf253faKI4AlOLWWjhORB4BC4A+q+l1D91ljsG5i6NbBRjqbhlHVF32wj69qaJtToOJqowNOj6dD3opzRdECZ9bDWCCrqfF4Y322WwrDGp6bNa8Tg4j8AmdSngxgODAO+Jd71jNNVXd5uZ+2wFvADFXdJyIxQGdgLHA08LqI9Kt+2Swi03Hng+7Tx7uzmYx9hbSIjqJz6xZebW9MTUSkG84f6QNUdUsjdzcD+EREHsU5uTqk44aqLhKRhTjHmgBPqOq6Rn5eg6RlOj2SbAxD89aQ1qXPcC6Jx6vqBlWdjXPWnwCs8mYHIhKLkxTmquo8d/E2YJ46lgLlQHz196rqTFVNUdWUhIQErwLOzC2ka4c4oqJsUJtpGBHpICIvugPctuP0Uqr8aKxrgRtVtTdwI/BcDZ89ABiK037XE5ggIsfVEud0EVkmIstycpo+7i41y0Or2Gh6dWrV5H2Z8NWQxHCyqt6lqgca4VR1E/AL4N/1vVmcIcfPAetU9W+VVr2DW6hPRAbhnJntbEBctcrILaR7e/uBm0Z5FDgcOBfnFuclwC04JzK/bsJ+pwIVJ0Vv4IyXqO48YLGq5rlFKj/CuUI/RGNOmOqyPiuPQV3b2slUM+d1YlDVr2pZXq6qf/ZiF+OBKThnPyvdx0ScuRz6ichq4FVgqq96X2TmFtK9o7UvmEY5Hfidqn4ClAHL3ROa24Crm7DfHTgNyQATgPU1bLMFOF5EYtyr7OOBgNxKSs3y2MA207DGZxHphHPA9OHQe6731fVeVf0G535pTSbXsrzRysuVzNxCa3g2jdWRgzMT5gJdgA3AIpw5SOolIq/g9DiKF5FtwD04vfAed9vWCnHbzUQkBbhGVafhzDs9AecWreJ08njfN1+rdnv2F5PjKbKGZ9OgxuexwIdAEU67wnagu/t6M1BnYgi03fnFFJeV093mXjCN8zPQD+fsfR1wkYgsBc4HvOqJp6oX17LqqBq2XQZMc5+X0bSrkkapKIUxyLp3N3sNaWN4BGcMQ0+cM50JOFcOywjBGdwycwsB6NbB2hhMo8zi4DiCv+L8oS7GOQ5C7vfuCwcSg83z3Ow15FbSSOAqVVURKQPiVHWjiPwReBknaYSMDDcxdLdbSaYRVPXvlZ7/V0SGACk4sxZ61Qsv3KRmeWjXMoZudpXd7DUkMRRXep4FJOFcYucBPXwZlC9k5jqD26zx2fiCO26hsWMXwkJaVh6Du7azOUtMg24lrcAZgAbO0Pw/i8hU4J/Ajz6Oq8l25BYSEyXEt2mWBTKND4jIdW5hx3wR6ecuu01ELgx2bL6mqqRZjyTjakhiuJODw/fvwpnF6l9AJ9yeFaEkM7eQru1bWn9s0ygiMgPndz6Tqr3ptuObOkohJcdTxN78EgZb+4KhYUX0llV6noPTbTVkZeQWWPuCaYprgN+o6ociUnmczgqckjARJS3LqZFkPZIMRPCczzaGwTRREk4V4epKgIjr6paaZTWSzEH1XjGIyHve7EhVz256OL6hqmTkFnLq8G7BDsWEr43AkRwc5FZhIrA28OH4V1qmhy5tWhDf1trkjHe3ks7EOTi+8G8ovrMnv4Si0nLrdmea4lHgCRFpjdPGME5EpuBMWHVlUCPzg7Rsj10tmAO8SQyP4NQ4+gXwAjBLVbf5Naomyqjoqmq3kkwjqeoLbtmKB4HWwGyczhe/V9XXghqcj6kqaZkeLjiqV7BDMSGi3jYGVf0jUFEiOAVYLyIficgFboGvkHNw1LMlBtN4qvqMqiYBiUA3Ve2lqoeUyQ532/cWsL+4zBqezQFeNT6rapmqvqeq5wJ9gYXAn4Ht7sQ7IaVi1HOPjhHXRmiCQFV3+nLK2VCzPstmbTNVNXRqT4A2OJUn2+KMeg7IBOUNkZFbQHSUWEOaabBw7GzRVBU9kmxwm6ngVWIQkVbAhcBVOLeT3saZN+FzP8bWaBm5hXRtF0e0DW4zDRd2nS2aKi3TQ7f2LenQKiTvDJsg8Ka76jM4SWE9zgxsZ6vq3oZ+kIj0xpkatCvOVcZMVX280vqbcXqCJKhqk2ZwszEMpgnCrrNFU6Vle6x9wVThTRvDVcAenInJTwdeEpH3qj+82E8pcLOqDgPGAteLyDA4kDROxUdFyjJzC+lu5bZNI4RjZ4umKCtXZzrPxJBrKjRB5E1ieAmnsXknsKuOR51UNUNVV7jPPTiVWXu6q/+O0z+8ye0VFYPbrKuqaSxfdbYQkedFJNudtrZi2SgRWexObbtMRGqa8xkR6SMin4rIOhFZKyLJTftWNdu6O5+i0nK7YjBV1HsrSVUv9/WHuj/yI4AlInIOsF1Vf/BFud/cghIKSsrsVpLxlaZ0tpgFPIFzclXhYeBeVf3InfP8YZzpP6t7CXhAVRe4yai84aHXz0phmJoEvFaS+yN/C5iBc3vpDuBuL9433T3DWpaTk1Prdgcn6LFbSaZxRKSViEwVka9w5l1Owuls0U9V93u7H1X9ikOnAVWgvfu8AwcrFlf+/GFAjKoucPeTp6r5jfgq9UrLdHsk2a0kU0ljuqs2mnuP9i1grqrOE5HDcC7VK64WegErRGS0qmZWfq+qzsQpgUxKSkqtZ202uM00ha86W9RhBvCJiDyKc2J2TA3bDAL2isg8nOPjM+A2dy7o6vFOxy1736dPnwYHk5adR+/OrWgTF9A/BSbEBezXIM5f/ueAdar6NwB3isTESttsBlKa0ivJpvQ0TXQVTieIis4Wp9d0i7MJ4xiuBW5U1bfcCX+eA06utk0McBzO7dYtwGvA5e621ePw6oSpNmmZHgYl2m0kU1UgbyWNx+kGOMFteFvp3mP1qczcAqIEEtvZ4DbTKD7pbFGHqcA89/kbQE2Nz9uAlaq6UVVLgXdwKr36VElZORt35lnDszlEwK4YVPUbqs6EVdM2yU39nB25hSS2a0lMdMRONWH8yB+dLarZARyPM4BuAs4tq+q+AzqKSII7KdYEYFkN2zXJ5p37KSlTK4VhDhFxNxZtcJsJFSLyCk6Po3gR2QbcA/wGeNyt3FqI2z4gIinANao6TVXLROQPwOfuLdjlwDO+ju9gKQxreDZVRVxiyMgtsK53JiSo6sW1rDqqhm2XAdMqvV4AjPRTaIDTvhAl0D/BEoOpKqLutxwc3GZdVY2pT1pWHsnxbWgZGx3sUEyIiajEsK+wlPziMuuRZIwX0rKsR5KpWUQlBhvDYIx3CkvK2Lxrv/VIMjWKqMRgU3oa452fc/IoV5ucx9QsohKDXTEY4520AzWSrOHZHCqiEkNGbiEi0LW9JQZj6pKamUdstJAc3ybYoZgQFGGJoYCEtnHE2uA2Y+q0PstD/4S2dqyYGkXUr8LmYTDGO6lZHpvj2dQqohKDjXo2pn4rtuxh254Chna3xGBqFnGJwQa3GVO7HE8R185ZTp/Orbl0dFKwwzEhKmJKYngKS/AUldqtJGNqUVJWzvUvryC3oIR5146mQ+uIm8La+EjEJAbrqmpM3f760U8s3bSbv//6cIb1aF//G0yzFTG3kmxKT2Nq994PO3jum01cfkwy5x3RK9jhmBAXsMQgIr1FZKGIrBWRNSJyg7v8ERH5SUR+FJG3RaRjY/afaTO3GVOj1EwPf3zzR1KSOnHHxKHBDseEgUBeMZQCN6vqMGAscL076fkCYISqjgTSgNsbs/OKK4bE9jZzmzEVcgtKuGbOctq2jOHflx5Ji5iIuUlg/ChgvxJVzVDVFe5zD7AO6Kmqn7rTFwIsBhp1nZuRW0B82zjiYqyEsAkNIvK8iGSLyOpKy0aJyGJ3attlIlLT1J4V27YXkW0i8kRjY3jum01s3Z3Pvy89kkSrCGC8FJTGZxFJxpnofEm1VVfiTHzeYDedOogp46z7nQkps4AncOaRrvAwcK+qfuTOef4wzixvNbkf+KopAfx+wgCOHRDP0cmdm7Ib08wE/LpSRNoCbwEzVHVfpeV34txumlvL+6a7Z1jLcnJyDlmf2K4lw3t08FPUxjScqn4F7K6+GKjoEtQBZw7oQ4jIUUBX4NOmxBATHcXovpYUTMME9IpBRGJxksJcVZ1XafnlwJnASaqqNb1XVWcCMwFSUlJq3MaYMDAD+EREHsU5MTum+gYiEgU8BkwGTg5seMYEtleSAM8B61T1b5WWnwbcCpytqvmBiseYILkWuFFVewM34hwT1V0HzFfVbfXtrL4raWMaI5BXDOOBKcAqEVnpLrsD+CcQByxwcgeLVfWaAMZlTCBNBW5wn78BPFvDNuOA40TkOqAt0EJE8lT1tuob2pW08YeAJQZV/QaQGlbND1QMxoSAHcDxwBfABGB99Q1U9dKK5+5t1pSakoIx/iK13NIPaSKSA6TXsCoe2BngcALBvlfDJalqgp/27RUReQWnx1E8kAXcA6QCj+OclBUC16nqchFJAa5R1WnV9nE5TmL4rRefZ8dFZAj6cRGWiaE2IrJMVVOCHYev2fcyTRGp/872vfzHhkEaY4ypwhKDMcaYKiItMcwMdgB+Yt/LNEWk/jvb9/KTiGpjMMYY03SRdsVgjDGmiSwxGGOMqSJiEoOInCYiqSKyQUTCdjBQLaWaO4vIAhFZ7/63UzBjbIw6JmoK++8Wyuy4CG2helxERGIQkWjgSeB0YBhwsTsJUDiaBZxWbdltwOeqOhD43H0dbmqbqCkSvltIsuMiLITkcRERiQEYDWxQ1Y2qWgy8CpwT5JgapZZSzecAL7rPXwTODWhQPlDbRE1EwHcLYXZchLhQPS4iJTH0BLZWer3NXRYpuqpqhvs8E6dOf9iqNlFTRH23EGPHRRgJpeMiUhJDs+HOVxG2fYxrm6gJwv+7meAJ999OqB0XkZIYtgO9K73u5S6LFFki0h3A/W92kONplFomaoqI7xai7LgIA6F4XERKYvgOGCgifUWkBXAR8F6QY/Kl93Dq+OP+990gxtIotU3URAR8txBmx0WIC9XjImJGPrsTq/8DiAaeV9UHghxSo9RSqvkd4HWgD05Z5QtVtXpDXEgTkWOBr4FVQLm7+A6c+6lh/d1CmR0XoS1Uj4uISQzGGGN8I1JuJRljjPERSwzGGGOqsMRgjDGmCksMxhhjqrDEYIwxpgpLDM2UW9HxskqvVUQuqGP7pSLyq8BEZ0xw2HHhsMTgJyKSICL/FpHNIlIkIlki8rmInOKu3ywifwhSbGfgjIid24C33Q/8VUTsN2MazY6L8BBRXybEvIVT3fIqYBBwJvAR0CWYQbluAGapalkD3jMfaIdTwtmYxrLjIhyoqj18/AA64hS9OrmW9V+46w88Kq07BvgSyMepa/MfoH219z4FPA7scR+PAFGVtjkf+BEowClV/CVOtUaABJwRlodXi0mBCyq9/iOwExhbadnzwJxg//vaIzwfdlyEz8OuGPwjz32cLSIta1h/Pk4J5PuA7u4DETkM+BSnTsrh7najcH54lV2Kc7U3DrgamA7McPfRDafu/ovAUOAXwOxK7z0WKAJWUwNxPAr8DjheVRdXWr0UOL7eb29Mzey4CBfBzkyR+gB+hXNWUggsAh4FxlRavxn4Q7X3vAQ8V23ZKJyzlkT39RdAGm45E3fZXcA29/mR7vZJtcQ1A0ivYbkCvwZecPd/yPuBs3HOqmKC/e9rj/B82HERHg+7YvATVX0L6AGchXMP9RhgsYjcUcfbjgImi0hexQP4n7uuf6XtFqv7i3QtAnqKSHvgB+AzYLWIvCUi14pIQqVtW+EclDV5FKdQ2bGqml7D+gJAgJrO9oyplx0X4cESgx+paqGqLlDV+1T1GJzyuv/nlkCuSRTwLM7ZUMXjcGAgsNLLzywDTnUfP+I08q0XkcPdTXYCtU0svgDoBkysZX1noFBV87yJxZia2HER+mKCHUAzsxbn37wlUIxTCrmyFcBwVd1Qz37GiIhUOjsaC+xQd+Ynd/kiYJGI3Aeswbkc/gH4HkgQkXhV3Vltv/OBecAb7u5frLZ+hBujMb5kx0WIsSsGPxCRLiLyXxGZLCIj3YlSJgG3Ap+7P9TNwHEi0lNE4t23PgSMFpGnROQIERkgImeKyNPVPqIH8A8RGewOvrkF+Lv72WNF5C4ROVpE+uDc/+yNc/CBcwBk4zS2HUJVPwAmAU9VHujjOg74uNH/MKZZs+MijAS7kSMSH0Ac8CDODFp7cLrYrQf+BnR2txmLc6ZSSNVueSk4P7J9wH6cCTzuq7T+C5xueU8Ae939PwZEu+uH4ty7zcLpZbEBuLVafH8B3qi2rHq3vLNw7p1e5r7uiXM21yvY/772CM+HHRfh87CJesKMiHwBrFbV3zZhH4k4Z0pHq+omL9/zCNBBVac39nON8Rc7LnzLbiU1Q6qaDVyJM22gt7KBP/knImOCz46Lg6zxuZlS1QZNCq+qj/grFmNChR0XDruVZIwxpgq7lWSMMaYKSwzGGGOqsMRgjDGmCksMxhhjqrDEYIwxpgpLDMYYY6qwxGCMMaYKSwzGGGOqsMRgjDGmCksMxhhjqrDEYIwxpgpLDMb4iYg8LyLZIrK60rJRIrJYRFaKyDIRGV3H+9uLyDYReSIwERvjsMRgjP/MAk6rtuxh4F5VHQXc7b6uzf3AV/4JzZjaWWIwxk9U9Stgd/XFQHv3eQdgR03vFZGjgK7Ap34L0JhaWGIwJrBmAI+IyFbgUeD26huISBTOtJR/qG9nIjLdvSW1TEQiahYxEzxhOVFPfHy8JicnBzsME8KWL1++U1UTgh1HDa4FblTVt0TkQuA54ORq21wHzFfVbSJS585UdSYwE5zjIiUl5Wk/xGwihLfHRVgmhuTkZJYtWxbsMEwIE5H0YMdQi6nADe7zN4Bna9hmHHCciFwHtAVaiEieqt5W147tuDD18fa4CMvEYEwY2wEcD3wBTADWV99AVS+teC4ilwMp9SUFY3zJEoMxfiIirwAnAPEisg24B/gN8LiIxACFwHR32xTgGlWdFqRwjTnAEoMxfqKqF9ey6qgatl0GHJIUVHUWTrdXYwLGEoMJWarK/uIy9uYXsze/hNyCEvbml7C34ODr608cQIdWscEO1RifUlWKSsspLCmjsMT5b0FJ2cHXpWUUHbLO3b60jAtTetM/oW2jP98SgwmKrbvzWZia7fyhd//Y5+aXsLeghL35xQeSQGm51rqPlrFRXHR0b0sMJmJ8vDqTW978AU9haaP30SImirH9ulhiMOGjpKycmV9t5J+fr6eotByAtnExdGgVS8fWzmNIt/Z0aB1Lx4plrVpUet2Cjq1j6dAqlpax0UH+Nsb4jqewhD+9u5ruHVpyxfjutIyNomVMNC1jo53nVf4b7a6r9Do2iriYaKKj6u7i7A1LDCZglqfv4Y55q0jN8nD6iG788bQh9OzUithoG2dpzL/+u4EcTxHPXJbCqN4dgxpLwBODiEQDy4DtqnqmiPQFXgW6AMuBKapaHOi4jP/kFpTw8Mc/8fLSLXRv35JnL0vh5GFdgx2WMSFjQ3Yez3+ziQtTegU9KUBwrhhuANZxsF7MQ8DfVfVVEXkKuAr4TxDiMj6mqsxflcn/vb+GXXlFXHFMX246dRBt4+xC1ZgKqsp9H6ylVWw0t/xySLDDAQJcK0lEegFn4I72FGe8/wTgTXeTF4FzAxmT8Y9te/K56sVlXP/yChLbxfHu9cdy91nDLCkYU82CtVl8lZbDjFMGkdAuLtjhAIG/YvgHcCvQzn3dBdirqhVN8NuAnjW90S0QNh2gT58+fg7TNFZpWTmzvt3MY5+mIQJ3nTGUy49JJsbaEYw5RGFJGfd/uJaBiW25bFxSsMM5IGCJQUTOBLJVdbmInNDQ91cuFpaSklJ7H0YTND9u28vt81axZsc+ThqSyL3nDKdXp9bBDsuYkPXMVxvZuruAOVeNCalOGIG8YhgPnC0iE4GWOG0MjwMdRSTGvWroBWwPYEzGB/KKSnns01Re/HYz8W3j+PelR3L6iG7UVxnUmOZsx94CnvxiA6cN78axA+ODHU4VAUsMqno7bu1594rhD6p6qYi8AVyA0zNpKvBuoGIyTbdgbRZ3v7uazH2FTB6TxC2nDaZ9SxtwZkx9Hpi/DlW484yhwQ7lEKHQEvhH4FUR+TPwPU59ehPiMnML+b/31vDxmkwGd23HE5ccyVFJnYIdljFhYdHPu/jwxwxmnDyQ3p1D73ZrUBKDqn6BU3YYVd0I1DohugktZeXKnMXpPPJJKiVl5dx62mB+c1y/kLo/akwoKy0r597319CzYyuuOb5/sMOpUShcMZgwsXbHPu54exUrt+7luIHx/PncESR1aRPssIwJK3OXbOGnTA9PTT4yZMu6WGIw9SooLuMfn6fx7Neb6NgqlscvGsXZh/ewxmVjGmhXXhGPfZrK+AFd+OXwbsEOp1aWGEydvkjN5q53VrNtTwG/TunN7ROH0LF1i2CHZUxYevTTVPYXl/F/Zw0P6RMrSwymRjmeIu77YC3v/7CD/glteG36WMb06xLssIwJW6u25fLqd1u54pi+DOzarv43BJElBlNFebny2rKt/GX+OgpLyrnx5EFcc0I/4mJC816oMeGgvFy5573VdGnTghmnDAx2OPWyxGAOWJ/l4Y63V/Hd5j2M7deZB847rEmTfRhjHO+s3M6KLXt5+IKRYTHOxxKDobCkjCcXbuCpL3+mTVwMD18wkklH9Qrpe6DGhAtPYQl/+egnDu/dkQuO7BXscLxiiaGZ+3bDTu58ZzWbdu7n/CN6cucZQ+nSNjQqPBoTCZ6oNAFPlA9mVwsESwzN1O79xTzw4TreWrGNpC6tmXPVmJCr12JMuPs5J4/n/7eJSUeFxgQ83rLE0MyoKm+t2M4DH67FU1jK9Sf253cTBobsQJtgcmcXTAZaATnAKlUtDGpQJmyoKve+v5aWMdHcelpoTMDjLUsMzcimnfu58+1VfPvzLo5K6sSD5x3G4G6h3W0u0EQkGbgWuBhnbpDK1/7FIvI1Tvn3t1S1POABmrDx2bpsvkrL4a4zhobMBDzessTQDBSXlvP0lz/zr4UbiIuJ4oHzRnDx0X3C5n5noIjIP3Eq/H4K3AksBXYABUBnYARwHHA/cI+IXKGq3wUpXBPCCkvKuP8DZwKeqcckBzucBrPEEOG+27yb2+etYkN2HmeM7M49Zw4jsX3LYIcVqgqB/qq6s4Z12cB/3ce97rwiSUCtiUFEngcqJqga4S4bBTyFMydJKXCdqi6t9r5ROPOetwfKgAdU9bUmfjcTQM9+vZEtu/NDbgIeb1liiFC5+SX89eN1vLJ0Kz07tuKFy4/mxCGJwQ4rpKnqrQ3Ydr4Xm80CngBeqrTsYeBeVf3ITS4PAydUe18+cJmqrheRHsByEflEVfd6G58Jnh17C3hy4c8hOQGPtxqcGEQkCmipqvl+iMc0kary/o8Z3Pf+WvbkFzP9F/2YcfJAWrewc4DGEpF4YAwQDSxV1Uxv3qeqX7ltFlUW41wJAHTAuVVV/X1plZ7vEJO03LkAACAASURBVJFsIAGwxBAGHpy/jnLVkJyAx1te/bUQkdNxGuOOx22QE5FCYAXwMfCCqh7yAzeBtXV3Pne9s5ov03IY2asDL155NMN7dAh2WGFNRM7BOfNfD7QABonIdFWd08hdzgA+EZFHgSjgmHo+f7T7uT/Xsn46MB2gT58+jQzJ+Mqin3fxwY8Z3HBSaE7A4606E4OInAc8BLQD5gMPcmhj3MnAn0RkFvAnVc3xZ8DmUCVl5Tz3zSb+8Vka0SLcc9YwLhuXTLQ1LjeYiMSpalGlRfcD41V1rbv+HOBJoLGJ4VrgRlV9S0QuxJmx8ORaYukOzAam1tYDSlVn4vSSIiUlRRsZk/GByhPwXHtCaE7A4636rhhuA24C5tfyw3wdQER6AjcAlwGP+TRCU6fvt+zh9nmr+CnTw6nDunLvOcPp3qFVsMMKZytF5DpVXei+VpxG4gpN7aI6FedYAXgDeLamjUSkPfAhcKeqLm7iZ5oAqJiA5z+Xhu4EPN6qMzGo6hhvdqKq2wGvG+5M03kKS3jkk1RmL06na7uWPD3lqJCe+COMXALMFJFVwM3A3cASEUkDYoHBwDVN2P8OnFuyXwATcG5RVSEiLYC3gZdU9c0mfJYJkMoT8Jw2IvyPw0a3SIpIGyBaVff5MB5TD1XlkzWZ3PPeGrI9RUwdl8zNpw6iXRhUbAwHqvq9iIyB/2fvvMOrqrI+/K40OqG3hA6C9BIEBewNEB2xjL33sc6oM+P4DY7OOMU29u5Yx4YoDEVFBREVMIFA6DWBFJJAEiAJSUiyvj/2id6EhNwkt9/9Ps95uHefctcNOVlnr7X2+nEPkIhZz3AMMAGTE/jJ3XyaiLyPqTjqJCLpwEzgRuBpEYnClMfe5BybANyiqjcAFwMnAh1F5BrncteoarJHvqTF4zz+5ZagEOBxl8ZUJQ3CxD0TABWR9ZgY6GpPG2epTmbBIf48Zz1fbczm2O5tefnKhKDqvxIsOGHTJ0RkNvACJkR6i6ruauB1Lq1j19hajk0EbnBev0vjcxgWH2MEeHYFhQCPuzRmxvAy8CrmSagZcB/wFjDCc2ZZXKmoVN76IZUnvtxMpcIDUwdz3cS+RAXhwplgQESGAoMxvZGmiMjlwBIReRp4RlVtktcCmBl8MAnwuEu9f1lE5F0RcdV07A68p6rFqpoPfAjU22RcRJqLyEoRWSMi60XkL874myKyU0SSnW1UY79MKLJpzwHOf+F7Hp63gXF9O/DlPSdy04n9rVPwEiLyW8xq5vuAH0XkRlV9DxgHjAaWi4h9CLIA8OlqI8Bz/1mDg0KAx13cmTGsAH4SkZmq+g7wX2CliMzHJOMuwMwY6qMUOFVVC0UkGlgmIgudfffZJNuRfLY6gz/MXkvrZtE8e+lozhnRPSTilwHO/cA0VV0sIr0x63ReVdV9wDUicjrmYSh4Vy9ZPEJhabkR4ImP5cKxwSHA4y71OgZVfVZEPgOeF5ErMRUZiZiKCsGUs8524zoKFDpvo53NTslroay8kkcXbOTNH1I5rm8HnrtsNF3a2P5GPkL4pSS1gurdVVHVr0RktM+tsgQcz369ldyDpbxy5diQa0jpVjxCVXer6rmYmutFwFDMU/49qvqJuzFXEYkUkWRMQ7JFqrrC2fU3EVkrIk+JSHD1p/Uw2QdKuOzV5bz5Qyo3TOrLezeMt07BtzwGLBCRH4Bk4MmaB1hNBourAM/oXu39bY7HcTtQLSIdVfUjYAwwABNeGtOQD1PVClUdhclJHCciw4A/YhJ94zCrqX9fx+ffJCKJIpKYmxuai6tX7szjnGeXsSHrAM9eOpoHzxkSlJ0ZgxlVfRxTmvoUMMlZWWyx/Iyq8nCQCvC4izvJ59NEJBvIdWqxh6jqTZgQ0jsi8oSINGiprdMlcjFwtqpmqaEU+A9wXB3nvKKqCaqa0Llz54Z8XMCjqryxbCeXvbqc1s2i+Ow3E5k+soe/zQpbVDVFVT9W1U3+tiWYqKxUDpQc9rcZXuerjTl8uyWXu04fGHQCPO7izuPo85jWwC2B24F/A6jqt5gqjUOYKfdREZHOItLOed0COAPY5PSDQUxW9VfAuoZ/jeCluKycuz5I5uF5GzhlcBfm3D6RY0KkFjrYEJEHnYWb7hw7UUSme9umYOLjpN2M/9vX7NoXuo2XqwR4BgSpAI+7uOMYugPznbjq55j2vwCoapmqPgjMcPM6i0VkLaYccJGqzgPec9oPpACdgL828DsELTv3FnH+8z8wb20m9501iJevGBtSJW9BSH9gl4i8IiLTqx5a4Ody6zEicqeIrMQs8sz3m6UByNItezl0uIJ/f72l/oODlCoBnoemDw3pMK875apzgVkiMheYhOmyWg1VXV/fRVR1LWaGUXP8VDdsCDm+2pDNPR8lExUhvHXdcUweGFrhsWBEVa8VkeGYmfHbQFsRUeAwpvW1YFrNvwK8VaMLa1ijqiSm5REdKXy2OoPbTu7PgC6hNfMNBQEed3HH5V2PWe0ci1mmf7dXLQpxKiqVJ77czA1vJ9KnYyv+d8ck6xQCCCe/cDPQEdO64gLgWuAsoIuT53rFOoXqpOcfIvtAKXecOpAW0ZE8teiI3oBBTygI8LiLO+sYyoBnfWBLyJNfVMZdHyazdEsuv07oyV/OGxr07XlDFadfUjJu5M8skJiWB8AZQ7pSXlHJM99s49aM/QyLCw2hqOU7QkOAx12OOmMQkUnuXkhEWjvTcEstrMvYz/TnlrF8+z7+PmM4/7xwhHUKQYJTOPFnEflXQ+6JcOKn1HzaNIvimK5tuH5yP9o2j+LJRaGRayivqOShuUaA55aTgluAx13qCyW9JiJfi8iljnDIEYjICBH5F7ANGOlxC0OAjxN3c8GLP1BZqXx0y/FcepyVYAxUnMTzqy7vW2GKJR7EtMheLCJn+8u+QCUpNZ8xvdsTGSHEtojm5pP6882mHJLSgj8//9+VRoDnwWnH0iImPB7m6nMMwzDtLmYCeSKyWUQWi8hCEVkuIvmYXkpxmD5ItlWwC6XlFfzp0xTum7WWsb3b8787Jtk22YHPZOAzl/dXAG2BgUB7TJ7tPj/YFbDsLz7MlpyDJPT+ZQXwtRP70Kl1DI9/sdmPljWdvKIynvhyS8gI8LjLUR2Dqpar6vOqOhizGvRFTMw1DfgK0z8+TlUvr9LEtRiy9h/i1y8v570Vu7jlpP68fd1xdGwdmothQox4wHVh2+nALFVNc1q/PI1pCWNxWLUrH1UY2+cXx9AyJorbTh7Ajzv28f22vX60rmk89sVmCkvLmRkiAjzu4rYegyMkkuhFW0KGH7bv5Y7/rqbkcAUvXTGGs4d1r/8kS6BQDrjGC8YDD7m8L8DMICwOiWl5REXIEbPhy8b34tXvdvDYF5s5oX/HoPvDui7jFwGecFt02ugVGiJyoohMrVrNbDG13K8s3c4Vr62gfasY5tw+yTqF4GMjcD6Y/BkmTLrYZX9vINsPdgUsian5DO3RlpYx1Z8zm0dHcudpA0neXcA3m3L8ZF3jMAI86+nQMoa7Tg8dAR53cadX0u0i8qcaY/MwYubzgPUiEvqFvfVQWFrO7f9dzaMLNnH2sG589puJDOjS2t9mWRrOv4BHRGQpJly6QFV3uuyfCqz0i2UBSFl5Jcm7C0jo06HW/ReOjad3x5Y8/uUWKiuDp8v+Z8kZJKXl8/uzBxPbIvy6EbgzY7gak1MAQETOxyz2uRKj+5wG/J9XrAsStuUU8qvnv2fhuiwemDqY5y8bQ+tmjVFNtfgbVf0MmAIkAU8Av65xSDEm12YB1mfup7S8slri2ZXoyAjuOf0YNmYdYMG6LB9b1zgKS8v5+4LQFOBxF3f+evUHVru8nwrMdeQOEZEHcE/BLST5fF0W9368lmZREbx7w3hO6B/aS+XDAVX9Gvi6jn1/EZEBPjYpYKkqR3VNPNdk+sgePL94G08u2sLZQ7sFvCzts99sJedgKS+HoACPu7jzP9QMKHJ5fzyw1OX9DqCLJ40KBsorKvnHwk3c8u4q+ndpzbw7J1mnEMI4TfSuEJElQHDXYHqQn1Lz6N2x5VHFpCIjhN+deQw7cov4dHWGD61rONtzC3ljWegK8LiLO44hDSOig4h0wWjdLnPZ3w1TqRE25BWVcdUbK3np2+1cPr4XH908ge6xDZKksAQJTkfV54FMjLrbBkwJa9ijqiSl5TO2jjCSK2cN7cbwuFie/norZeWV9R7vD8JBgMdd3HEMb2H0nh8GZgEbVTXJZf8JmJbZYcPf5m8kMS2fxy4cwd/OH06zqPBYDRkuiEisiNwmIquAbzENJNsAp6nqbaq6+OhXCA9S9xWzt7CMhN61J55dETGzhvT8Q3z40y4fWNdwvg4DAR53cccxPAa8BEzHhJQuqrF/IvCRh+0KWErLK/hy/R7OG9mDixJ6+tsci4cRkXeAdIzGyNNAN1W9wr9WBSaJqaZx3rij5BdcOemYzozr055nv9nGobIKb5rWYEoOV/BwGAjwuEu9jkFVK1X1z6o6WlWnqOrGGvsvUtXXvGdiYLFs614OlpYzdYRdnxCiXAo8A1ykqm+palF9J9SFiLwhIjkiss5lbJTTTibZ0TCvVcpWRK4Wka3OdnVjbfAmSWn5xLaIpn9n98qyRYR7zxxEzsFS3l2eVv8JPuT1ZTvZlVfMzOlWZx2asMANQER6ichDIhJY/8teZEHKHto2j2KiTTSHKhcDo4B0EflURM4XkcYWsr8J1Gy49y/gL6o6Cviz874aItIB059sPEYDfaaIBFwm9KfUPMb2bt+gyp3x/ToyeWAnXvx2O4Wl5V60zn0yCw7x3DfbOGtoV6uN4tBgxyAi0SJykYh8galIuhhzA4Q8ZeWVLNqwhzOGdCMmyj5VhCKqOltVpwGDMGptjwN7MPfKaGlAXwdVXQrk1Rzml5YasZikdk3Owkjf5qlqPrCIIx2MX8kvKmN7bhEJboaRXLn3zEHkFZXxxrKd9R/sA/6+cBOVqjw4bYi/TQkY3P7rJiJDRORJIANzs5wGTFfVIao601sGBhLfb9vLgZJypo0Iny6L4YqqpqvqI6raH7PI7WPgNSBLRJqywO1u4DER2Y25j/5YyzFxwG6X9+nOWMBQtX7BncRzTUb2bMeZQ7ry6tIdFBSXedq0BrF8xz7+tyaTW07qHxYCPO7iTkuM60VkObAc03b410BfzJNP2ISQABakZNGmeRQTB9gwUjihql+p6iVAD+DvmEq8xnIrcI+q9gTuAV5vim0icpOTq0jMzc1tyqUaxE+OvvOI+MYptP32zGMoLCvn5aU7PGyZ+4SjAI+7uDNjeBkzle2iqteq6mJH9jCsOFxRyZcbsjnj2K62PDVMUdV8VX1aVZsiSHU1RuMEzCyktuRzBuBa8hbvjNVm0yuODnVC586+i48npeYzPC620SqEg7u1ZfqIHrz5fSo5B0s8bJ17hKMAj7u44xhewDzlfCcid4pIWGZnvt+2l/2HDjN1uK1GCmVEZG4d2zsi8qCzyLMpZAInOa9PBbbWcswXwJki0t5JOp/pjAUEJYcrWJu+v87Gee5yzxnHUFZRyQuLt3vIMvepEuA5oX94CfC4izvlqndiptBPAucCu0VkPiBA2LQdXJiyh9bNopg00IaRQpx9dWytgeuAzSIyzJ0Licj7wI/AIBFJF5HrgRuBJ0RkDfAoRi4UEUkQkdcAVDUPeAQjKfoT8LAzFhCsy9hPWUWlWyuej0bfTq24cEw8/12xi4yCQx6yzj0e/9II8Dx0bngJ8LiLWy1AVbUMeB94X0T6AtcDI4AfRGQh8LGqfni0a4hIc0yPpWbO585S1ZnO9T4AOmI6Wl7pfF7AcLiiki827OH0Y7s0eupsCQ5U9dq69olIBPAqJs8w3Y1rXVrHrrG1HJuIUUSsev8G8EZ9n+EPEn9OPDe9gvbO0wfy6eoMnvtmK3+fMaLJ13OHdRn7eX/lLq45oU/YCfC4S4NrLlV1p6o+iBEsuRijdvW2G6eWYnShR2LqxM8WkQnAP4GnVHUAkI9xOgHF8h37KCi2YaRwx8mtPUMtf9jDicTUPPp1auURqdq4di24bHwvPkpMJ3Vvo9cSuo2rAM/dpx/j9c8LVhpdjO+siJ6vqudTPVFW1/GqqoXO22hnU0ycdZYz/hbwq8ba5C0WpGTRKiaSE48Jy/SKpTrFQNjWNVY1zmvM+oW6uO2U/kRHCv/+aovHrlkX4S7A4y71hpJE5Ld17NoPbFbVZarqlm6fiERiwkUDgOeB7UCBqlYtgQy4eu3yikq+WJ/Nacd2tWEkC8AZgPf/ggUo23OLyC8+3Kj1C3XRpU1zrjmhLy8v3c6tJw9gUDfvhHesAI/7uJNjuKOO8XZArIj8AJzrTnJMVSuAUY5O9KeA271tReQmnERdr1693D2tyazYmUdeUZkNI4UJIjKjjl2xmBDSdUCdeYhQp6px3tGEeRrDzSf2473laTy5aDMvX5ng0WtXYQV43Kdex6CqfevaJyL9gHeBv2FKWt1CVQtEZDFG9KediEQ5s4aj1msDrwAkJCT4TDx2fkoWLWMiOXmQDSOFCbPqGD+IEei5rr5Ci1AmMS2fDq1i6NeplUev275VDNdP7su/v9rK2vQCRsS38+j1qwR4LgxzAR53aVLDH1XdAfwBU2d9VESkszNTQERaYKbkG4HFwIXOYVcDc5pikycpr6jki3V7OHWwrUYKF1Q1oo4tVlWPU9UP/G2jP6kS5vFGief1k/rSvmU0j3/p2UidqwDP78NcgMddPNEJbhfQ1Y3jugOLRWQtpjZ7karOA34P/FZEtmFKVpvUIsCTrEzNY58NI1ksAOQeLGXn3iKPlKnWRpvm0dxyUn+Wbsll5U7PLduwAjwNxxOOYQRu9ExS1bWOpsMIVR2mqg874zucJ7EBjrZDqQds8ggLUrJoER3JKYPCTtLaYjmCnxvnNXHF89G46vg+dG7TjMe/2Ixq0yPGVoCncbjTRK9DHVtfEbkQo3IVctPrikrl83XZnDq4i+2jYrEASWl5xERFMCyubf0HN5IWMZHcceoAVqbm8d3WvU2+nhXgaRzu/KT2Arm1bNswDuELzCK1kOKn1Dz2FpYyZbjto2KxAPyUms/I+FivN5G8ZFwv4tq14PEvmzZryNpvBXgaizvlqqfUMX4A2OqyaC2kWJCSRfPoCE4dbMNIFsuhsgrWZ+7nhsn9vP5ZMVER3HX6QO6ftZYvN2Rz1tDGPZw9usAK8DQWd8pVv/WFIYFEZaWycN0eThnUhZYxbrWTsoQwTjVdtdl1IDW18wVr0gs4XKFeSzzXZMboOF5asp0nv9zC6cd2JbKB6w5WOAI8d5420ArwNAIbdKuFxLR8cg+WMsVWI4UtItJbRBaKyCFMd9WqEGpVaDWsqEo8N7WjqrtERUZw9xnHsDn7IPPW1qZ+WjflFZXMdAR4brUCPI3CPg7XwoKULJpF2TBSmPMfzOr+6zEaCj5bVBmIJKbmMbBLa9q1jPHZZ54zvDsvLN7GU4u2MHV4d7eTx1UCPC9cPsYWjjQS6xhqYMJIWZx0TGdaN7M/njDmOGCCqq7ztyH+prLSNM6bNsK3M+iICOF3Zw7ixrcT+SQpnUuOq78VjqsAzxQrwNNobCipBqt25ZN9oNTnN4El4NiJ0Q4Je7bmFHKgpJyxHmyc5y6nH9uFkT3b8czXWyktr6j3eCvA4xncdgwi0uco+5oijh5QLEjZQ4wNI1ngLuDvIjLA34b4m8Q0k2cf5+HGee4gItx35iAy95fw/opdRz22SoDnquN7WwGeJtKQGcMaEbnCdUBEIkTkYUy/o6CnKox04sDOtGlue7WHOXOAkzFSnsUicsB187NtPiUxNZ9OrZvRy0/VPRMHdGRCvw48t3g7xWXltR6jqjxkBXg8RkOC6PcDL4nIVOAWoDPwHqYj6jQv2OZzVu8uIGt/CfefPcjfplj8z+3+NiBQSEzLY1wf7zTOcwcR4d4zB3HhSz/y1g9p3HrykZVGc5IzSUzL558XDLcCPB7Abcegqi+LyLcYZ7AOU7GxCJgaKjXdC1OyiImM4LRj3ekJaAllVPUtf9sQCGQfKGF33iGuPr6PX+1I6NOBkwd15qVvt3P5hF60dZnRF5aW8+iCjYyIj+WisfWKSVrcoKHJ5ywgFdNNtQXweag4BVWzqG3ywE7VfuksFhHpJiK9XDd/2+QrElO93zjPXe49cxD7Dx3mte92Vht/7ptt5Bws5S/nDrUCPB6iIcnnE4EUTOhoKEbJ6jER+VREOnrJPp+RvLuAjIJDtsW2BQARiRWRt5wFbhmYKiXXLSxITMujeXQEQ3t4r3GeuwyLi2XKsG68/t0O8orKANiRW8jry3ZYAR4P05AZw1fA28BEVd2mqu8AozG5hhRvGOdLFq7bQ3SkcPoQG0ayAPA4MBL4FVACXAbch9El/7Uf7fIpSWn5jOrZLmA6k/72jGMoPlzBS99uNwI88zbQLCrS5gU9TEOSz6er6lLXAVXd6cwkHvCsWb5FVZm/NotJAzrZxJWliinApar6nYhUAEmq+qGIZAE3U7cEaMhQVFrO+swDAdVWYmDXNpw/Ko63fkilT8dWLNmcy4PTjqVLm+b+Ni2kcPsxoKZTcBmvVNW/es4k35OSsd+GkSw1accvAlT7MeqCAD8CIbNu52is2V1ARaWS4If1C0fj7tOPoaJSeeDTFCvA4yUa1PNBRNpjnqR6AdWaplQpsgUj81OyiIoQzrBhJMsvbAf6YaRrNwKXiMhKYAbgVsGFiLwBnAPkqOowZ+xDoCru0Q4oUNVRtZx7D3ADpkdTCnCtqpY06Rs1kJ9S8xGBMT5qnOcuvTq25OJxPfnvil1WgMdLuO0YRGQCMB8oxeQVMjA6zqWYSqWgdAyqyoKULCYO6OTTBmGWgOdNjGztEuAfwDzM2oYIzKpod6/xHCY3B4Cq/pyfEJEnMLORaohIHHAnMERVD4nIR8AlzvV8RmJaHoO6tgnIKr0/nzOEC8fGM8YmnL1CQ2YMj2HWMNyFEek5FSgC3gde97xpvmF95gF25x3ijlMG+tsUSwChqk+5vP5GRAYDCRhxKreKLVR1aV2tZMSsFrsYcx/VRhTQQkQOAy0xHV59RkWlsnpXAb8a3cOXH+s2zaMjrVPwIg2Zg40AnlOjtVcBNFPVbOD3wENesM0nzE/JItKGkSz1oKq7VHW2u07BDSYD2aq6tZbPysBURe3CrB3ar6pfeuhz3WLTngMUlpaT4IfGeRb/0xDHUObyOhvo7bwuBALzsaIeqsJIJ/TvSPtWNoxkqY6I3CYi651eSf2csT+IyMUeuPylmNl2bZ/bHjgP6Iu5t1rV7FPmcuxNIpIoIom5uZ7TD/K1MI8lsGiIY1gFjHNeLwH+KiJXA88Aaz1sl0/YkHWAtH3FTLPVSJYaiMjdwIPAK4DrctoMmthHSUSiMEnsD+s45HRgp6rmquphYDZ1VEKp6iuqmqCqCZ07e07wPjE1n25tmxPfvoXHrmkJHhriGP7EL3HOBzHyhs8C7YGb6jtZRHqKyGIR2eA8hd3ljD8kIhkikuxsUxv4HRrNAieMdGYjxcYtIc0twI2q+jTg2tJzFWblf1M4Hdikqul17N8FTBCRlk4u4jRMZZTPSEzNY6wfG+dZ/EtDmuglurzOxZStNoRy4HequkpE2gBJIrLI2feUqj7ewOs1CRNG2sPx/TrSwYaRLEfSG9MssiaHMX3C6kVE3se07u4kIunATFV9HVNh9H6NY3sAr6nqVFVdISKzME6oHFiNmbn4hMyCQ2TuL+FGG0YKW3ymXamqWZhEGqp6UEQ2AnG++vyabNpzkJ17i7hhcl9/mWAJbHYAY/hlkVsVU4EN7lxAVS+tY/yaWsYynWtXvZ8JzHTTVo+S6OQXxgVA4zyLf6jXMYjIXHcupKrnuvuhTgnfaGAFMBG4XUSuAhIxs4r8Ws65CSdk1atX05tbLkjJIkLgLBtGstTO48BzItISk2M4XkSuxOiSXOdXy7xMYmoeLWMiGdzNqqCFK+7kGM4BhgP76tncQkRaA58Ad6vqAeBFoD8wCjOjeKK28zyZZFNV5qdkMaFfRzq1trK+liNR1f9gyrAfxawjeAe4EbhTVetKGocEian5jOnVnii7ojhscSeU9BhwJXAi8B/gzaMkzY6KiERjnMJ7qjobwFkLUbX/VcwKU6+yJbuQHblFXDvRhpEsdaOqrwKvikgnIEJVc/xtk7c5WHKYTXsOcMepdsFnOFPvI4Gq/h7oCdyDs/JTRBaKyIXOH3q3cKorXgc2quqTLuOutaLnU3vCz6PMd8JIZ9swksUNVHVvODgFgNW7CqhUAq5xnsW3uJV8VtUKYC4wV0S6AVcBfwVeEJF+qlroxmUmYmYeKSKS7Iw9AFwqIqMwzcJSMS2NvcrClCyO69uBzm1sGMlSHW/k1IKJxLR8IgQrehPmNKYqqRWmK2RrzKpndeckVV1G9YVCVSxohA2NZmv2QbbmFHLl8U0tRbeEKOdgKpGW+NkOj1FWXklMlHv5gsTUPI7t3pbWzXxWsGgJQNz6bRGRFiJytYgsxbQA7g1crar9VLXIqxZ6mAUpexAbRrLUzWNAM0xObTvwf6p6bc3Nvya6z3++38mIv3xByeGKeo8tr6gkeXcBCXb9QthTr2NwEsJ7gDswi3J6qOrlqvq1t43zBgtSshjXuwNd2lrFJ8uReCqnFij0aNeCksOVbMg6UO+xG7MOUlxWQYJdvxD2uDNjuB7Ix5SSTgHeFpG5NTevWukhtuUUsjn7IFOH29mCpW5UtUJV56rqrzCN7BZjcmoZTrl10DAyvh1g1Njq46dUoz9kE88WdwKJb+NmHiHQWZiSBcAU2zTP4j6NyqkFCt1im9OlTTPWph+hB3QESWn5xLVrQfdY2zgv3KnXMdS2fD9YmZ+S6AIv+AAAIABJREFURULv9nS1YSTLURCRFhgRnesx4aRPMTm1oAyfjohvx5r0o88YVJXEtDwm9Ot41OMs4UHYLG3ckVvIpj0HmWpnC5ajEGo5NYCR8bHsyC3iQMnhOo9Jzz9E9oFSm3i2AD5soudvFq7bA8AUm1+wHJ3r+UU5bQowpbbW08G0jmFkT5NnSEnfz8QBnWo9JjHN5BfGWsU2C2HkGOavzWJMr3Y2fmqpj5DJqVUxIj4WgDXpBXU6hp9S82nTLIpBtnGehTBxDKl7i9iQdYAHpx3rb1MsAU4o5dSqaNcyht4dW7J2d90J6KTUfEb3bk9khBXmsYRJjmHBOluNZAlvRsS3Y20dCej9xYfZknOQcTa/YHEID8eQksWonu2Ia2fDSJbwZGR8LJn7S8g5WHLEvlW78lGFsXb9gsUh5B3Drn3FrMs4wDQ7W7CEMVUJ6NrCSYlpeURGCKOcYyyWkHcMVWGks4fZaiRL+DK0R1sihFrDSYmp+Qzr0ZaWMWGRcrS4Qeg7hpQsRsbH0rNDS3+bYrH4jZYxURzTtQ1raqyALis3jfNsmarFlZB2DGn7ilibvt8uarNYMGWra9MLUP2lGnd95n5KyyttfyRLNULaMfzn+1SiI4XzRsX52xSLxe+M7NmO/OLDpOcf+nksKS0fwK54tlQjZB3D/uLDfJS4m+kje9At1vZGsliqOq0mu3Ra/Sk1j14dWto29JZqhKxjeG9lGsVlFdw4uZ+/TbFYAoJB3doQExXxcwJaVUlKy7ezBcsRhKRjKC2v4M3vU5k8sBPHdm/rb3MsloAgOjKCId3b/pyATt1XzN7CMivMYzmCkHQMc5MzyTlYamcLFksNRsbHsi5jPxWVSqIV5rHUQcg5BlXlte92MrhbGyYPrL1hmMXiC0TkDRHJEZF1LmMfikiys6WKSHId57YTkVkisklENorI8Z6waWTPdhSXVbA9t5CktHzaNo9iQOegEqWz+ICQcwxLt+5lc/ZBbpzcj9raJVssPuRN4GzXAVX9taqOUtVRwCfA7DrOfRr4XFUHAyOBjZ4waIRLAvqn1DwS+nQgwjbOs9Qg5BzDq0t30LVtM6aP7OFvUyxhjqouBfJq2yfmqeVijBhQzX2xwInA6851ylS1ftFmN+jXqRVtmkXx7ZZctucWMdYmni214DPHICI9RWSxiGwQkfUicpcz3kFEFonIVuffRv+mrs/cz7Jte7nmhL7ERIWcz7OEFpOBbFXdWsu+vkAu8B8RWS0ir4lIq9ouIiI3iUiiiCTm5ubW+6EREcKwuFg+d4SrbEWSpTZ8+dezHPidqg4BJgC/EZEhwB+Ar1V1IPC1875RvP7dTlrFRHLZ+F4eMdhi8SKXUstswSEKGAO8qKqjgSLquC9U9RVVTVDVhM6dO7v1wSN7tqOiUomOlJ+b61ksrvjMMahqlqqucl4fxMRM44DzgLecw94CftWY62ftP8TcNZlcPK4nsS2iPWGyxeIVRCQKmAF8WMch6UC6qq5w3s/COAqPMNJRdBsWF0vz6EhPXdYSQvgl3iIifYDRwAqgq6pmObv2AF3rOOeoU+Y3v0+lUpXrJvb1jtEWi+c4Hdikqum17VTVPcBuERnkDJ0GbPDUh49wZgk2jGSpC5/32RWR1phqjLtV9YBr5ZCqqojUqrerqq8ArwAkJCQccczx/TvSulmU7aJqCRhE5H3gZKCTiKQDM1X1deASaoSRRKQH8JqqTnWG7gDeE5EYYAdwrafsimvXgicvHlmn/rPF4lPHICLRGKfwnqpWlelli0h3Vc0Ske5ATmOuffKgLpw8qIunTLVYmoyqXlrH+DW1jGUCU13eJwMJ3rJtxph4b13aEgL4sipJMOV3G1X1SZddc4GrnddXA3N8ZZPFYrFYjsSXM4aJwJVAistqzweAfwAficj1QBqmtttisVgsfsJnjkFVlwF1LbE8zVd2WCwWi+Xo2FVgFovFYqmGuMr8BQsikosJO9WkE7DXx+b4Avu9Gk5vVXVvxVeIYO+LkMHv90VQOoa6EJFEVfVaJYe/sN/L0hRC9edsv5f3sKEki8VisVTDOgaLxWKxVCPUHMMr/jbAS9jvZWkKofpztt/LS4RUjsFisVgsTSfUZgwWi8ViaSLWMVgsFoulGiHjGETkbBHZLCLbRKTRYj/+pg4BeY+p3PkLXyj4WY7E3heBTaDeFyHhGEQkEngemAIMAS511OGCkTepISCPB1Xu/IjXFfws1bH3RVAQkPdFSDgG4Dhgm6ruUNUy4AOMMlzQUYeAvEdU7vyJtxX8LLVi74sAJ1Dvi1BxDHHAbpf36c5YqOCWyl2w0BgFP0ujsPdFEBFI90WoOIawQU19cdDWGNdU8HPdF+zfzeI/gv13J9Dui1BxDBlAT5f38c5YqJDtqNvRFJU7f3M0BT9nf9B+twDF3hdBQCDeF6HiGH4CBopIX0cj9xKMMlyoEPQqd1bBzy/Y+yLACdT7ImRWPovIVODfQCTwhqr+zc8mNQpXAXkgG5gJfAZ8BPTCUblT1ZqJuIBGRCYB3wEpQKUz/AAmnhrU3y2QsfdFYBOo90XIOAaLxWKxeIZQCSVZLBaLxUNYx2CxWCyWaljHYLFYLJZqWMdgsVgslmpYx2CxWCyWaljHEKY4HR2vcnmvInLhUY5fKSIX+MY6i8U/2PvCYB2DlxCRziLygoikikipiGSLyNcicoazP1VE7vWTbdMwK2Lfa8BpjwD/EBH7O2NpNPa+CA5C6ssEGJ9gulteDxwDnAMsBDr60yiHu4A3VbWiAecsANpgWjhbLI3F3hfBgKrazcMb0A7T9Or0OvYvcfb/vLnsOwH4FijG9LV5EWhb49yXgKeBfGd7DIhwOWYGsBY4hGlV/C2mWyNAZ8wKy5E1bFLgQpf3vwf2AhNcxt4A3vX3z9duwbnZ+yJ4Njtj8A6FznauiDSvZf8MTAvkh4HuzoaIDAe+xPRJGekcNwrzi+fK5ZjZ3vHAzcBNwN3ONbph+u6/BRwLnAi843LuJKAUWEctiOFx4A7gJFVd7rJ7JXBSvd/eYqkde18EC/72TKG6ARdgnkpKgB+Bx4HxLvtTgXtrnPM28HqNsVGYp5YuzvslwBacdibO2INAuvN6jHN87zrsuhtIq2VcgV8D/3Guf8T5wLmYp6oof/987Racm70vgmOzMwYvoaqfAD2A6ZgY6gnAchF54CinjQWuEJHCqg343tnX3+W45er8Rjr8CMSJSFtgDfAVsE5EPhGRW0Wks8uxLTA3ZW08jmlUNklV02rZfwgQoLanPYulXux9ERxYx+BFVLVEVRep6sOqegKmve5DTgvk2ogAXsM8DVVtI4GBQLKbn1kBnOlsazFJvq0iMtI5ZC9Ql7D4IqAbMLWO/R2AElUtdMcWi6U27H0R+ET524AwYwPmZ94cKMO0QnZlFTBUVbfVc53xIiIuT0cTgEx1lJ+c8R+BH0XkYWA9Zjq8BlgNdBaRTqq6t8Z1FwCzgY+dy79VY/8wx0aLxZPY+yLAsDMGLyAiHUXkGxG5QkRGOEIpFwH3A187v6ipwGQRiRORTs6p/wSOE5GXRGS0iAwQkXNE5OUaH9ED+LeIDHIW39wHPOV89gQReVBExolIL0z8syfm5gNzA+Rgkm1HoKrzgIuAl1wX+jhMBj5v9A/GEtbY+yKI8HeSIxQ3oBnwKEZBKx9TYrcVeBLo4BwzAfOkUkL1srwEzC/ZAaAII+DxsMv+JZiyvOeAAuf6TwCRzv5jMbHbbEyVxTbg/hr2/R34uMZYzbK86ZjY6VXO+zjM01y8v3++dgvOzd4XwbNZoZ4gQ0SWAOtU9fYmXKML5klpnKrudPOcx4BYVb2psZ9rsXgLe194FhtKCkNUNQe4DiMb6C45wP95xyKLxf/Y++IXbPI5TFHVBonCq+pj3rLFYgkU7H1hsKEki8VisVTDhpIsFovFUg3rGCwWi8VSDesYLBaLxVIN6xgsFovFUg3rGCwWi8VSDesYLBaLxVIN6xgsFovFUg3rGCwWi8VSDesYLBaLxVIN6xgsFovFUg3rGCwWi8VSDesYLBaLxVIN6xgsFovFUg3rGCyWJiIib4hIjoiscxkbJSLLRSRZRBJF5Lg6zr1aRLY629Uu42NFJEVEtonIMyIivvguFgtYx2CxeII3gbNrjP0L+IuqjgL+7Lyvhoh0AGYC44HjgJki0t7Z/SJwIzDQ2Wpe32LxGkEp1NOpUyft06ePv82wBDBJSUl7VbWzLz5LVZeKSJ+aw0Bb53UskFnLqWcBi1Q1D0BEFgFnOzKVbVV1uTP+NvArjGZxndj7wlIf7t4XQekY+vTpQ2Jior/NsAQwIpLmZxPuBr4QkccxM/MTajkmDtjt8j7dGYtzXtccPwIRuQm4CaBXr172vrAcFXfvCxtKsli8w63AParaE7gHeN0bH6Kqr6hqgqomdO7skwmSJQywjsFi8Q5XA7Od1x9jcgg1yQB6uryPd8YynNc1xy0Wn2Adg8XiHTKBk5zXpwJbaznmC+BMEWnvJJ3PBL5Q1SzggIhMcKqRrgLm+MJoiwWCNMdgCX1UlfJK5XBFJYcrzL/lzr9lLq8Hdm1Ns6hIv9oqIu8DJwOdRCQdU2l0I/C0iEQBJTh5ABFJAG5R1RtUNU9EHgF+ci71cFUiGrgNU+3UApN0PmriORCoqFQKisvo2LqZv02xNBGfOQYRaQ4sBZo5nztLVWeKSF/gA6AjkARcqaplvrLL4lsqK5UVO/OYlZRO8u78n//ouzqAqtfusOTek+nTqZWXrT46qnppHbvG1nJsInCDy/s3gDfqOG6Yp2z0BR8n7uah/61nyb2n0C22ub/NsTQBX84YSoFTVbVQRKKBZSKyEPgt8JSqfiAiLwHXY2q4LSHE7rxiPlmVzier0tmdd4jWzaI4oX9HWsZEEh0ZQVRkBDGRQnRkBNFREURH/PI6KkKIiYowx7m8jo6MoHMb+3QaKKxJL6DkcCWfJWdwy0n9/W2OpQn4zDGoqgKFzttoZ1NM/PUyZ/wt4CGsYwgJisvKWZiyh1lJ6fy4Yx8icEL/jvzujEGcNbQbLWL8GwKyeJZtOeb2/iQpnZtP7IddrB28+DTHICKRmHDRAOB5YDtQoKrlziFu12tbAhNV5afUfGYl7Wb+2iyKyiro3bElvzvjGM4fE0d8+5b+NtHiBVSVrTmFtG0exdacQtZlHGB4fKy/zbI0Ep86BlWtAEaJSDvgU2BwA859BXgFICEhwb0AtMVnZBQcYnZSOrNWpZO2r5hWMZFMG9GdC8f2ZFyf9vbpMcTZV1RGQfFh7jptIC9+u51PVqVbxxDE+KUqSVULRGQxcDzQTkSinFmDrdcOIg6VVfDFehMq+n77XlRhQr8O3HnqQM4e1o1WzWzRW7hQFUYa07s9Zwzpytw1mTww9VhiomxFfDDiy6qkzsBhxym0AM4A/gksBi7EVCZdja3XDmhUlVW7CpiVtJt5a7I4WFpOfPsW3HnqQC4cG0/PDjZUFI5sdRzDwC6tuWBMHPPXZrFkcw5nDu3mZ8ssjcGXj3TdgbecPEME8JGqzhORDcAHIvJXYDVeah1gaRp79pcwe3U6s5LS2ZFbRIvoSKYM78ZFY3syvm8HIiJsqCic2Z5TSKuYSLrHNqdLm2Z0ah3D7FUZ1jEEKb6sSloLjK5lfAe1twuw+JmSwxUs2pDNx0npLNuaS6XCcX06cMuJ/Zk6ojutbajI4rA15yD9u7RGRIiKFM4bFcfbP6aSX1RG+1Yx/jbP0kDsnW2phqqyJn0/Hyfu5n9rMjlQUk6P2Ob85pQBXDAm3u+LySyBybacQiYO6PTz+wvGxPP6sp3MW5vJlcf38Z9hlkZhHYMFgJwDJXy6OoNZSelszSmkWVQEU4Z146KEnhzfr6MNFVnq5EDJYbIPlDKgS+ufx4b0aMvgbm2YtSrDOoYgxDqGMKa0vIKvN+bwceJuvt1iQkVje7fn7zOGM21Ed9o2j/a3iZYgYNvPiec21cYvHBvPX+dvZFtOYTWnYQl8rGMIM1SVdRkHmJW0mzlrMikoPky3ts255aT+XDA2nv6d7Q1saRhVjqHmH/9zR/Xg7ws3MXtVOvef7faSJUsAYB1DmJB7sJQ5ySZUtGnPQWKiIjhraDcuHBvPpAGdiLShIksj2ZZTSExUBD3bt6g23qVNc04c2IlPV2dw75mDbDgyiLCOIYQpK6/km005zEpKZ8nmHMorlVE92/HXXw1j+ogexLa0oSJL09mWU0i/Tq2IijxyMduMMfHc8f5qftyxr1py2hLYWMcQgqzP3M+spHTmJGeSV1RG5zbNuH5yXy4cE8/Arm3qv4DFbUTkDeAcIEdVhzljHwKDnEPaYfqBjapx3iDgQ5ehfsCfVfXfIvIQRs8h19n3gKou8N63aBrbcgrrbH9xxpCutGkexSer0q1jCCKsYwgR8orK+MypKtqQdYCYyAjOGNKVC8fGM3lgp1qf5iwe4U3gOeDtqgFV/XXVaxF5Athf8yRV3QyMco6JxLSC+dTlkKdU9XHvmOw5Sg5XsDu/mBljau19SfPoSM4Z0Z05yZk8cl65bZMSJNj/pSDmcEUl327O5eOk3XyzKYfDFcrwuFgePm8o00f0sAuLfICqLhWRPrXtc2Q5L8a0lj8apwHbVTXNs9Z5n+25hagemXh25YIx8by/cjefr9vDBWPj6zzOEjhYxxCEbN5zkI8Td/NZcgZ7C8vo1DqGa07owwVj4xncra2/zbP8wmQgW1Vr03t25RLg/Rpjt4vIVUAi8DtVza/tRH+3o6+rVNWVsb3b07tjSz5ZlW4dQ5BgHUOQUFBcxpzkTGYlpZOSsZ+oCOG0Y7tw0dienDSoM9E2VBSIXMqRf/CrISIxwLnAH12GXwQewQhZPQI8AVxX2/n+bke/LaeQCIE+nepunigizBgdz7+/3kJGwSHi2rWo81hLYGAdQ4CzPnM/zy/exlcbciirqGRI97bMnD6Ec0f2sKLrAYyIRAEzqEX3uQZTgFWqml014PpaRF4F5nnFSA+wLaeQ3h1b0Szq6Gp8M8bE8dRXW/hsdQa/OWWAj6wLP3bnFfPwvA387VfD6NK28brb1jEEMB+s3MWf566nVUwkl0/oxYVj4xnaw4qfBAmnA5tUNb2e446YVYhId1XNct6eD6zzgn0eYaubq5p7dmjJcX078MmqdG47ub8VbvIwqsqspHT+8r8NCLAlu9A6hlCj5HAFM+es58PE3Uwe2ImnLxlNB5tI9hoi0hfoA7TAlIimqGqJm+e+D5wMdBKRdGCmqr5OLXkDEekBvKaqU533rTC6JDfXuOy/RGQUJpSUWsv+gOBwRSWpe4s4Y0hXt46/YEwcv/8kheTdBYzu1d7L1oUPeUVlPDA7hc/X7+G4vh148uKRTZbQtY4hwNidV8xt760iJWM/t58ygHvOOMauSvYCTiXRrZgn9jjA9YdcJiLfYWL3n6hqZV3XUdVL6xi/ppaxTGCqy/sioGMtx13pznfwN2n7iiivVAa62Qdp6vDu/HnOemavyrCOwUMs3pzD/bPWUlBcxh+nDOaGyf088vfCZxlLEekpIotFZIOIrBeRu5zxUSKyXESSRSRRRMJWm2HJ5hymP7eM1H1FvHZVAveeNcg6BS8gIs8AazCLyv4EDAFigRigG+aP9zJM4netiIzzk6kBTV09kuqiTfNozhrajblrMiktr/CmaSHPobIK/u+zdVz7n59o3zKaOb+ZxM0n9ffY3wtfzhjKMWV3q0SkDZAkIouAfwF/UdWFIjLVeX+yD+3yO5WVynOLt/HUV1sY1LUNL10x1uoeeJcSoL+q7q1lXw7wjbP9xfmd7A385EP7goKt2cYxNKTx4gVj45m7JpPFm3I4e1h3b5kW0qxNL+DuD5PZkVvE9ZP6ct9Zg2geffTkf0NpsGMQkQiguaoWN+Q8J5mW5bw+KCIbMVN4BaqK72OBzIbaFMzsLz7MPR8l882mHM4fHcej5w+nRYxn/5Mt1VHV+xtwbMC2ovA323ILiWvXokGrmScN6ESXNs2YlZRhHUMDKa+o5MUl23n66610btOM924Y77U2I279j4rIFEws9iSceKyIlACrgM+B/zjxU7dw4rujgRXA3cAXIvI4JrR1Qh3n+HUhjzdYn7mfW99dRdb+Qzxy3lCumNDbVmv4ERHpBIwHIoGVqrrHzyYFNNtyCunfQJ2FyAjh/NFxvL5sJ/sKS23JtZuk7Sving+TWbWrgHNH9uCR84Z5tQnmUXMMInK+iGwB3gAOA49iyufOAq4FFmPK8naIyEsi0rm+DxSR1sAnwN2qegCTALxHVXsC9wCv13aeqr6iqgmqmtC5c70fE/DMSkpnxgs/UFZeyYc3H8+Vx/exTsGPiMh5wFZgJvAw5nf6Cv9aFbhUVirbcwvdTjy7MmNMPOWVytw1YRUcaBSqygcrdzHl6e/YmlPI05eM4plLR3u9M3J9M4Y/AL8FFtRRmfERgIjEAXcBV2FWadaKiERjnMJ7qjrbGb7aORfgY+A1t60PQkrLK3j4fxt4b8Uuju/XkWcvG00n+9Tkc0SkmaqWugw9AkxU1Q3O/vOA54F3/WFfoJNRcIiSw5WNUmYb1K0Nw+LaMntVBtdO7OsF60KDvYWl/OGTFL7amM0J/Tvy+EUj6eGjVeNHdQyqOt6di6hqBnDUuK3TUOx1YKOqPumyKxMTolqCaTZWX1+ZoCWz4BC3vreKNbsLuOWk/tx75jG266n/SBaR21R1sfNeMQUSVdRZomqBrTkHARo1YwCYMTqeh+dtYEv2QY6xreCP4OuN2fz+k7UcKCnnwWnHct3Evj4VOmp0VZKzOCfSCQe5w0TgSiBFRJKdsQcwfeefdloIlODkEUKN77ft5Y73V1NWXslLV4zl7GHd/G1SuHMZ8IqIpAC/A/4MrHBCp9EYPYVb/GhfQNPQUtWanDuqB48u2Mgnq9L545RjPWlaUFNcVs4j8zby/spdDO7WhvdumMCgbr53nI2pShoEvAMkACoi64GrVXX10c5T1WVUX0TkSn39ZIKWykrlxW+388SXmxnQpTUvXTGWflZX2e+o6moRGY/JayVi1jMcA0zA5N5+akhBRbixLaeQTq1jaNeycSvyO7VuxsmDOvPZ6gzuP2uwXa8DbMs5yK3vrmJbbiE3n9SP355xTL09qLxFY+IYLwOvAq2BTpgGX2950qhQ4UDJYW5+N4nHvtjMtBE9+PS2idYpBBCqWqmqT2AKKK7G/B6vUdU51ikcHXd7JB2NC8bEk32glGXbaltOEl7MSc7g3Oe+J6+ojHeuG88fpxzrN6cAbjgGEXlXRFyX7XfHJI+LnR7xHwK2yXoNNu05wLnPLmPxphxmTh/CM5eMsupVAYaIDBWRC4BoVZ0CvAcsEZG7xJaI1Ymqss0DjuHUY7sQ2yKa2avq6zMYupSWmxXMd32QzJDubZl/52QmDfS/BKo7M4YVwE8iUtW/5b/AShH5p4g8CczFzhiqMSc5g/Of/4Hisgo+uGkC107sa0tRAwwR+S1mNfN9wI8icqOqvgeMw6yxWS4iI/xpY6CSc7CUgyXlRxXncYdmUZFMH9mdL9bv4WDJYQ9ZFzzszivmopd+5J3ladw4uS/v3zSBbrGN74jqSep1DKr6LKZq6CIR+RKTX/g9Jj8hmHLW33rTyGChrLySh+au564PkhkeH8u8OyeR0KeDv82y1M79wDRVnQCMwfkdVtV9TgO8P2Fmw5YaNDXx7MqMMfGUHK5kYUp4rSX8emM25zy7jJ25Rbx0xVj+NG1IQIltuRXbUNXdwLkicjGwCJNnuO9oXSfDjT37S7jtvSRW7Srghkl9+f2UwQH1H205AuGXktQKahRGqOpXIjLa51YFAb/IeTbdMYzu2Y5+nVoxa1U6F4/r2eTrBTrlFZU8uWgLLyzZzpDubXnxijH07hh4fdHc/sslIh1V9SPM09UATHhpjNcsCyJ+3L6Pc579jk17DvL8ZWN48JzA8v6WWnkMWCAiPwDJwJM1D2iAJsMbIpIjIutcxj50OgYni0iqS4l2zXNTRSSlqruwy3gHEVkkIludfwOmT/XWnIO0aR5F5zZNX5gpIswYE8fKnXnszmtQ+7WgI+dgCZe/toIXlmzn0uN6Mvu2EwLSKYB7yefTRCQbyHWESIao6k2Yqfc7IvKEiISliKuq8urSHVzx+gpiW0Qz9/aJTBthG4MFA6r6OKY09SlgkqOd3FjeBM6ucf1fq+ooVR2FWe0/u7YTHU5xjk1wGfsD8LWqDgS+dt4HBFWJZ0/lzc4fY2pXZq/K8Mj1ApHlO/Yx7ZllrEkv4ImLRvL3GSM83hHVk7jzWPs8phV2S+B24N8AqvotJkl3CPPEFVYUlpbzm/+u4m8LNnLW0K7MuX0SA5qYjLP4FlVNUdWPVXVTE6+zFMirbZ9T3XQxNdTc3OA8finqeAv4VaMN9DDbchrXI6ku4tq14Ph+HZm9Oh1V9dh1A4HKSuWFJdu47NXltGkexZzfTOKCsYFfxOmOY+gOzHem1Z8DP3ewU9UyVX0QI3oeNqTuLeK855bxxfpsHpx2LM9fNobWthQ1aBCRB52V++4cO1FEpjfh4yYD2apaV6sXBb4UkSSng3AVXV10n/cAtepnishNjsBVYm5ubhPMdI+C4jL2FpZ5JPHsygVj40nbV0xSWr5Hr+tPCorLuPHtRP71+WamDu/O3Nsn+WUVc2NwxzHMBWaJyKPAl8AR/elVdb2nDQtkHp63gb2FZbx3w3humNzPlqIGH/2BXSLyiohMF5Gf438i0lxExojInSKyElOF15S/Vpdy9NnCJFUdA0wBfiMiJ9Y8QM1jdK2P0r7uOvxL4tmzf+CmDOtGi+hIPgmRcNKa3QVMe2YZS7fm8vB5Q3n20tFB9fDojmO4HlOFFIvpNHm3Vy0KcLIPlLBkcw7oZwG/AAAgAElEQVRXTOjFhH5HyPVaggBVvRajEqjA20C6iJSLyCGgCNMi4ypMp99jnXYuDcbp/zWDo5S9Og0oUdUc4FOgSto2u8phOf/mNMYGT7PVg6WqrrRqFsWUYd2YtzaTksPBK/upqrzzYyoXvfQjAB/fcgJXBWFL/XpdmKqWAc/6wJagYPaqDCoVLhob+qV1oYyqpgA3i8itwAiMfGcLYC+QXIfsZ0M5HdikqrUu7XXCWRGOomEr4EyMFgSYmfrVwD+cf+d4wJ4msy2nkObREcR5of3zBWPjmb06g682ZnPOiB4ev763KSot5w+zU/jfmkxOGdSZJy8eRftWjesl5W/qE+qZ5O6FRKS1iAxvukmBi6rycdJujuvTwWoyhwhOv6Rkpz/SB6r6VUOdgoi8D/wIDBKRdBG53tl1CTXCSCLSQ0SqwrFdgWUisgZYicnlfe7s+wdwhohsxTiYfzTuG3qWrTmF9O/c2istoCf060j32OZ8khR8LTK2ZB/k3OeWMX9tJvedNYjXrx4XtE4B6p8xvCYiGZgp9fzaWmw7bQOuwEy97wVSPG5lgLBqVz47cou45aT+/jbF4gUcBcJbMQ0i57obQlLVS+sYv6aWsUxgqvN6BzCyjnP3Aae5ZbgP2Z5TSEIf7yypqJL9fHnpDnIOltClTWC0h6iPT1en88DsdbRqFsm7N4znhP7+73XUVOrLMQzD1F/PBPJEZLOILBaRhSKyXETyMb2U4oBTVTWk1a4+TkynZUwk04bbtQrBjpN4ftXlfStM76QHMZogi0Xk7LrOD0eKSsvJKDjk0VLVmswYE09FpTI3OfCb25YcruCBT1O458M1DI+LZf6dk0PCKUA9jkFVy1X1eVUdjFkM9CJmzUIa8BVwAxCnqpdXSSLWhYj0dJzKBhFZLyJ3uey7Q0Q2OeP/avK38gLFZeX8b00m04Z3t11SQ4PJwGcu768A2gIDgfaYQov7/GBXwLI91zuJZ1cGdGnNyJ7tAr46ade+Yi586Qf+u2IXt5zUn//eOJ6ubYNjhuMObv+FU9VETLVGYykHfqeqq0SkDZAkIoswcdbzgJGqWioiXZrwGV5jYcoeisoquCjBJp1DhHjAdWHb6cAsVU0DEJGnMet2LA6/NM/zbi3+BWPi+POc9WzIPMCQHm29+lmNYdGGbH77UTICvHpVAmcMqXWJSVDT6IY+InKiiEwVkXbuHK+qWaq6ynl9ENiICUHdCvyjSpjdKdsLOD5O2k2fji0Z56X4qsXnlAOuPQnGA8td3hdgZhAWh605hURFCL07tvTq50wf0YPoSAk4nYbyikr+vnAjN76dSO+OLZl/5+SQdArgXq+k20XkTzXG5gFLMOpt60WkQaKtItIH005jBUZOcbKIrBCRb0VkXB3n+HSFpyu79hWzfEceFyX0DLp6ZEudbATOh58LKOKAxS77ewPZfrArYNmWU0ifTq283iCyfasYTh3chc+SMymvCIwGztkHSrjs1RW8/O0OLh/fi1m3nEDPDt51kP7Enf/hqzE5BQBE5HzgLOBKjO5zGvB/7n6giLTGNBW726lyigI6YHIY9wEf1aae5esVnq7MStpNhMCMMf/f3pmHV1ld+//zZZ7nABJAZXBAFESch6pVq2gdqBP32urVp7ZWrXZQ+7P93dvaudo6XG2ttji01gqC1tahtSpa61RABRRpwqCE6STIlEAYknX/2G/kJJwkJ8mZcs76PM/7cM7e77Dfw7uz3r33Wt9VnNHrOmnlZ8D3Jb1CWC97xsyWx9VPIbiQOhFLU6yR1BSfmzScisrt/KMk+2k/Xyut4My7/sHCVZu446KJ/PC8g3NaAC8VJGMYRgNvx32fQnDleySaGroZODaZi0nqTDAKj5hZndpkGTDbAm8RNPJzZmm/ptZ4fF4Zx48tYq++BSkim5eY2ZMEGYp5wM+BixrsspXgbOEQUlCuWF+V1oXneE7cfzD9e3Tm8SxOJ9XWGne/WFJPPfncQwvj5TCZxeeuBJmAOo4G7o/7vgxodsE4GgX8FlhsZvHa908CJxHcA/cDuhCiT3OC15ZWsHpTNTef2aLZMqcdYGYvECStE9V9T9KYDDcpZ1lRsZVaS69HUjxdOnXgnInF/OGtj9i0bSd9u3fOyHXr2FC1g6/NeIc5S8o5Z+IwfnTewQXljZjMiOFDQh5cIo+hA4H4wJ+hhIW65jiWMP10clwCkynAdGBUlOTkj8CllkPauzPnltGvR+e8XWRy6hOJ6F0iaQ6wJNvtyRVKYluAzBkGCFO3O3bV8vSCNc3vnELe/mgDZ/3vq7xWup4fnDueOy6aWFBGAZIbMTwE3BPJXZxIeOOfF1d/DElEO0dRpI2t3F6SRDsyzqatO3nuvbVMO3wEXTvl95xioRNlI7yCoIa6nSBo972sNiqHKI1VIsHooswZhoOL+zJ2cC9mzy/jP44cmfbrmRkPvbaCHz6zmCF9uvH4VUdzyPCknC7zjmQMw61AT+CzBF34LzWoPxaYkeJ25QRPLVjNjl21HruQp0jqC/wnIVBzLEGorjdBCrvJgM1CozRWyYj+PTK66BrSfg7np899wIqKqrTqk22p3sm3Zi3k6YVr+PQBg/nFhRPp2yOz01e5RLNTSZHI2H+b2aFmdoaZLW5Qf4GZ/SZ9TcweM+eu5MC9+nBQDgbZOG1D0u8Ijg9TgTuBoWaWkyPXXKAunWemOe/QYiSY/Xb6IqE/WLuZc+7+J88uWsNNpx/A/V+YXNBGAdoQ4AYgaaSk70r6sPm92xdL1m5hQdkmLjhsuMcu5CfTgLuAC8zsITOrau6AQmVXTS3LKqoy5qoaz9C+3ThuzCBmzy+jtjb1S4+Pzyvj3Hv+yZbtu/jDF4/iqhNHp0U5tr3RYsMgqbOkCyT9leCRdCEhGXpeMXPuSjp3VMG4pxUgFwITCUl6npB0XuRO3SIkTZcUi5wn6soei3OwWCFpj5zozWiHfVfSqgZOGllj5YZt7NhVy+gsGAYIMQ1lG7bxrxUJ02q3iuqdNXxr1gK+OfNdJo7ox9NfPc4Tb8WR9FK7pHGEudhLgG2ESNHPmtmzaWpb1thZU8sTb6/ilAOHMKAda6o7jRPF0cyWNBz4L+A2grx8B+BQSYuT9I57ELibkAmu7tyfxERI+jmwKcFxCbXD4tY2bjez21pxaylndzrP7BiG0w4aQs8uHZk1v4wjU/DH+8P1VVz1+/m8v2YzV580mq+dsh+d0hzN3d5IRhLjCklvEHRk+hMCgfYlpEXMuykkgBc/iLG+agcXTB6e7aY4acbMyszs+2Y2mvBszyQYiDWSmg1wM7NXgISvslHszoUkyPnchHZYzlHnqpqtEUOPLp2YcvBePLNwLdt2tC3t53OL1nLWXa+yauM2pl82mRs+c4AbhQQk84v8GngeGGxm/2VmL5lZbgiYpImZc8sY3LsrJ4zNrPSGk12i7G0XA8OAHxNcsdvC8cA6MytpaqcG2mF1XCNpQTRVlVXlxtJYJUP6dKVPt+wtyE6dNJzK7bv42/trW3X8zppafvCX9/ny7+cxqqgnf7n2OE4+wGOTGiMZw/BLggLqPyR9NcpylbfEtlTz0pIYUycN9zeJAsXMNpjZnWaWMLtaC5hGgtFCPAm0wyBIcYwmrIGsIUh2NHZ82sUlg0ZSeqW2m+PIfQdQ3K97q/I0rN1UzbT73uA3ry7nC0fvzYwvH53XAnipoNk1BjP7qqRvAp8jBAD9TNILhGC1vPPpevLtVdTUmk8j5TmSnmqkahMh4vm+tkjAS+pEcIU9rIl9EmmHYWbr4va5n6BinBAzuw+4D2Dy5Mkpd9sxM0pjlVmP5enQQUydVMw9L5WybnN10klxXi2p4Lo/vs22nTXcNe1Qzp4wLM0tzQ+SeiU2sx1m9qiZnUKQxHib8CbzmqTHJTUUIGuXmBkz5pZx2N79Mxrh6WSF9Y1svYDLgSWSxrfh/KcAH5hZQhW4JrTDkBSfO/Y8YBFZYs2maqp21GQlhqEhUycNp9bCy1tz1NYad/69hM9Pf5MBPbvw1DXHulFoAS2eKzGz5Wb2HYJe/YWEZCcPN31U++CdlRvD29FhPlrId6L1skTbecAYQq7zHzd3HkmPAq8D+0sqk3RFVHUxDaaRJA2T9Ez0tTHtMAij8oWSFhAEJr/W5htuJSWx9KfzTJZ9B/Vk0sh+zJpfRlMOYx9X7eCyB//F7X//N+dOLOZP1xyb9qxz+UarlaGiBeingadzNR1nS5k5r4zunTty5iF7Nb+zk7eYWa2ku4BmXbHNbFoj5ZclKFtNkK1vUjvMzD7fkvamk9IcMgwAnztsON9+YhHvrd7M+OK+e9TP+3AD1/xhPusrd/Cj8w5m2hGeXKs1NGsYJH29kapNwBIzezVX03G2hG07avjzO6s54+Ch9M6i94WTM2wFCn6FsjRWSf8enRmYI/E8Zx08jO/9+X0en1dWzzCYGQ/8cwU/emYxe/XrxuyvHJPQcDjJkcyI4dpGyvsBfSW9BpxtZqkLS8wCf31vLVu27+KCw1wwzwHgVODf2W5EtimNbWHM4F4589bdt0dnTj1wCE+9u5pvn3kgnTt2YHP1Tm56fAHPLlrLqeOGcNv5Ewpe66itJOOVtG9jdZJGAb8HfkhwaW23zJi7kpEDenDkvgOy3RQnA0ia2khVX4In0eWEiOiCxcwoiVVyxvjcmlqdOqmYpxeuYc6Scor7decrj8xj5YZt3DzlAL54/KicMWLtmTZlnzCzZZK+BTzQ3L6SRhAWqYcQoqbvM7M74+q/QZAlKDKzjGZwW/nxVl5bup6vn7qfC2gVDo83Ur6F4K56uZk9lsH25Bzrq3awcevOnFlfqOOE/YoY1KsLP33uA1Z+vJW+3Tvz6BeP4gh/qUsZqUhL9BHhj31zNKoNExmN06JzZZxZ88uQwsKWUxiYmUcvNkO2NZIao3PHDpw9oZjp/1zOMaMHcufFh1LUu2u2m5VXpMIwHEISmklmtoYQ+4CZbZFUpw3zPnA7cCMhUUpGqa01Zs4t47gxgyju1z3Tl3ecnCXXPJLiuf7UsRy2d39OHz+Ujj7KTznJeCU1Nj6rm4u9lZC3OWnitWEknQOsMrN3szE3+May9azauI0bT98/49d2nFymNFZJzy4d2atvclHGmaRPt87uVp5GkhkxVBDWBBJhBCXKnyZ7wXhtGML00s2EaaTmjrsSuBJg5MjU5X+dMXclfbp14jMHDU3ZOR0nH6jL2uaLuYVHMobhpEbKNwMlZlaZ7MUaasNIOpgg4V03WhgOzJd0hJnVk1FMhybM5uqdPLtoLRdMHp7RXLaO0x4oiW3h2DGDst0MJwsk4676cioulEgbxswWAoPj9lkBTM6UV9Jf3l3D9l21HrvgOA3YXL2TdZu3Z11V1ckOqVh8TpY6bZiFcakObzazZ5o4Jq3MmLuS/Yf05pDhHiHpgKR+NNAPa++Bm61laQ4vPDvpJ2OGoSltmLh99slMa6Bk3RbeWbmR75x5oM+hFjCS9gbuBU4E4nUfRFhDK8g5xlwSz3MyTyZHDDnFzHlldOogzj00J7MpOpnjAYK8yxXAahp3tCgolsYq6dKpAyP6uwt3IVKQhmFnTS2z56/i5AMGM6iXB8YUOEcAR5lZ1nIe5CIlsUpGDerpWQwLlKT/16PYg8bq2pobN6O8vKScisrtWc9K5eQEy4E2vR1EeZljkhbFlT0Wl2dhRdy6WsNjT5e0RFJpJC9TV76vpDej8sckZVTetM5V1SlMWvI68K6kS+ILJHWQdAvwUmqblV5mzF3JoF5dOXH/vE5f7STHdcCPJY1pwzkeBE6PLzCzi8xsoplNJLhoz254kKSOwD3AGcA4YJqkcVH1T4HbzWwMsIEw1ZURqnfWsHLDVjcMBUxLDMONwL2S/iCpj6TRwGsEFcoz09K6NFBRuZ0XP4gxdVIxnX2Y7AQZlhMJqTy3StocvyVzAjN7BUjovRS5aV9Ig2xuEUcApWa2zMx2AH8EzomOOZndQn8PAee25KbawtLySsx84bmQSXqNwcx+Lell4BFCDtp+wPPAlPbk0vfk26vYVWuevtOp45o0n/94YJ2ZlSSoKwZWxn0vA44EBgIbzWxXXHnGvCR2i+d5DEOh0tLF5zXACmA8YbTxXHsyCmZBMG/iiH6MHeIPvQNm9lCaLzGNxKOFlJAOqZjSWCUdBPsMKvgEdgVLSxafTwAWEmQrDiJMId0q6QlJA9PUvpSycNUmlqzbwgWTfbTg7ImkoZJGxm9tPF8nYCrQWF6HVUC8B8TwqGw90C86Pr58D8zsPjObbGaTi4pSs2ZWGqtkn4E96dqpIEM4HFq2xvB3QqKdY82s1Mx+R1BILSIYjJxnxtyVdO3Ugc9OGJbtpjg5gqS+kh6StI3wx3d5g60tnAJ8YGZljdT/CxgbeSB1AS4GnjIzIzh0nB/tdykZlKQvjVUy2tcXCpqWGIZTzOw7cfOemNly4ATglylvWYqp3lnDU++s5ozxQ+nTzfPBOp9wGzCBsLhbDfwHcANhXv+iZE4g6VHgdWB/SWWS6jyILqbBNJKkYZKeAYj60jXAX4HFwAwzey/a9Sbg65JKCWsOv231HbaAnTW1LK+oyrnkPE5macni8yuNlNcCP0hZi9LEX99by+bqXR674DTkDGCamf1DUg0wz8wek7QG+BKNpwD9BDOb1kj5ZQnKVgNT4r4/A+yhF2ZmywheSxnlw/Vb2VVr7pFU4LRo8VlSf0JHGkl9XRnM7JYUtivlPD6vjOJ+3Tl6VLtYDnEyRz92ZyDcRHg7LyWMAH6TrUZli9LYFsBdVQudpA2DpKOAp4HthHWFVcBe0fcVQM4ahlUbt/FqaQVfPXksHTwNoFOfpcAoQr7xxcDFkt4iLBq3G4+7VFHnqjq6yA1DIdOSNYZbCTEMxYS52JMJI4e5tCCDWzaYNa8MMzjfYxecPXmQkLcc4CeE6aMdhOc9p5/rdFAaq6S4X3d6di1IGTUnoiX/+4cAV5iZRXOxXc1smaSbgD8QjEbOUVtrPD6vjGNGD2TEAPfLdupjZrfHfX5R0gHAZEJ2wnbhbZdKSlwjyaFlI4YdcZ/XAXtHnyuBZv0/JY2Q9JKk9yW9J+m6qPxWSR9IWhDFRPRrQZua5c3lH/PRx1s9dsFJCjP7yMxmF6JRqK01lpa7YXBaZhjmA4dHn+cAP5B0KXAXsCCJ43cB3zCzccBRwNWRYNjzwHgzOwT4N/D/WtCmZpk5byW9u3bi9IP2SuVpnTxC0leil5WtkkZFZd+SdGG225ZJVm3cRvXOWjcMTosMw7cJiUwAvgOUA/8L9CcKyW8KM1tjZvOjz1sIC33FZva3uNiINwhRnimhcvsunl24lrMmDKN7F4/idPZE0vWE5/k+6mcYXEX6dZRyit0aSW4YCp2WxDHMjftcTnBbbRVRbodDgTcbVF1O4/IBLebdlRvZtrOGM8YPTdUpnfzjy8AXzexpSfHxOPMJ0i8FQ4m7qjoRGdedltSLoE9/vZltjiv/NmG6KeEitqQrJc2VNLe8vDypay0rj96AhviD7jTK3gS14IbsBAoqr2VprJJBvbrSr0dGcwI5OUizIwZJTyVzIjM7O4lzdSYYhUfMbHZc+WXAWcCnI52YROe/jzDcZ/LkyUnl5V1aXkWPLh0Z2qdbMrs7hckyYBK7g9zqmAK8n/nmZI+Qta1ntpvh5ADJTCWdReg0c9pyoSj5yG+BxWb2i7jy0wlJgD5lZlvbco2GLC2vZFRRT8KlHSchtwF3S+pBWGM4WtLnCc/k5VltWQYxM0pilZw7MWNpH5wcJhnDcCvweYJY3gPAg02oRTbFsdF5Fsblv72Z4NXUFXg++gP+hpl9uRXn34Nl5VVM3qd/Kk7l5Clm9kAkb/0joAfwO4KTxVfNLGXrXblO+ZbtbKne5esLDpCEYTCzmyTdTEjfeTnwbUlzCG//fzKznclcyMxepb7XRx17CIilgm07ali1cRsXDnLRPKdpzOx+4H5Jg4AOZhbLdpsyTUnkkeSGwYEkF5/NrMbMnjKzc4F9CVrxPwBWRYvJOceyikjzxedMnSQxs4pCNArgrqpOfVojiNKToEjZixD1nNRCcKZZVl4FwKhB/qA7e5IqpwpJ0wnrcDEzGx9Xfi1wNVADPG1mNzY4bn/qu2aPAv7bzO6Q9F3gi4RYIYCbI3nutFEaq6R3t04U9e6azss47YSkDIOk7sCFwBUEHZkngEvN7IU0tq1NLC2vRIJ9B/mIwUlISpwqCCJ8dxOyGwIg6STgHGCCmW2XNLjhQWa2BJgY7d+REFD3RNwut5vZbW1sW9KUxLYwdnAvd9RwgOTcVe8nGIUSwrrC2Wa2Md0NayvLyqsY1re7Rzw7jZESpwozeyUK2IznKuAnZrY92qe56alPA0vNrKHLbMYojVVx8gGpyRnttH+SWWO4AtgArCFEOz8s6amGW1pb2QqWlnveWqdxzOwmYATwNSI1VUnPSjo/irdpC/sBx0t6U9LLkg5vZv89UoAC10TCktOjBFlpY+PWHVRUbveFZ+cTkjEMDxMWmyuA9U1sOUNtrbGsvIpRPo3kNEEanSo6AQMIYpE3ADPUyByNpC7A2cDMuOJfAaMJU01rgJ83dqHWKAI0ZPfCc+9WHe/kH8m4q16WgXaklLWbq9m2s8ZHDE5LSKVTRRkwO4rif0tSLTCI3YvJ8ZwBzDezdXUF8Z+jqdy/NHah1igCNKTUXVWdBmRcKykT1HkkjfYRg9MEkrpLulTSK8BCgm7SpWY2ysyq2nDqJ4GTomvsR8iPXtHIvtNoMI0kKV4j/jwSazmljJJYJd06d6C4X0FJQzlNkJf5+5aW18Uw+BuQk5hUOVVIehQ4ERgkqQz4H2A6MF3SIkKCq0ujzIfDgN+Y2ZTo2J7AqYR0ovH8TNJEwqhlRYL6lFIaq2R0US/Ph+58Ql4ahmXllfTs0pHB7pPtNM4VwEfsdqo4I9EyQHNxDGY2rZGqSxLsu5ogzlf3vQoYmGC/zzd1zVRTGqvkcJeOceLIS8OwtLyK0e6T7TTNw+RocGYmqdq+i1UbtzFtsEvHOLvJS8OwrLySI/YdkO1mODlMe3SqSAd163G+8OzEk3eLz1t37GL1pmpGF/mD7jjNsTtrm7uqOrvJO8PwiUaSGwbHaZbSWCWdOoi9B/bIdlOcHCL/DENF5KrqqqqO0ywlsUr2HdSTzh3z7k+B0wby7mlYGgviefsMdMPgOM2xNFbp6wvOHmTMMEgaIeklSe9Lek/SdVH5AEnPSyqJ/m2T39yyiiqG9+9Ot84unuc4ECRiErF9Vw0ffrzVDYOzB5kcMewCvmFm4wgaMldLGgd8C3jBzMYCL0TfW83SWKXnYHCciNnzyzjtjleo3lmzR92Kiq3U1JobBmcPMmYYzGyNmc2PPm8BFgPFBN36h6LdHgLObe01amuN5RVV7pHkOBFD+3ajNFbJzHl7Koq7RpLTGFlZY4j06w8F3gSGmNmaqGotMKSRY5pVkVwTieeNKvL1BccBOHrUQA7buz/3zlnKzpraenUlsS1I+IuUswcZNwyRnPEs4Hoz2xxfF6lRJpwQNbP7zGyymU0uKkqcUGRZnUaSP+iOA4AkrjlpDKs2buOJt1fVqyuNVTKifw9fj3P2IKOGIUqAMgt4xMxmR8Xr6tQko39bnYx9aazOMPiIwXHqOHH/Ig4a1odfzVlKTdxCdGmskrE+jeQkIJNeSSKoWC42s1/EVT0FXBp9vhT4U2uvsayiit5dPaG548QjiWtPHsPyiiqeXhhmbWtqjWUVVb6+4CQkkyOGYwk5dk+W9E60TQF+ApwqqQQ4JfreKpaWVzKqqKeL5zlOA04bN5Sxg3txz4ul1NYaKz/eyo5dtS5N7yQkYyJ6ZvYq0Nhf7E+n4hrLyqs4etQeKsaOk1YkTQfOAmJmNj6u/FrgaqAGeNrMbkxw7ApgS7TPLjObHJUPAB4D9iHkZLjQzDa0to0dOoirTxrD9Y+9w98Xr/vk5cmnkpxE5E3kc9X2XazZVO0eSU42eBA4Pb5A0kkEV+wJZnYQcFsTx59kZhPrjEJESuN7AM46ZC9GDujB3S+VfuKq6iMGJxF5YxiW12kkuUeSk2HM7BXg4wbFVwE/MbPt0T4tdapIWXxPHZ06duArJ45mQdkmHn3rI4b26Uafbp3belonD8kbw1CXztNVVZ0cYT/geElvSnpZ0uGN7GfA3yTNk3RlXHnK4nvimTppOHv17cZHLoXhNEEeGYYqOgiXD3ZyhU7AAIL8yw3ADCX2ijjOzCYR0oteLemEhju0Nb4nni6dOvClE0YBHvHsNE7eGIaa2loOGtbXg3WcXKEMmG2Bt4BaYFDDncxsVfRvDHgCOCKqSll8T0MuPmIkJ+xXxKnjEg5CHCd/DMMNnzmAP197XLab4Th1PAmcBCBpP6ALUBG/g6SeknrXfQZOAxZF1SmL72lIt84defjyIzh2zB52ynGAPDIMjpMtJD0KvA7sL6lM0hXAdGCUpEXAH4FLzcwkDZP0THToEOBVSe8CbxFcWp+L6lIW3+M4LSVjcQyOk6+Y2bRGqi5JsO9qYEr0eRkwoZFzridF8T2O01J8xOA4juPUww2D4ziOUw83DI7jOE49FFyk2xeSyoEPE1QNooHnR57g99Vy9jaz5h378wjvF3lD1vtFuzQMjSFpbgO9mbzA78tpC/n6O/t9pQ+fSnIcx3Hq4YbBcRzHqUe+GYb7st2ANOH35bSFfP2d/b7SRF6tMTiO4zhtJ99GDI7jOE4byRvDIOl0SUsklUpqc7arbCFpuqRYpLFTVzZA0vOSSqJ/+2ezja1B0ghJL0l6X9J7kq6Lytv9veUy3i9ym1ztF3lhGCR1BO4haNqPA6ZJGpfdVrWaB2mQJpI0pHnMAruAb5jZOEKOgquj/6N8uLecxPtFuyAn+0VeGJs3LuAAAAU0SURBVAaChn2pmS0zsx0ENctzstymVtFImsiUp3nMNGa2xszmR5+3AIuBYvLg3nIY7xc5Tq72i3wxDMXAyrjvZVFZvpBUmsf2gqR9gEOBN8mze8sxvF+0I3KpX+SLYSgYmkrz2B6Q1AuYBVxvZpvj69r7vTnZo70/O7nWL/LFMKwCRsR9Hx6V5QtpS/OYSSR1Jjz8j5jZ7Kg4L+4tR/F+0Q7IxX6RL4bhX8BYSftK6gJcTEiNmC+kLc1jppAk4LfAYjP7RVxVu7+3HMb7RY6Tq/0ibwLcJE0B7gA6AtPN7IdZblKriNJEnkhQWFwH/A8hf/AMYCRBPfNCM2u4EJfTSDoO+AewEKiNim8mzKe263vLZbxf5Da52i/yxjA4juM4qSFfppIcx3GcFOGGwXEcx6mHGwbHcRynHm4YHMdxnHq4YXAcx3Hq4YahQIkUHb8Q990knd/E/m9J+lxmWuc42cH7RcANQ5qQVCTpl5JWSNouaZ2kFySdGtWvkPTNLLXtTEJE7CMtOOz7wE8k+TPjtBrvF+2DvLqZHGMWQd3yCmA/4CzgWWBgNhsVcR3woJnVtOCYZ4DeBAlnx2kt3i/aA2bmW4o3oB9B9OqURurnRPWfbHF1xwAvA1sJuja/Avo0OPZe4E5gQ7TdCnSI22cqsADYRpAqfpmg1ghQRIiwnNCgTQacH/f9JqACOCqubDrw+2z/vr61z837RfvZfMSQHiqj7WxJ3RLUTyVIIN8C7BVtSDoY+BtBJ2VCtN9EwoMXz38SRntHA18CrgSuj84xlKC7/xBwIHAC8Lu4Y48DtgOLSIACtwHXAp8yszfiqt8CPtXs3TtOYrxftBeybZnydQM+R3grqQZeB24DjoyrXwF8s8ExDwO/bVA2kfDWMjj6Pgf4N5GcSVT2HaAs+jwp2n/vRtp1PfBhgnIDLgIeiM6/x/HA2YS3qk7Z/n19a5+b94v2sfmIIU2Y2SxgGPBZwhzqMcAbkm5u4rDDgEskVdZtwD+jutFx+71h0RMZ8TpQLKkP8C7wd2CRpFmSrpJUFLdvd0KnTMRtBKGy48zswwT12wABid72HKdZvF+0D9wwpBEzqzaz583sFjM7hiCv+91IAjkRHYDfEN6G6rYJwFjgnSSvWQOcFm0LCIt8JZImRLtUAI0lFn8eGApMaaR+AFBtZpXJtMVxEuH9IvfplO0GFBjvE37zbsAOghRyPPOBg8ystJnzHClJcW9HRwGrLcr8FJW/Drwu6RbgPcJw+F3gbaBI0iAzq2hw3meA2cDM6PQPNagfH7XRcVKJ94scw0cMaUDSQEkvSrpE0iFRopQLgBuBF6IHdQVwvKRiSYOiQ38KHCHpXkmHShoj6SxJv25wiWHAHZL2j4JvbgBuj659lKTvSDpc0kjC/OcIQueD0AFihMW2PTCzvwAXAPfGB/pEHA881+ofxilovF+0I7K9yJGPG9AV+BEhg9YGgotdCfALYEC0z1GEN5Vq6rvlTSY8ZJuBKkICj1vi6ucQ3PLuBjZG5/850DGqP5Awd7uO4GVRCtzYoH0/BmY2KGvolvdZwtzpF6LvxYS3ueHZ/n19a5+b94v2s3minnaGpDnAIjO7pg3nGEx4UzrczJYnecytQF8zu7K113WcdOH9IrX4VFIBYmYx4HJC2sBkiQH/Pz0tcpzs4/1iN774XKCYWYuSwpvZrelqi+PkCt4vAj6V5DiO49TDp5Icx3GcerhhcBzHcerhhsFxHMephxsGx3Ecpx5uGBzHcZx6uGFwHMdx6vF/T67BmtU1l/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean UAS(%)')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BL+YU41Mn4d082xKecajqTzqwr8160knb99Zw69/X8HHxPq6YMoy7z7O+edHGr8QnImfj1CmciluvICK1wGrgTeBpVd0ZtCijXGsJ76ZZo8gYYAnPtE9EUoCpQDyQo6q7PQ4pYv1z7Q5+9o/1iMCfr5zIueOsb140OmLiE5GLcEZsT8aZN+/XfLVO4Qzg5yLyDPBzVS0LZsDR5KsJL52bZo22hGf8JiIXAs8A+UA34GgRmaeqz3saWIQ5WNfIPYs28MqqEiYN789D1jcvqrX3xPdT4IfAG220EFsAICJDgduAa4HfBTTCKLS7qpbH3tvC33K24/NZwjP+E5EkVa1rtuo+4GRV3ehuvxD4M2CJz0/rd1Rx64trKNp7kFtPG81tp4+xvnlR7oiJT1Wn+nMSVd0B+F3/EKtK99fy6FJLeKZL1orITar6rvtegcZm260Lg59Ulac+KOJ//72JAb268bfrp3HSKOubFws63apTRHoB8aq6P4DxRKWWCe+SSencPNsSnumUK4H5IrIO+BFwN7BSRDYDicAxwI3tnUREMoC/Amk4yXO+qj7UYp++OE+Ow3D+r3hAVZ8O4HfxzF63b967n5VxxrGD+O0l4xlgffNiRmdadR4DPAdkAyoiG4C5qrom0MFFOkt4JtBUdY2ITAV+AOTh9Oc7GpiGM6Fsrp8NzRqBH6nqahFJBlaJyOLDRaaum4GNqnq+O2P7ZyLygqrWB/RLhdgHBeXc/tJaqg418MsLjuPak6xvXqzpzBPf48ATwCwgCfgx8CwwLnBhRbY9+2t59L0t/G3ldhp9yiUTnYQ3bKAlPNN1bn3770RkIfAITt36jaq6vQPn2AXscl8fEJFNOC22myc+BZLFyQq9cWZhb2x5rkjR0OTjwcWbeey9LYxM6cWz355C1lHWNy8WtZv4ROR54DZV3euuGgK84PbjqxGRl/CjaCUWWMIzoSAixwFjccbmPFtErgKWishDwB9VVTt4vkyc2dhXttj0MLAIpyV3MnB5a43cRGQeMA9g2LBhHfsyIVJcUcOtL65hbfE+vjk5g7vPz6JnNxu/I1b58y+/EsgVkXtU9Tngb0COiLyOU6fwDZwnviNqq05BRAYAL+EMtlsEXKaqlZ34Lp6qrmvkrIeWUXWogW9MHMots8dYwjMBJyI/BH4FfAKMEZGfquoTIvImTovqFSJyg6p+4uf5egOvAre3Ul//NWAtcBowClgsIsta7qeq83HGByU7O7tDSTdU5j6dQ9mBOh6+cgLnjTvK63CMx9pts6uqf8LpuH6piLyFU793B07SFJzuDj/047MO1ylk4dRH3CwiWThdJpao6hhgifs+4qzYspeKg/U8ce0kfnvJeEt6Jlh+ApyrqtOAibjXnqruVdVv4dT5veTPiUQkESfpvaCqC1vZ5dvAQnUUAIU4T5oRZce+Q2wtO8gPzjjakp4B/KzjU9Vi4AIRuQxYjFPP9+OOjP5+hDqFC3HqC8F5clyKk1gjyvKCcronxnHy6BSvQzHRTfiiy0ITX56dAVV9W0QmtHsSp97uSWCTqj7Yxm7bgdOBZSKShtNidGtnA/dKbmEFAFNGDPA4EhMu/O6lKSIDVXUBzl3maJziz4md+dAWdQppblIE2I1TFNraMfNEJE9E8srKwm9wmGX5ZUwdMZCkBBvTzwTV/cAbIvIhTjHkV5KWn3PynQxcA5wmImvd5RwRuVFEDtfZ3wdMd7tOLAHuaGOA7LCWU1RBclKCDTJtPudP45bTcer1UkVkJ3Cpqs4TkVOB59y6hbtU9ZA/H9iyTqF5M2JVVRFptY4gnOsRdu47xJayg1wxJTwr9k30UNUHROQ/fNG45dNOnmc5LZ4WW9lnJ3BmZ84fTnILK5g4vD/xcdZlwTj8eeL7M/BboCdwC/AHAFV9D+ep7RDOnWe72qhTKBWRIe72ITjTq0SU5fnOTfCMMVbMaYJPVdep6sudTXqxpPJgPfl7qq2Y03yJP4lvCPC6W3zyJpB6eIOq1qvqXcDF7Z3kCHUKi3Am18T9859+xh42lhWUk5qcxDFpyV6HYqKYiNzljpjkz74ni8j5wY4p3OUWOfV7kzMt8Zkv+NO4ZRHwiogsAmbgzNLwJaq6wY/zHK5TWCcih58Q7wT+F1ggItcB24DL/Ak8XPh8ygcF5cw6OtVGfzDBNgrYLiKvAv8H5B2uHxeR7kAWzjV6NZCC07E9puUWVdAtPo5x6X29DsWEEX8S33XAd3HqFJ4HnurMB7VTp3B6Z84ZDjbu2k/FwXor5jRBp6rfFpETcKoc/gr0cevEG3CmJBKcOTLnA8+2mMUhJuUUVTI+oy/dE63RmflCu4nPHZfvTyGIJSItO1y/Z90YTAio6jrguyLyPZxhAocDPYByYG0ktroMlpr6RjbsqGLeKSO9DsWEmfYmop3hPqm1y22tOcK9MGPG8oIyxg5OZlCf7l6HYmKI24d2LX42LItFa7bvo9GnTLaGLaaF9hq3/EVElojIFSLSaicYERknIr8FCoDxAY8wjB2qbyK3qNKe9oynRCRVRO4Wkd+KyAyv4wkXOYUViMCk4f29DsWEmfaKOo/Hqd+7B6fP3hacAWtrgf44Izl0BxYCp7WY0iTq5RRVUN/os/o9EzIiMh8QVb3Bfd8LyAWOAmqAH4jI+ar6podhhoXcogqOHdyHPt0TvQ7FhJkjPvGpaqOq/llVx+KMr/koTtHKNuBt4HpgqKpeFWtJD2B5fhnd4uOYOsJmbTYhMxN4rdn7q4E+wBicm9HncaYKi2kNTT7WbN9n/fdMq/yel0NV83AmvjSuZfnlZGf2p0c3azFmQiYdaN5x/QzgFVXdBuBOTRTzT3vrd1RxqKHJ+u+ZVvk9VmdLInKKO7Zfv0AGFCn2HKjl090HrJjThFoj0PxOayqwotn7fThPgDHt847rI6x+z3xVu4lPRG4RkZ+1WPcvnFkU/gVsEJFjgxNe+PqgwGk1fsqY1Hb2NCagNgEXgdOwDGeGk3ebbR8OlHoQV1jJKawkc2BPBiVba2vzVf488c3FqdMDQEQuwpmg8hog293286BEF8aW5ZczoFc3smzEdxNavwXuE5H3cerZ31DVwmbbzwFyPIksTPh8St62CivmNG3yp45vFLCm2ftzgEWq+gKAiNyJHzOwRxNVZXl+OdNHDSTORnw3IaSqr4nI2cB5wOt8dXCJGpxGaDGroKyafTUN1n/PtMmfxJcEHGz2/iTgiWbvtwKDAhlUuNtcWs2eA3XMtPo94wFVXYIzP15r234pIqNDHFJYyTk88aw98Zk2+FPUuQ2YDCAig4BjgeajuQzGqVCPGcvynYlwZ1j9ngkTItJdRK4WkaXAZ37snyEi74rIRhHZICK3tbHfLHeS2g0i8l6g4w6G3KIKUpOTGD6wp9ehmDDlzxPfs8Cf3cFxZ+FMK7Sq2fbpQIwNU1bOyNReDO3Xw+tQTIwTkYk4A8lfAdQB/wB+6cehjcCPVHW1iCQDq0RkcfP+uG6L7UeAs1R1u3vjG/ZyCyuYkjnAZksxbfIn8d0P9ALOB3bjjOTS3MnAggDHFbbqGptYsXUvl2dneB2KiVEi0he4CmcAiTE4c1gmAzP8HUjCnc5ol/v6gIhswmkh2vz4K4GFqrrd3S/sJ4kuqaxhZ1Ut8zKtG4NpW7tFnarqU9W7VXWCqp6tqptabL9UVf8SvBDDy6ptldQ2+JhpxZzGAyLyHFCCM/nzQ8BgVb26i+fMBCYAK1tsOhroLyJLRWSViLQ6v5+IzBORPBHJKysr60ooXfZF/z2r3zNt63QHdgARGSYivxCRbe3vHR2W55eTECdMG2XDlBlPXAH8EbhUVZ9V1YPtHXAk7qwqrwK3q+r+FpsTgEnAuThdmH4uIke3PIeqzlfVbFXNTk319oYwp7CS5KQExg62bkambR1OfCKSKCKXish/cFp0XgY8E+jAwtWy/HImDOtH7yS/R3szJpAuA04ESkTkHyJykYh0ahRm97hXgRdUdWEru5QA/1HVg+48f+8T5jOw5BZVMCmzP/HWzcgcgd+JT0SyRORBYAfwAM6s6eerapaq3hOsAMNJ5cF61u+ssmJO4xlVXaiq5+LMjLIa51rcjXMtTxA/W3S4+z2J01jtwTZ2+ycwQ0QSRKQnzvBom9rY13MVB+sp2FNtHddNu/wZsuw6EVmBMx5gf+ByYASgNBvRJRZ8sKUcVWx8TuM5VS1R1ftUdRTONfky8Bdgl4j404H9ZJzRl05zuyusdcfevVFEbnQ/YxPOgNef4IwG8xdVXR+ULxQAn9fvWeIz7fCnvO5x4H+AWapae3hlLDYVXp5fTnL3BMYN7et1KMZ8TlXfBt4Wkf7AtcB3/DhmOdDuRayq9+O07A57uYUVdIuPY1y6XZ/myPwp6nwE+B6wTES+LyIxWc6nqixzhylLiO9SmyBjgkJVK1X1IVUN63q4YMktqmB8Rl+6J9o0YebI/OnO8H2c2Z0fBC4AikXkdZy7Rb8r1UXkKRHZIyLrm637hYjsaF7U0vGvEBqF5QfZse+Q1e8ZT4nIojaW50TkrkjpZB5oB+saWb9zvxVzGr/49eiiqvWq+qKqnoEzZNkanM6vH4rIKyJyuR+neQY4q5X1v1fVE93lDX8DD7Xl7jRENj6n8djeNpbeOEWcn4nI8d6F54012/fR5FPrv2f80uE2+e4UKHeJyN3A2TijR/wVeKmd4953O8pGpPc3l5MxoAfDB/byOhQTw1T1221tE5E4nAHk/wdnpKWYkVNUgQhMGm4jtpj2dbqyyh3R5XVVvQjoyvhdt4jIJ25RaJu/Wi9Hh2ho8rFi614r5jRhTVV9OJ3bJ3kdS6jlFlZw7OA+9OneqS6NJsa0+8QnIj9sY1MV8JmqLu/CGH6PAvfhdI24D/gdbbRIU9X5wHyA7Oxs7eTndcrHxfuormtk5mgr5jRhrwaIqWkJ6ht9rCmu5JuTh3kdiokQ/hR13trG+n5AXxH5ELhAVSs6+uGqWnr4tYg8Afyro+cIhffzy4kTmD7KEp8Je3OAzV4HEUrrd1ZR2+Czhi3Gb+0mPlUd0dY2ERkJPA/8N06Xhw4RkSHuKPEAFwFh2Tl2eX4ZJ6T3o29PK0Yx3hKRi9vY1BeniPM7QJv1gNEot/DwwNRWv2f806UBJ1V1q4j8FHi6vX1F5EWc+fxSRKQEuAeYJSIn4hR1FvHVKY88t7+2gY9Lqrhp1iivQzEG4JU21h/AmYD2O6p6xIZm0Sa3qILMgT0ZlNzd61BMhAjESMvbgbT2dlLVK1pZ/WQAPj+oPtqylyafMsPq90wYUFUbPaEZn0/JLarkzKx2/wsy5nOBuIjGEcVjdi7LL6Nnt3gmDLNiFGPCTf6eaqoONVj/PdMh/rTqbOsXdbhO4X7gqUAGFU6W55dz0siBdEuwG21jwk2OOzD1FGvYYjrAn6LOcpw6uNYozojwvwlYRGGkuKKGor01zJ2e6XUoxphW5BZWkJqcxPCBMdWDw3SRP4lvdhvr9wP5qlodwHjCyrJ8G6bMmHClquQWVTAlc0BMzhZjOs+f7gzvhSKQcLS8oIzBfbozKrW316EYE1AikoEz1GAaTsnNfFV9qI19JwMfAd9U1bZalYZcSeUhdlXVMjnT6t9NxwSiVWdUavIpHxTs5cysNLubNGFNRPrRoqGaHwNKNAI/UtXVIpIMrBKRxaq6scW543GqMt4KZMyB8PnEs9awxXSQtdhow7odVVQdarDZ1k1YEpHhIvJvETmEMztDmbuUu38ekaruUtXV7usDwCZgaCu73gq8CnR2WMKgyS2qIDkpgbGD+3gdiokw9sTXhuX5zv8dJ1v/PROensYZNvA6YCdtN0BrlztrygRgZYv1Q3FGVJoNTD7C8fOAeQDDhoVuvMycwgomZfYnPs5KZEzHWOJrw7L8co47qg8pvZO8DsWY1kwBpqlql4b5E5HeOE90t6vq/hab/wDcoaq+IxX3ezGA/N7qOraUHeTiiemh+DgTZfwu6jzSXHoiMj0QwYSLg3WNrN5eacWcJpwVAl26KxORRJyk94KqLmxll2zg7yJSBFwCPCIiX+/KZwZKblElAFOsfs90Qkfq+D4WkaubrxCROBG5F3g3sGF5a2V6r2xFAAAUAklEQVThXhqalJmjbf49E7ZuA/5HREZ35mBxHuGeBDap6oOt7aOqI1Q1U1UzccYIvUlVX+tswIGUW1RBt4Q4xqX39ToUE4E6UtT5E+AxETkHuBFIBV4A0oFzgxCbZ5bll5OUEEe2NZM24eufOE98n4lIHU4rzc+panstPk4GrgHWichad92dwDD3+McCG25g5RZVcGJ6P5IS4r0OxUQgvxOfqj4uIu/hJLv1OBXri4FzOjMXXzhbnl/OlBED6J5oF5UJW7d05WBVXQ743SpEVb/Vlc8LpIN1jWzYuZ8bTx3pdSgmQnW0ccsunOmDjscpJn0z2pLerqpD5O+p5tJsqzQ34UtVn/U6Bq+s3l5Jk09t4lnTaX4nPhE5BWfS2V3AccBJwJ/cos/rVXVvcEIMreXuMGUzrH7PRAgRGQx0a75OVbd7FE7Q5RZWECcwabhVRZjO6Ujjlrdxhjg6WVULVPU5nL4/qcC6YATnheUF5aT0TmLs4GSvQzGmTSLSV0SedTuw78Bp5dl8iVo5RRUcO6QPyd0TvQ7FRKiOJL4zVPUuVf28El1VC4FTgEcCHpkHfD7lg4JyZoweSJx1ijXh7QFgPPB1oBa4EvgxUAJc7mFcQVXf6GPN9n1WzGm6pCONW95vY70P+FXAIvLQpt37Ka+uZ8YYK+Y0Ye9s4ApVXSYiTcAqVX1JRHYB38XpfhB11u2ooq7RZ/33TJd0qHGLiPTHueCG8dU6hXsDGJcnlts0RCZy9AO2ua+rgIFAAc4sCn/xKqhgyzs8MLU98Zku6EjjlmnA60AdTr3eDmCI+74IiPzEV1DO0Wm9SevT3etQjGnPFmAksB1ngOlvikgOcDEQVS2tm8stqmBESi9Sk20oQdN5Hanjux+nD99QnDqF03Ce/PKIghnYaxuaWFlYYa05TaR4Bhjnvv5fnOLNepzrNOKvx9b4fEpuUaXNv2e6rCNFneOA61RV3TqFJFXdKiJ3AH/DSYoRK7eogvpGnxVzmoigqr9v9vodERmLM7ZmvqpGTSvr5vL3VFN1qMGKOU2XdeSJr77Z61JguPu6GjiqvYNF5CkR2SMi65utGyAii0Uk3/3Ts1u55fnlJMYLU0faRWUij6puV9WF0Zr0wOnGADYwtem6jiS+1XwxJ9dS4FciMhf4I/CJH8c/A5zVYt1PgSWqOgZY4r73xPv55Uwa3p+e3WymJhMZROQmEdkgIjUiMtJd91MRuczr2IIht7CCQclJDBvQ0+tQTITrSOL7Gc6ElwB34czy/CegP+4klEfidodoWel+IXB46KVncfokhVzZgTo27drPTOvGYCKEiNyOcx3O58tjbu6gi+N4hiNVJbeogskjBnCkuQGN8UdH+vHlNXtdhtOtoavSVHWX+3o3kNbWjsGc5fnDLdaNwUScG4EbVPV1EWnej3Y1zpCCUaWk8hC7qmqZYvV7JgA68sQXVKqqQJuzN6vqfFXNVtXs1NTAPpktyy+nX89EjjvK5vYyEWM4ziwpLTUAPUIcS9DlWv89E0DtPvGJyCJ/TqSqF3Ti80tFZIiq7hKRIcCeTpyjS1SVZfllnDwqhXgbpsxEjq3ARL7oxH7YOcDG9g4WkQycsXfTcG4456vqQy32uQq4A6co9QDwPVX9uOuhd1xuUQXJ3RM4xsbQNQHgT1HneTgX19IgfP4iYC5OP6S5OJNrhlTBnmpK99cxw4o5TWR5AHhYRHriJKaTROQanAmjv+PH8Y3Aj1R1tYgkA6tEZLGqNk+ahcCpqlopImfj1CdODezX8E9OYQXZw/vbzakJCH8S3/04MzWfAjwNPKOqJR39IBF5EZgFpIhICXAPTsJbICLX4STXkLdGW/b5NESW+EzkUNWnRSQB+DXQE3gOp/HZ91X1JT+O34UzxRiqekBENuEMTrGx2T4fNjtkBeDJJJV7q+vYUnaQb0yyOTJNYLSb+FT1DhG5EzgX507yZyKyFHgS+KeqNvjzQap6RRubTvcz1qBYll/GiJReZFgTaRNhVPUJ4AkRSQHiVLVTVQUikokzxdjKI+x2HfDvNo4PWsMzgNyiSgBr2GICxq/GLarapKqLVPXrwAjgXZwZGXaISO9gBhhM9Y0+d5gye9ozkUtVy7uQ9HoDrwK3q+r+NvaZjZP47mjj84PW8Ayc+r1uCXGckG6Nz0xgdKa3di+ckeF744za0mZLzHC3enslNfVN1o3BRIxANjYTkUScpPeCqi5sY59xOLM9nK2qezsSa6DkFlVwYkY/khLivfh4E4X8euITkR4iMldE3seZbX04MFdVR6rqwaBGGETL88uJjxOmjRrodSjG+Os84ARgbzvLEYnTC/xJYJOqPtjGPsOAhcA1qro5INF30MG6Rjbs3G/FnCag/OnO8AROo5N8nAvlAlXdF+zAQmFZfhknZvSjT/dEr0Mxxl8BaWwGnOyeZ52IrHXX3Ykz4wqq+hhwN848f4+4o6U0qmp218LvmNXbK2nyKZNtfE4TQP4UdV6HM+fXLpzRWs5ubcigTvbj88y+mno+2VHFbaeP8ToUY/wWwMZmy/nyUGet7XM9cH3XIu6a3MIK4gQmDuvnZRgmyviT+P5KBNfjteXDLXtRtWHKTORR1SacPrCLRGQwcC1OY7NHRGSkqlZ7GmAA5RRVkHVUH5KtVMYEkD/dGb4VgjhCbll+GclJCYxPtztJE9GiprFZS/WNPtZs38eVUwPfRcLEtrAZqzOUnGHKypk2aiAJ8TH5V2AiWLQ2Nmtp3Y4q6hp91rDFBFxMTj63bW8NJZWH+O4pI70OxZgOiebGZi0dHpg62xKfCbCYTHzL8ssAmGHz75nIE5WNzVqTW1jByJRepCYneR2KiTIxmvjKGdqvB5kDbZgyE3GisrFZSz6fkretkrOOG+x1KCYKxVzia2zy8dGWvZw3fojN5GwiTrQ2Nmtp854DVB1qsP57JihirmXHxyVVHKhrZMZoK+Y0JlzlFjr1e9awxQRDzCW+ZflliMB0G6bMmLCVU1RJWp8kMgZE3WTyJgzEXOJbnl/OCUP70r9XN69DMca0QlXJLaxgcuYAq44wQRFTie9AbQNrivfZaC3GhLGSykPs3l/LFKvfM0ESU4nvoy17afKp1e8ZE8Zy3Pq9yVa/Z4IkphLf8oJyeiTGM3G4DVNmTLjKLaqgT/cEjklL9joUE6ViK/HllzNt5ACb0NKYMJZTVEF25gDi4qx+zwRHzCS+ksoatpYftNFajAFEJENE3hWRjSKyQURua2UfEZE/ikiBiHwiIhODHVd5dR1byw5aMacJqpjpwL48vxywaYiMcTUCP1LV1SKSDKwSkcWqurHZPmcDY9xlKvCo+2fQ5Lnjc04Z0T+YH2NiXMw88S0rKCetTxJjBvX2OhRjPKequ1R1tfv6ALAJGNpitwuBv6pjBdBPRIYEM66cwkqSEuI4YajVw5vgCYsnPhEpAg4ATUCjqmYH8vxNPuWDgnJOH5tm/YKMaUFEMoEJwMoWm4YCxc3el7jrdrU4fh4wD2DYsK7NnZdbVMGJGf3olhAz9+TGA+H065qtqicGOukBrC2uZF9NgxVzGtOCiPQGXgVuV9X9nTmHqs5X1WxVzU5N7XwdenVdIxt2Vln/PRN04ZT4gubRpVvo2yOR048d5HUoxoQNEUnESXovqOrCVnbZAWQ0e5/urguK1dsq8an13zPBFy6JT4G3RGSVW2zyFSIyT0TyRCSvrKzM7xOvK6ni7U17uGHmCJK7JwYqXmMimjhl/k8Cm1T1wTZ2WwRc67bunAZUqequNvbtstyiCuIEJg63hi0muMKijg+Yoao7RGQQsFhEPlXV95vvoKrzgfkA2dnZfs9H9tCSzfTtkcjc6ZkBDdiYCHcycA2wTkTWuuvuBIYBqOpjwBvAOUABUAN8O5gB5RRWcNxRfemdFC7/LZloFRa/MFXd4f65R0T+AUwB3j/yUe07/LT3X2cebU97xjSjqsuBI7b0UlUFbg5FPHWNTawt3sdVU4eH4uNMjPO8qFNEern9iBCRXsCZwPpAnNue9oyJDOt3VFHX6LP+eyYkwuGJLw34h9vNIAH4m6q+2dWT2tOeMZEjp7ASgGxr2GJCwPPEp6pbgfGBPq897RkTOXKLKhiZ2ouU3kleh2JigOdFncFgLTmNCS+1DU0sy2+9NbbPp+QVVTDFnvZMiERl4vvD25vp19Oe9owJF8+v2MY1T+bwvedXsavq0Je2fVZ6gP21jdZ/z4SM50WdgfZJyT6WfLqHH3/tGHvaMyZMXHtSJnWNPv64JJ/3N5fxgzlH863pmSTEx5H7+cDUlvhMaETdE99Db+fTr2ci155kzaKNCRfdEuK4efZoFv/gVKaMGMCvXt/E+Q9/wOrtleQUVjC4T3fS+/fwOkwTI6Iq8R1+2rth5kh72jMmDA0b2JOnvjWZx66eyL6aei5+5EPe2ljK5BEDbAB5EzJRlfj+tnK7Pe0ZE+ZEhLOOH8LiH57K9TNG0ORTTh9r4+ia0ImqOr77vn48c6dn2tOeMRGgd1ICd52XxQ/PPJoeifFeh2NiSFQlvsT4OI4d0sfrMIwxHdCzW1T9N2QiQFQVdRpjjDHtscRnjDEmpljiM8YYE1Ms8RljjIkplviMMcbEFHHmmowsIlIGbGtjcwpQHsJwwol99+AbrqqpIficiGTXZpvsu4eGX9dnRCa+IxGRPFXN9joOL9h3j83vHili+d/Ivnt4fXcr6jTGGBNTLPEZY4yJKdGY+OZ7HYCH7LubcBbL/0b23cNI1NXxGWOMMUcSjU98xhhjTJss8RljjIkpUZX4ROQsEflMRApE5KdexxNMIvKUiOwRkfXN1g0QkcUiku/+2d/LGINFRDJE5F0R2SgiG0TkNnd9THz/SGTXZmz8NiPl2oyaxCci8cCfgbOBLOAKEcnyNqqgegY4q8W6nwJLVHUMsMR9H40agR+pahYwDbjZ/beOle8fUezaBGLntxkR12bUJD5gClCgqltVtR74O3ChxzEFjaq+D1S0WH0h8Kz7+lng6yENKkRUdZeqrnZfHwA2AUOJke8fgezajJHfZqRcm9GU+IYCxc3el7jrYkmaqu5yX+8G0rwMJhREJBOYAKwkBr9/hLBrMwZ/m+F8bUZT4jPNqNNPJar7qohIb+BV4HZV3d98Wyx8fxOZYuG3Ge7XZjQlvh1ARrP36e66WFIqIkMA3D/3eBxP0IhIIs6F9YKqLnRXx8z3jzB2bcbQbzMSrs1oSny5wBgRGSEi3YBvAos8jinUFgFz3ddzgX96GEvQiIgATwKbVPXBZpti4vtHILs2Y+S3GSnXZlSN3CIi5wB/AOKBp1T1vz0OKWhE5EVgFs6UH6XAPcBrwAJgGM7UMJepastK9ognIjOAZcA6wOeuvhOnLiHqv38ksmvTrk3C6PtHVeIzxhhj2hNNRZ3GGGNMuyzxGWOMiSmW+IwxxsQUS3zGGGNiiiU+Y4wxMcUSn2mXO9r6tc3eq4hccoT9c0TkG6GJzpjYZtdnx1niCwMikioij4hIkYjUiUipiCwRkTnu9iIR+S+PYjsXZ9SNFzpw2H3A/4qI/b5MxLPrM/rE7BcPM6/ijGB/HXA0cB7wb2Cgl0G5bgOeUdWmDhzzBpCMMw2NMZHOrs9oo6q2eLgA/XAGbD2jje1L3e2fL822TQfeA2pwxj58FOjT4tjHgIeASne5H4hrts/FwCfAIZypVN7DGUkdIBVn9IXxLWJS4JJm7+8AyoFpzdY9BTzv9d+vLbZ0ZbHrMzoXe+LzXrW7XCAi3VvZfjHONC73AkPcBRE5AXgLZwy88e5+J+L8oJu7CufJ/iTgu8A84Hb3HINx5kZ7FjgWOAV4rtmxM4A6YD2tEMcDwK3Aqaq6otnmHODUdr+9MeHNrs9o5HXmtUUBvoFzN1cLfAQ8AExttr0I+K8Wx/wVeLLFuhNx7vYGue+XAptxh6Zz190FlLivJ7r7D28jrtuBba2sV+By4Gn3/F85HrgA5240weu/X1ts6cpi12f0LfbEFwZU9VXgKOB8nLqD6cAKEbnzCIdNAq4WkerDC/CBu21Us/1WqPtLd30EDBWRPsDHwNvAehF5VUS+JyKpzfbtgXOxt+YBnIF4Z6jqtla2HwIEaO0u2ZiIYddn9LHEFyZUtVZVF6vqvao6HWdqj1+407i0Jg74C85d5OFlPDAGWOvnZzYBZ7rLJziV9/kiMt7dpRzo38bhi4HBwDltbB8A1KpqtT+xGBPO7PqMLgleB2DatBHn36c7UI8znUtzq4HjVLWgnfNMFRFpdlc5Ddip7qzI7vqPgI9E5F5gA04xycfAGiBVRFJUtbzFed8AFgIvu6d/tsX2490YjYlGdn1GMHvi85iIDBSRd0TkahEZ507WeSnwE2CJewEUATNFZKiIpLiH/gaYIiKPicgEERktIueJyOMtPuIo4A8icozbqfXHwO/dz54mIneJyGQRGYZT7p+Bc1GDc2HtwalE/wpV/RdwKfBY8w60rpnAm53+izEmDNj1GaW8rmSM9QVIAn6NM0t1JU7T53zgQWCAu880nDu8Wr7cXDob58e7HziIM/njvc22L8VpLv0wsM89/++AeHf7sTh1FqU4rcMKgJ+0iO9/gJdbrGvZXPp8nDqDa933Q3HugtO9/vu1xZauLHZ9RudiE9FGMRFZCqxX1Vu6cI5BOHeYk1W10M9j7gf6quq8zn6uMdHOrk/vWFGnOSJV3QN8BxjWgcP2AD8PTkTGmMPs+uwca9xi2qWqizq4//3BisUY82V2fXacFXUaY4yJKVbUaYwxJqZY4jPGGBNTLPEZY4yJKZb4jDHGxBRLfMYYY2LK/weYKVdq7tDGcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure=plt.figure()\n",
    "figure=plt.figure(figsize=(6,10))\n",
    "# plt.subplots_adjust(wspace=1,hspace=0.1)\n",
    "plt.subplots_adjust(wspace=0.45,hspace=0.35)\n",
    "x = [0,1,5,10,20,23]\n",
    "y = [acc_matrix.max() for acc_matrix in rstdt_sentcons]\n",
    "plt.subplot(3,2,1)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Max UAS(%)',fontsize=14)\n",
    "    \n",
    "y = [acc_matrix.max() for acc_matrix in instruction_sentcons]\n",
    "plt.subplot(3,2,3)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Max UAS(%)',fontsize=14)\n",
    "y = [acc_matrix.max() for acc_matrix in gum_sentcons]\n",
    "plt.subplot(3,2,5)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Max UAS(%)',fontsize=14)\n",
    "\n",
    "y = [acc_matrix.mean() for acc_matrix in rstdt_sentcons]\n",
    "plt.subplot(3,2,2)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Mean UAS(%)',fontsize=14)\n",
    "y = [acc_matrix.mean() for acc_matrix in instruction_sentcons]\n",
    "plt.subplot(3,2,4)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Mean UAS(%)',fontsize=14)\n",
    "y = [acc_matrix.mean() for acc_matrix in gum_sentcons]\n",
    "plt.subplot(3,2,6)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Mean UAS(%)',fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "figure=plt.figure(figsize=(7,3))\n",
    "plt.subplots_adjust(wspace=0.45)\n",
    "plt.subplot(1,2,1)\n",
    "y = [(rstdt_nocons[i].max()+instruction_nocons[i].max()+gum_nocons[i].max())/3 for i in range(len(rstdt_sentcons))]\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Max UAS(%)',fontsize=14)\n",
    "plt.subplot(1,2,2)\n",
    "y = [(rstdt_nocons[i].mean()+instruction_nocons[i].mean()+gum_nocons[i].mean())/3 for i in range(len(rstdt_sentcons))]\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('Mean UAS(%)',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'ROUGE-L')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ibCUoE9jWJGUyZf5OVu45QosGtXn6ql7ceG47aofb/9vG+EJBUTFfbz7AjBXJJOw9yl+v7UO92v4x2szXUbwIPAaUrITcFMhyMsCDJyt8m3LKzQWuwrPWXF08SwOVXawbEZmMk/OqffvAThFhqu7tVcnUEmHiUPtvbwJXwt6jTJm/k2WJh2lWrzZPjOvJzUPa2w8SY3wk40Qus1enMmv1XjJO5NGhaV2evLInN8S1czu0H/is8SYi44AMVU0QkVFVLD4YKAJaA42B70RkgaruKX2Sqk4DpgHExcVp9aM2gSInv5D31qYytldLvxiPYExVbUjNYsr8nXy78xBNoyP5w+XncMvQDtSJtEabMTVNVVmXksXMlcl8+f1+CoqUkd1ieO66WEZ2i/G7fIm+7Hk7DxgvIpcDUXjGvL2EZwmfcKf3rS2QXk7Zm/Fkui4AMkRkORAH7CnnXBOCPlm/j+O5lh7EBJ7N6ceYMn8nC7dn0LhuBL+7rAe3DetA3Uj/eDxjTDDLLSjivxv3MWNlMpvTj1O/dji3DO3AbcNi6dgs2u3wKuSzvw6q+jjwOIDT8/aoqk4UkQ/wrDc3B8+C25+WUzwFz9i4t0UkGhiK5xGsMZ70ICuSOadVA86Nbex2OMZUypZ9x3hxwS7mbz1IwzoR/ObS7kwaHus3Y2qMCWZpR3OYtTqFOWtSOJpTQNfm9Xjm6t5cM6AN0QFQB/0hwt8Cc0TkGWA98AaAiIwH4lT1CWAq8KaIbAEEeNNZAsUYVu3JZMfBEzx3XR9LD2L83o4DJ3hxwU6+2nyA+lHh/Oribtx+XiwNoiLcDs2YoKaqrNx9hLdWJLNg20EALu7ZgknDYxnWqWlA3T9cabyp6hJgifN+D54xbWXP+Qz4zHmfjSddiDE/MWNFMo3qRnBV//LmuhjjHxIzTvDigl188f1+oiPDefCirtx1fkca1rFGmzE16WReIR+tT2fmimR2ZWTTuG4EPx/ZmVuGdqBNo8AcI+0PPW/GnLX0rFN8s/UA91zQyWbjGb+0+1A2Ly/cxWcb91EnIox7R3XmnhGdaFQ30u3QjAlqew5l8/aqvcyN9yyZ2LtNA/52fV+u7Nc64O8X1ngzAe2dVXsBuHVoB5cjMebHkg+f5OVFu/hkfTq1w8P4+QWdmXxBJ5pEW6PNmJpSXKws2ZnBjBV7+XbnISLChMv7tGLS8FgGtGsUUI9GT8cabyZg5RYUMXtNCmPOaUHbxnXdDscYwLN49T8X7eLDdemE1xLuOr8jPx/ZmWb1arsdmjFB61hOAR8kpDJz5V5SMnNoXr82j4zpxoQh7WheP8rt8LzOGm8m4Kgq8XuPMm3pHrJyCrjd0oMYP5B2NIepixP5ID6NWrWE24Z14JcjO9O8QfDdOIzxF9sPHGfGir18sj6dUwVFnBvbmN9c2p2xvVsG9bq/1ngzAeN4bgGfrE9n1qoUdhw8Qf3a4dw7qjPDOjd1OzQTwvYfO8XUxYm8tzYVQbh5SHvuHdWFlg2t0WZMTSgsKuabrQeZsSKZ1UmZ1A6vxdX923Db8A70at3Q7fB8whpvxu9tTj/GO6v28umGfZwqKKJPm4Y8d10fruzX2hKZGtccPJ7LvxYnMntNKopyQ1w77ruwC60DdPaaMf7ucHYec9akMGt1CvuP5dK2cR0ev6wHN57bLuQmANmdz/ilU/merNezVu9lY9oxoiJqcVW/Nkwc2p6+bRu5HZ4JYRkncnl1yR5mrd5LYbHys0Ftue/CLrRrYuMujakJG1KzmLkimc837Se/qJgRXZvx9FW9Gd2jOWF+tmyVr1jjzfiVXQdPMGt1Ch+uS+NEbiFdm9fjT1f25JqBbS0flnHVkew8Xlu6h5krk8kvLObagW15YHQXOjT13yV0go2I9AIWq2rz05wTBSwFauO5x81V1SdF5DugvnNac2CNql5dTvn2wOtAO0CBy1U12atfxJxRXmERX2zaz4yVe9mYmkV0ZBgTBrfj1mGxdGlez+3wXOeVxpuInAN8oaqdvHE9E1ryCov4evMBZq1OYU1SJpFhtbisT0smDunAubGNA3Zqt4icB1wNHAXeVtXUUscaAx+q6mi34jOVc/RkPtO+28OMFcnkFhRxdf82PHBRV79e9zCIhQNnGuSaB4xW1WwRiQCWichXqjqi5AQR+ZDyl2IEmAk8q6rzRaQeUOyNwE3l7D92ilmrUpi9JoUjJ/PpFBPNU+N7ce3ANtS3VUh+4K2et0jAEm2ZKkk5ksO7a1L4ID6VIyfzad+kLr+7rAc/G9SWpgGeVkFErgQ+BhLw/Nr/rYhMUNUvnVMigZGVuM50YByQoaq9nX39gVeBKKAQuFdV11RQvgGwFfhEVe+v3rcKLVk5+bz+XRJvLk8ip6CIK/u25sGLutqvfj+nqgpkO5sRzktLjjt1YjRwR9myItITCFfV+c61ssueY7xPVVmdlMnMlcnM23KQYlUu6tGCScM7cH6XZgH7A74mVarx5txATqeJF2IxIaCwqJiF2zOYtTqFpTsPEVZLuKhHc24Z6qmktYJn/MIfgKdV9WkAEbkPeF9EblXVj6twnbeAV/D0BpR4HnhKVb8Skcud7VEVlP8znkdIppKOnSpg+rIkpi9L4kReIVf0bcVDF3WlW4v6Zy5s/IKIhOH54dQFmKqqq0sdvhpYqKrHyynaDcgSkY+AjsAC4HeqWlTm+pOByQDt27evgW8QGnLyC/lk/T5mrkxm+4ETNKwTwd3nd+SWoR1sDOkZVLbn7TZgFZ7HP+Vp4J1wTLA6cCyXOWtTmLMmlQPHc2nZIIqHx3TlxnPb0aphUM7O6wncXLKhqlNF5ADwjojcBiyrzEVUdamIxJbdzf/qXENgX3llRWQQ0AL4GoirSvCh6ERuAW8tT+Y/3+3heG4hY3u15KExXTmnlf15CzROY6u/iDQCPhaR3qq62Tk8Ac+YtvKEAyOAAUAK8B5wO/BGmetPA6YBxMXFKaZK9h45ydsr9/J+fCrHcws5p1UDnruuD+P7taFOZGAvW+UrlW287QKmqerM8g46j3ESvBaVCQrFxcqyxMPMWr2XBdsyKCpWLugWw9NX9WJ0j+aEB3ECRSAXT4/0npIdqvqhePr/ZwK/q8a1HwbmicgLQC1geNkTRKQW8HfgFmBMNT4r6J3MK+StFZ5GW1ZOAWPOacHDY7rSu01o5IvyJyJyglKPOMtRpTu7qmaJyGJgLLBZRJoBg4FrKiiSBmxQ1T1OPJ8AQynTeDNVV1ysLN11iJkr97J4RwZhIozt3ZJJw2OJ6xC4Y5vdUtnG2zpgID9+dFOaAvZv3gCeWXkfJKTx7uoUUjJzaBIdyT0jOnHz4Pa0bxoyXeHr8YyriS+9U1XnOg2rd6px7V8CjziNwRvw3FjKNtDuBb5U1bQz/VEM5UdA3+48xCPvbSDzZD4Xdo/hkYu7WSoad1V7XKaIxAAFTsOtDnAx8Jxz+Hrgc1XNraD4WqCRiMSo6iHKqcOmao7nFjA3Po23V+0l6fBJmtWrzQOju3Lz4PaWyLoaKtt4+zWeadflUtWNeHoATIhSVdYmH2XW6r189f0B8ouKGdyxCb++pBtje7ekdnjIdYW/SgUTElT1facB9/OzvPYk4CHn/QeU/whoGDBCRO4F6gGRIpKtqj/p8QvVR0CFRcU88elmGtWN4I1JcQxo39jtkEKeqs7wwmVaATOccW+1gPdV9XPn2E3AX0ufLCJxwC9U9W5VLRKRR4GFTi95AvAfL8QUcnYdPMGMlcl8tC6dnPwiBrRvxEs39eey3q2IDLfmQnVVqvGmqgdqOhATmI7nFvDxunRmrd7LzoPZ1I8K5+Yh7Zk4pD1dQ3iAtzMpocKJCao6B5hzlpffh6dhuARPz8Cucq4/seS9iNwOxJXXcAtln2/az94jObx26yBruPkxEfkX8ISqHq7M+aq6Cc+YtfKOjSpnXzxwd6nt+UDfswo2xJVMSJuxIpkVu48QGV6L8f1ac9uwDtaj7WVnnSqkqhXKBJdNaVnMWpXCZxs9S1b1a9uQ56/ry7h+rWzJqgqcTZ0Rkdl4ZpI2E5E04EngHuAlEQnHM7ZusnPuDz0I3o492BQXK68sTqR7i/pcfE4Lt8Mxp3cL8AJg9xo/lXkynzlrU5i1KoX0rFO0bhjFY2O7c2Ncu4BP++SvqnOXtQoVYnLyC50lq1LYlHaMOhFhXNW/NROHdKBPWxvcXQlVrjOqOqGCQ4PKOfdHPQil9r+FJ+WIcXy95QCJGdn8c8KAYEpPE6zsP5Cf2px+jLdWJPPZxn3kFxYzrFNT/jiuJ2POCfoJaa6rTuPNKlSI2HnwBLNW7eWjdemcyCukW4t6PH1VL64e0IYGlvG6KqzO+AFV5Z+LEunULJrL+7RyOxxjAs7CbQeZujiRdSlZ1I0M44a4ttw2LNZyIfqQPd8y5fphyapVKaxJ9ixZdXmflkwc2sGmdZuAtnBbBtv2H+eFn/UL2UWtA4mqWovAj+w8eIK7Z8bTvkldnhjXk+sG2brTbjjrxtvZVihnBlA8kK6q40SkI56B203xzOy5VVXzyynXF3gNT3LSYuDc00z3Nmdp75GTzpJVaWSezKdD07r8/vIeXD+oHU2iI90OL6DZTch9qso/FyfSrkkdrurf2u1wTBlO2poZqprnbPcCdqhqobMdDfxWVZ9wMcyQ9sK8HdSLDOeTe8+jsd0TXFOph9IiMllEapfa7uUMli7ZjhaRpyv5mQ8B20ptPwdMUdUueFZwuKuczw/HkxfrF6raC88A7oJKfp45g8KiYr7efIBb31jNyL8t4fXvkhgc24S37xrM4l+PYvIFna3hVkVerjPGS5YlHmZjaha/HNmFCBuT44/+jWfVkBIrgdLJB+vhWXrOuGB9ylG+2XqQey7oZA03l1X2r5dXKpSItAWuwMlL5eTRGQ3MdU6ZgWfdubIuATY5+eRQ1SNl15ozVbf/2CmmzN/Jec8t4hfvJJCYkc0jY7qx4nejefXWQYzoGmODuc+e3YT80D8XJtKqYRTXDWrjdiimfGX/4NgfID/ywjc7aBodyZ3nd3Q7lJBX2cem3qpQLwKPASWPj5oCWSVd4niWJinvr2o3QEVkHhADzFHV588yhpBWXKx8l3iYd1btZeG2gygwslsMz1zdgQu7x9gMIe+xm5CfWb3nCGuSM/nTlT1DMWm0MdWyPPEwyxOP8MS4ntSrbcPl3eaz/wIiMg7IUNUEERlVxeLhwPnAuUAOnuzXCaq6sMxnhOwyP2dyODuPD+LTeHfNXlIzT9E0OpKfj+zMhHNDaskqE8JeWZxIs3q1uWmw/W0wpipUlefn7aB1wyhuHmL1xx/4svl8HjBeRC4HovBMPHgJzzpy4U7vW1sgvZyyacDSkuSmIvIlnrVWf9R4C9VlfiqiqqxJymTW6hS+2ryfgiJlSMcm/ObSHlzaq4X1PpiQsT7lKN/tOszvL+9BVIT9f+/nrhCRY877WsClInLQ2bY0/S74ZutBNqZm8fx1fa3++ImqNN6qVaFU9XHgcQCn5+1RVZ0oIh/gWSx4Dp41Gz8tp/g84DERqQvk41kaaEoVYg8px04V8NG6NGatTiExI5sGUeHcMrQDE4e0p0tzm/DoQ3YT8hOvLEqkUd0IJg7p4HYo5szeKLM9tcx2yP8w96WiYuWFeTvoFBPNtQNtrKi/qErjraYq1G+BOSLyDLC+5HNEZDye9RifUNWjIvIPYK3zOV+q6hdn+XlBa3P6MWau9GS7zi0opl+7Rjx/fV+u7NuaOpH2a8kFdhPyA5vTj7Fwewa/vrgb0TZWx6+pqg269TOfrE9nV0Y2U28eaGOi/UhlF6b36n8xVV2CZ1FtVHUPMLiccz4DPiu1/Q6edCGmHFv3HeeqqcupHV6Lawa0ZeKQ9vRuY0tWucVuQv5j6uJE6keFM+m8WLdDMSag5BcWM2XBTnq3acBlvVu6HY4pxX6GBok3liURGVaLpY9dSDNbCNgYwJMN/qvNB3hgdBdbyi0AiEgEnicuK53t3wGlE4oVAc+rquX59IE5a1NIO3qKZ6/pY2mj/EylGm9Wofxbxolc/rtxHzcNbmcNNz9hdcY/TF2cSN3IMO48z/JSBYgJwG3AGGf7D8BQhJqOAAAgAElEQVQ+PGOdwZNK6gjwqu9DCy05+YW8vDCRIR2bcEHXZm6HY8qobM+bVSg/9s6qFPKLirnDblD+xOqMy5IOn+S/G/dxzwjLBh9AJgH/KrPvMmd4DSJyN3ArVm9q3JvLkzmcncdrtw60taz9UGXH5UzCkzG+tMtUtY+q9sGTeHeCVyMzlZJbUMSsVXu5qEdzOjaLdjsc8z9WZ1z27yWJRITV4q4R9qMmgHTHM3GtIsuAvj6KJWQdyyngtW93c1GP5gzq0MTtcEw5Ktt4swrlpz7bsI8jJ/O5y5Yr8TdWZ1yUmpnDR+vSmTC4Pc3rR7kdjqm8Zvz4vtQGSCq1XQTU8WlEIei1pbs5nlvIo5d2dzsUU4HKPjYtr0KdKLVtFcoFqsr05Un0aFmfYZ2buh2O+TGrMy56beluRODnIzu5HYqpmgN4fvgkAqjq8TLHz3HOMTUk40Quby5PZny/1pzTqoHb4ZgKVLbnraRCAZ4Kpaqlc1RZhXLBit1H2H7gBHee19HGJPgfqzMuOXg8l/fXpnH9oHa0amjt4wCzAM/40J8QkVrA74H5p7uAiESJyBoR2SgiW0TkKWf/dyKywXntE5FPTnONBiKSJiKvVOO7BKRXFiVSUFTMry7u5nYo5jQq2/NWUqF+khi3shXKeN/0ZUk0jY5kfP/WbodifsrqjEte+3YPRarcO6qz26GYqnsWWC8iq4EXgJ3O/h54xol2Bm4+wzXygNGqmu3M+l4mIl+p6oiSE0TkQ8pfzafEn4GlZ/kdAlZqZg6z16Rww7ntiLUx1H6tso03b1Qo40V7DmWzcHsGD17U1daa809WZ1xwODuPd9fs5er+bWjXpK7b4ZgqUtUkERkDzADe43+rkAiwDbi4ZObpaa6hQLazGeG8fuj1FpEGwGjgjvLKi8ggoAXwNRB31l8mAE1ZsJNaIjw4uqvboZgzqOwKC9WuUMa73lqRTGRYLW4Z2t7tUEw5rM64441lSeQVFnPvhdbrFqhUNR7oJSIDgJJWxE5V3VDZa4hIGJAAdAGmqurqUoevBhaWM56upFf878At/C/NT0jYefAEH69P554RnWjZ0Cb5+LtKr7DgjQplvONYTgEfxKdxZb/WNpPOj1md8a2snHxmrkhmXN/WdI6p53Y4pppUdT2nn7F9urJFQH8RaQR8LCK9VXWzc3gC8HoFRe/Fs3Z22unGEYvIZGAyQPv2wfED+oV5O6gXGc4vR9oPn0BQ5eWxqlOhjHfMWZvCqYIi7jw/1u1QTCVUp86IyHRgHJChqr2dff3xJCmNAgqBe1V1TZly/fHkmWuAZ2brs6r63ll/iQDw5vJkTuYXcZ/1ugUsEfmsgkPHgB3ANFXNqOz1VDVLRBYDY4HNItIMz1ra11RQZBgwQkTuBeoBkSKSraq/K3PdacA0gLi4OP3pZQLLhtQsvtl6kF9d3M0SWgeIyi6P5dUKZc5eYVExM1YkM7RTE3q1toXn/ZUX68xbwCvAzFL7ngeeUtWvRORyZ3tUmXI5wG2quktEWgMJIjJPVbOq8DUCxoncAt5cnsSlvVrQo6WlNwhgRyrY3wi4E/i1iIwo1Yv2EyISAxQ4Dbc6wMXAc87h64HPVTW3vLKqOrHUdW7Hs8Td78o7N5j8bd52mkZHcqflCw0Yle15q3aFMt7x9ZYD7DuWy1NX9XY7FHN6XqkzqrpURGLL7sbTowbQEM+yW2XL7Sz1fp+IZAAxQFA23mau3Mvx3ELuv9AGWgcyVS13EgH8MB7tP8D/A648zWVaATOccW+1gPdV9XPn2E3AX8tcNw74hareXZ3YA9XyxMMsTzzCH8f1pF7tKj+MMy6p7IQFb1Qo4wXTlyXRoWldRvdo7nYo5jRquM48DMwTkRfw3JyGn+5kERkMRAK7Kzge0ON3cvILeWNZEqO6x9CnrfVGBytVLRaRl4GvznDeJmBABcdGlbMvHvhJw01V38LT8x20VJXn5+2gdcMoJg4JvLofyiqbpLdCqloMvAwMqn445nTWpxxlXUoWdwyPJayWJeUNVF6oM78EHlHVdsAjwBsVnSgirYC3gTuczy0vnmmqGqeqcTExMWcZknveXZ1C5sl8HrD0BqEgB7AcMF7yzdaDbEzN4uEx3SzlVICpduPNYRXKB6YvT6Z+7XCuj2vndiim+qpTZyYBHznvP8AzAPsnnHxWXwB/UNVVZ/lZfi23oIjXlu5heOemDOrQ2O1wTM27mP/lTDTVUFSsvDBvB51iorl2YBu3wzFV5K0H3Fahati+rFN8+f1+7jwv1sYlBIfq1Jl9wEhgCZ5ko7vKniAikcDHwExVnXuWn+P3PohP5dCJPF66qb/boRgvEJFrKzjUEE9P9Z1UkFzXVM0n69PZlZHN1JsHEh7mrX4c4yuVnW1qFcplM1fuRVW5bVis26GYSvBWnRGR2XhmkjYTkTTgSeAe4CURCQdyccarlRl4fQNwAdDUmTUHcHsw5ZjLLyzm30t2E9ehMcM6NXU7HOMdFf3QOIFnlvadwZ7yxhfyC4uZsmAnvds04LLeLd0Ox5yFynbhWIVyUU5+IbPXpHBpr5a25E/g8EqdUdUJFRz6yXi50gOvVfUd4J3KhRqYPl6fxr5jufzl2j6cLqGqCRyqal1APjBnbQppR0/xzNW9qWXjpwNSZWebWoVy0Yfr0jl2qoC7LAdPwLA6U7MKi4r515Ld9G3bkJHdAm+ShTFuyckv5OWFiQzu2MTqTgDz+Q1GRMJEZL2IfO5sdxSR1SKSKCLvOWN1KirbXkSyReRR30XsruJi5c3lSfRt29AGZBvj+O+mfew9ksP9F3axXrcgIyLhIvJrEUlw/t5ni8g6EfmVM1TAVMNbK5I5nJ3Hb8d2t7oTwCrdePNihXoIz8LcJZ4DpqhqF+AocNdpyv6DM+T4CTbf7jzEnkMnuev8jlbRAozdhGpGcbHyyqJEerSsz5hzWrgdjvEiEakNLMSzasgRPGuQvg4cBv4GzD/dD3xzesdyCnh1yW4u6tGcQR2auB2OqYZKNd68VaFEpC1whVMW8bRGRvO/8UEzgKsrKHs1kARsqUzMwWL68iRaNKjNZb1buR2KqQK7CdWcrzYfYPehk9w/uouN1wk+vwU641mW6hJVfdh5XQKcC3RxzjFn4bWluzmeW8ivL+nudiimmir76790hfrRAtsiMhD41Dnnz2e4zovAY0B9Z7spkKWqhc52GvCThDMiUs+5/sVAyDwy3XHgBN/tOsxvLu1OZLgNoQow3qozphRV5Z+LdtEpJtp+0ASnCcCvy9YZAFVdJyK/wTPj2upNFWWcyOXN5cmM79eanq1t/d9AV9kWwWkrFPAb4ObTXUBExgEZqppQ5SjhT3gerWaf4TMmi0i8iMQfOnToLD7Gv7y5PImoiFrcPNiWLQlA1a4z5qcWbstg+4ET3Deqi60yEpw6AitOc3w5EOubUILL1EWJ5BcV86uLu7kdivGCyva8eaNCnQeMF5HLgSg8C2u/BDQSkXCn960tkF5O2SHA9SLyPJ6FvYtFJFdVXyl9kqpOA6YBxMXF6Rm/lR87kp3HR+vTuX5QWxpH29O1AGQ3IS8r6XVr16QOV/Vv7XY4pmacxPNEJrWC403xrE5iqiA1M4d316RwQ1w7YptFux2O8YLK9ryVVKiKnLFCqerjqtpWVWOBm4BFqjoRWAxc75w2Cc/jpLJlR6hqrFP2ReAvZRtuwebd1SnkFxZz53mxbodizk6164z5se92HWZj2jHuHdXFMsIHr2XA/ac5fj+eHz6mCqYs2EktER66yNb/DRaV/QtYkxXqt8CvRCQRzw3tDQARGS8iT5/lNQNaXmERM1ftZWS3GLo0r3/mAsYf2U3Iy15ZlEirhlG2DmNw+wtwq4h8KCJDRKSh8xomIh8BtwLPuhxjQNl58AQfr09n0vBYWjaMcjsc4yWVfWz6F2CpiDTGM3tuu7O/J56xO1fgWYqnUlR1CZ51GVHVPZSzsLaqfgZ8Vs7+P1X2cwLVF5v2c+hEHnf+zJLyBjCv1plQt2rPEdYkZ/LU+F7UDg9zOxxTQ1R1tYj8DM/M7LKZBzKBG1V1te8jC1x//2YH0ZHh/HJkZ7dDMV5U2RUWrEL5iKryxrIkujSvxwVdm7kdjjlLVme865VFiTSrV5sbz23ndiimhqnqZyLSARgLlDzn2wl8o6o5ItJOVSsaE2dK2ZCaxbwtB3lkTDcbOx1kKp0o1CqUb6xJymTLvuP85RpbrzHQWZ3xjnUpR1mWeJg/XH4OURHW6xYKVPUU8HHpfSLSUkT+BtwJ1HElsADzt3nbaRodyV0j7ClOsKlSlnerUDVv+vIkGtWN4JoBNq4nGFidqb5XFiXSuG4ENw+xlDnBTkQaAVOBS4AC4K/AP4En8IyP3oqn3pgzWJ54mOWJR/jjuJ7Uq20LugSbyq6w0EhEZonIIRHZJyIPiseTwB48qTysQlVTypEcvtl6kIlD2lMn0noYApnVGe/YnH6MRdszuOv8jkTbDSgU/AXPWNAZeIYXTMEz9nkkcJmqxqnqbBfjCwiqyvPzdtC6YRQT7UdPUKrKhIWSCjUWT4W6GIjGU6G+rZnwQstbK5IJE+HWobFuh2Kqz+qMF7yyKJH6UeHcNjzW7VCMb1wB3KGqC0TkX0AisFtVH3Y5roDyzdaDbEzN4rnr+thQgyBV2VQhJRXqUWA8IHgq1Gi7CXnHidwC3o9PZVzfVjadOzhYnammnQdP8PWWA9wxPJYGURFuh2N8ozWeR6MlmQhygf9U5QIiEiUia0Rko4hsEZGnnP3ficgG57VPRD4pp2x/EVnplNskIjd64Tv5VFGx8sK8HXSKiea6gW3dDsfUkMr2vP2oQolIlSuUOb3349PIzivkzvNtYGmQsDpTTVMXJxIdGcYd51mdCCG18Ix1K1FE1ZNZ5wGjVTVbRCKAZSLylaqOKDlBRD6knITwzmfdpqq7RKQ1kCAi81Q1q4oxuObTDensyshm6s0DLZl1EKts480bFcpUoKhYeWtFEufGNqZv20Zuh2O8w+pMNSQdPsl/N+7jngs6WYqD0CLAOyKS52xHAf8RkR/VHVUdX9EFVFWBknWwI5zXD8slikgDYDRwRzlld5Z6v09EMoAYICAab/mFxUxZsJNerRtwWe+WbodjalBlG2/VrlCmYvO3HiQ18xS/v+wct0Mx3mN1phr+tTiRyPBa3H1+J7dDMb41o8z2O2dzEREJAxKALsDUMjkVrwYWqurxM1xjMBAJ7C7n2GRgMkD79v4zIWDO2hRSM0/x1h29qVXLUk0Fs8o23rxSoUz5pi9Lom3jOlzSy34pBRGrM2cpNTOHj9enc+uwDsTUr+12OMaHVPUnvWFneZ0ioL+TeuRjEemtqpudwxPwJM+ukIi0At4GJqlqcTnXnwZMA4iLi9Oyx92Qk1/IywsTGdyxCSO7xbgdjqlhlV1hwSsVyvzU92nHWJOcyf9dcQ5h9kspaFidOXuvfrubWiJMvsB63Uz1qGqWiCzGM+N7s4g0w7Mc4zUVlXEeq34B/EFVV/km0up7a0Uyh7PzePWWgZbgPQTYaEaXvbk8iejIMG6wZX9MOURkuohkiMjmUvv6i8gqZ9ZcvPN4p7yyk0Rkl/Oa5Luoz96BY7l8EJ/G9XFtadXQ8hebqhORGKfHDRGpgydFT8nawtcDn6tqbgVlI/Ek1Z6pqnN9Ea83HMsp4NUluxndozlxsU3cDsf4gDXeXJRxPJf/btrHz+LaWSoEU5G38PQalPY88JSq9seTef75soVEpAnwJJ5kwIOBJ0Wkcc2GWn3Tlu6hSNUW0TbV0QpYLCKbgLXAfFX93Dl2E/CjJL8iEiciJY9Rb8CTn/H2UmlF+vsq8LP12tLdHM8t5NFLursdivERS1nuordX7aWwWLnjvFi3QzF+SlWXikhs2d1AA+d9Q2BfOUUvxXPTygQQkfl4GoF+m53+cHYe767ZyzUD2tCuSV23wzEBSlU3AQMqODaqnH3xwN3O+3cIsPGpGSdyeXN5MuP7taZn6wZnLmCCgjXeXJJbUMSs1SmMOacFHZpGux2OCSwPA/NE5AU8vefDyzmnDVB60fs0Z99P+MvMude/SyK/sJh7R1mvmzGVNXVRIvlFxTxycTe3QzE+ZI9NXfLJ+nQyT+ZzpyUgNVX3S+ARVW0HPAK8UZ2Lqeo0Z83IuJgYd2apHT2Zz9srkxnXtzWdYuq5EoMxgSY1M4d316RwQ1w7OjazToBQYo03F6gq05cn0bNVA4Z2ssGlpsomAR857z/AM6atrHSg9CyYts4+v/TmimRO5hdx34Vd3A7FmIDx4oJdiAgPXdTV7VCMj1njzQXLEg+z82A2d57f0aZ0m7OxDxjpvB8N7CrnnHnAJSLS2JmocImzz+8kHT7JG9/t4dJeLejesr7b4RgTELbtP87H69OYNKyDrYcdgmzMmwveWJZEs3q1ubJfK7dDMX5ORGYDo4BmIpKGZwbpPcBLIhKOZ+Huyc65ccAvVPVuVc0UkT/jmW0H8HTJ5AV/kldYxAOz1xERXosnr+zldjjGBISc/EIemL2eJtG1uXeU9VaHImu8+VhiRjZLdhzikTHdqB0e5nY4xs+p6oQKDg0q59wfZs0529OB6TUUmlf8vy+3szn9OP+5LY7WjSyvmzGV8cdPtrD7UDaz7hpia/+GKHts6mNvrUgiMrwWE4f6z3p4xrjhmy0HeGtFMnecF8vFPVu4HY4xAeGD+FQ+XJfGQxd1ZXiXZm6HY1zi88abiISJyHoR+dzZ7igiq0UkUUTeczJcly1zsYgkiMj3zj9H+zpub8jKyefDhHSu7t+aZvVszUYTutKzTvGbuZvo3aYBv7ush9vhGBMQdh48wR8/3czwzk15YLRNUghlbvS8PQRsK7X9HDBFVbsAR4G7yilzGLhSVfvgmWn3do1HWQNmr0nlVEERd55v6UFM6CosKuah2espKlZemTDQhg8YUwk5+YXcO2sd9WpH8OJN/W0t7BDn08abiLQFrgBed7YFz2y5kjXkZgBXly2nqutVtSSL/BagjogEVNdVQVExM1Ykc16XpvRoaVmwTeh6ccEu4vce5dlrehNruamMqZSScW4v3dSf5vVtdmmo83XP24vAY0Cxs90UyFLVQme7wizwpVwHrFPVvLIHRGSys1B3/KFDh7wVs1d8tfkAB47ncpf1upkQtmzXYaYuSeSGuLZc1f9MVd0YA/8b5/bg6K6cZ+PcDD5svInIOCBDVROqcY1eeB6z/ry84/6QKb48qsoby5Lo2CyaUd2aux2OMa44dCKPh9/bQOeYevxpvKUFMaYySsa5DevUlActGa9x+DJVyHnAeBG5HIjCs7D2S0AjEQl3et8qzALvPHL9GLhNVXf7KGavWJeSxcbULJ6+qhe1bJyCCUHFxcqv3t/AidwCZt09hLqRlqXImDMpPc7tpQk2zs38j8963lT1cVVtq6qxwE3AIlWdCCwGrndOmwR8WrasiDQCvgB+p6rLfRSy10xflkSDqHCuG9jW7VCMccWrS3fz3a7DPHllL1tFwZhKsnFupiL+kOftt8CvRCQRzxi4NwBEZLyIPO2ccz/QBXhCRDY4r4B4/ph2NIevNu9nwuD2RNe23gYTehL2ZvL3b3ZyRd9WTBjc7swFjDE2zs2cliutCVVdAixx3u+hnIW1VfUz4DPn/TPAM76L0HtmrtyLiHDb8Fi3QzHG57Jy8nlw9gbaNKrD/7u2j63la0wl2Dg3cybWFVSDTuYVMntNCmN7t6SNLf1jQoyq8tjcTWScyGXuL4bTICrC7ZCM8Xs5+YXcZ+PczBn4w2PToPXhujRO5BZaehATkt5etZdvth7kt2N70K9dI7fDMSYgPPHpFhJtnJs5A2u81ZDiYuXN5cn0b9eIge0bux2OMT61Zd8xnvl8Gxd2j+HO8+zHizGV8UF8KnMT0njAxrmZM7DGWw1ZvCODpMMnrdfNhJyTeYU88O56GkdH8Pcb+lt6HONTIhIlImtEZKOIbBGRp5z935Wa8LZPRD6poPwkEdnlvCb5Ku6ScW5DOzXhIRvnZs7AxrzVkDeWJdGqYRRje7d0OxRjfOqPn2wm+chJ3r1nKE2iI90Ox4SePGC0qmaLSASwTES+UtURJSeIyIeUn5aqCfAkEAcokCAin6nq0ZoM+H/j3MJ5+aYBNs7NnJH1vNWAbfuPs2L3ESYNjyUizP4Vm9AxNyGNj9an8+BFXRnaqanb4ZgQpB7ZzmaE89KS4yLSAM+a2uX1vF0KzFfVTKfBNh8YW8Mh/zDO7cUbB9C8gY1zM2dmLYsaMH1ZEnUiwrjpXMtpZUJHYkY2f/zE89jngdH22Me4R0TCRGQDkIGnMba61OGrgYWqerycom2A1FLblVlvu1rmJqT9MM7t/K42zs1UjjXevOxwdh6fbtjHdYPa0KiuPTIyoSG3oIj7311HncgwXrLHPsZlqlqkqv3xLLk4WER6lzo8AZhdneuLyGQRiReR+EOHDp31dXYdPPHDDx4b52aqwhpvXlJYVMynG9KZMG0V+UXF3GEz7EwIeeaLrWw/cIK//6wfLeyxj/ETqpqFZwnGsQAi0gxPUvgvKiiSDpR+ZFLuetuqOk1V41Q1LiYm5qxiK1m3NLp2mI1zM1VmjbdqKigq5v21qYz5x7c8NGcDAK/eMojOMfVcjswY3/jq+/28syqFe0Z05MIeAbFqnQliIhLjrIeNiNQBLga2O4evBz5X1dwKis8DLhGRxiLSGLjE2ed1T9o4N1MNNtv0LOUWFPFBfCqvfruH9KxT9GrdgFdvGcglPVtaagQTMlIzc3jsw030a9eI31zaw+1wjAFoBcwQkTA8HRTvq+rnzrGbgL+WPllE4oBfqOrdqpopIn8G1jqHn1bVTG8HODchjQ8S0njwIhvnZs6ONd6qKCe/kHdXpzBt6R4yTuQxsH0jnrm6N6O6x9i6jcbrRGQ6MA7IUNXezr73gO7OKY2ALGd8T9myjwB345lp9z1wx2l6HKqsoKiYB+esB4V/3jSAyHDryDfuU9VNwIAKjo0qZ188nnpSsj0dmF5T8dk4N+MN1nirpOO5Bby9ci9vLEsi82Q+wzo15cUb+zOsc1NrtJma9BbwCjCzZIeq3ljyXkT+DhwrW0hE2gAPAj1V9ZSIvI+n1+EtbwX2wjc7WJ+SxdSbB9K+aV1vXdaYoGXj3Iy3WOPtDI6ezOfN5Um8uSKZE7mFjOoew/0XdiEutonboZkQoKpLRSS2vGPi+dVwA56cVeUJB+qISAFQF9jnrbiW7MjgtW/3cPOQ9lzRt5W3LmtMUCsZ5zbzzsE2zs1UizXeKpBxIpc3vkvinVV7OZlfxKW9WnD/hV3p07ah26EZU2IEcFBVd5U9oKrpIvICkAKcAr5R1W+88aEHj+fy6/c30qNlfZ4Y19MblzQm6P0wzm10F0Z0PbsZqsaUsMZbGfuPneK1b/cwe00KBUXFjOvbmvsu7EL3lvXdDs2YsirMV+XMlLsK6AhkAR+IyC2q+k45504GJgO0b9/+tB9YVKw8PGcDOflFvHLzAKIiwqr5FYwJfj8a5zamm9vhmCBgjTdHypEc/v3tbuYmpKIK1wxowy9HdaaTpfwwfkhEwoFrgUEVnDIGSFLVQ875HwHDgZ803lR1GjANIC4uTsseL23q4kRW7jnC89f3pUtz+0FjzJmcyi/ivndtnJvxrpBvvCVmZPOvJYl8umEfYSLceG47fn5BZ9o1sQHYxq+NAbaraloFx1OAoSJSF89j04uA+Op84JqkTF5csJOr+rfmZ4PaVudSxoSMJz/bzK4MG+dmvCtkG2/b9h/nlcWJfPn9fmqH1+L24bFMvqCTZYc3fkVEZgOjgGYikgY8qapv4Jk5OrvMua2B11X1clVdLSJzgXVAIbAep3ftbBw9mc9Dc9bTvkldnr2mj82wNqYSPkxI4/14G+dmvC/kGm8bUrN4ZVEiC7YdpF7tcH45sjN3nt+RZvVqux2aMT+hqhMq2H97Ofv2AZeX2n4SeNILMfDoBxs5kp3PR/cOp17tkPuzYUyVJWac4P9snJupISH1V1hV+dNnW0g6fJJHxnTj9uGxNKwb4XZYxvi1hL1HWbQjgyfG9aR3G5ttbUxl/HNRoo1zMzXG5403Z8mSeCBdVceJSEdgDtAUSABuVdX8cso9DtwFFAEPqmqV15sTEabc2J+Y+rWt98CYSoqLbcJn951P7zYN3A7FmIDx3HV92Xskx8a5mRrhxno2DwHbSm0/B0xR1S7AUTwNtB8RkZ54xvj0AsYC/3IagVXWsVm0NdyMqaI+bRvaODdjqiAqIsxSTJka49PGm4i0Ba4AXne2BU92+LnOKTOAq8spehUwR1XzVDUJSAQG13zExhhjjDH+xdc9by8CjwHFznZTPItqFzrbaUCbcsq1AVJLbZd7nohMFpF4EYk/dOiQ96I2xhhjjPETPmu8icg4IENVE2rqM1R1mqrGqWpcTIxNyzbGGGNM8PHl4K/zgPEicjkQBTQAXgIaiUi40/vWFkgvp2w60K7UdkXnGWOMMcYENZ/1vKnq46raVlVj8Uw+WKSqE4HFwPXOaZOAT8sp/hlwk4jUdmandgXW+CBsY4wxxhi/IqqnXcqwZj5UZBTwqJMqpBOeVCFN8GSBv0VV80RkPBCnqk84Zf4A3IknW/zDqvrVGT7jEHASOFxz38QvNSO0vrMvvm8HVQ2J5/AhWm9Crc6A1RuvCdE6A6FXb/yqzrjSePMVEYlX1Ti34/ClUPvOofZ9fSHU/p2G2veF0PzONSkU/32G2nf2t+/rRp43Y4wxxhhzlqzxZowxxhgTQIK98TbN7QBcEGrfOdS+ry+E2r/TUPu+EJrfuSaF4r/PUPvOfvV9g3rMmzHGGGNMsAn2njdjjDHGmKASlI03ERkrIjtEJFFEfud2PDVBRKaLSByYehIAAAa2SURBVIaIbC61r4mIzBeRXc4/G7sZo7eJSDsRWSwiW0Vki4g85OwP6u/tK1Zvgu//H6szNc/qTfD9PxQI9SboGm8iEgZMBS4DegITRKSnu1HViLeAsf+/vfuPvWu+4zj+fKkZid9axVdLQyeMFaO+mvrxBxLmx+LXJKSSSciCkKxMxEK6sEk7KhEqUdSPTEjFmsWWUYosLRJbKRIa2qgfbZpOuo4SvPfH58OO6/ut7/nq+d57Pn09knd67/mcc+7nfPt5JZ9z7zn3diy7BlgYEROBhfl5ST4Hfh0RBwH9wKX5/7b0426cc1Ps+HFmGuTcFDuGej43xU3egMnA8oh4OyI+I30B8Bld7tNmFxHPAes6Fp8BzMuP5wE/H9FONSwiPoiIl/Pj/wBvAH0UftwjxLlJiho/zkzjnJukqDHUhtyUOHnrA96tPF+Vl20JxkbEB/nxh8DYbnamSZL2BQ4DXmALOu4GOTdJsePHmWmEc5MUO4Z6NTclTt4MiHQbcZG3EkvaHphP+pm09dW2ko/bmlfq+HFmrEmljqFezk2Jk7f3gHGV53vnZVuC1ZL2BMj/rulyfzY7ST8ghemhiHgsLy7+uEeAc0OZ48eZaZRzQ5ljqNdzU+Lk7SVgoqQJkrYBzgMWdLlPI2UBcGF+fCHw5y72ZbOTJGAu8EZE3FJpKvq4R4hzkxQ1fpyZxjk3SVFjqA25KfJLeiWdAswGRgH3RMSNXe7SZifpT8DxwGhgNXA98DjwCDAeWAmcGxGdF5m2lqSpwPPAq8CXefG1pGsRij3ukeLclDd+nJnmOTfljaE25KbIyZuZmZlZqUr82NTMzMysWJ68mZmZmbWIJ29mZmZmLeLJm5mZmVmLePJmZmZm1iKevFktkp6RNK3yPCSdvYn1X5R01sj0zqw3OTdm9Tk3g/PkrcdIGiPpDkkrJH0qabWkhZJOzO0rJE3vUt9+Rvo28YdqbPY74A+SPNasMc6NWX3OTXsVf4AtNB+YDFwE/Ag4FfgrsFs3O5VdAdwXEV/U2OYJYAfg5Ga6ZAY4N2bD4dy0VUS4eqSAnUk/dHvCIO2LcvvXVWmbAjwLfEz6bb07gR07tp0D3Ab8O9dMYKvKOmcCrwCfAOvy/sbmtjGkb5qe1NGnAM6uPP8NsBboryy7B3iw239fV5nl3Lhc9cu5aXf5nbfesiHX6ZK2HaD9TGAVMAPYMxeSDgH+TvrdtUl5vUNJg7jqfNK7rUcDlwAXA1fmfewBPAzMAw4EjgUeqGw7FfgUWDZQx5XMAi4HjouIJZXmF4HjvvPozYbHuTGrz7lps27PHl3fLOAs0lnIRmAxMAs4qtK+Apjesc39wNyOZYeSzlJ2z88XAW+SfxItL7sOWJUfH57X32eQfl0JrBxgeQC/AO7N+//W9sDppLOorbv993WVWc6Ny1W/nJv2lt956zERMR/YCziNdO3BFGCJpGs3sdlPgQskbfiqgH/ktv0q6y2JPLqzxUCfpB2BpcBTwDJJ8yX9StKYyrrbkQI+kFmkHy2eGhErB2j/BBAw0Nmd2ffm3JjV59y0lydvPSgiNkbEkxExIyKmAHOBGyRtM8gmWwF3k85+vqpJwETgX0N8zS+Ak3K9QrqA9S1Jk/Iqa4FdBtn8SWAP4JRB2ncFNkbEhqH0xWw4nBuz+pybdtq62x2wIXmd9H+1LfAZMKqj/WXgxxGx/Dv2c5QkVc6G+oH3I2I95KtR09nRYkkzgNdIb1EvBf4JjJE0OiLWduz3CeAx4NG8+3kd7QfnPpqNJOfGrD7npgX8zlsPkbSbpKclXSDpJ5ImSDoHuBpYmAf9CuAYSX2SRudNbwYmS5oj6TBJ+0s6VdJdHS+xFzBb0gH5iw6vAm7Nr90v6TpJR0oaT7puYBwpyJDCtIZ0Iem3RMRfgHOAOdUvVcyOAf427D+M2SY4N2b1OTct1+2L7lz/L+CHwE3AS6Rbqz8G3gJuAXbN6/STzkw28s1bt48gDdj1wH+BV4EZlfZFpFu3bwc+yvv/IzAqtx9IuuZhNekun+XA1R39+z3waMeyzlu3TyNdczAtP+8jnb3t3e2/r6vMcm5crvrl3LS7lA/YCidpEbAsIi77HvvYnXRmdGREvDPEbWYCO0XExcN9XbNucW7M6nNumuePTW3IImIN8EtgfI3N1gC/baZHZr3PuTGrz7nZNN+wYLVExIKa689sqi9mbeHcmNXn3AzOH5uamZmZtYg/NjUzMzNrEU/ezMzMzFrEkzczMzOzFvHkzczMzKxFPHkzMzMzaxFP3szMzMxa5H/KgZGvGddXHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,5,10,20,23]\n",
    "r1=[28.2430,40.0250,40.1040,40.5680,40.7340,41.0210]\n",
    "# r2=[8.5160,17.7630,17.9800,18.3920,18.5950,18.7410]\n",
    "# rl=[25.69,36.9030,37.1310,37.5370,37.7180,37.9470]\n",
    "r1=[40.0250,40.1040,40.5680,40.7340,41.0210]\n",
    "r2=[17.7630,17.9800,18.3920,18.5950,18.7410]\n",
    "rl=[36.9030,37.1310,37.5370,37.7180,37.9470]\n",
    "figure=plt.figure(figsize=(10,3))\n",
    "# plt.subplots_adjust(wspace=1,hspace=0.1)\n",
    "plt.subplots_adjust(wspace=0.4,hspace=0.3)\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(x,r1)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('ROUGE-1',fontsize=14)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(x,r2)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('ROUGE-2',fontsize=14)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(x,rl)\n",
    "plt.xlabel('Steps(k)',fontsize=14)\n",
    "plt.ylabel('ROUGE-L',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=plt.figure(figsize=(10,3))\n",
    "for i in range(len(rstdt_sentcons)):\n",
    "    ax=plt.subplot(2, 6, i+1) \n",
    "    acc_matrix = (rstdt_nocons[i]+instruction_nocons[i]+gum_nocons[i])/3\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=0, vmax=35)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "#     plt.xlabel('Head',fontsize=14)\n",
    "#     plt.ylabel('Layer',fontsize=14) \n",
    "    \n",
    "    ax=plt.subplot(2, 6, i+7) \n",
    "    acc_matrix = (rstdt_sentcons[i]+instruction_sentcons[i]+gum_sentcons[i])/3\n",
    "    im = plt.imshow(acc_matrix,cmap=plt.get_cmap('Greens'),origin='lower',vmin=15, vmax=50)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "#     plt.xlabel('Head',fontsize=14)\n",
    "#     plt.ylabel('Layer',fontsize=14) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of documents: 127\n",
      "avg number of edus: 106.850394\n",
      " avg number of sentences: 45.031496\n",
      " avgnumber of words each edu: 8.178998\n",
      " avg number of words per sentence: 19.407064\n",
      " avg number of words per doc:873.929134 \n"
     ]
    }
   ],
   "source": [
    "data_list=['./em_dataset/all/', './instruction_dataset/all','./GUM_binary/']\n",
    "all_data = torch.load('./GUM_binary/all.pt')\n",
    "num_edus_total = 0\n",
    "num_sentences_total = 0\n",
    "length_doc_total = 0\n",
    "num_data = len(all_data)\n",
    "for d in all_data:\n",
    "    num_edus_total+=len(d['disco_txt'])\n",
    "    num_sentences_total +=len(d['sentence_boundary'])\n",
    "    length_doc_total+=sum([len(d['disco_txt'][i]) for i in range(len(d['disco_txt']))])\n",
    "avg_num_edus = num_edus_total/num_data\n",
    "avg_num_sent = num_sentences_total/num_data\n",
    "avg_length_edus = length_doc_total/num_edus_total\n",
    "avg_length_sent=length_doc_total/num_sentences_total\n",
    "avg_length_doc=length_doc_total/num_data\n",
    "print('number of documents: %d'%(num_data))\n",
    "print('avg number of edus: %f\\n avg number of sentences: %f\\n avgnumber of words each edu: %f\\n avg number of words per sentence: %f\\n avg number of words per doc:%f '%(avg_num_edus,avg_num_sent,avg_length_edus,avg_length_sent,avg_length_doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tree_property(deptree):\n",
    "    resultdict={}\n",
    "    all_node_out = set(deptree.keys())\n",
    "    all_node_in = set([n for v in deptree.values() for n in v])\n",
    "#     print(all_node_in)\n",
    "    root = all_node_out.difference(all_node_in).pop()\n",
    "    leaf = all_node_in.difference(all_node_out)\n",
    "    \n",
    "#     print(all_node_out.difference(all_node_in))\n",
    "    resultdict['branch_max']  = max([len(v) for v in deptree.values()])\n",
    "    resultdict['branch_min']  = min([len(v) for v in deptree.values()])\n",
    "    resultdict['branch_avg'] = sum([len(v) for v in deptree.values()])/len(deptree.values())\n",
    "    resultdict['branch_median']= np.median([len(v) for v in deptree.values()])\n",
    "    resultdict['depth'] = get_depth(deptree,root)\n",
    "    resultdict['num_leaf']=len(leaf)\n",
    "    resultdict['num_node'] = len(all_node_in)+1\n",
    "    resultdict['normalized_arc_length']=(get_avg_arc_length(deptree)/len(all_node_in))\n",
    "    resultdict['vacuous_tree']=check_vacuous_tree(deptree)\n",
    "    return resultdict\n",
    "\n",
    "def get_depth(deptree,start):\n",
    "    if start not in deptree.keys():\n",
    "        return 1\n",
    "    return 1+max([get_depth(deptree,n) for n in deptree[start]])\n",
    "\n",
    "def get_avg_arc_length(deptree):\n",
    "    arc_length=0\n",
    "    arc_num=0\n",
    "    for k in deptree.keys():\n",
    "        for v in deptree[k]:\n",
    "            arc_length+=abs(v-k)\n",
    "            arc_num+=1\n",
    "    return arc_length/arc_num\n",
    "    \n",
    "def check_vacuous_tree(deptree):\n",
    "    if len(deptree.keys())==1:\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: rstdt-full\n",
      "{1: [0, 2]}\n",
      "{0: [1]}\n",
      "{0: [1]}\n",
      "{0: [1]}\n",
      "{0: [1, 2, 3]}\n",
      "{0: [1, 2]}\n",
      "{1: [0, 2, 3, 4]}\n",
      "GT tree\n",
      " avg branch max: 5.412987, avg branch min: 1.051948, avg branch avg: 2.100000, avg depth: 8.189610, avg median: 1.767532,     avg leaf ratio(micro): 0.515489, avg leaf ratio(macro): 0.535549, avg norm. arc. length: 0.130829, vacuous tree ratio: 0.018182\n",
      "{1: [2, 0]}\n",
      "{1: [0]}\n",
      "{1: [0]}\n",
      "{1: [0]}\n",
      "{2: [1, 3, 0]}\n",
      "{2: [0, 1]}\n",
      "{2: [0, 1]}\n",
      "{2: [1, 0]}\n",
      "{2: [1, 3, 0]}\n",
      "{2: [0, 3, 1]}\n",
      "Candidate tree\n",
      " avg branch max: 5.511688, avg branch min: 1.025974, avg branch avg: 1.499450, avg depth: 27.064935, avg median: 1.111688,     avg leaf ratio(micro): 0.366378, avg leaf ratio(macro): 0.352447, avg norm. arc. length: 0.098739, vacuous tree ratio: 0.025974\n",
      "Dataset: instructional\n",
      "{0: [1]}\n",
      "GT tree\n",
      " avg branch max: 3.835227, avg branch min: 1.000000, avg branch avg: 1.586123, avg depth: 8.494318, avg median: 1.238636,     avg leaf ratio(micro): 0.409802, avg leaf ratio(macro): 0.405430, avg norm. arc. length: 0.146935, vacuous tree ratio: 0.005682\n",
      "{1: [3, 0, 2]}\n",
      "{1: [0]}\n",
      "{3: [1, 2, 0, 4]}\n",
      "{1: [2, 0]}\n",
      "{2: [1, 3, 0]}\n",
      "Candidate tree\n",
      " avg branch max: 4.738636, avg branch min: 1.062500, avg branch avg: 1.564903, avg depth: 15.738636, avg median: 1.241477,     avg leaf ratio(micro): 0.387556, avg leaf ratio(macro): 0.386382, avg norm. arc. length: 0.126522, vacuous tree ratio: 0.028409\n",
      "Dataset: GUM\n",
      "GT tree\n",
      " avg branch max: 7.448819, avg branch min: 1.000000, avg branch avg: 2.024103, avg depth: 12.173228, avg median: 1.551181,     avg leaf ratio(micro): 0.505306, avg leaf ratio(macro): 0.507772, avg norm. arc. length: 0.037869, vacuous tree ratio: 0.000000\n",
      "Candidate tree\n",
      " avg branch max: 10.881890, avg branch min: 1.000000, avg branch avg: 1.611781, avg depth: 44.937008, avg median: 1.015748,     avg leaf ratio(micro): 0.396242, avg leaf ratio(macro): 0.379435, avg norm. arc. length: 0.047345, vacuous tree ratio: 0.000000\n"
     ]
    }
   ],
   "source": [
    "dataset_list=['rstdt-full','instructional','GUM']\n",
    "for idata,dataset in enumerate(dataset_list):\n",
    "    print('Dataset: %s'%(dataset))\n",
    "    f = '/scratch/wenxiao/summ_guided_dp_result/%s-cnndm-6-8-dep_perhead_cle.pt'%(dataset)\n",
    "    all_trees= torch.load(f)\n",
    "    ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "    dep_tree_gt_list = []\n",
    "    all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "    for i in ids:\n",
    "    #     if all_gt[i]==[]:\n",
    "    #         continue\n",
    "    #     ids.append(i)\n",
    "    #     print(all_gt[i])\n",
    "        dep_tree_gt=all_gt[i]['dep_tree']\n",
    "        dep_tree_gt_list.append(dep_tree_gt)\n",
    "        \n",
    "    dep_tree_cand_list=[]\n",
    "    for i in ids:\n",
    "        dep_tree_cand = {k: list(all_trees['1-1-sent_cons-cle'][i][k].keys()) for k in all_trees['1-1-sent_cons-cle'][i].keys()}\n",
    "        dep_tree_cand_list.append(dep_tree_cand)\n",
    "        \n",
    "    \n",
    "    \n",
    "    branch_max_total=0\n",
    "    branch_min_total=0\n",
    "    branch_avg_total=0\n",
    "    branch_median_total=0\n",
    "    depth_total=0\n",
    "    leaf_num_total=0\n",
    "    node_num_total=0\n",
    "    leaf_ratio_total=0\n",
    "    normalized_arc_length_total=0\n",
    "    vacuous_tree_num=0\n",
    "    for deptree in dep_tree_gt_list:\n",
    "        resultdict= get_tree_property(deptree)\n",
    "        branch_max_total+=resultdict['branch_max']\n",
    "        branch_min_total+=resultdict['branch_min']\n",
    "        branch_avg_total+=resultdict['branch_avg']\n",
    "        branch_median_total+=resultdict['branch_median']\n",
    "        depth_total+=resultdict['depth']\n",
    "        leaf_num_total+=resultdict['num_leaf']\n",
    "        node_num_total+=resultdict['num_node']\n",
    "        leaf_ratio_total+=(resultdict['num_leaf']/resultdict['num_node'])\n",
    "        normalized_arc_length_total+=resultdict['normalized_arc_length']\n",
    "        if resultdict['vacuous_tree']:\n",
    "            vacuous_tree_num+=1\n",
    "            print(deptree)\n",
    "    print('GT tree\\n avg branch max: %f, avg branch min: %f, avg branch avg: %f, avg depth: %f, avg median: %f, \\\n",
    "    avg leaf ratio(micro): %f, avg leaf ratio(macro): %f, avg norm. arc. length: %f, vacuous tree ratio: %f'\\\n",
    "         %(branch_max_total/len(dep_tree_gt_list),branch_min_total/len(dep_tree_gt_list),\\\n",
    "           branch_avg_total/len(dep_tree_gt_list),depth_total/len(dep_tree_gt_list),\\\n",
    "           branch_median_total/len(dep_tree_gt_list),leaf_num_total/node_num_total,leaf_ratio_total/len(dep_tree_gt_list),\\\n",
    "          normalized_arc_length_total/len(dep_tree_gt_list),vacuous_tree_num/len(dep_tree_gt_list) ))\n",
    "    \n",
    "    branch_max_total=0\n",
    "    branch_min_total=0\n",
    "    branch_avg_total=0\n",
    "    branch_median_total=0\n",
    "    depth_total=0\n",
    "    leaf_num_total=0\n",
    "    node_num_total=0\n",
    "    leaf_ratio_total=0\n",
    "    normalized_arc_length_total=0\n",
    "    vacuous_tree_num=0\n",
    "    for deptree in dep_tree_cand_list:\n",
    "        resultdict= get_tree_property(deptree)\n",
    "        branch_max_total+=resultdict['branch_max']\n",
    "        branch_min_total+=resultdict['branch_min']\n",
    "        branch_avg_total+=resultdict['branch_avg']\n",
    "        branch_median_total+=resultdict['branch_median']\n",
    "        depth_total+=resultdict['depth']\n",
    "        leaf_num_total+=resultdict['num_leaf']\n",
    "        node_num_total+=resultdict['num_node']\n",
    "        leaf_ratio_total+=(resultdict['num_leaf']/resultdict['num_node'])\n",
    "        normalized_arc_length_total+=resultdict['normalized_arc_length']\n",
    "        if resultdict['vacuous_tree']:\n",
    "            vacuous_tree_num+=1\n",
    "            print(deptree)\n",
    "    print('Candidate tree\\n avg branch max: %f, avg branch min: %f, avg branch avg: %f, avg depth: %f, avg median: %f, \\\n",
    "    avg leaf ratio(micro): %f, avg leaf ratio(macro): %f, avg norm. arc. length: %f, vacuous tree ratio: %f'\\\n",
    "         %(branch_max_total/len(dep_tree_gt_list),branch_min_total/len(dep_tree_gt_list),\\\n",
    "           branch_avg_total/len(dep_tree_gt_list),depth_total/len(dep_tree_gt_list),\\\n",
    "           branch_median_total/len(dep_tree_gt_list),leaf_num_total/node_num_total,leaf_ratio_total/len(dep_tree_gt_list),\\\n",
    "           normalized_arc_length_total/len(dep_tree_gt_list),vacuous_tree_num/len(dep_tree_gt_list)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: rstdt-full\n",
      "GT tree\n",
      " med branch max: 5.000000, med branch min: 1.000000, med branch avg: 2.062500, med depth: 7.000000, med median: 2.000000\n",
      "Cand tree\n",
      " med branch max: 3.000000, med branch min: 1.000000, med branch avg: 1.548387, med depth: 23.000000, med median: 1.000000\n",
      "Dataset: instructional\n",
      "GT tree\n",
      " med branch max: 3.000000, med branch min: 1.000000, med branch avg: 1.597297, med depth: 8.000000, med median: 1.000000\n",
      "Cand tree\n",
      " med branch max: 3.000000, med branch min: 1.000000, med branch avg: 1.666667, med depth: 10.000000, med median: 1.500000\n",
      "Dataset: GUM\n",
      "GT tree\n",
      " med branch max: 7.000000, med branch min: 1.000000, med branch avg: 2.014706, med depth: 11.000000, med median: 2.000000\n",
      "Cand tree\n",
      " med branch max: 15.000000, med branch min: 1.000000, med branch avg: 2.057692, med depth: 46.000000, med median: 1.000000\n"
     ]
    }
   ],
   "source": [
    "dataset_list=['rstdt-full','instructional','GUM']\n",
    "for idata,dataset in enumerate(dataset_list):\n",
    "    print('Dataset: %s'%(dataset))\n",
    "    f = '/scratch/wenxiao/summ_guided_dp_result/%s-cnndm-6-8-dep_perhead_cle.pt'%(dataset)\n",
    "    all_trees= torch.load(f)\n",
    "    ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "    dep_tree_gt_list = []\n",
    "    all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "    for i in ids:\n",
    "    #     if all_gt[i]==[]:\n",
    "    #         continue\n",
    "    #     ids.append(i)\n",
    "    #     print(all_gt[i])\n",
    "        dep_tree_gt=all_gt[i]['dep_tree']\n",
    "        dep_tree_gt_list.append(dep_tree_gt)\n",
    "        \n",
    "    dep_tree_cand_list=[]\n",
    "    for i in ids:\n",
    "        dep_tree_cand = {k: list(all_trees['1-1-nocons-cle'][i][k].keys()) for k in all_trees['1-1-nocons-cle'][i].keys()}\n",
    "        dep_tree_cand_list.append(dep_tree_cand)\n",
    "        \n",
    "    \n",
    "    \n",
    "    branch_max_total=[]\n",
    "    branch_min_total=[]\n",
    "    branch_avg_total=[]\n",
    "    branch_median_total=[]\n",
    "    depth_total=[]\n",
    "    for deptree in dep_tree_gt_list:\n",
    "        branch_max,branch_min,branch_avg,depth, branch_median= get_depth_branch_tree(deptree)\n",
    "        branch_max_total.append(branch_max)\n",
    "        branch_min_total.append(branch_min)\n",
    "        branch_avg_total.append(branch_avg)\n",
    "        branch_median_total.append(branch_median)\n",
    "        depth_total.append(depth)\n",
    "        \n",
    "    print('GT tree\\n med branch max: %f, med branch min: %f, med branch avg: %f, med depth: %f, med median: %f'\\\n",
    "         %(np.median(branch_max_total),np.median(branch_min_total),\\\n",
    "           np.median(branch_avg_total),np.median(depth_total),np.median(branch_median_total)))\n",
    "    \n",
    "    branch_max_total=[]\n",
    "    branch_min_total=[]\n",
    "    branch_avg_total=[]\n",
    "    branch_median_total=[]\n",
    "    depth_total=[]\n",
    "    for deptree in dep_tree_cand_list:\n",
    "        branch_max,branch_min,branch_avg,depth, branch_median= get_depth_branch_tree(deptree)\n",
    "        branch_max_total.append(branch_max)\n",
    "        branch_min_total.append(branch_min)\n",
    "        branch_avg_total.append(branch_avg)\n",
    "        branch_median_total.append(branch_median)\n",
    "        depth_total.append(depth)\n",
    "    print('Cand tree\\n med branch max: %f, med branch min: %f, med branch avg: %f, med depth: %f, med median: %f'\\\n",
    "         %(np.median(branch_max_total),np.median(branch_min_total),\\\n",
    "           np.median(branch_avg_total),np.median(depth_total),np.median(branch_median_total)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def local_distant_accuracy(dep_tree_gt,dep_tree_cand):\n",
    "    total=0\n",
    "    correct_local=0\n",
    "    correct_distant=0\n",
    "    local_gt=0\n",
    "    local_cand=0\n",
    "    for key in dep_tree_gt.keys():\n",
    "        if key in dep_tree_cand.keys():\n",
    "            correct_keys=set(dep_tree_gt[key]).intersection(set(dep_tree_cand[key]))\n",
    "            correct_all=len(correct_keys)\n",
    "            if key-1 in correct_keys:\n",
    "                correct_local+=1\n",
    "                correct_all-=1\n",
    "            if key+1 in correct_keys:\n",
    "                correct_local+=1\n",
    "                correct_all-=1              \n",
    "            correct_distant+=correct_all\n",
    "        total+=len(dep_tree_gt[key])\n",
    "        \n",
    "    for key in dep_tree_gt.keys():\n",
    "        if key-1 in dep_tree_gt[key]:\n",
    "            local_gt+=1\n",
    "        if key+1 in dep_tree_gt[key]:\n",
    "            local_gt+=1\n",
    "            \n",
    "    for key in dep_tree_cand.keys():\n",
    "        if key-1 in dep_tree_cand[key]:\n",
    "            local_cand+=1\n",
    "        if key+1 in dep_tree_cand[key]:\n",
    "            local_cand+=1\n",
    "    \n",
    "    return correct_local,correct_distant,total,local_gt,local_cand\n",
    "\n",
    "\n",
    "def local_distant_accuracy_list(dep_tree_gt_list,dep_tree_cand_list):\n",
    "    correct_all=0\n",
    "    correct_local_all=0\n",
    "    correct_distant_all=0\n",
    "    total_all=0\n",
    "    total_macro=0\n",
    "    local_gt_all=0\n",
    "    local_cand_all=0\n",
    "    for i in range(len(dep_tree_gt_list)):\n",
    "        correct_local,correct_distant,total,local_gt,local_cand=local_distant_accuracy(dep_tree_gt_list[i],dep_tree_cand_list[i])\n",
    "        correct_local_all+=correct_local\n",
    "        correct_distant_all+=correct_distant\n",
    "        total_all+=total\n",
    "        total_macro+=((correct_local+correct_distant)/total)\n",
    "        local_gt_all+=local_gt\n",
    "        local_cand_all+=local_cand\n",
    "    correct_all = correct_local_all+correct_distant_all\n",
    "    micro_f1 = correct_all/total_all\n",
    "    correct_local_ratio = correct_local_all/correct_all\n",
    "    correct_distant_ratio = correct_distant_all/correct_all\n",
    "    macro_f1 = total_macro/len(dep_tree_gt_list)\n",
    "    local_correct_recall=correct_local_all/local_cand_all\n",
    "    local_correct_precision = correct_local_all/local_gt_all\n",
    "    print('correct num: ',correct_all)\n",
    "    print('total_num: ',total_all)\n",
    "    print('micro f1: ',micro_f1)\n",
    "    print('macro f1: ', macro_f1)\n",
    "    print('local ratio: ', correct_local_ratio)\n",
    "    print('distant ratio: ', correct_distant_ratio)\n",
    "    print('local correct recall: ', local_correct_recall)\n",
    "    print('local correct precision: ', local_correct_precision)\n",
    "    print('local ratio in gt: ',local_gt_all/total_all)\n",
    "    print('local ratio in cand: ',local_cand_all/total_all)\n",
    "    return micro_f1,macro_f1\n",
    "\n",
    "\n",
    "\n",
    "def local_minus_plus_distant_accuracy(dep_tree_gt,dep_tree_cand):\n",
    "    total=0\n",
    "    correct_local_minus=0\n",
    "    correct_local_plus=0\n",
    "    correct_distant=0\n",
    "    for key in dep_tree_gt.keys():\n",
    "        if key in dep_tree_cand.keys():\n",
    "            correct_keys=set(dep_tree_gt[key]).intersection(set(dep_tree_cand[key]))\n",
    "            correct_all=len(correct_keys)\n",
    "            if key-1 in correct_keys:\n",
    "                correct_local_minus+=1\n",
    "                correct_all-=1\n",
    "            if key+1 in correct_keys:\n",
    "                correct_local_plus+=1\n",
    "                correct_all-=1\n",
    "            correct_distant+=correct_all\n",
    "        total+=len(dep_tree_gt[key])\n",
    "    return correct_local_minus,correct_local_plus,correct_distant,total\n",
    "\n",
    "\n",
    "\n",
    "def local_minus_plus_distant_accuracy_list(dep_tree_gt_list,dep_tree_cand_list):\n",
    "    correct_all=0\n",
    "    correct_local_minus_all=0\n",
    "    correct_local_plus_all=0\n",
    "    correct_distant_all=0\n",
    "    total_all=0\n",
    "    total_macro=0\n",
    "    for i in range(len(dep_tree_gt_list)):\n",
    "        correct_local_minus,correct_local_plus,correct_distant,total=local_minus_plus_distant_accuracy(dep_tree_gt_list[i],dep_tree_cand_list[i])\n",
    "        correct_local_minus_all+=correct_local_minus\n",
    "        correct_local_plus_all+=correct_local_plus\n",
    "        correct_distant_all+=correct_distant\n",
    "        total_all+=total\n",
    "        total_macro+=((correct_local_minus+correct_local_plus+correct_distant)/total)\n",
    "    correct_all = correct_local_minus_all+correct_local_plus_all+correct_distant_all\n",
    "    micro_f1 = correct_all/total_all\n",
    "    correct_local_minus_ratio = correct_local_minus_all/correct_all\n",
    "    correct_local_plus_ratio = correct_local_plus_all/correct_all\n",
    "    correct_distant_ratio = correct_distant_all/correct_all\n",
    "    macro_f1 = total_macro/len(dep_tree_gt_list)\n",
    "    print('correct num: ',correct_all)\n",
    "    print('total_num: ',total_all)\n",
    "    print('micro f1: ',micro_f1)\n",
    "    print('macro f1: ', macro_f1)\n",
    "    print('local minus ratio: ', correct_local_minus_ratio)\n",
    "    print('local plus ratio: ', correct_local_plus_ratio)\n",
    "    print('distant ratio: ', correct_distant_ratio)\n",
    "\n",
    "    return micro_f1,macro_f1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: rstdt-full\n",
      "correct num:  5900\n",
      "total_num:  21404\n",
      "micro f1:  0.275649411324986\n",
      "macro f1:  0.3210666193485579\n",
      "local ratio:  0.7777966101694915\n",
      "distant ratio:  0.22220338983050847\n",
      "local correct recall:  0.46088179170432864\n",
      "local correct precision:  0.40286190852427356\n",
      "local ratio in gt:  0.5321902448140534\n",
      "local ratio in cand:  0.4651934217903196\n",
      "Dataset: instructional\n",
      "correct num:  1926\n",
      "total_num:  5578\n",
      "micro f1:  0.345285048404446\n",
      "macro f1:  0.4030802730747217\n",
      "local ratio:  0.8115264797507789\n",
      "distant ratio:  0.18847352024922118\n",
      "local correct recall:  0.5849550898203593\n",
      "local correct precision:  0.4683847767455799\n",
      "local ratio in gt:  0.5982430978845464\n",
      "local ratio in cand:  0.4790247400501972\n",
      "Dataset: GUM\n",
      "correct num:  3094\n",
      "total_num:  13443\n",
      "micro f1:  0.23015695901212527\n",
      "macro f1:  0.25376496333021525\n",
      "local ratio:  0.7798965740142211\n",
      "distant ratio:  0.22010342598577892\n",
      "local correct recall:  0.44909733854457473\n",
      "local correct precision:  0.33687002652519893\n",
      "local ratio in gt:  0.5328423714944581\n",
      "local ratio in cand:  0.3996875697388976\n"
     ]
    }
   ],
   "source": [
    "dataset_list=['rstdt-full','instructional','GUM']\n",
    "for idata,dataset in enumerate(dataset_list):\n",
    "    print('Dataset: %s'%(dataset))\n",
    "    f = '/scratch/wenxiao/summ_guided_dp_result/%s-cnndm-6-8-dep_perhead_cle.pt'%(dataset)\n",
    "    all_trees= torch.load(f)\n",
    "    ids = all_trees['0-0-sent_cons-cle'].keys()\n",
    "    dep_tree_gt_list = []\n",
    "    all_gt = torch.load('/scratch/wenxiao/summ_guided_dp_gt/%s-gt.pt'%(dataset))\n",
    "    for i in ids:\n",
    "    #     if all_gt[i]==[]:\n",
    "    #         continue\n",
    "    #     ids.append(i)\n",
    "    #     print(all_gt[i])\n",
    "        dep_tree_gt=all_gt[i]['dep_tree']\n",
    "        dep_tree_gt_list.append(dep_tree_gt)\n",
    "        \n",
    "    dep_tree_cand_list=[]\n",
    "    for i in ids:\n",
    "        dep_tree_cand = {k: list(all_trees['1-1-nocons-cle'][i][k].keys()) for k in all_trees['1-1-nocons-cle'][i].keys()}\n",
    "        dep_tree_cand_list.append(dep_tree_cand)\n",
    "    local_distant_accuracy_list(dep_tree_gt_list,dep_tree_cand_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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