#-*- encoding:utf-8 -*-
"""
@author:   letian
@homepage: http://www.letiantian.me
@github:   https://github.com/someus/
"""
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import os
import math
import networkx as nx
import numpy as np
import sys
from . import compute_yingda

try:
    reload(sys)
    sys.setdefaultencoding('utf-8')
except:
    pass
    
sentence_delimiters = ['?', '!', ';', '？', '！', '。', '；', '……', '…', '\n']
allow_speech_tags = ['an', 'i', 'j', 'l', 'n', 'nr', 'nrfg', 'ns', 'nt', 'nz', 't', 'v', 'vd', 'vn', 'eng']

PY2 = sys.version_info[0] == 2
if not PY2:
    # Python 3.x and up
    text_type    = str
    string_types = (str,)
    xrange       = range

    def as_text(v):  ## 生成unicode字符串
        if v is None:
            return None
        elif isinstance(v, bytes):
            return v.decode('utf-8', errors='ignore')
        elif isinstance(v, str):
            return v
        else:
            raise ValueError('Unknown type %r' % type(v))

    def is_text(v):
        return isinstance(v, text_type)

else:
    # Python 2.x
    text_type    = unicode
    string_types = (str, unicode)
    xrange       = xrange

    def as_text(v):
        if v is None:
            return None
        elif isinstance(v, unicode):
            return v
        elif isinstance(v, str):
            return v.decode('utf-8', errors='ignore')
        else:
            raise ValueError('Invalid type %r' % type(v))

    def is_text(v):
        return isinstance(v, text_type)

__DEBUG = None

def debug(*args):
    global __DEBUG
    if __DEBUG is None:
        try:
            if os.environ['DEBUG'] == '1':
                __DEBUG = True
            else:
                __DEBUG = False
        except:
            __DEBUG = False
    if __DEBUG:
        print( ' '.join([str(arg) for arg in args]) )

class AttrDict(dict):
    """Dict that can get attribute by dot"""
    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self


def combine(word_list, window = 2):
    """构造在window下的单词组合，用来构造单词之间的边。
    
    Keyword arguments:
    word_list  --  list of str, 由单词组成的列表。
    windows    --  int, 窗口大小。
    """
    if window < 2: window = 2
    for x in xrange(1, window):
        if x >= len(word_list):
            break
        word_list2 = word_list[x:]
        res = zip(word_list, word_list2)
        for r in res:
            yield r

def get_similarity(word_list1, word_list2):
    """默认的用于计算两个句子相似度的函数。

    Keyword arguments:
    word_list1, word_list2  --  分别代表两个句子，都是由单词组成的列表
    """
    words   = list(set(word_list1 + word_list2))        
    vector1 = [float(word_list1.count(word)) for word in words]
    vector2 = [float(word_list2.count(word)) for word in words]
    
    vector3 = [vector1[x]*vector2[x]  for x in xrange(len(vector1))]
    vector4 = [1 for num in vector3 if num > 0.]
    co_occur_num = sum(vector4)

    if abs(co_occur_num) <= 1e-12:
        return 0.
    
    denominator = math.log(float(len(word_list1))) + math.log(float(len(word_list2))) # 分母
    
    if abs(denominator) < 1e-12:
        return 0.
    
    return co_occur_num / denominator

def sort_words(vertex_source,  window = 2, pagerank_config = {'alpha': 0.85,}):
    """将单词按关键程度从大到小排序

    Keyword arguments:
    vertex_source   --  二维列表，子列表代表句子，子列表的元素是单词，这些单词用来构造pagerank中的节点
    edge_source     --  二维列表，子列表代表句子，子列表的元素是单词，根据单词位置关系构造pagerank中的边
    window          --  一个句子中相邻的window个单词，两两之间认为有边
    pagerank_config --  pagerank的设置
    """
    sorted_words   = []
    word_index     = {}
    index_word     = {}
    _vertex_source = vertex_source
    words_number   = 0
    for word_list in _vertex_source:
        for word in word_list:
            if not word in word_index:
                word_index[word] = words_number
                index_word[words_number] = word
                words_number += 1

    graph = np.zeros((words_number, words_number))
    
    for word_list in _vertex_source:
        for w1, w2 in combine(word_list, window):
            if w1 in word_index and w2 in word_index:
                index1 = word_index[w1]
                index2 = word_index[w2]
                graph[index1][index2] = 1.0
                graph[index2][index1] = 1.0

    debug('graph:\n', graph)
    
    nx_graph = nx.from_numpy_matrix(graph)
    scores = nx.pagerank(nx_graph, **pagerank_config)          # this is a dict
    sorted_scores = sorted(scores.items(), key = lambda item: item[1], reverse=True)
    for index, score in sorted_scores:
        item = AttrDict(word=index_word[index], weight=score)
        sorted_words.append(item)

    return sorted_words

def sort_sentences(sentences, words, sim_func = get_similarity,decay_rate = 1.0, pagerank_config = {'alpha': 0.85,}):
    """将句子按照关键程度从大到小排序

    Keyword arguments:
    sentences         --  列表，元素是句子
    words             --  二维列表，子列表和sentences中的句子对应，子列表由单词组成
    sim_func          --  计算两个句子的相似性，参数是两个由单词组成的列表
    pagerank_config   --  pagerank的设置
    """

    while "ou" in sentences:
        sentences.remove("ou")

    sorted_sentences = []
    _source = words

    sentences_num = len(sentences)        
    graph = np.zeros((sentences_num, sentences_num))
 
    for x in xrange(sentences_num):
        for y in xrange(x, sentences_num):
            #similarity = sim_func( _source[x], _source[y] )
            similarity = compute_yingda.pipei(sentences[x],sentences[y])
            graph[x, y] = similarity
            graph[y, x] = similarity

    #进行咨询新闻特殊处理
    chapital = []
    duanluo_index = [x for x in xrange(sentences_num) if sentences[x].find("ou") >= 0]
    chapital.append((0,duanluo_index[0]))
    for i in range(len(duanluo_index)-1):
        ft = (duanluo_index[i],duanluo_index[i+1])
        chapital.append(ft)
    chapital.append((duanluo_index[-1],sentences_num))

    #归一化
    for x in xrange(sentences_num):
        sum_lie = 0
        for y in xrange(sentences_num):
            if x != y :
                sum_lie += graph[y,x]
        if sum_lie >0 :
            graph [x,x] = 0
        else:
            graph [x,x] = 1.0
            sum_lie = 1.0
        for y in xrange(sentences_num):
            graph [y,x] = float(graph[y,x]) / sum_lie
            graph [x,y] = graph [y,x]


    #权重倾斜
    for i in xrange(len(chapital)):
        for j in xrange(chapital[i][0],chapital[i][1]):
            if chapital[i][1] - chapital[i][0] <= 1 and i != len(chapital) - 1 and sentences[chapital[i][0]].find("，") < 0:
                for k in xrange(chapital[i+1][0],chapital[i+1][1]):
                    graph [chapital[i][0] , k] += (1.0/decay_rate - 1.0)
            elif j < chapital[i][1] - 1 and chapital[i][1] - chapital[i][0] >=3 :
                graph[j , j+1] += (1.0/decay_rate -1.0)

    for x in xrange(sentences_num):
        sum_lie = 0
        for y in xrange(sentences_num):
            sum_lie += graph[y , x]
        for y in xrange(sentences_num):
            graph[y,x] = float(graph[y,x]) / sum_lie

    nx_graph = nx.from_numpy_matrix(graph)
    scores = nx.pagerank(nx_graph, **pagerank_config)              # this is a dict
    sorted_scores = sorted(scores.items(), key = lambda item: item[1], reverse=True)

    for index, score in sorted_scores:
        item = AttrDict(index=index, sentence=sentences[index].replace("ou","").strip(), weight=score)
        sorted_sentences.append(item)

    return sorted_sentences

if __name__ == '__main__':
    pass

numpy.zeros