import sys
from textmatch.models.text_embedding.model_factory_sklearn import ModelFactory


if __name__ == '__main__':
    # doc
    doc_dict = {"0":"我去玉龙雪山并且喜欢玉龙雪山玉龙雪山", "1":"我在玉龙雪山并且喜欢玉龙雪山", "2":"我在九寨沟", "3":"你好"}   
    # query
    query = "我在九寨沟,很喜欢"
    
    # 模型工厂，选择需要的模型加到列表中: 'bow', 'tfidf', 'ngram_tfidf', 'bert', 'albert', 'w2v'
    mf = ModelFactory( match_models=['bow', 'tfidf', 'ngram_tfidf'] )
    # 模型处理初始化
    mf.init(words_dict=doc_dict, update=True)

    # query 与 doc的相似度
    search_res = mf.predict(query)
    print ('search_res>>>>>', search_res) 
    # search_res>>>>> {'bow': [('0', 0.2773500981126146), ('1', 0.5303300858899106), ('2', 0.8660254037844388), ('3', 0.0)], 'tfidf': [('0', 0.2201159065358879), ('1', 0.46476266418455736), ('2', 0.8749225357988296), ('3', 0.0)], 'ngram_tfidf': [('0', 0.035719486884261346), ('1', 0.09654705406841395), ('2', 0.9561288696241232), ('3', 0.0)]}
    
    # query的embedding
    query_emb = mf.predict_emb(query)
    print ('query_emb>>>>>', query_emb) 
    '''
    pre_emb>>>>> {'bow': array([1., 0., 0., 1., 1., 0., 1., 0.]), 'tfidf': array([0.61422608, 0.        , 0.        , 0.4842629 , 0.4842629 ,
       0.        , 0.39205255, 0.        ]), 'ngram_tfidf': array([0.        , 0.        , 0.37156534, 0.37156534, 0.        ,
       0.        , 0.        , 0.29294639, 0.        , 0.37156534,
       0.37156534, 0.        , 0.        , 0.37156534, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.29294639, 0.37156534, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        ])}
    '''

import sys
import json 
import time
import faiss
import numpy as np
from faiss import normalize_L2
from textmatch.config.constant import Constant as const
from textmatch.core.text_embedding import TextEmbedding
from textmatch.tools.decomposition.pca import PCADecomposition
from textmatch.tools.faiss.faiss import FaissSearch

test_dict = {"id0": "其实事物发展有自己的潮流和规律",
   "id1": "当你身处潮流之中的时候，要紧紧抓住潮流的机会",
   "id2": "想办法脱颖而出，即使没有成功，也会更加洞悉时代的脉搏",
   "id3": "收获珍贵的知识和经验。而如果潮流已经退去",
   "id4": "这个时候再去往这个方向上努力，只会收获迷茫与压抑",
   "id5": "对时代、对自己都没有什么帮助",
   "id6": "但是时代的浪潮犹如海滩上的浪花，总是一浪接着一浪，只要你站在海边，身处这个行业之中，下一个浪潮很快又会到来。你需要敏感而又深刻地去观察，略去那些浮躁的泡沫，抓住真正潮流的机会，奋力一搏，不管成败，都不会遗憾。",
   "id7": "其实事物发展有自己的潮流和规律",
   "id8": "当你身处潮流之中的时候，要紧紧抓住潮流的机会" }


if __name__ == '__main__':
    # ['bow', 'tfidf', 'ngram_tfidf', 'bert']
    # ['bow', 'tfidf', 'ngram_tfidf', 'bert', 'w2v']
    # text_embedding = TextEmbedding( match_models=['bow', 'tfidf', 'ngram_tfidf', 'w2v'], words_dict=test_dict ) 
    text_embedding = TextEmbedding( match_models=['bow', 'tfidf', 'ngram_tfidf', 'w2v'], words_dict=None, update=False ) 
    feature_list = []
    for sentence in test_dict.values():
        pre = text_embedding.predict(sentence)
        feature = np.concatenate([pre[model] for model in ['bow', 'tfidf', 'ngram_tfidf', 'w2v']], axis=0)
        feature_list.append(feature)
    pca = PCADecomposition(n_components=8)
    data = np.array( feature_list )
    pca.fit( data )
    res = pca.transform( data )
    print('res>>', res)

   

    pre = text_embedding.predict("潮流和规律")
    feature = np.concatenate([pre[model] for model in ['bow', 'tfidf', 'ngram_tfidf', 'w2v']], axis=0)
    test = pca.transform( [feature] )

    faiss_search = FaissSearch( res, sport_mode=False )
    faiss_res = faiss_search.predict( test )
    print( "faiss_res:", faiss_res )
    '''
    faiss kmeans result times 8.0108642578125e-05
    faiss_res: [{0: 0.7833399, 7: 0.7833399, 3: 0.63782495}]
    '''

    
    faiss_search = FaissSearch( res, sport_mode=True )
    faiss_res = faiss_search.predict( test )
    print( "faiss_res:", faiss_res )
    '''
    faiss kmeans result times 3.266334533691406e-05
    faiss_res: [{0: 0.7833399, 7: 0.7833399, 3: 0.63782495}]
    '''