from sentence_transformers import util
from nltk.tokenize import sent_tokenize
from nltk import word_tokenize, pos_tag
import torch
import numpy as np
import tqdm

def compute_sentencewise_scores(model, query_sents, candidate_sents):
    # list of sentences from query and candidate
    q_v, c_v = get_embedding(model, query_sents, candidate_sents)
    
    return util.cos_sim(q_v, c_v)
    
def get_embedding(model, query_sents, candidate_sents):
    q_v = model.encode(query_sents)
    c_v = model.encode(candidate_sents)
   
    return q_v, c_v

def get_top_k(score_mat, K=3):
    """
    Pick top K sentences to show
    """
    idx = torch.argsort(-score_mat)
    picked_sent = idx[:,:K]
    picked_scores = torch.vstack(
        [score_mat[i,picked_sent[i]] for i in range(picked_sent.shape[0])]
    )
    
    return picked_sent, picked_scores

def get_words(sent):
    """
    Input: list of sentences
    Output: list of list of words per sentence, all words in, index of starting words for each sentence
    """
    words = []
    sent_start_id = [] # keep track of the word index where the new sentence starts
    counter = 0
    for x in sent:
        #w = x.split()
        w = word_tokenize(x)
        nw = len(w)
        counter += nw
        words.append(w)
        sent_start_id.append(counter)
    words = [word_tokenize(x) for x in sent]
    all_words = [item for sublist in words for item in sublist]
    sent_start_id.pop()
    sent_start_id = [0] + sent_start_id
    assert(len(sent_start_id) == len(sent))
    return words, all_words, sent_start_id

def get_match_phrase(w1, w2, method='pos'):
    """
    Input: list of words for query and candidate text
    Output: word list and binary mask of matching phrases between the inputs
    """
    mask1 = np.zeros(len(w1))
    mask2 = np.zeros(len(w2))
    if method == 'pos':
        # POS tags that should be considered for matching phrase
        include = [
            'NN',
            'NNS',
            'NNP',
            'NNPS',
            'LS',
            'SYM',
            'FW'
        ]
        pos1 = pos_tag(w1)
        pos2 = pos_tag(w2)
        for i, (w, p) in enumerate(pos2):
            if w.lower() in w1 and p in include:
                j = w1.index(w.lower())
                mask2[i] = 1
                mask1[j] = 1
    return mask1, mask2

def remove_spaces(words, attrs):
    # make the output more readable by removing unnecessary spacings from the tokenizer
    # e.g.
    # 1. spacing for parenthesis
    # 2. spacing for single/double quotations
    # 3. spacing for commas and periods
    # 4. spacing for possessive quotations
    assert(len(words) == len(attrs))
    word_out, attr_out = [], []
    idx, single_q, double_q = 0, 0, 0
    while idx < len(words):
        # stick to the word that appears right before
        if words[idx] in [',', '.', '%', ')', ':', '?', ';', "'s"]:
            ww = word_out.pop()
            aa = attr_out.pop()
            word_out.append(ww + words[idx])
            attr_out.append(aa)
            idx += 1
        # stick to the word that appears right after
        elif words[idx] in ["("]:
            word_out.append(words[idx] + words[idx+1])
            attr_out.append(attrs[idx+1])
            idx += 2
        # quotes 
        elif words[idx] == '"':
            double_q += 1
            if double_q == 2:
                # this is closing quote: stick to word before
                ww = word_out.pop()
                aa = attr_out.pop()
                word_out.append(ww + words[idx])
                attr_out.append(aa)
                idx += 1
                double_q = 0
            else:
                # this is opening quote: stick to the word after
                word_out.append(words[idx] + words[idx+1])
                attr_out.append(attrs[idx+1])
                idx += 2
        elif words[idx] == "'":
            single_q += 1
            if single_q == 2:
                # this is closing quote: stick to word before
                ww = word_out.pop()
                aa = attr_out.pop()
                word_out.append(ww + words[idx])
                attr_out.append(aa)
                idx += 1
                single_q = 0
            else:
                if words[idx-1][-1] == 's': #possessive quote
                    # stick to the word before, reset counter
                    ww = word_out.pop()
                    aa = attr_out.pop()
                    word_out.append(ww + words[idx])
                    attr_out.append(aa)
                    idx += 1
                    single_q = 0
                else:
                    # this is opening quote: stick to the word after
                    word_out.append(words[idx] + words[idx+1])
                    attr_out.append(attrs[idx+1])
                    idx += 2
        else:
            word_out.append(words[idx])
            attr_out.append(attrs[idx])
            idx += 1         
            
    assert(len(word_out) == len(attr_out))
    return word_out, attr_out

def mark_words(query_sents, words, all_words, sent_start_id, sent_ids, sent_scores):
    """
    Mark the words that are highlighted, both by in terms of sentence and phrase
    """
    num_query_sent = sent_ids.shape[0]
    num_words = len(all_words)
    
    output = dict()
    output['all_words'] = all_words
    output['words_by_sentence'] = words
    
    # for each query sentence, mark the highlight information
    for i in range(num_query_sent):
        query_words = word_tokenize(query_sents[i])
        is_selected_sent = np.zeros(num_words)
        is_selected_phrase = np.zeros(num_words)
        word_scores = np.zeros(num_words)
        
        # for each selected sentences from the candidate, compile information
        for sid, sscore in zip(sent_ids[i], sent_scores[i]):
            #print(len(sent_start_id), sid, sid+1)
            if sid+1 < len(sent_start_id):
                sent_range = (sent_start_id[sid], sent_start_id[sid+1])
                is_selected_sent[sent_range[0]:sent_range[1]] = 1
                word_scores[sent_range[0]:sent_range[1]] = sscore
                _, is_selected_phrase[sent_range[0]:sent_range[1]] = \
                    get_match_phrase(query_words, all_words[sent_range[0]:sent_range[1]])
            else:
                is_selected_sent[sent_start_id[sid]:] = 1
                word_scores[sent_start_id[sid]:] = sscore
                _, is_selected_phrase[sent_start_id[sid]:] = \
                    get_match_phrase(query_words, all_words[sent_start_id[sid]:])
                    
        # update selected phrase scores (-1 meaning a different color in gradio)
        word_scores[is_selected_sent+is_selected_phrase==2] = -0.5
            
        output[i] = {
            'is_selected_sent': is_selected_sent,
            'is_selected_phrase': is_selected_phrase,
            'scores': word_scores
        }

    return output

def get_highlight_info(model, text1, text2, K=None):
    """
    Get highlight information from two texts
    """
    sent1 = sent_tokenize(text1) # query
    sent2 = sent_tokenize(text2) # candidate
    if K is None: # if K is not set, select based on the length of the candidate
        K = int(len(sent2) / 3)
    score_mat = compute_sentencewise_scores(model, sent1, sent2)

    sent_ids, sent_scores = get_top_k(score_mat, K=K)
    words2, all_words2, sent_start_id2 = get_words(sent2)
    info = mark_words(sent1, words2, all_words2, sent_start_id2, sent_ids, sent_scores)
    
    # get top sentence pairs from the query and candidate (score, index_pair)
    top_pair_num = 5
    top_pairs = []
    ii = np.unravel_index(np.argsort(np.array(sent_scores).ravel())[-top_pair_num:], sent_scores.shape)
    for i, j in zip(ii[0][::-1], ii[1][::-1]):
        score = sent_scores[i,j].item()
        index_pair = (i, sent_ids[i,j].item())
        top_pairs.append((score, index_pair)) # list of (score, (sent_id_query, sent_id_candidate))
        
    # convert top_pairs to corresponding highlights format for GRadio Interpretation component
    top_pairs_info = dict()
    count = 0
    for s, (sidq, sidc) in top_pairs:
        q_sent = sent1[sidq]
        c_sent = sent2[sidc]
        q_words = word_tokenize(q_sent)
        c_words = word_tokenize(c_sent)
        mask1, mask2 = get_match_phrase(q_words, c_words)
        sc = 0.5
        mask1 *= -sc # mark matching phrases as blue (-1: darkest)
        mask2 *= -sc # mark matching phrases as blue
        assert(len(mask1) == len(q_words) and len(mask2) == len(c_words))
        
        # spacing
        q_words, mask1 = remove_spaces(q_words, mask1)
        c_words, mask2 = remove_spaces(c_words, mask2)
        
        top_pairs_info[count] = {
            'query': {
                'original': q_sent,
                'interpretation': list(zip(q_words, mask1))
            },
            'candidate': {
                'original': c_sent,
                'interpretation': list(zip(c_words, mask2))
            },
            'score': s,
            'sent_idx': (sidq, sidc) 
        }
        count += 1
    
    return sent_ids, sent_scores, info, top_pairs_info

### Document-level operations

def predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=20):
    # compute document scores for each papers
    
    # concatenate title and abstract
    title_abs = []
    for t, a in zip(titles, abstracts):
        if t is not None and a is not None:
            title_abs.append(t + ' [SEP] ' + a)    
            
    num_docs = len(title_abs) 
    no_iter = int(np.ceil(num_docs / batch))
    scores = []
    with torch.no_grad():
        # batch
        for i in tqdm.tqdm(range(no_iter)):
            # preprocess the input
            inputs = tokenizer(
                [query] + title_abs[i*batch:(i+1)*batch], 
                padding=True, 
                truncation=True, 
                return_tensors="pt", 
                max_length=512
            )
            inputs.to(doc_model.device)
            result = doc_model(**inputs)
        
            # take the first token in the batch as the embedding
            embeddings = result.last_hidden_state[:, 0, :].detach().cpu().numpy()
        
            # compute cosine similarity
            q_emb = embeddings[0,:]
            p_emb = embeddings[1:,:]
            nn = np.linalg.norm(q_emb) * np.linalg.norm(p_emb, axis=1)
            scores += list(np.dot(p_emb, q_emb) / nn)

    assert(len(scores) == num_docs)
    
    return scores

def compute_document_score(doc_model, tokenizer, query, papers, batch=5):
    scores = []
    titles = []
    abstracts = []
    urls = []
    for p in papers:
        if p['title'] is not None and p['abstract'] is not None:
            titles.append(p['title'])
            abstracts.append(p['abstract'])
            urls.append(p['url'])
    scores = predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=batch)
    assert(len(scores) == len(abstracts))
    idx_sorted = np.argsort(scores)[::-1]
    
    titles_sorted = [titles[x] for x in idx_sorted]
    abstracts_sorted = [abstracts[x] for x in idx_sorted]
    scores_sorted = [scores[x] for x in idx_sorted]
    urls_sorted = [urls[x] for x in idx_sorted]
    
    return titles_sorted, abstracts_sorted, urls_sorted, scores_sorted