import numpy as np
from numpy.linalg import norm

class DirectedCentralityRnak(object):
    def __init__(self, 
                document_feats,
                extract_num=20,
                beta=0.2, 
                lambda1=1, 
                lambda2=0.8,
                alpha=1,
                processors=8):
        self.extract_num = extract_num
        self.processors = processors
        self.beta = beta
        self.lambda1 = lambda1
        self.lambda2 = lambda2
        self.alpha = alpha

        self.candidate_phrases = [x['candidate_phrases'] for x in document_feats]
        self.doc_embeddings = [x['sentence_embeddings'] for x in document_feats]
        self.tokens_embeddings = [x['candidate_phrases_embeddings'] for x in document_feats]
    
    def flat_list(self, l):
        return [x for ll in l for x in ll]
    
    def extract_summary(self,):
        paired_scores = self.rank()
        

        rank_list_phrases = []
        for candidate, paired_score in zip(self.candidate_phrases, paired_scores):
            candidates = []
            for i in range(len(candidate)):
                phrase = candidate[i]
                candidates.append([phrase, paired_score[i][0], paired_score[i][1]])
            rank_list_phrases.append(candidates)


        predicted_candidation = []
        for i in range(len(rank_list_phrases)):
            final_score = []
            position_weight = 1 / (np.array(list(range(1, len(rank_list_phrases[i]) + 1))))
            position_weight = np.exp(position_weight) / np.sum(np.exp(position_weight))
            cnt = 0
            for candidate, index, score in rank_list_phrases[i]:
                final_score.append([candidate, score * position_weight[cnt]])
                cnt += 1
            final_score.sort(key = lambda x: x[1], reverse = True)
            candidates = [x[0].strip() for x in final_score]
            predicted_candidation.append(candidates)
        return predicted_candidation


    def pairdown(self, scores, pair_indice, length):
        out_matrix = np.ones((length, length))
        for pair in pair_indice:
            out_matrix[pair[0][0]][pair[0][1]] = scores[pair[1]]
            out_matrix[pair[0][1]][pair[0][0]] = scores[pair[1]]
            
        return out_matrix

    def get_similarity_matrix(self, sentence_embeddings):
        pairs = []
        scores = []
        cnt = 0
        for i in range(len(sentence_embeddings)-1):
            for j in range(i, len(sentence_embeddings)):
                if type(sentence_embeddings[i]) == float or type(sentence_embeddings[j]) == float:
                    scores.append(0)
                else:
                    scores.append(np.dot(sentence_embeddings[i], sentence_embeddings[j])) 

                pairs.append(([i, j], cnt))
                cnt += 1
        return self.pairdown(scores, pairs, len(sentence_embeddings))

    def compute_scores(self, similarity_matrix, edge_threshold=0):

        forward_scores = [1e-10 for i in range(len(similarity_matrix))]
        backward_scores = [1e-10 for i in range(len(similarity_matrix))]
        edges = []
        n = len(similarity_matrix)
        alpha = self.alpha
        for i in range(len(similarity_matrix)):
            for j in range(i+1, len(similarity_matrix[i])):
                edge_score = similarity_matrix[i][j]
                # boundary_position_function
                db_i = min(i, alpha * (n-i))
                db_j = min(j, alpha * (n-j))
                if edge_score > edge_threshold:
                    if db_i < db_j:
                        forward_scores[i] += edge_score
                        backward_scores[j] += edge_score
                        edges.append((i,j,edge_score))
                    else:
                        forward_scores[j] += edge_score
                        backward_scores[i] += edge_score
                        edges.append((j,i,edge_score))

        return np.asarray(forward_scores), np.asarray(backward_scores), edges
    
    def _rank_part(self, similarity_matrix, doc_vector, candidate_phrases_embeddings):
        min_score = np.min(similarity_matrix)
        max_score = np.max(similarity_matrix)
        threshold = min_score + self.beta * (max_score - min_score)
        new_matrix = similarity_matrix - threshold
        dist = []
        for emb in candidate_phrases_embeddings:
            if type(doc_vector) == float or type(emb) == float:
                dist.append(0)
            else:
                dist.append(1/np.sum(np.abs(emb - doc_vector)))

        forward_score, backward_score, _ = self.compute_scores(new_matrix)

        paired_scores = []
        for node in range(len(forward_score)):
            paired_scores.append([node,  (self.lambda1 * forward_score[node] + self.lambda2 * backward_score[node]) * (dist[node])])

        return paired_scores

    def rank(self,):
        
        similarity_matrix = []
        extracted_list = []
        for embedded in self.tokens_embeddings:
            similarity_matrix.append(self.get_similarity_matrix(embedded))
        for matrix, doc_vector, candidate_phrases_embeddings in zip(similarity_matrix, self.doc_embeddings, self.tokens_embeddings):
            extracted_list.append(self._rank_part(matrix, doc_vector, candidate_phrases_embeddings))
        return extracted_list