# Copyright (c) Microsoft Corporation. 
# Licensed under the MIT license.

# -*- coding:utf-8 -*-
import argparse
from .bleu import corpus_bleu
from .weighted_ngram_match import corpus_weighted_ngram_match
from .syntax_match import corpus_syntax_match
from .dataflow_match import corpus_dataflow_match
import os

def calculate(predictions, references, language="python", alpha=0.25, beta=0.25, gamma=0.25, theta=0.25):

    # preprocess inputs
    pre_references = [[s.strip() for s in my_list] for my_list in references]
    hypothesis = [s.strip() for s in predictions]

    for i in range(len(pre_references)):
        assert len(hypothesis) == len(pre_references[i])

    references = []
    for i in range(len(hypothesis)):
        ref_for_instance = []
        for j in range(len(pre_references)):
            ref_for_instance.append(pre_references[j][i])
        references.append(ref_for_instance)
    assert len(references) == len(pre_references)*len(hypothesis)


    # calculate ngram match (BLEU)
    tokenized_hyps = [x.split() for x in hypothesis]
    tokenized_refs = [[x.split() for x in reference] for reference in references]

    ngram_match_score = corpus_bleu(tokenized_refs,tokenized_hyps)

    # calculate weighted ngram match
    # from os import listdir
    # from os.path import isfile, join
    # onlyfiles = [f for f in listdir("./keywords") if isfile(join("keywords", f))]
    # print(onlyfiles)
    curr_path = os.path.dirname(os.path.abspath(__file__))
    keywords = [x.strip() for x in open(curr_path + "/keywords/" + language +'.txt', 'r', encoding='utf-8').readlines()]
    def make_weights(reference_tokens, key_word_list):
        return {token:1 if token in key_word_list else 0.2 \
                for token in reference_tokens}
    tokenized_refs_with_weights = [[[reference_tokens, make_weights(reference_tokens, keywords)]\
                for reference_tokens in reference] for reference in tokenized_refs]

    weighted_ngram_match_score = corpus_weighted_ngram_match(tokenized_refs_with_weights,tokenized_hyps)

    # calculate syntax match
    syntax_match_score = corpus_syntax_match(references, hypothesis, language)

    # calculate dataflow match
    dataflow_match_score = corpus_dataflow_match(references, hypothesis, language)

    code_bleu_score = alpha*ngram_match_score\
                    + beta*weighted_ngram_match_score\
                    + gamma*syntax_match_score\
                    + theta*dataflow_match_score

    return {
        "ngram_match_score": ngram_match_score,
        "weighted_ngram_match_score": weighted_ngram_match_score,
        "syntax_match_score": syntax_match_score,
        "dataflow_match_score": dataflow_match_score,
        "code_bleu_score": code_bleu_score
    }