import pandas as pd

import pandas as pd

from .pycocoevalcap.bleu.bleu import Bleu

from .pycocoevalcap.meteor import Meteor

from .pycocoevalcap.rouge import Rouge

import re

import errno

import os

try:

    os.mkdir('results')

except OSError as exc:

    if exc.errno != errno.EEXIST:

        raise

    pass

def preprocess_captions(images_captions):

    """

    :param images_captions: Dictionary with image ids as keys and captions as values

    :return: Dictionary with the processed captions as values

    """

    # Clean for BioASQ

    bioclean = lambda t: re.sub('[.,?;*!%^&_+():-\[\]{}]', '',

                                t.replace('"', '').replace('/', '').replace('\\', '').replace("'",

                                                                                              '').strip().lower())

    pr_captions = {}

    # Apply bio clean to data

    for image in images_captions:

        # Save caption to an array to match MSCOCO format

        pr_captions[image] = [bioclean(images_captions[image])]

    return pr_captions

def compute_scores(gts:str, res:str, save_scores:bool=True):

    """

    Performs the MS COCO evaluation using the Python 3 implementation (https://github.com/salaniz/pycocoevalcap)

    :param gts: Dictionary with the image ids and their gold captions,

    :param res: Dictionary with the image ids ant their generated captions

    :print: Evaluation score (the mean of the scores of all the instances) for each measure

    """

    # convert pd.Dataframe to dict

    gold_captions_df = pd.read_csv(gts, sep='|', names=['ID', 'caption'])

    pred_captions_df = pd.read_csv(res, sep='|', names=['ID', 'caption'])

    gold_captions = preprocess_captions(dict( zip( gold_captions_df.ID.to_list(), gold_captions_df.caption.to_list() ) ) )

    pred_captions = preprocess_captions( dict( zip( pred_captions_df.ID.to_list(), pred_captions_df.caption.to_list() ) ) )

    # Set up scorers

    scorers = [

        (Bleu(4), ["BLEU_1", "BLEU_2", "BLEU_3", "BLEU_4"]),

        (Meteor(), "METEOR"),

        (Rouge(), "ROUGE_L")

    ]

    metrics_scores = {}

    # Compute score for each metric

    for scorer, method in scorers:

        try:

            score, scores = scorer.compute_score(gold_captions, pred_captions, verbose=0)

        except TypeError:

            score, scores = scorer.compute_score(gold_captions, pred_captions)

        if type(method) == list:

            for sc, m in zip(score, method):

                metrics_scores[m] = [round(sc*100, 1)]

        else:

            metrics_scores[method] = [round(score*100, 1)]

    if save_scores:

        scores_df = pd.DataFrame.from_dict(metrics_scores)

        scores_df.to_csv('results/scores.csv', sep='\t')

    return metrics_scores