import csv

import numpy as np

# pdf codes might be updated in the meantime in the UDHR website
udhr_lang_code = {"amh": "amh", "hye": "arm", "ast": "aub", "bel": "ruw", "ben": "bng", "bul": "blg", "mya": "bms",
                  "kat": "geo", "ell": "grk", "guj": "gjr", "heb": "hbr", "hin": "hnd", "jpn": "jpn", "kan": "kjv",
                  "kaz": "kaz", "khm": "khm", "kor": "kkn", "kir": "kdo", "lao": "nol", "mkd": "mkj", "mal": "mjs",
                  "mar": "mrt", "npi": "nep", "pbu": "pbu", "pan": "pnj1", "rus": "rus", "srp": "src5", "tgk": "pet",
                  "tam": "tcv", "tel": "tcw", "tha": "thj", "ukr": "ukr", "urd": "urd", "vie": "vie", "tur": "trk",
                  "uzn": "uzb", "wol": "wol", "arb": "arz", "ceb": "ceb", "cmn": "chn", "fuv": "fuv", "lug": "lap1",
                  "isl": "ice", "lin": "lin", "mri": "mbf", "khk": "khk", "nya": "nyj", "ron": "rum", "ckb": "kdb1",
                  "zul": "zuu", "sna": "shd", "umb": "mnf", "swh": "swa", "som": "som", "swe": "swd", "pol": "pql",
                  "slk": "slo", "slv": "slv", "gaz": "gax", "por": "por"}

lang_code_xml = {"ron": "ron_1953", "ell": "ell_monotonic", "npi": "nep", "fuv": "fuv2", "srp": "srp_cyrl",
                 "uzn": "uzn_latn", "cmn": "cmn_hans", "nya": "nya_chechewa", "por": "por_PT", "gaz": "gax"}


def get_languages():
    languages = []
    lang_code = []
    with open("Data/language_codes/languages.csv", newline="", encoding="utf-8") as file:
        for row in csv.reader(file):
            languages.append(row[0])
            lang_code.append(row[1])
    return languages[1:], lang_code[1:]


def create_dictionary_lang():
    return dict(zip(*get_languages()))


def merge_dicts(dict1, dict2):
    """ Merge dictionaries and keep values of common keys in list"""
    dict3 = {**dict1, **dict2}
    for key, value in dict3.items():
        if key in dict1 and key in dict2:
            dict3[key] = list(set(value + dict1[key]))
    return dict3


# articles that had an CER > mean + 2 * std -> must be anomalies (not matching pdf & text)
def check_annotations_anomaly(list_error_rates):
    error_rates = np.asarray([er for _, er in list_error_rates])

    mean = error_rates.mean(axis=0)
    std = error_rates.std(axis=0)

    list_anomalies = []
    print("mean:", mean, "std:", std)
    for index, error_rate in list_error_rates:
        if error_rate >= mean + 2 * std:
            print("Articles: " + str(index) + " CER: " + str(error_rate))
            list_anomalies.append(index)
    return list_anomalies


# I already computed them running both Google & Tesseract
def return_all_anomalies():
    dict_anomalies_google = {'amh': [], 'hye': [], 'ast': [15, 17, 20], 'bel': [20], 'ben': [11], 'bul': [17, 20],
                             'mya': [3, 17], 'kat': [8], 'ell': [15, 17], 'guj': [3, 30], 'heb': [17], 'hin': [9],
                             'jpn': [17, 20], 'kan': [9], 'kaz': [15, 20], 'khm': [5, 11], 'kor': [], 'kir': [12, 27],
                             'lao': [3, 6], 'mkd': [12], 'mal': [10, 13], 'mar': [9], 'npi': [24, 28], 'pbu': [26],
                             'pan': [17], 'rus': [3], 'srp': [15, 17, 20], 'tgk': [17], 'tam': [15, 24], 'tel': [15],
                             'tha': [], 'ukr': [6], 'urd': [3], 'vie': [24], 'tur': [15, 20], 'uzn': [5], 'wol': [3],
                             'zul': [15, 17, 20], 'arb': [17], 'ceb': [20], 'cmn': [], 'fuv': [15], 'lug': [14, 21],
                             'isl': [6, 10], 'lin': [15, 20], 'mri': [4], 'khk': [13, 17], 'nya': [15, 20],
                             'ron': [1, 20], 'ckb': [2], 'sna': [15, 17], 'umb': [9, 6, 7, 8, 10, 11, 12, 13, 14],
                             'swh': [15, 17, 20], 'som': [15, 17, 20], 'swe': [11], 'pol': [15, 17, 20], 'slk': [14],
                             'slv': [17, 30], 'gaz': [15, 20], 'por': [17]}
    dict_anomalies_tesseract = {'amh': [13, 20, 27], 'hye': [], 'ast': [15, 17, 20], 'bel': [17, 20], 'ben': [13],
                                'bul': [17, 20], 'mya': [], 'kat': [8], 'ell': [15, 17], 'guj': [3, 30],
                                'heb': [13, 17], 'hin': [6], 'jpn': [2, 30], 'kan': [9], 'kaz': [15, 17],
                                'khm': [5, 11], 'kor': [24], 'kir': [15, 20], 'lao': [6], 'mkd': [17, 20],
                                'mal': [10, 21], 'mar': [29], 'npi': [28], 'pbu': [26], 'pan': [7], 'rus': [15, 17, 20],
                                'srp': [20], 'tgk': [4], 'tam': [3, 15, 24], 'tel': [15], 'tha': [6, 10],
                                'ukr': [17, 20], 'urd': [3, 6, 9], 'vie': [], 'tur': [15, 17, 20], 'uzn': [5],
                                'wol': [15, 17, 20], 'zul': [15, 20], 'arb': [17], 'ceb': [17, 20], 'cmn': [22],
                                'fuv': [], 'lug': [14, 21], 'isl': [10], 'lin': [15, 20], 'mri': [4], 'khk': [15, 20],
                                'nya': [20], 'ron': [15, 20], 'ckb': [3, 6], 'sna': [17], 'umb': [9],
                                'swh': [15, 17, 20], 'som': [15], 'swe': [11], 'pol': [15, 17, 20], 'slk': [14],
                                'slv': [17, 30], 'gaz': [17, 20], 'por': [6, 13, 24]}
    return merge_dicts(dict_anomalies_google, dict_anomalies_tesseract)


def sentence_split(input_, output, mode):
    with open(input_ + ".txt", encoding="utf8") as file:
        input_txt_ocr = file.readlines()

    new_ocr_txt = " ".join(input_txt_ocr)
    new_ocr_txt = " ".join(new_ocr_txt.split())  # remove double spaces

    with open(output, mode, encoding="utf-8") as file:
        file.write(new_ocr_txt)