# -*- coding: utf-8 -*-
import configparser as ConfigParser
import csv
import functools
import logging
import random
import re
import sys
from pathlib import Path
import pickle
import spacy
import textacy
from scipy import *
import os
import glob, os
from textacy.fileio import open_sesame
import json
from spacy.tokens.doc import Doc as SpacyDoc

csv.field_size_limit(sys.maxsize)
FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"



# load config
config_ini = FILEPATH + "config.ini"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)



# config logging
filename = FILEPATH + config.get("logging","filename")
level = config.get("logging","level")
if level == "INFO":
    level = logging.INFO
elif level == "DEBUG":
    level = logging.DEBUG
elif level == "WARNING":
    level = logging.WARNING
logging.basicConfig(filename=filename, level=level)



def logprint(string, level="INFO"):
    """log and prints"""
    print(string)
    if level == "INFO":
        logging.info(string)
    elif level == "DEBUG":
        logging.debug(string)
    elif level == "WARNING":
        logging.warning(string)


def compose(*functions):
    def compose2(f, g):
        return lambda x: f(g(x))

    return functools.reduce(compose2, functions, lambda x: x)


def get_calling_function():
    """finds the calling function in many decent cases.
    https://stackoverflow.com/questions/39078467/python-how-to-get-the-calling-function-not-just-its-name
    """
    fr = sys._getframe(1)  # inspect.stack()[1][0]
    co = fr.f_code
    for get in (
            lambda: fr.f_globals[co.co_name],
            lambda: getattr(fr.f_locals['self'], co.co_name),
            lambda: getattr(fr.f_locals['cls'], co.co_name),
            lambda: fr.f_back.f_locals[co.co_name],  # nested
            lambda: fr.f_back.f_locals['func'],  # decorators
            lambda: fr.f_back.f_locals['meth'],
            lambda: fr.f_back.f_locals['f'],
    ):
        try:
            func = get()
        except (KeyError, AttributeError):
            pass
        else:
            if func.__code__ == co:
                return func
    raise AttributeError("func not found")


def save_obj(obj, path):
    with open(path , 'wb') as f:
        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)

def load_obj(path):
    with open(path, 'rb') as f:
        return pickle.load(f)


def replaceRockDots():
    return lambda string: re.sub(r'[ß]', "ss",
                                 (re.sub(r'[ö]', "oe",
                                         (re.sub(r'[ü]', "ue", (re.sub(r'[ä]', "ae", string.lower())))))))

def list_from_files(*paths):
    """
    create string-list from file like
        n1
        n2
        n3

    :param paths: list(str) or str if single path
    :return: list(str)
    """

    listlist = []
    for path in paths:
        listlist.append(list(textacy.fileio.read_file_lines(path)))

    #liste von listen zu einer liste
    liste = [item for sublist in listlist for item in sublist]

    return list(map(textacy.preprocess.normalize_whitespace, liste))

def debug():
    pass

def normalize(string):
    # replaceRockDots
    string = re.sub(r'[ß]', "ss", string.lower())
    string = re.sub(r'[ö]', "oe", string)
    string = re.sub(r'[ü]', "ue", string)
    string = re.sub(r'[ä]', "ae", string)
    string = textacy.preprocess.normalize_whitespace(string)
    return string


def deprecated(func):
    """This is a decorator which can be used to mark functions
    as deprecated. It will result in a warning being emmitted
    when the function is used."""

    @functools.wraps(func)
    def new_func(*args, **kwargs):
        warnings.simplefilter('always', DeprecationWarning) #turn off filter
        warnings.warn("Call to deprecated function {}.".format(func.__name__), category=DeprecationWarning, stacklevel=2)
        warnings.simplefilter('default', DeprecationWarning) #reset filter
        return func(*args, **kwargs)

    return new_func


def printRandomDoc(textacyCorpus):
    """
    printlogss random doc out of a textacy-Corpus
    :param textacyCorpus:
    """
    print()
    if len(textacyCorpus) == 0:
        logprint("NO DOCS IN CORPUS")
    else:
        #printlog("len(textacyCorpus) = %i" % len(textacyCorpus))
        randIndex = int((len(textacyCorpus) - 1) * random.random())
        logprint("Index: {0} \n Text: {1} \n categoryName: {2}\n".format(randIndex, textacyCorpus[randIndex].text,
                                                                         textacyCorpus[randIndex].metadata['categoryName']))

    print()

def get_list_from_config(section,option):
    return list(map(textacy.preprocess.normalize_whitespace,config.get(section,option).split(",")))

def corpus2Text(corpus):
    for doc in corpus:
        yield doc.text

def corpus2Meta(corpus):
    for doc in corpus:
        yield doc.metadata

def savelabledCorpiLines(corpus,filepath):

    textacy.fileio.write_file_lines(gen_labledLines(corpus), filepath=filepath)

def gen_labledLines(corpus):
    for doc in corpus:
        # generate [topic1, topic2....] tok1 tok2 tok3 out of corpi
        yield "[" + doc.metadata["categoryName"] + "] " + doc.text


def save_corpus(corpus, corpus_path, corpus_name):
    """
    saves a textacy-corpus including spacy-parser
    :param corpus: textacy-Corpus
    :param corpus_path: str
    :param corpus_name: str (should content the language like "_de_")
    """

    # save parser
    parser = corpus.spacy_lang
    parserpath = corpus_path + str(parser.lang) + '_parser'
    parser.save_to_directory(parserpath)

    # save plain content + meta
    plainpath = corpus_path + corpus_name + "_content.json"
    textacy.fileio.write_json_lines(gen_dicts(corpus), plainpath)



def gen_dicts(corpus):
    for doc in corpus:
        dict = {"index" : doc.corpus_index, "content" : doc.text}
        dict.update(doc.metadata)
        yield dict



def load_corpus(corpus_path, corpus_name, lang="de"):
    """
    Load textacy-Corpus including spacy-parser out from file
    :param corpus_path: str
    :param corpus_name: str (should content the language like "_de_")
    :param lang: str (language code) ir spacy.Language
    :return: texracy.Corpus, spacy.language
    """

    #ckeck for language
    if "de_" in corpus_name:
        lang="de"
    elif "en_" in corpus_name:
        lang ="en"


    # load parser
    parser = spacy.load(lang)

    stringstorepath = corpus_path + str(lang) + '_parser'+'/vocab/strings.json'
    with open(stringstorepath) as file:
        parser.vocab.strings.load(file)

    vocabpath = Path(corpus_path + str(lang) + '_parser'+'/vocab/lexemes.bin')
    parser.vocab.load_lexemes(vocabpath)

    #load corpus
    corpus = textacy.Corpus(parser)

    plainpath = corpus_path + corpus_name + "_content.json"
    plain_stream = textacy.fileio.read_json_lines(plainpath)  # yields {int : str}

    for plain in plain_stream:
        meta = {}
        for key,value in plain.items():
            if key != "content" and key != "index":
                meta[key] = value
        corpus.add_doc(textacy.Doc(plain["content"], lang=corpus.spacy_lang, metadata=meta))

    return corpus, corpus.spacy_lang