__author__ = 'thorwhalen'


import pandas as pd
import mappyng.ot as pdict_ot
import tapyoca.ulist as util_ulist
from collections import OrderedDict
import re
import typyst.trans as pstr_trans
# import nitpyck.html2text_formated as html2text_formated
from pattern.web import plaintext


# import venere.data_source as venere_data_source

########################################################################################################################
# FACTORIES
def lower_ascii_slash_w_terms():
    w_terms_re = re.compile('[^\w]+')
    w_terms = lambda x: ' '.join(w_terms_re.sub(' ', x).split()).strip()
    return TextProcessor(text_processors=[preprocess_text_lower_ascii, w_terms]).process


def lower_ascii_term_replacer(map_spec, rep_col=None, by_col=None, term_padding_exp=r'\b'):
    term_replacer = TermReplacer(map_spec, rep_col=rep_col, by_col=by_col, term_padding_exp=term_padding_exp)
    return TextProcessor(text_processors=[preprocess_text_lower_ascii, term_replacer.replace_terms]).process


def erenev_kw_str_term_replacer(rep_col=None, by_col=None, term_padding_exp=r'\b'):
    DeprecationWarning("aspyre.text_processors.erenev_kw_str_term_replacer is depreciated: "
                       "Use the ut.erenev.aw version instead")
    print("misc.semantics.text_processors.erenev_kw_str_term_replacer is depreciated: " \
          "Use the ut.venere.aw version instead")
    #venere_term_replacer = TermReplacer(venere_data_source.term_map, rep_col=None, by_col=None, term_padding_exp=r'\b')
    #return TextProcessor(text_processors=[aw_manip.kw_str, venere_term_replacer.replace_terms]).process


########################################################################################################################
# Class that composes mutilple text processors
class TextProcessor(object):
    def __init__(self, text_processors):
        self.text_processors = text_processors

    def process(self, text):
        for processor in self.text_processors:
            text = processor(text)
        return text

    # TODO: Replace TextProcessor composition using util_pfunc.multi_compose


########################################################################################################################
# A menu of text processors

def preprocess_text_lower_ascii(text):
    """
    Preprocesses the text before it will be fed to the tokenizer.
    Here, we should put things like lower-casing the text, casting letters to "simple" ("ascii", "non-accentuated")
    letters, replacing some common strings (such as "bed and breakfast", "New York" by singular token representatives
    such as "b&b", "new_york"), and what ever needs to be done before tokens are retrieved from text.
    """
    return pstr_trans.toascii(text).lower()


class TermReplacer(object):
    def __init__(self, map_spec, rep_col=None, by_col=None, term_padding_exp=r'\b'):
        if isinstance(map_spec, pd.DataFrame):
        # if map_spec is given by a dataframe, make a mapto dict out of it
            map_spec = pdict_ot.keyval_df(map_spec, key_col=rep_col, val_col=by_col, warn=True)

        self.pattern = list_to_token_matcher_re(list(map_spec.keys()), term_padding_exp=term_padding_exp)
        # replaces:
        # key_lengths = map(len,map_spec.keys())
        # keys = util_ulist.sort_as(map_spec.keys(), key_lengths, reverse=True)
        # self.pattern = re.compile(term_padding_exp + '(' + '|'.join(keys) + ')' + term_padding_exp)

        self.replace_by = lambda x: map_spec[x.group()]

    def replace_terms(self, s):
        return self.pattern.sub(self.replace_by, s)


def list_to_token_matcher_re(str_list, term_padding_exp=r'\b'):
    str_lengths = list(map(len, str_list))
    str_list = util_ulist.sort_as(str_list, str_lengths, reverse=True)
    return re.compile(term_padding_exp + '(' + '|'.join(str_list) + ')' + term_padding_exp)


def html2text(text):
    return plaintext(text)
    # return html2text_formated.html2text(text).replace('**', '')
