"""Helper functions for main pipeline"""

import inflection, re, unicodedata, sqlite3
from collections import OrderedDict
from itertools import combinations
from dateutil.parser import parse
from lexmapr.definitions import synonym_db
from nltk import pos_tag
from nltk.tokenize import word_tokenize
from nltk.tokenize.treebank import TreebankWordDetokenizer


def _lookup_correction(sample, lookup_table, lookup_x, micro_status, status_title):
    '''Apply corections, if available in resource'''
    sample = ' ' + sample + ' '
    for x in lookup_table[lookup_x]:
        find_x = re.findall(' '+x+' ', sample)
        if find_x != []:
            micro_status.append(status_title + x)
            sample = sample.replace(' '+x+' ', ' '+lookup_table[lookup_x][x]+' ')
    return(' '.join(sample.split()), micro_status)


def _remove_annotated_synonyms(input_annotations):
    '''Remove annotations to see original phrase'''
    output_sample = ''
    copy_char = True
    for x in input_annotations:
        if x == '{':
            copy_char = False
        elif x == '}':
            copy_char = True
        else:
            if copy_char == True:
                output_sample += x
    while re.search('  ', output_sample):
        output_sample = output_sample.replace('  ', ' ')
    return(output_sample)


def _retrieve_map_id(search_results, c):
  '''Get resource id from database'''
  return_list = []
  for x in search_results:
    c.execute('SELECT * FROM non_standard_resource_ids WHERE key=:key', {'key':x[1]})
    for y in c.fetchall():
      result_dic = {'term':y[1], 'id':y[0], 'status':[]}
      if not result_dic in return_list:
        return_list.append(result_dic)
  return(return_list)


def _map_term_helper(term, c):
    '''Maps term to resource or resource permutation'''
    c.execute('SELECT * FROM standard_resource_labels WHERE key=:key', {'key':term})
    search_results = c.fetchall()
    if len(search_results) == 0:
        c.execute('SELECT * FROM standard_resource_permutations WHERE key=:key', {'key':term})
        search_results = c.fetchall()
        if len(search_results) != 0:
            return(_retrieve_map_id(search_results, c))
    else:
        return(_retrieve_map_id(search_results, c))
    return(None)


def _ngrams(input_phrase, gram_value):
    '''Get ngrams with a given value of gram_value'''
    input_phrase = input_phrase.split()
    output = []
    for i in range(len(input_phrase) - gram_value + 1):
        output.append(input_phrase[i:i + gram_value])
    return(output)


def process_sample(sample, lookup_table, micro_status):
    '''Apply corrections to input sample'''
    sample, micro_status = _lookup_correction(sample, lookup_table, 'spelling_mistakes',
                                          micro_status, 'Spelling Correction Treatment: ')
    sample, micro_status = _lookup_correction(sample, lookup_table, 'abbreviations',
                                          micro_status, 'Abbreviation-Acronym Treatment: ')
    sample, micro_status = _lookup_correction(sample, lookup_table, 'non_english_words',
                                          micro_status, 'Non English Language Words Treatment: ')
    return(sample, micro_status)


def punctuation_treatment(untreated_term):
    '''Remove punctuations from term'''
    punctuations_regex_char_class = '[~`!@#$%^*()_\|/{}:;,.<>?]'
    ret_term = ''
    for word_token in untreated_term.split():
        if word_token.count('-') > 1:
            ret_term += word_token.replace('-',' ') + ' '
        else:
            ret_term += word_token + ' '
    ret_term = ret_term.lower().replace('\"','').replace('\'ve','').replace('\'m','')
    ret_term = ret_term.replace('\'s','').replace('\'t','').replace('\'ll','').replace('\'re','')
    ret_term = ret_term.replace('\'','').replace('-','').replace('[','').replace(']','')
    ret_term = ret_term.replace('&',' and ').replace('+',' and ').replace('=',' is ')
    ret_term = re.sub(punctuations_regex_char_class, ' ', ret_term).lower()
    return(' '.join(ret_term.split()))


def further_cleanup(sample_text):
    '''Remove terms indicated to not be relevant and some compound words'''
    new_text = []
    neg_words = [r'no ',r'non',r'not',r'neither',r'nor',r'without']
    stt_words = ['animal','cb','chicken','environmental','food','human','large','medium','necropsy',
                 'organic','other','poultry','product','sausage','small','stool','swab','wild',]
    end_words = ['aspirate','culture','environmental','fluid','food','intestine','large','meal','medium',
                 'mixed','necropsy','other','poultry','product','research','sample','sausage','slaughter',
                 'small','swab','water','wild',]
    not_replace = ['agriculture','apiculture','aquaculture','aquiculture','aviculture',
                   'coculture','hemoculture','mariculture','monoculture','sericulture',
                   'subculture','viniculture','viticulture',
                   'semifluid','subfluid','superfluid',
                   'superlarge','reenlarge','enlarge','overlarge','largemouth','larges',
                   'bonemeal','cornmeal','fishmeal','inchmeal','oatmeal','piecemeal','premeal',
                   'wholemeal','biosample','ensample','resample','subsample','backwater',
                   'another','bother','brother','foremother','frother','godmother','grandmother',
                   'housemother','mother','otherguess','otherness','othernesse','otherwhere',
                   'otherwhile','otherworld','pother','soother','smoother','smother','stepbrother',
                   'stepmother','tother',
                   'byproduct','coproduct','production','productive','subproduct',
                   'ultrasmall','smaller','smallmouth','smalltime','smallpox','smallpoxe',
                   'smallsword','smallsholder','mediumship',
                   'bathwater','bilgewater','blackwater','breakwater','cutwater','deepwater',
                   'dewater','dishwater','eyewater','firewater','floodwater','freshwater',
                   'graywater','groundwater','headwater','jerkwater','limewater','meltwater',
                   'overwater','polywater','rainwater','rosewater','saltwater','seawater',
                   'shearwater','springwater','tailwater','tidewater','underwater','wastewater',
                   'semiwild','wildcard','wildcat','wildcatter','wildcatted','wildebeest','wilded',
                   'wilder','wilderment','wilderness','wildernesse','wildest','wildfire','wildflower',
                   'wildfowl','wildfowler','wildish','wildland','wildling','wildlife','wildwood',
                  ]

    found_comp = []
    for comp_word in stt_words:
        found_comp.extend(re.findall(f'({comp_word})(\w+)', sample_text))
    for comp_word in end_words:
        found_comp.extend(re.findall(f'(\w+)({comp_word})', sample_text))
    for x in found_comp:
        if x[0]+x[1] not in not_replace and x[0]+x[1]+'s' not in not_replace:
            sample_text = sample_text.replace(x[0]+x[1], x[0]+' '+x[1])

    for sample_word in sample_text.split():
        if len(sample_word) > 1:
            new_text.append(sample_word.strip())

    if 'nor' in new_text:
        if 'neither' not in new_text:
            word_ind = new_text.index('nor')
            new_text.insert(max[0,word_ind-2], 'neither')

    for neg_word in neg_words:
        if neg_word in new_text:
            word_ind = new_text.index(neg_word)
            del(new_text[word_ind:word_ind+2])
    return(' '.join(new_text))


def is_number(input_string):
    '''Determine whether a string is a number'''
    try:
        unicodedata.numeric(input_string)
        return(True)
    except(TypeError, ValueError):
        return(False)


def is_date(input_string):
    '''Determine  whether a string is a date or day'''
    try:
        parse(input_string)
        return(True)
    except(ValueError, OverflowError):
        return(False)


def singularize_token(token, lookup_table, micro_status, c):
    '''Singularize the string token, if applicable'''
    if token in lookup_table['inflection_exceptions']:
        return(token, micro_status)

    exception_tail_chars_list = ['us', 'ia', 'ta', 'ss'] # TODO: add as, is?
    for char in exception_tail_chars_list:
        if token.endswith(char):
            return(token, micro_status)

    taxon_names = c.execute('''SELECT * FROM standard_resource_labels WHERE key LIKE :key AND 
                                                                           value LIKE :value''',
                           {'key':'% '+token,'value':'NCBITaxon%'}).fetchall()
    remove_finds = []
    for x in taxon_names:
        if len(x[0].split()) > 2:
            remove_finds.append(x)
    for x in remove_finds:
        taxon_names.remove(x)
    if taxon_names != []:
        return(token, micro_status)

    lemma = inflection.singularize(token)
    micro_status.append('Inflection (Plural) Treatment: ' + token)
    return(lemma, micro_status)


def get_cleaned_sample(input_sample, token, lookup_table):
    '''Prepare the cleaned sample phrase using the input token'''
    if input_sample == '' and token not in lookup_table['stop_words']:
        return(token)
    elif token not in lookup_table['stop_words']:
        return(input_sample + ' ' + token)
    else:
        return(input_sample)


def get_annotated_sample(annotated_sample, lemma):
    '''Embed synonyms in the sample, if available'''
    # TODO: able to annotate permuatations instead of just left to right?
    synonym_map = {}
    if not annotated_sample:
        annotated_sample = lemma
    else:
        annotated_sample = f'{annotated_sample} {lemma}'

    conn_syn = sqlite3.connect(synonym_db)
    d = conn_syn.cursor()
    for y in [lemma, _remove_annotated_synonyms(annotated_sample)]:
        d.execute('SELECT * FROM label_synonyms WHERE key=:key', {'key':y})
        for x in d.fetchall():
            if not re.search(x[1], annotated_sample):
                annotated_sample = annotated_sample+' {'+x[1]+'}'
                synonym_map[y] = x[1]
    conn_syn.close()
    return(annotated_sample, synonym_map)


def map_term(term, lookup_table, c, consider_suffixes=False):
    '''Map term to some resource in database'''
    if consider_suffixes:
        for suffix in lookup_table['suffixes']:
            mapping = _map_term_helper(term+' '+suffix, c)
            if mapping:
                for x in mapping:
                    x['status'].insert(-2, 'Suffix Addition')
                return(mapping)
    else:
        mapping = _map_term_helper(term, c)
        if mapping:
            return(mapping)
    return([])


def annotation_reduce(annotated_sample, synonym_map):
    '''Remove annotations on shorter phrases included in longer phrases with annotations'''
    remove_list = []
    for x in list(synonym_map.keys()):
        for y in list(synonym_map.keys()):
            if x != y:
                if x.startswith(y) or x.endswith(y) == True:
                    remove_list.append(y)
    for x in remove_list:
        annotated_sample = annotated_sample.replace('{'+synonym_map[x]+'}',' ')
    return(' '.join(annotated_sample.split()))


def get_annotated_synonyms(input_annotations):
    '''Get list of the annotations'''
    synonym_list = []
    for x in input_annotations.split('{')[1:]:
        synonym_list.append(x.split('}')[0])
    return(synonym_list)


def get_gram_chunks(input_phrase, num):
    '''Make num-gram chunks from input'''
    input_tokens = input_phrase.split()
    if len(input_tokens) < 15:
        return(list(combinations(input_tokens, num)))
    else:
        return(_ngrams(input_phrase, num))