from flair.data import Sentence
from flair.datasets import FlairDatapointDataset

def nolabel2o(line, i):
    return 'O' if line[i] == 'NoLabel' else line[i]

def conllu2flair(sentences, label=None):
    if label == "frame":
        return conllu2flair_frame(sentences, label)
    else:
        return conllu2flair_slot(sentences, label)

def conllu2flair_frame(sentences, label=None):
    fsentences = []
    for sentence in sentences:
        tokens = [token["form"] for token in sentence]
        fsentence = Sentence(' '.join(tokens), use_tokenizer=False)

        for i in range(len(fsentence)):
            fsentence[i:i+1].add_label(label, sentence[i][label])

        fsentences.append(fsentence)

    return FlairDatapointDataset(fsentences)

def conllu2flair_slot(sentences, label=None):
    fsentences = []

    for sentence in sentences:
        fsentence = Sentence(' '.join(token['form'] for token in sentence), use_tokenizer=False)
        start_idx = None
        end_idx = None
        tag = None

        if label:
            for idx, (token, ftoken) in enumerate(zip(sentence, fsentence)):
                if token[label].startswith('B-'):
                    start_idx = idx
                    end_idx = idx
                    tag = token[label][2:]
                elif token[label].startswith('I-'):
                    end_idx = idx
                elif token[label] == 'O':
                    if start_idx is not None:
                        fsentence[start_idx:end_idx+1].add_label(label, tag)
                        start_idx = None
                        end_idx = None
                        tag = None

            if start_idx is not None:
                fsentence[start_idx:end_idx+1].add_label(label, tag)

        fsentences.append(fsentence)
    return FlairDatapointDataset(fsentences)

def __predict(model, csentence):
    fsentence = conllu2flair([csentence])[0]
    model.predict(fsentence)
    return fsentence

def __csentence(sentence, label_type):
    if label_type == "frame":
        return [{'form': word } for word in sentence]
    else:
        return [{'form': word, 'slot': 'O'} for word in sentence]

def predict_single(model, sentence, label_type):
    csentence = __csentence(sentence, label_type)
    fsentence = __predict(model, csentence)
    intent = {}

    for span in fsentence.get_spans(label_type):
        tag = span.get_label(label_type).value
        if tag in intent:
            intent[tag] += 1
        else:
            intent[tag] = 1

    return max(intent, key=intent.get)

def predict_multiple(model, sentence, label_type):
    csentence = __csentence(sentence, label_type)
    fsentence = __predict(model, csentence)

    return set(span.get_label(label_type).value for span in fsentence.get_spans(label_type))

def predict_and_annotate(model, sentence, label_type):
    csentence = __csentence(sentence, label_type)
    fsentence = __predict(model, csentence)

    for span in fsentence.get_spans(label_type):
        tag = span.get_label(label_type).value
        if label_type == "frame":
            csentence[span.tokens[0].idx-1]['frame'] = tag
        else:
            csentence[span.tokens[0].idx - 1]['slot'] = f'B-{tag}'
            for token in span.tokens[1:]:
                csentence[token.idx - 1]['slot'] = f'I-{tag}'

    return csentence