from typing import List, Dict
from copy import deepcopy

from overrides import overrides
import numpy

from allennlp.common.util import JsonDict
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import TextField, SequenceLabelField
from allennlp.data.tokenizers.word_splitter import SpacyWordSplitter
from allennlp.models import Model
from allennlp.predictors.predictor import Predictor


@Predictor.register('sentence-tagger')
class SentenceTaggerPredictor(Predictor):
    """
    Predictor for any model that takes in a sentence and returns
    a single set of tags for it.  In particular, it can be used with
    the :class:`~allennlp.models.crf_tagger.CrfTagger` model
    and also
    the :class:`~allennlp.models.simple_tagger.SimpleTagger` model.
    """
    def __init__(self, model: Model, dataset_reader: DatasetReader, language: str = 'en_core_web_sm') -> None:
        super().__init__(model, dataset_reader)
        self._tokenizer = SpacyWordSplitter(language=language, pos_tags=True)

    def predict(self, sentence: str) -> JsonDict:
        return self.predict_json({"sentence" : sentence})

    @overrides
    def _json_to_instance(self, json_dict: JsonDict) -> Instance:
        """
        Expects JSON that looks like ``{"sentence": "..."}``.
        Runs the underlying model, and adds the ``"words"`` to the output.
        """
        sentence = json_dict["sentence"]
        tokens = self._tokenizer.split_words(sentence)
        return self._dataset_reader.text_to_instance(tokens)

    @overrides
    def predictions_to_labeled_instances(self,
                                         instance: Instance,
                                         outputs: Dict[str, numpy.ndarray]) -> List[Instance]:
        """
        This function currently only handles BIOUL tags.

        Imagine an NER model predicts three named entities (each one with potentially
        multiple tokens). For each individual entity, we create a new Instance that has
        the label set to only that entity and the rest of the tokens are labeled as outside.
        We then return a list of those Instances.

        For example:
        Mary  went to Seattle to visit Microsoft Research
        U-Per  O    O   U-Loc  O   O     B-Org     L-Org

        We create three instances.
        Mary  went to Seattle to visit Microsoft Research
        U-Per  O    O    O     O   O       O         O

        Mary  went to Seattle to visit Microsoft Research
        O      O    O   U-LOC  O   O       O         O

        Mary  went to Seattle to visit Microsoft Research
        O      O    O    O     O   O     B-Org     L-Org
        """
        predicted_tags = outputs['tags']
        predicted_spans = []

        i = 0
        while i < len(predicted_tags):
            tag = predicted_tags[i]
            # if its a U, add it to the list
            if tag[0] == 'U':
                current_tags = [t if idx == i else 'O' for idx, t in enumerate(predicted_tags)]
                predicted_spans.append(current_tags)
            # if its a B, keep going until you hit an L.
            elif tag[0] == 'B':
                begin_idx = i
                while tag[0] != 'L':
                    i += 1
                    tag = predicted_tags[i]
                end_idx = i
                current_tags = [t if begin_idx <= idx <= end_idx else 'O' \
                    for idx, t in enumerate(predicted_tags)]
                predicted_spans.append(current_tags)
            i += 1

        # Creates a new instance for each contiguous tag
        instances = []
        for labels in predicted_spans:
            new_instance = deepcopy(instance)
            text_field: TextField = instance['tokens']  # type: ignore
            new_instance.add_field('tags', SequenceLabelField(labels, text_field), self._model.vocab)
            instances.append(new_instance)
        instances.reverse() # NER tags are in the opposite order as desired for the interpret UI

        return instances
