import nltk
from nltk import Tree

from .base_parser import BaseParser, IS_PY2, STRING_TYPES, PTB_TOKEN_ESCAPE

    'en': 'english',
    'de': 'german',
    'fr': 'french',
    'pl': 'polish',
    'sv': 'swedish',

class Parser(BaseParser):
    Berkeley Neural Parser (benepar), integrated with NLTK.

    Sample usage:
    >>> parser = benepar.Parser("benepar_en")
    >>> parser.parse("The quick brown fox jumps over the lazy dog.")

    Note that the self-attentive parsing model is only tasked with constructing
    constituency parse trees from tokenized, untagged, single-sentence inputs.
    Any other elements of the NLP pipeline (i.e. sentence segmentation,
    tokenization, and part-of-speech tagging) will be done using the default
    NLTK models.
    def __init__(self, name, batch_size=64):
        Load a parsing model given a short model name (e.g. "benepar_en") or a
        filename on disk.

        name (str): Model name, or path to uncompressed TensorFlow model graph
        batch_size (int): Maximum number of sentences to process per batch
        super(Parser, self).__init__(name, batch_size)
        self._tokenizer_lang = TOKENIZER_LOOKUP.get(self._language_code, None)

    def _make_nltk_tree(self, sentence, tags, score, p_i, p_j, p_label):
        # The optimized cython decoder implementation doesn't actually
        # generate trees, only scores and span indices. When converting to a
        # tree, we assume that the indices follow a preorder traversal.
        last_splits = []

        # Python 2 doesn't support "nonlocal", so wrap idx in a list
        idx_cell = [-1]
        def make_tree():
            idx_cell[0] += 1
            idx = idx_cell[0]
            i, j, label_idx = p_i[idx], p_j[idx], p_label[idx]
            label = self._label_vocab[label_idx]
            if (i + 1) >= j:
                if self._provides_tags:
                    word = sentence[i]
                    tag = self._tag_vocab[tags[i]]
                    word, tag = sentence[i]
                tag = PTB_TOKEN_ESCAPE.get(tag, tag)
                word = PTB_TOKEN_ESCAPE.get(word, word)
                tree = Tree(tag, [word])
                for sublabel in label[::-1]:
                    tree = Tree(sublabel, [tree])
                return [tree]
                left_trees = make_tree()
                right_trees = make_tree()
                children = left_trees + right_trees
                if label:
                    tree = Tree(label[-1], children)
                    for sublabel in reversed(label[:-1]):
                        tree = Tree(sublabel, [tree])
                    return [tree]
                    return children

        tree = make_tree()[0]
        tree.score = score

        return tree

    def _nltk_process_sents(self, sents):
        for sentence in sents:
            if isinstance(sentence, STRING_TYPES):
                if self._tokenizer_lang is None:
                    raise ValueError(
                        "No word tokenizer available for this language. "
                        "Please tokenize before calling the parser."
                sentence = nltk.word_tokenize(sentence, self._tokenizer_lang)

            if IS_PY2:
                sentence = [
                    word.decode('utf-8', 'ignore') if isinstance(word, str) else word
                    for word in sentence

            if not self._provides_tags:
                sentence = nltk.pos_tag(sentence)
                yield [word for word, tag in sentence], sentence
                yield sentence, sentence

    def parse(self, sentence):
        Parse a single sentence

        The argument "sentence" can be a list of tokens to be passed to the
        parser. It can also be a string, in which case the sentence will be
        tokenized using the default NLTK tokenizer.

        sentence (str or List[str]): sentence to parse

        Returns: nltk.Tree
        return list(self.parse_sents([sentence]))[0]

    def parse_sents(self, sents):
        Parse multiple sentences

        If "sents" is a string, it will be segmented into sentences using NLTK.
        Otherwise, each element of "sents" will be treated as a sentence.

        sents (str or Iterable[str] or Iterable[List[str]]): sentences to parse

        Returns: Iter[nltk.Tree]
        if isinstance(sents, STRING_TYPES):
            if self._tokenizer_lang is None:
                raise ValueError(
                    "No tokenizer available for this language. "
                    "Please split into individual sentences and tokens "
                    "before calling the parser."
            sents = nltk.sent_tokenize(sents, self._tokenizer_lang)

        for parse_raw, tags_raw, sentence in self._batched_parsed_raw(self._nltk_process_sents(sents)):
            yield self._make_nltk_tree(sentence, tags_raw, *parse_raw)