# Natural Language Toolkit: Senna POS Tagger
#
# Copyright (C) 2001-2022 NLTK Project
# Author: Rami Al-Rfou' <ralrfou@cs.stonybrook.edu>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
Senna POS tagger, NER Tagger, Chunk Tagger

The input is:

- path to the directory that contains SENNA executables. If the path is incorrect,
  SennaTagger will automatically search for executable file specified in SENNA environment variable
- (optionally) the encoding of the input data (default:utf-8)

Note: Unit tests for this module can be found in test/unit/test_senna.py

>>> from nltk.tag import SennaTagger
>>> tagger = SennaTagger('/usr/share/senna-v3.0')
>>> tagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
[('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'), ('airspeed', 'NN'),
('of', 'IN'), ('an', 'DT'), ('unladen', 'NN'), ('swallow', 'NN'), ('?', '.')]

>>> from nltk.tag import SennaChunkTagger
>>> chktagger = SennaChunkTagger('/usr/share/senna-v3.0')
>>> chktagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
('?', 'O')]

>>> from nltk.tag import SennaNERTagger
>>> nertagger = SennaNERTagger('/usr/share/senna-v3.0')
>>> nertagger.tag('Shakespeare theatre was in London .'.split()) # doctest: +SKIP
[('Shakespeare', 'B-PER'), ('theatre', 'O'), ('was', 'O'), ('in', 'O'),
('London', 'B-LOC'), ('.', 'O')]
>>> nertagger.tag('UN headquarters are in NY , USA .'.split()) # doctest: +SKIP
[('UN', 'B-ORG'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'),
('NY', 'B-LOC'), (',', 'O'), ('USA', 'B-LOC'), ('.', 'O')]
"""

from nltk.classify import Senna


class SennaTagger(Senna):
    def __init__(self, path, encoding="utf-8"):
        super().__init__(path, ["pos"], encoding)

    def tag_sents(self, sentences):
        """
        Applies the tag method over a list of sentences. This method will return
        for each sentence a list of tuples of (word, tag).
        """
        tagged_sents = super().tag_sents(sentences)
        for i in range(len(tagged_sents)):
            for j in range(len(tagged_sents[i])):
                annotations = tagged_sents[i][j]
                tagged_sents[i][j] = (annotations["word"], annotations["pos"])
        return tagged_sents


class SennaChunkTagger(Senna):
    def __init__(self, path, encoding="utf-8"):
        super().__init__(path, ["chk"], encoding)

    def tag_sents(self, sentences):
        """
        Applies the tag method over a list of sentences. This method will return
        for each sentence a list of tuples of (word, tag).
        """
        tagged_sents = super().tag_sents(sentences)
        for i in range(len(tagged_sents)):
            for j in range(len(tagged_sents[i])):
                annotations = tagged_sents[i][j]
                tagged_sents[i][j] = (annotations["word"], annotations["chk"])
        return tagged_sents

    def bio_to_chunks(self, tagged_sent, chunk_type):
        """
        Extracts the chunks in a BIO chunk-tagged sentence.

        >>> from nltk.tag import SennaChunkTagger
        >>> chktagger = SennaChunkTagger('/usr/share/senna-v3.0')
        >>> sent = 'What is the airspeed of an unladen swallow ?'.split()
        >>> tagged_sent = chktagger.tag(sent) # doctest: +SKIP
        >>> tagged_sent # doctest: +SKIP
        [('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
        ('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
        ('?', 'O')]
        >>> list(chktagger.bio_to_chunks(tagged_sent, chunk_type='NP')) # doctest: +SKIP
        [('What', '0'), ('the airspeed', '2-3'), ('an unladen swallow', '5-6-7')]

        :param tagged_sent: A list of tuples of word and BIO chunk tag.
        :type tagged_sent: list(tuple)
        :param tagged_sent: The chunk tag that users want to extract, e.g. 'NP' or 'VP'
        :type tagged_sent: str

        :return: An iterable of tuples of chunks that users want to extract
          and their corresponding indices.
        :rtype: iter(tuple(str))
        """
        current_chunk = []
        current_chunk_position = []
        for idx, word_pos in enumerate(tagged_sent):
            word, pos = word_pos
            if "-" + chunk_type in pos:  # Append the word to the current_chunk.
                current_chunk.append(word)
                current_chunk_position.append(idx)
            else:
                if current_chunk:  # Flush the full chunk when out of an NP.
                    _chunk_str = " ".join(current_chunk)
                    _chunk_pos_str = "-".join(map(str, current_chunk_position))
                    yield _chunk_str, _chunk_pos_str
                    current_chunk = []
                    current_chunk_position = []
        if current_chunk:  # Flush the last chunk.
            yield " ".join(current_chunk), "-".join(map(str, current_chunk_position))


class SennaNERTagger(Senna):
    def __init__(self, path, encoding="utf-8"):
        super().__init__(path, ["ner"], encoding)

    def tag_sents(self, sentences):
        """
        Applies the tag method over a list of sentences. This method will return
        for each sentence a list of tuples of (word, tag).
        """
        tagged_sents = super().tag_sents(sentences)
        for i in range(len(tagged_sents)):
            for j in range(len(tagged_sents[i])):
                annotations = tagged_sents[i][j]
                tagged_sents[i][j] = (annotations["word"], annotations["ner"])
        return tagged_sents
