# Natural Language Toolkit: SemCor Corpus Reader
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Nathan Schneider <nschneid@cs.cmu.edu>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Corpus reader for the SemCor Corpus.
"""
from __future__ import absolute_import, unicode_literals
__docformat__ = 'epytext en'

from nltk.corpus.reader.api import *
from nltk.corpus.reader.xmldocs import XMLCorpusReader, XMLCorpusView
from nltk.tree import Tree

class SemcorCorpusReader(XMLCorpusReader):
    """
    Corpus reader for the SemCor Corpus.
    For access to the complete XML data structure, use the ``xml()``
    method.  For access to simple word lists and tagged word lists, use
    ``words()``, ``sents()``, ``tagged_words()``, and ``tagged_sents()``.
    """
    def __init__(self, root, fileids, wordnet, lazy=True):
        XMLCorpusReader.__init__(self, root, fileids)
        self._lazy = lazy
        self._wordnet = wordnet

    def words(self, fileids=None):
        """
        :return: the given file(s) as a list of words and punctuation symbols.
        :rtype: list(str)
        """
        return self._items(fileids, 'word', False, False, False)

    def chunks(self, fileids=None):
        """
        :return: the given file(s) as a list of chunks,
            each of which is a list of words and punctuation symbols
            that form a unit.
        :rtype: list(list(str))
        """
        return self._items(fileids, 'chunk', False, False, False)

    def tagged_chunks(self, fileids=None, tag=('pos' or 'sem' or 'both')):
        """
        :return: the given file(s) as a list of tagged chunks, represented
            in tree form.
        :rtype: list(Tree)

        :param tag: `'pos'` (part of speech), `'sem'` (semantic), or `'both'`
            to indicate the kind of tags to include.  Semantic tags consist of
            WordNet lemma IDs, plus an `'NE'` node if the chunk is a named entity
            without a specific entry in WordNet.  (Named entities of type 'other'
            have no lemma.  Other chunks not in WordNet have no semantic tag.
            Punctuation tokens have `None` for their part of speech tag.)
        """
        return self._items(fileids, 'chunk', False, tag!='sem', tag!='pos')

    def sents(self, fileids=None):
        """
        :return: the given file(s) as a list of sentences, each encoded
            as a list of word strings.
        :rtype: list(list(str))
        """
        return self._items(fileids, 'word', True, False, False)

    def chunk_sents(self, fileids=None):
        """
        :return: the given file(s) as a list of sentences, each encoded
            as a list of chunks.
        :rtype: list(list(list(str)))
        """
        return self._items(fileids, 'chunk', True, False, False)

    def tagged_sents(self, fileids=None, tag=('pos' or 'sem' or 'both')):
        """
        :return: the given file(s) as a list of sentences. Each sentence
            is represented as a list of tagged chunks (in tree form).
        :rtype: list(list(Tree))

        :param tag: `'pos'` (part of speech), `'sem'` (semantic), or `'both'`
            to indicate the kind of tags to include.  Semantic tags consist of
            WordNet lemma IDs, plus an `'NE'` node if the chunk is a named entity
            without a specific entry in WordNet.  (Named entities of type 'other'
            have no lemma.  Other chunks not in WordNet have no semantic tag.
            Punctuation tokens have `None` for their part of speech tag.)
        """
        return self._items(fileids, 'chunk', True, tag!='sem', tag!='pos')

    def _items(self, fileids, unit, bracket_sent, pos_tag, sem_tag):
        if unit=='word' and not bracket_sent:
            # the result of the SemcorWordView may be a multiword unit, so the
            # LazyConcatenation will make sure the sentence is flattened
            _ = lambda *args: LazyConcatenation((SemcorWordView if self._lazy else self._words)(*args))
        else:
            _ = SemcorWordView if self._lazy else self._words
        return concat([_(fileid, unit, bracket_sent, pos_tag, sem_tag, self._wordnet)
                       for fileid in self.abspaths(fileids)])

    def _words(self, fileid, unit, bracket_sent, pos_tag, sem_tag):
        """
        Helper used to implement the view methods -- returns a list of
        tokens, (segmented) words, chunks, or sentences. The tokens
        and chunks may optionally be tagged (with POS and sense
        information).

        :param fileid: The name of the underlying file.
        :param unit: One of `'token'`, `'word'`, or `'chunk'`.
        :param bracket_sent: If true, include sentence bracketing.
        :param pos_tag: Whether to include part-of-speech tags.
        :param sem_tag: Whether to include semantic tags, namely WordNet lemma
            and OOV named entity status.
        """
        assert unit in ('token', 'word', 'chunk')
        result = []

        xmldoc = ElementTree.parse(fileid).getroot()
        for xmlsent in xmldoc.findall('.//s'):
            sent = []
            for xmlword in _all_xmlwords_in(xmlsent):
                itm = SemcorCorpusReader._word(xmlword, unit, pos_tag, sem_tag, self._wordnet)
                if unit=='word':
                    sent.extend(itm)
                else:
                    sent.append(itm)

            if bracket_sent:
                result.append(SemcorSentence(xmlsent.attrib['snum'], sent))
            else:
                result.extend(sent)

        assert None not in result
        return result

    @staticmethod
    def _word(xmlword, unit, pos_tag, sem_tag, wordnet):
        tkn = xmlword.text
        if not tkn:
            tkn = "" # fixes issue 337?

        lemma = xmlword.get('lemma', tkn) # lemma or NE class
        lexsn = xmlword.get('lexsn') # lex_sense (locator for the lemma's sense)
        if lexsn is not None:
            sense_key = lemma + '%' + lexsn
            wnpos = ('n','v','a','r','s')[int(lexsn.split(':')[0])-1]   # see http://wordnet.princeton.edu/man/senseidx.5WN.html
        else:
            sense_key = wnpos = None
        redef = xmlword.get('rdf', tkn)	# redefinition--this indicates the lookup string
        # does not exactly match the enclosed string, e.g. due to typographical adjustments
        # or discontinuity of a multiword expression. If a redefinition has occurred,
        # the "rdf" attribute holds its inflected form and "lemma" holds its lemma.
        # For NEs, "rdf", "lemma", and "pn" all hold the same value (the NE class).
        sensenum = xmlword.get('wnsn')  # WordNet sense number
        isOOVEntity = 'pn' in xmlword.keys()   # a "personal name" (NE) not in WordNet
        pos = xmlword.get('pos')    # part of speech for the whole chunk (None for punctuation)

        if unit=='token':
            if not pos_tag and not sem_tag:
                itm = tkn
            else:
                itm = (tkn,) + ((pos,) if pos_tag else ()) + ((lemma, wnpos, sensenum, isOOVEntity) if sem_tag else ())
            return itm
        else:
            ww = tkn.split('_') # TODO: case where punctuation intervenes in MWE
            if unit=='word':
                return ww
            else:
                if sensenum is not None:
                    try:
                        sense = wordnet.lemma_from_key(sense_key)   # Lemma object
                    except Exception:
                        # cannot retrieve the wordnet.Lemma object. possible reasons:
                        #  (a) the wordnet corpus is not downloaded;
                        #  (b) a nonexistant sense is annotated: e.g., such.s.00 triggers: 
                        #  nltk.corpus.reader.wordnet.WordNetError: No synset found for key u'such%5:00:01:specified:00'
                        # solution: just use the lemma name as a string
                        try:
                            sense = '%s.%s.%02d' % (lemma, wnpos, int(sensenum))    # e.g.: reach.v.02
                        except ValueError:
                            sense = lemma+'.'+wnpos+'.'+sensenum  # e.g. the sense number may be "2;1"

                bottom = [Tree(pos, ww)] if pos_tag else ww

                if sem_tag and isOOVEntity: