# -*- coding: utf-8 -*-
# Natural Language Toolkit: Interface to the Stanford Parser
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Steven Xu <xxu@student.unimelb.edu.au>
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from __future__ import unicode_literals

import tempfile
import os
import re
import warnings
from subprocess import PIPE
from io import StringIO

from six import text_type

from nltk.internals import find_jar, find_jar_iter, config_java, java, _java_options, find_jars_within_path

from nltk.parse.api import ParserI
from nltk.parse.dependencygraph import DependencyGraph
from nltk.tree import Tree

_stanford_url = 'https://nlp.stanford.edu/software/lex-parser.shtml'

class GenericStanfordParser(ParserI):
    """Interface to the Stanford Parser"""

    _MODEL_JAR_PATTERN = r'stanford-parser-(\d+)(\.(\d+))+-models\.jar'
    _JAR = r'stanford-parser\.jar'
    _MAIN_CLASS = 'edu.stanford.nlp.parser.lexparser.LexicalizedParser'

    _USE_STDIN = False
    _DOUBLE_SPACED_OUTPUT = False

    def __init__(self, path_to_jar=None, path_to_models_jar=None,
                 model_path='edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz',
                 encoding='utf8', verbose=False,
                 java_options='-mx1000m', corenlp_options=''):

        # find the most recent code and model jar
        stanford_jar = max(
            find_jar_iter(
                self._JAR, path_to_jar,
                env_vars=('STANFORD_PARSER', 'STANFORD_CORENLP'),
                searchpath=(), url=_stanford_url,
                verbose=verbose, is_regex=True
            ),
            key=lambda model_path: os.path.dirname(model_path)
        )

        model_jar=max(
            find_jar_iter(
                self._MODEL_JAR_PATTERN, path_to_models_jar,
                env_vars=('STANFORD_MODELS', 'STANFORD_CORENLP'),
                searchpath=(), url=_stanford_url,
                verbose=verbose, is_regex=True
            ),
            key=lambda model_path: os.path.dirname(model_path)
        )


        #self._classpath = (stanford_jar, model_jar)

        # Adding logging jar files to classpath
        stanford_dir = os.path.split(stanford_jar)[0]
        self._classpath = tuple([model_jar] + find_jars_within_path(stanford_dir))

        self.model_path = model_path
        self._encoding = encoding
        self.corenlp_options = corenlp_options
        self.java_options = java_options

    def _parse_trees_output(self, output_):
        res = []
        cur_lines = []
        cur_trees = []
        blank = False
        for line in output_.splitlines(False):
            if line == '':
                if blank:
                    res.append(iter(cur_trees))
                    cur_trees = []
                    blank = False
                elif self._DOUBLE_SPACED_OUTPUT:
                    cur_trees.append(self._make_tree('\n'.join(cur_lines)))
                    cur_lines = []
                    blank = True
                else:
                    res.append(iter([self._make_tree('\n'.join(cur_lines))]))
                    cur_lines = []
            else:
                cur_lines.append(line)
                blank = False
        return iter(res)

    def parse_sents(self, sentences, verbose=False):
        """
        Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
        list where each sentence is a list of words.
        Each sentence will be automatically tagged with this StanfordParser instance's
        tagger.
        If whitespaces exists inside a token, then the token will be treated as
        separate tokens.

        :param sentences: Input sentences to parse
        :type sentences: list(list(str))
        :rtype: iter(iter(Tree))
        """
        cmd = [
            self._MAIN_CLASS,
            '-model', self.model_path,
            '-sentences', 'newline',
            '-outputFormat', self._OUTPUT_FORMAT,
            '-tokenized',
            '-escaper', 'edu.stanford.nlp.process.PTBEscapingProcessor',
        ]
        return self._parse_trees_output(self._execute(
            cmd, '\n'.join(' '.join(sentence) for sentence in sentences), verbose))

    def raw_parse(self, sentence, verbose=False):
        """
        Use StanfordParser to parse a sentence. Takes a sentence as a string;
        before parsing, it will be automatically tokenized and tagged by
        the Stanford Parser.

        :param sentence: Input sentence to parse
        :type sentence: str
        :rtype: iter(Tree)
        """
        return next(self.raw_parse_sents([sentence], verbose))

    def raw_parse_sents(self, sentences, verbose=False):
        """
        Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
        list of strings.
        Each sentence will be automatically tokenized and tagged by the Stanford Parser.

        :param sentences: Input sentences to parse
        :type sentences: list(str)
        :rtype: iter(iter(Tree))
        """
        cmd = [
            self._MAIN_CLASS,
            '-model', self.model_path,
            '-sentences', 'newline',
            '-outputFormat', self._OUTPUT_FORMAT,
        ]
        return self._parse_trees_output(self._execute(cmd, '\n'.join(sentences), verbose))

    def tagged_parse(self, sentence, verbose=False):
        """
        Use StanfordParser to parse a sentence. Takes a sentence as a list of
        (word, tag) tuples; the sentence must have already been tokenized and
        tagged.

        :param sentence: Input sentence to parse
        :type sentence: list(tuple(str, str))
        :rtype: iter(Tree)
        """
        return next(self.tagged_parse_sents([sentence], verbose))

    def tagged_parse_sents(self, sentences, verbose=False):
        """
        Use StanfordParser to parse multiple sentences. Takes multiple sentences
        where each sentence is a list of (word, tag) tuples.
        The sentences must have already been tokenized and tagged.

        :param sentences: Input sentences to parse
        :type sentences: list(list(tuple(str, str)))
        :rtype: iter(iter(Tree))
        """
        tag_separator = '/'
        cmd = [
            self._MAIN_CLASS,
            '-model', self.model_path,
            '-sentences', 'newline',
            '-outputFormat', self._OUTPUT_FORMAT,
            '-tokenized',
            '-tagSeparator', tag_separator,
            '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer',
            '-tokenizerMethod', 'newCoreLabelTokenizerFactory',
        ]
        # We don't need to escape slashes as "splitting is done on the last instance of the character in the token"
        return self._parse_trees_output(self._execute(
            cmd, '\n'.join(' '.join(tag_separator.join(tagged) for tagged in sentence) for sentence in sentences), verbose))

    def _execute(self, cmd, input_, verbose=False):
        encoding = self._encoding
        cmd.extend(['-encoding', encoding])
        if self.corenlp_options:
            cmd.append(self.corenlp_options)

        default_options = ' '.join(_java_options)

        # Configure java.
        config_java(options=self.java_options, verbose=verbose)

        # Windows is incompatible with NamedTemporaryFile() without passing in delete=False.
        with tempfile.NamedTemporaryFile(mode='wb', delete=False) as input_file:
            # Write the actual sentences to the temporary input file
            if isinstance(input_, text_type) and encoding:
                input_ = input_.encode(encoding)
            input_file.write(input_)
            input_file.flush()

            # Run the tagger and get the output.
            if self._USE_STDIN:
                input_file.seek(0)
                stdout, stderr = java(cmd, classpath=self._classpath,
                                      stdin=input_file, stdout=PIPE, stderr=PIPE)
            else:
                cmd.append(input_file.name)
                stdout, stderr = java(cmd, classpath=self._classpath,
                                      stdout=PIPE, stderr=PIPE)

            stdout = stdout.replace(b'\xc2\xa0',b' ')
            stdout = stdout.replace(b'\x00\xa0',b' ')
            stdout = stdout.decode(encoding)

        os.unlink(input_file.name)

        # Return java configurations to their default values.
        config_java(options=default_options, verbose=False)

        return stdout

class StanfordParser(GenericStanfordParser):
    """
    >>> parser=StanfordParser(
    ...     model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
    ... )

    >>> list(parser.raw_parse("the quick brown fox jumps over the lazy dog")) # doctest: +NORMALIZE_WHITESPACE
    [Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
    Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']),
    Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])])]

    >>> sum([list(dep_graphs) for dep_graphs in parser.raw_parse_sents((
    ...     "the quick brown fox jumps over the lazy dog",
    ...     "the quick grey wolf jumps over the lazy fox"
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
    Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']),
    Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])]), Tree('ROOT', [Tree('NP',
    [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['grey']), Tree('NN', ['wolf'])]), Tree('NP',
    [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']), Tree('NP', [Tree('DT', ['the']),
    Tree('JJ', ['lazy']), Tree('NN', ['fox'])])])])])])]

    >>> sum([list(dep_graphs) for dep_graphs in parser.parse_sents((
    ...     "I 'm a dog".split(),
    ...     "This is my friends ' cat ( the tabby )".split(),
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('ROOT', [Tree('S', [Tree('NP', [Tree('PRP', ['I'])]), Tree('VP', [Tree('VBP', ["'m"]),
    Tree('NP', [Tree('DT', ['a']), Tree('NN', ['dog'])])])])]), Tree('ROOT', [Tree('S', [Tree('NP',
    [Tree('DT', ['This'])]), Tree('VP', [Tree('VBZ', ['is']), Tree('NP', [Tree('NP', [Tree('NP', [Tree('PRP$', ['my']),
    Tree('NNS', ['friends']), Tree('POS', ["'"])]), Tree('NN', ['cat'])]), Tree('PRN', [Tree('-LRB-', ['-LRB-']),
    Tree('NP', [Tree('DT', ['the']), Tree('NN', ['tabby'])]), Tree('-RRB-', ['-RRB-'])])])])])])]

    >>> sum([list(dep_graphs) for dep_graphs in parser.tagged_parse_sents((
    ...     (
    ...         ("The", "DT"),
    ...         ("quick", "JJ"),
    ...         ("brown", "JJ"),
    ...         ("fox", "NN"),
    ...         ("jumped", "VBD"),
    ...         ("over", "IN"),
    ...         ("the", "DT"),
    ...         ("lazy", "JJ"),
    ...         ("dog", "NN"),
    ...         (".", "."),
    ...     ),
    ... ))],[]) # doctest: +NORMALIZE_WHITESPACE
    [Tree('ROOT', [Tree('S', [Tree('NP', [Tree('DT', ['The']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
    Tree('NN', ['fox'])]), Tree('VP', [Tree('VBD', ['jumped']), Tree('PP', [Tree('IN', ['over']), Tree('NP',
    [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])]), Tree('.', ['.'])])])]
    """

    _OUTPUT_FORMAT = 'penn'

    def _make_tree(self, result):
        return Tree.fromstring(result)


class StanfordDependencyParser(GenericStanfordParser):

    """
    >>> dep_parser=StanfordDependencyParser(
    ...     model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
    ... )

    >>> [parse.tree() for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE
    [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy'])])]

    >>> [list(parse.triples()) for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE
    [[((u'jumps', u'VBZ'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det', (u'The', u'DT')),
    ((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'), u'amod', (u'brown', u'JJ')),
    ((u'jumps', u'VBZ'), u'nmod', (u'dog', u'NN')), ((u'dog', u'NN'), u'case', (u'over', u'IN')),
    ((u'dog', u'NN'), u'det', (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ'))]]

    >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.raw_parse_sents((
    ...     "The quick brown fox jumps over the lazy dog.",
    ...     "The quick grey wolf jumps over the lazy fox."
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy'])]),
    Tree('jumps', [Tree('wolf', ['The', 'quick', 'grey']), Tree('fox', ['over', 'the', 'lazy'])])]

    >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.parse_sents((
    ...     "I 'm a dog".split(),
    ...     "This is my friends ' cat ( the tabby )".split(),
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('dog', ['I', "'m", 'a']), Tree('cat', ['This', 'is', Tree('friends', ['my', "'"]), Tree('tabby', ['the'])])]

    >>> sum([[list(parse.triples()) for parse in dep_graphs] for dep_graphs in dep_parser.tagged_parse_sents((
    ...     (
    ...         ("The", "DT"),
    ...         ("quick", "JJ"),
    ...         ("brown", "JJ"),
    ...         ("fox", "NN"),
    ...         ("jumped", "VBD"),
    ...         ("over", "IN"),
    ...         ("the", "DT"),
    ...         ("lazy", "JJ"),
    ...         ("dog", "NN"),
    ...         (".", "."),
    ...     ),
    ... ))],[]) # doctest: +NORMALIZE_WHITESPACE
    [[((u'jumped', u'VBD'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det', (u'The', u'DT')),
    ((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'), u'amod', (u'brown', u'JJ')),
    ((u'jumped', u'VBD'), u'nmod', (u'dog', u'NN')), ((u'dog', u'NN'), u'case', (u'over', u'IN')),
    ((u'dog', u'NN'), u'det', (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ'))]]

    """

    _OUTPUT_FORMAT = 'conll2007'

    def _make_tree(self, result):
        return DependencyGraph(result, top_relation_label='root')


class StanfordNeuralDependencyParser(GenericStanfordParser):
    '''
    >>> from nltk.parse.stanford import StanfordNeuralDependencyParser
    >>> dep_parser=StanfordNeuralDependencyParser(java_options='-mx3g')

    >>> [parse.tree() for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE
    [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy']), '.'])]

    >>> [list(parse.triples()) for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE
    [[((u'jumps', u'VBZ'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det',
    (u'The', u'DT')), ((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'),
    u'amod', (u'brown', u'JJ')), ((u'jumps', u'VBZ'), u'nmod', (u'dog', u'NN')),
    ((u'dog', u'NN'), u'case', (u'over', u'IN')), ((u'dog', u'NN'), u'det',
    (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ')), ((u'jumps', u'VBZ'),
    u'punct', (u'.', u'.'))]]

    >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.raw_parse_sents((
    ...     "The quick brown fox jumps over the lazy dog.",
    ...     "The quick grey wolf jumps over the lazy fox."
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over',
    'the', 'lazy']), '.']), Tree('jumps', [Tree('wolf', ['The', 'quick', 'grey']),
    Tree('fox', ['over', 'the', 'lazy']), '.'])]

    >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.parse_sents((
    ...     "I 'm a dog".split(),
    ...     "This is my friends ' cat ( the tabby )".split(),
    ... ))], []) # doctest: +NORMALIZE_WHITESPACE
    [Tree('dog', ['I', "'m", 'a']), Tree('cat', ['This', 'is', Tree('friends',
    ['my', "'"]), Tree('tabby', ['-LRB-', 'the', '-RRB-'])])]
    '''

    _OUTPUT_FORMAT = 'conll'
    _MAIN_CLASS = 'edu.stanford.nlp.pipeline.StanfordCoreNLP'
    _JAR = r'stanford-corenlp-(\d+)(\.(\d+))+\.jar'
    _MODEL_JAR_PATTERN = r'stanford-corenlp-(\d+)(\.(\d+))+-models\.jar'
    _USE_STDIN = True
    _DOUBLE_SPACED_OUTPUT = True

    def __init__(self, *args, **kwargs):
        super(StanfordNeuralDependencyParser, self).__init__(*args, **kwargs)
        self.corenlp_options += '-annotators tokenize,ssplit,pos,depparse'

    def tagged_parse_sents(self, sentences, verbose=False):
        '''
        Currently unimplemented because the neural dependency parser (and
        the StanfordCoreNLP pipeline class) doesn't support passing in pre-
        tagged tokens.
        '''
        raise NotImplementedError(
            'tagged_parse[_sents] is not supported by '
            'StanfordNeuralDependencyParser; use '
            'parse[_sents] or raw_parse[_sents] instead.'
        )

    def _make_tree(self, result):
        return DependencyGraph(result, top_relation_label='ROOT')


def setup_module(module):
    from nose import SkipTest

    try:
        StanfordParser(
            model_path='edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz'
        )
        StanfordNeuralDependencyParser()
    except LookupError:
        raise SkipTest('doctests from nltk.parse.stanford are skipped because one of the stanford parser or CoreNLP jars doesn\'t exist')