#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Natural Language Toolkit: Interface to the Stanford Segmenter
# for Chinese and Arabic
#
# Copyright (C) 2001-2017 NLTK Project
# Author: 52nlp <52nlpcn@gmail.com>
#         Casper Lehmann-Strøm <casperlehmann@gmail.com>
#         Alex Constantin <alex@keyworder.ch>
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from __future__ import unicode_literals, print_function

import tempfile
import os
import json
from subprocess import PIPE

from nltk import compat
from nltk.internals import find_jar, find_file, find_dir, \
                           config_java, java, _java_options
from nltk.tokenize.api import TokenizerI

from six import text_type

_stanford_url = 'https://nlp.stanford.edu/software'


class StanfordSegmenter(TokenizerI):
    """
    Interface to the Stanford Segmenter
    >>> from nltk.tokenize.stanford_segmenter import StanfordSegmenter
    >>> seg = StanfordSegmenter()
    >>> seg.default_config('zh')
    >>> sent = u'这是斯坦福中文分词器测试'
    >>> print(seg.segment(sent))
    \u8fd9 \u662f \u65af\u5766\u798f \u4e2d\u6587 \u5206\u8bcd\u5668 \u6d4b\u8bd5
    <BLANKLINE>
    >>> seg.default_config('ar')
    >>> sent = u'هذا هو تصنيف ستانفورد العربي للكلمات'
    >>> print(seg.segment(sent.split()))
    \u0647\u0630\u0627 \u0647\u0648 \u062a\u0635\u0646\u064a\u0641 \u0633\u062a\u0627\u0646\u0641\u0648\u0631\u062f \u0627\u0644\u0639\u0631\u0628\u064a \u0644 \u0627\u0644\u0643\u0644\u0645\u0627\u062a
    <BLANKLINE>
    """

    _JAR = 'stanford-segmenter.jar'
    _SLF4J = 'slf4j-api.jar'

    def __init__(self,
                 path_to_jar=None, path_to_slf4j=None,
                 java_class=None,
                 path_to_model=None,
                 path_to_dict=None,
                 path_to_sihan_corpora_dict=None,
                 sihan_post_processing='false',
                 keep_whitespaces='false',
                 encoding='UTF-8', options=None,
                 verbose=False, java_options='-mx2g'):

        stanford_segmenter = find_jar(
                self._JAR, path_to_jar,
                env_vars=('STANFORD_SEGMENTER',),
                searchpath=(), url=_stanford_url,
                verbose=verbose)
        slf4j = find_jar(
                self._SLF4J, path_to_slf4j,
                env_vars=('SLF4J', 'STANFORD_SEGMENTER',),
                searchpath=(), url=_stanford_url,
                verbose=verbose)

        # This is passed to java as the -cp option, the segmenter needs slf4j.
        self._stanford_jar = os.pathsep.join(
            [_ for _ in [stanford_segmenter, slf4j] if not _ is None])

        self._java_class = java_class
        self._model = path_to_model
        self._sihan_corpora_dict = path_to_sihan_corpora_dict
        self._sihan_post_processing = sihan_post_processing
        self._keep_whitespaces = keep_whitespaces
        self._dict = path_to_dict

        self._encoding = encoding
        self.java_options = java_options
        options = {} if options is None else options
        self._options_cmd = ','.join('{0}={1}'.format(key, json.dumps(val)) for key, val in options.items())

    def default_config(self, lang):
        """
        Attempt to intialize Stanford Word Segmenter for the specified language
        using the STANFORD_SEGMENTER and STANFORD_MODELS environment variables
        """

        search_path = ()
        if os.environ.get('STANFORD_SEGMENTER'):
            search_path = {os.path.join(os.environ.get('STANFORD_SEGMENTER'), 'data')}

        # init for Chinese-specific files
        self._dict = None
        self._sihan_corpora_dict = None
        self._sihan_post_processing = 'false'

        if lang == 'ar':
            self._java_class = 'edu.stanford.nlp.international.arabic.process.ArabicSegmenter'
            model = 'arabic-segmenter-atb+bn+arztrain.ser.gz'

        elif lang == 'zh':
            self._java_class = 'edu.stanford.nlp.ie.crf.CRFClassifier'
            model = 'pku.gz'
            self._sihan_post_processing = 'true'

            path_to_dict = 'dict-chris6.ser.gz'
            try:
                self._dict = find_file(path_to_dict, searchpath=search_path,
                                       url=_stanford_url, verbose=False,
                                       env_vars=('STANFORD_MODELS',))
            except LookupError:
                raise LookupError("Could not find '%s' (tried using env. "
                    "variables STANFORD_MODELS and <STANFORD_SEGMENTER>/data/)" % path_to_dict)

            sihan_dir = './data/'
            try:
                path_to_sihan_dir = find_dir(sihan_dir,
                                             url=_stanford_url, verbose=False,
                                             env_vars=('STANFORD_SEGMENTER',))
                self._sihan_corpora_dict = os.path.join(path_to_sihan_dir, sihan_dir)
            except LookupError:
                raise LookupError("Could not find '%s' (tried using the "
                    "STANFORD_SEGMENTER environment variable)" % sihan_dir)
        else:
            raise LookupError("Unsupported language '%'" % lang)

        try:
            self._model = find_file(model, searchpath=search_path,
                                    url=_stanford_url, verbose=False,
                                    env_vars=('STANFORD_MODELS', 'STANFORD_SEGMENTER',))
        except LookupError:
            raise LookupError("Could not find '%s' (tried using env. "
                "variables STANFORD_MODELS and <STANFORD_SEGMENTER>/data/)" % model)

    def tokenize(self, s):
        super().tokenize(s)

    def segment_file(self, input_file_path):
        """
        """
        cmd = [
            self._java_class,
            '-loadClassifier', self._model,
            '-keepAllWhitespaces', self._keep_whitespaces,
            '-textFile', input_file_path
        ]
        if self._sihan_corpora_dict is not None:
            cmd.extend(['-serDictionary', self._dict,
                        '-sighanCorporaDict', self._sihan_corpora_dict,
                        '-sighanPostProcessing', self._sihan_post_processing])

        stdout = self._execute(cmd)

        return stdout

    def segment(self, tokens):
        return self.segment_sents([tokens])

    def segment_sents(self, sentences):
        """
        """
        encoding = self._encoding
        # Create a temporary input file
        _input_fh, self._input_file_path = tempfile.mkstemp(text=True)

        # Write the actural sentences to the temporary input file
        _input_fh = os.fdopen(_input_fh, 'wb')
        _input = '\n'.join((' '.join(x) for x in sentences))
        if isinstance(_input, text_type) and encoding:
            _input = _input.encode(encoding)
        _input_fh.write(_input)
        _input_fh.close()

        cmd = [
            self._java_class,
            '-loadClassifier', self._model,
            '-keepAllWhitespaces', self._keep_whitespaces,
            '-textFile', self._input_file_path
        ]
        if self._sihan_corpora_dict is not None:
            cmd.extend(['-serDictionary', self._dict,
                        '-sighanCorporaDict', self._sihan_corpora_dict,
                        '-sighanPostProcessing', self._sihan_post_processing])

        stdout = self._execute(cmd)

        # Delete the temporary file
        os.unlink(self._input_file_path)

        return stdout

    def _execute(self, cmd, verbose=False):
        encoding = self._encoding
        cmd.extend(['-inputEncoding', encoding])
        _options_cmd = self._options_cmd
        if _options_cmd:
            cmd.extend(['-options', self._options_cmd])

        default_options = ' '.join(_java_options)

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

        stdout, _stderr = java(cmd, classpath=self._stanford_jar, stdout=PIPE, stderr=PIPE)
        stdout = stdout.decode(encoding)

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

        return stdout


def setup_module(module):
    from nose import SkipTest

    try:
        seg = StanfordSegmenter()
        seg.default_config('ar')
        seg.default_config('zh')
    except LookupError as e:
        raise SkipTest('Tests for nltk.tokenize.stanford_segmenter skipped: %s' % str(e))
