# Natural Language Toolkit: Tokenizer Interface
#
# Copyright (C) 2001-2014 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
#         Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Tokenizer Interface
"""

from nltk.internals import overridden
from nltk.tokenize.util import string_span_tokenize

class TokenizerI(object):
    """
    A processing interface for tokenizing a string.
    Subclasses must define ``tokenize()`` or ``tokenize_sents()`` (or both).
    """
    def tokenize(self, s):
        """
        Return a tokenized copy of *s*.

        :rtype: list of str
        """
        if overridden(self.tokenize_sents):
            return self.tokenize_sents([s])[0]
        else:
            raise NotImplementedError()

    def span_tokenize(self, s):
        """
        Identify the tokens using integer offsets ``(start_i, end_i)``,
        where ``s[start_i:end_i]`` is the corresponding token.

        :rtype: iter(tuple(int, int))
        """
        raise NotImplementedError()

    def tokenize_sents(self, strings):
        """
        Apply ``self.tokenize()`` to each element of ``strings``.  I.e.:

            return [self.tokenize(s) for s in strings]

        :rtype: list(list(str))
        """
        return [self.tokenize(s) for s in strings]

    def span_tokenize_sents(self, strings):
        """
        Apply ``self.span_tokenize()`` to each element of ``strings``.  I.e.:

            return [self.span_tokenize(s) for s in strings]

        :rtype: iter(list(tuple(int, int)))
        """
        for s in strings:
            yield list(self.span_tokenize(s))


class StringTokenizer(TokenizerI):
    """A tokenizer that divides a string into substrings by splitting
    on the specified string (defined in subclasses).
    """

    def tokenize(self, s):
        return s.split(self._string)

    def span_tokenize(self, s):
        for span in string_span_tokenize(s, self._string):
            yield span


if __name__ == "__main__":
    import doctest
    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
