# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2025 NLTK Project
# Author: Marcus Uneson <marcus.uneson@gmail.com>
#   based on previous (nltk2) version by
#   Christopher Maloof, Edward Loper, Steven Bird
# URL: <https://www.nltk.org/>
# For license information, see  LICENSE.TXT

from abc import ABCMeta, abstractmethod


class Feature(metaclass=ABCMeta):
    """
    An abstract base class for Features. A Feature is a combination of
    a specific property-computing method and a list of relative positions
    to apply that method to.

    The property-computing method, M{extract_property(tokens, index)},
    must be implemented by every subclass. It extracts or computes a specific
    property for the token at the current index. Typical extract_property()
    methods return features such as the token text or tag; but more involved
    methods may consider the entire sequence M{tokens} and
    for instance compute the length of the sentence the token belongs to.

    In addition, the subclass may have a PROPERTY_NAME, which is how
    it will be printed (in Rules and Templates, etc). If not given, defaults
    to the classname.

    """

    json_tag = "nltk.tbl.Feature"
    PROPERTY_NAME = None

    def __init__(self, positions, end=None):
        """
        Construct a Feature which may apply at C{positions}.

        >>> # For instance, importing some concrete subclasses (Feature is abstract)
        >>> from nltk.tag.brill import Word, Pos

        >>> # Feature Word, applying at one of [-2, -1]
        >>> Word([-2,-1])
        Word([-2, -1])

        >>> # Positions need not be contiguous
        >>> Word([-2,-1, 1])
        Word([-2, -1, 1])

        >>> # Contiguous ranges can alternatively be specified giving the
        >>> # two endpoints (inclusive)
        >>> Pos(-3, -1)
        Pos([-3, -2, -1])

        >>> # In two-arg form, start <= end is enforced
        >>> Pos(2, 1)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
          File "nltk/tbl/template.py", line 306, in __init__
            raise TypeError
        ValueError: illegal interval specification: (start=2, end=1)

        :type positions: list of int
        :param positions: the positions at which this features should apply
        :raises ValueError: illegal position specifications

        An alternative calling convention, for contiguous positions only,
        is Feature(start, end):

        :type start: int
        :param start: start of range where this feature should apply
        :type end: int
        :param end: end of range (NOTE: inclusive!) where this feature should apply
        """
        self.positions = None  # to avoid warnings
        if end is None:
            self.positions = tuple(sorted({int(i) for i in positions}))
        else:  # positions was actually not a list, but only the start index
            try:
                if positions > end:
                    raise TypeError
                self.positions = tuple(range(positions, end + 1))
            except TypeError as e:
                # let any kind of erroneous spec raise ValueError
                raise ValueError(
                    "illegal interval specification: (start={}, end={})".format(
                        positions, end
                    )
                ) from e

        # set property name given in subclass, or otherwise name of subclass
        self.PROPERTY_NAME = self.__class__.PROPERTY_NAME or self.__class__.__name__

    def encode_json_obj(self):
        return self.positions

    @classmethod
    def decode_json_obj(cls, obj):
        positions = obj
        return cls(positions)

    def __repr__(self):
        return f"{self.__class__.__name__}({list(self.positions)!r})"

    @classmethod
    def expand(cls, starts, winlens, excludezero=False):
        """
        Return a list of features, one for each start point in starts
        and for each window length in winlen. If excludezero is True,
        no Features containing 0 in its positions will be generated
        (many tbl trainers have a special representation for the
        target feature at [0])

        For instance, importing a concrete subclass (Feature is abstract)

        >>> from nltk.tag.brill import Word

        First argument gives the possible start positions, second the
        possible window lengths

        >>> Word.expand([-3,-2,-1], [1])
        [Word([-3]), Word([-2]), Word([-1])]

        >>> Word.expand([-2,-1], [1])
        [Word([-2]), Word([-1])]

        >>> Word.expand([-3,-2,-1], [1,2])
        [Word([-3]), Word([-2]), Word([-1]), Word([-3, -2]), Word([-2, -1])]

        >>> Word.expand([-2,-1], [1])
        [Word([-2]), Word([-1])]

        A third optional argument excludes all Features whose positions contain zero

        >>> Word.expand([-2,-1,0], [1,2], excludezero=False)
        [Word([-2]), Word([-1]), Word([0]), Word([-2, -1]), Word([-1, 0])]

        >>> Word.expand([-2,-1,0], [1,2], excludezero=True)
        [Word([-2]), Word([-1]), Word([-2, -1])]

        All window lengths must be positive

        >>> Word.expand([-2,-1], [0])
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
          File "nltk/tag/tbl/template.py", line 371, in expand
            :param starts: where to start looking for Feature
        ValueError: non-positive window length in [0]

        :param starts: where to start looking for Feature
        :type starts: list of ints
        :param winlens: window lengths where to look for Feature
        :type starts: list of ints
        :param excludezero: do not output any Feature with 0 in any of its positions.
        :type excludezero: bool
        :returns: list of Features
        :raises ValueError: for non-positive window lengths
        """
        if not all(x > 0 for x in winlens):
            raise ValueError(f"non-positive window length in {winlens}")
        xs = (starts[i : i + w] for w in winlens for i in range(len(starts) - w + 1))
        return [cls(x) for x in xs if not (excludezero and 0 in x)]

    def issuperset(self, other):
        """
        Return True if this Feature always returns True when other does

        More precisely, return True if this feature refers to the same property as other;
        and this Feature looks at all positions that other does (and possibly
        other positions in addition).

        #For instance, importing a concrete subclass (Feature is abstract)
        >>> from nltk.tag.brill import Word, Pos

        >>> Word([-3,-2,-1]).issuperset(Word([-3,-2]))
        True

        >>> Word([-3,-2,-1]).issuperset(Word([-3,-2, 0]))
        False

        #Feature subclasses must agree
        >>> Word([-3,-2,-1]).issuperset(Pos([-3,-2]))
        False

        :param other: feature with which to compare
        :type other: (subclass of) Feature
        :return: True if this feature is superset, otherwise False
        :rtype: bool


        """
        return self.__class__ is other.__class__ and set(self.positions) >= set(
            other.positions
        )

    def intersects(self, other):
        """
        Return True if the positions of this Feature intersects with those of other

        More precisely, return True if this feature refers to the same property as other;
        and there is some overlap in the positions they look at.

        #For instance, importing a concrete subclass (Feature is abstract)
        >>> from nltk.tag.brill import Word, Pos

        >>> Word([-3,-2,-1]).intersects(Word([-3,-2]))
        True

        >>> Word([-3,-2,-1]).intersects(Word([-3,-2, 0]))
        True

        >>> Word([-3,-2,-1]).intersects(Word([0]))
        False

        #Feature subclasses must agree
        >>> Word([-3,-2,-1]).intersects(Pos([-3,-2]))
        False

        :param other: feature with which to compare
        :type other: (subclass of) Feature
        :return: True if feature classes agree and there is some overlap in the positions they look at
        :rtype: bool
        """

        return bool(
            self.__class__ is other.__class__
            and set(self.positions) & set(other.positions)
        )

    # Rich comparisons for Features. With @functools.total_ordering (Python 2.7+),
    # it will be enough to define __lt__ and __eq__
    def __eq__(self, other):
        return self.__class__ is other.__class__ and self.positions == other.positions

    def __lt__(self, other):
        return (
            self.__class__.__name__ < other.__class__.__name__
            or
            #    self.positions is a sorted tuple of ints
            self.positions < other.positions
        )

    def __ne__(self, other):
        return not (self == other)

    def __gt__(self, other):
        return other < self

    def __ge__(self, other):
        return not self < other

    def __le__(self, other):
        return self < other or self == other

    @staticmethod
    @abstractmethod
    def extract_property(tokens, index):
        """
        Any subclass of Feature must define static method extract_property(tokens, index)

        :param tokens: the sequence of tokens
        :type tokens: list of tokens
        :param index: the current index
        :type index: int
        :return: feature value
        :rtype: any (but usually scalar)
        """
