#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# GuessIt - A library for guessing information from filenames
# Copyright (c) 2011 Nicolas Wack <wackou@gmail.com>
#
# GuessIt is free software; you can redistribute it and/or modify it under
# the terms of the Lesser GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# GuessIt is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# Lesser GNU General Public License for more details.
#
# You should have received a copy of the Lesser GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#

from __future__ import unicode_literals
from guessit import UnicodeMixin, s, u, base_text_type
from guessit.language import Language
from guessit.country import Country
import json
import datetime
import logging

log = logging.getLogger(__name__)


class Guess(UnicodeMixin, dict):
    """A Guess is a dictionary which has an associated confidence for each of
    its values.

    As it is a subclass of dict, you can use it everywhere you expect a
    simple dict."""

    def __init__(self, *args, **kwargs):
        try:
            confidence = kwargs.pop('confidence')
        except KeyError:
            confidence = 0

        dict.__init__(self, *args, **kwargs)

        self._confidence = {}
        for prop in self:
            self._confidence[prop] = confidence


    def to_dict(self):
        data = dict(self)
        for prop, value in data.items():
            if isinstance(value, datetime.date):
                data[prop] = value.isoformat()
            elif isinstance(value, (Language, Country, base_text_type)):
                data[prop] = u(value)
            elif isinstance(value, list):
                data[prop] = [u(x) for x in value]

        return data

    def nice_string(self):
        data = self.to_dict()

        parts = json.dumps(data, indent=4).split('\n')
        for i, p in enumerate(parts):
            if p[:5] != '    "':
                continue

            prop = p.split('"')[1]
            parts[i] = ('    [%.2f] "' % self.confidence(prop)) + p[5:]

        return '\n'.join(parts)

    def __unicode__(self):
        return u(self.to_dict())

    def confidence(self, prop):
        return self._confidence.get(prop, -1)

    def set(self, prop, value, confidence=None):
        self[prop] = value
        if confidence is not None:
            self._confidence[prop] = confidence

    def set_confidence(self, prop, value):
        self._confidence[prop] = value

    def update(self, other, confidence=None):
        dict.update(self, other)
        if isinstance(other, Guess):
            for prop in other:
                self._confidence[prop] = other.confidence(prop)

        if confidence is not None:
            for prop in other:
                self._confidence[prop] = confidence

    def update_highest_confidence(self, other):
        """Update this guess with the values from the given one. In case
        there is property present in both, only the one with the highest one
        is kept."""
        if not isinstance(other, Guess):
            raise ValueError('Can only call this function on Guess instances')

        for prop in other:
            if prop in self and self.confidence(prop) >= other.confidence(prop):
                continue
            self[prop] = other[prop]
            self._confidence[prop] = other.confidence(prop)


def choose_int(g1, g2):
    """Function used by merge_similar_guesses to choose between 2 possible
    properties when they are integers."""
    v1, c1 = g1 # value, confidence
    v2, c2 = g2
    if (v1 == v2):
        return (v1, 1 - (1 - c1) * (1 - c2))
    else:
        if c1 > c2:
            return (v1, c1 - c2)
        else:
            return (v2, c2 - c1)


def choose_string(g1, g2):
    """Function used by merge_similar_guesses to choose between 2 possible
    properties when they are strings.

    If the 2 strings are similar, or one is contained in the other, the latter is returned
    with an increased confidence.

    If the 2 strings are dissimilar, the one with the higher confidence is returned, with
    a weaker confidence.

    Note that here, 'similar' means that 2 strings are either equal, or that they
    differ very little, such as one string being the other one with the 'the' word
    prepended to it.

    >>> s(choose_string(('Hello', 0.75), ('World', 0.5)))
    ('Hello', 0.25)

    >>> s(choose_string(('Hello', 0.5), ('hello', 0.5)))
    ('Hello', 0.75)

    >>> s(choose_string(('Hello', 0.4), ('Hello World', 0.4)))
    ('Hello', 0.64)

    >>> s(choose_string(('simpsons', 0.5), ('The Simpsons', 0.5)))
    ('The Simpsons', 0.75)

    """
    v1, c1 = g1 # value, confidence
    v2, c2 = g2

    if not v1:
        return g2
    elif not v2:
        return g1

    v1, v2 = v1.strip(), v2.strip()
    v1l, v2l = v1.lower(), v2.lower()

    combined_prob = 1 - (1 - c1) * (1 - c2)

    if v1l == v2l:
        return (v1, combined_prob)

    # check for common patterns
    elif v1l == 'the ' + v2l:
        return (v1, combined_prob)
    elif v2l == 'the ' + v1l:
        return (v2, combined_prob)

    # if one string is contained in the other, return the shortest one
    elif v2l in v1l:
        return (v2, combined_prob)
    elif v1l in v2l:
        return (v1, combined_prob)

    # in case of conflict, return the one with highest priority
    else:
        if c1 > c2:
            return (v1, c1 - c2)
        else:
            return (v2, c2 - c1)


def _merge_similar_guesses_nocheck(guesses, prop, choose):
    """Take a list of guesses and merge those which have the same properties,
    increasing or decreasing the confidence depending on whether their values
    are similar.

    This function assumes there are at least 2 valid guesses."""

    similar = [guess for guess in guesses if prop in guess]

    g1, g2 = similar[0], similar[1]

    other_props = set(g1) & set(g2) - set([prop])
    if other_props:
        log.debug('guess 1: %s' % g1)
        log.debug('guess 2: %s' % g2)
        for prop in other_props:
            if g1[prop] != g2[prop]:
                log.warning('both guesses to be merged have more than one '
                            'different property in common, bailing out...')
                return

    # merge all props of s2 into s1, updating the confidence for the
    # considered property
    v1, v2 = g1[prop], g2[prop]
    c1, c2 = g1.confidence(prop), g2.confidence(prop)

    new_value, new_confidence = choose((v1, c1), (v2, c2))
    if new_confidence >= c1:
        msg = "Updating matching property '%s' with confidence %.2f"
    else:
        msg = "Updating non-matching property '%s' with confidence %.2f"
    log.debug(msg % (prop, new_confidence))

    g2[prop] = new_value
    g2.set_confidence(prop, new_confidence)

    g1.update(g2)
    guesses.remove(g2)


def merge_similar_guesses(guesses, prop, choose):
    """Take a list of guesses and merge those which have the same properties,
    increasing or decreasing the confidence depending on whether their values
    are similar."""

    similar = [guess for guess in guesses if prop in guess]
    if len(similar) < 2:
        # nothing to merge
        return

    if len(similar) == 2:
        _merge_similar_guesses_nocheck(guesses, prop, choose)

    if len(similar) > 2:
        log.debug('complex merge, trying our best...')
        before = len(guesses)
        _merge_similar_guesses_nocheck(guesses, prop, choose)
        after = len(guesses)
        if after < before:
            # recurse only when the previous call actually did something,
            # otherwise we end up in an infinite loop
            merge_similar_guesses(guesses, prop, choose)


def merge_append_guesses(guesses, prop):
    """Take a list of guesses and merge those which have the same properties by
    appending them in a list.

    DEPRECATED, remove with old guessers

    """
    similar = [guess for guess in guesses if prop in guess]
    if not similar:
        return

    merged = similar[0]
    merged[prop] = [merged[prop]]
    # TODO: what to do with global confidence? mean of them all?

    for m in similar[1:]:
        for prop2 in m:
            if prop == prop2:
                merged[prop].append(m[prop])
            else:
                if prop2 in m:
                    log.warning('overwriting property "%s" with value %s' % (prop2, m[prop2]))
                merged[prop2] = m[prop2]
                # TODO: confidence also

        guesses.remove(m)


def merge_all(guesses, append=None):
    """Merge all the guesses in a single result, remove very unlikely values,
    and return it.
    You can specify a list of properties that should be appended into a list
    instead of being merged.

    >>> s(merge_all([ Guess({ 'season': 2 }, confidence = 0.6),
    ...               Guess({ 'episodeNumber': 13 }, confidence = 0.8) ]))
    {'season': 2, 'episodeNumber': 13}

    >>> s(merge_all([ Guess({ 'episodeNumber': 27 }, confidence = 0.02),
    ...               Guess({ 'season': 1 }, confidence = 0.2) ]))
    {'season': 1}

    """
    if not guesses:
        return Guess()

    result = guesses[0]
    if append is None:
        append = []

    for g in guesses[1:]:
        # first append our appendable properties
        for prop in append:
            if prop in g:
                result.set(prop, result.get(prop, []) + [g[prop]],
                           # TODO: what to do with confidence here? maybe an
                           # arithmetic mean...
                           confidence=g.confidence(prop))

                del g[prop]

        # then merge the remaining ones
        dups = set(result) & set(g)
        if dups:
            log.warning('duplicate properties %s in merged result...' % dups)

        result.update_highest_confidence(g)

    # delete very unlikely values
    for p in list(result.keys()):
        if result.confidence(p) < 0.05:
            del result[p]

    # make sure our appendable properties contain unique values
    for prop in append:
        if prop in result:
            result[prop] = list(set(result[prop]))

    return result
