#!/usr/pkg/bin/python3.13


__license__   = 'GPL v3'
__copyright__ = '2013, Kovid Goyal <kovid at kovidgoyal.net>'
__docformat__ = 'restructuredtext en'

import copy
from functools import partial
from polyglot.builtins import iteritems, native_string_type

from calibre.ebooks.metadata import author_to_author_sort
from calibre.utils.config_base import tweaks, prefs
from calibre.utils.icu import sort_key, collation_order

CATEGORY_SORTS = ('name', 'popularity', 'rating')  # This has to be a tuple not a set


class Tag:

    __slots__ = ('name', 'original_name', 'id', 'count', 'state', 'is_hierarchical',
            'is_editable', 'is_searchable', 'id_set', 'avg_rating', 'sort',
            'use_sort_as_name', 'category', 'search_expression', 'original_categories')

    def __init__(self, name, id=None, count=0, state=0, avg=0, sort=None,
                 category=None, id_set=None, search_expression=None,
                 is_editable=True, is_searchable=True, use_sort_as_name=False,
                 original_categories=None):
        self.name = self.original_name = name
        self.id = id
        self.count = count
        self.state = state
        self.is_hierarchical = ''
        self.is_editable = is_editable
        self.is_searchable = is_searchable
        self.id_set = id_set if id_set is not None else set()
        self.avg_rating = avg/2.0 if avg is not None else 0
        self.sort = sort
        self.use_sort_as_name = use_sort_as_name
        self.category = category
        self.search_expression = search_expression
        self.original_categories = None

    @property
    def string_representation(self):
        return '%s:%s:%s:%s:%s:%s'%(self.name, self.count, self.id, self.state,
                                    self.category, self.original_categories)

    def __str__(self):
        return self.string_representation

    def __repr__(self):
        return native_string_type(self)

    __calibre_serializable__ = True

    def as_dict(self):
        return {k: getattr(self, k) for k in self.__slots__}

    @classmethod
    def from_dict(cls, d):
        ans = cls('')
        for k in cls.__slots__:
            setattr(ans, k, d[k])
        return ans


def find_categories(field_metadata):
    for category, cat in field_metadata.iter_items():
        if (cat['is_category'] and cat['kind'] not in {'user', 'search'}):
            yield (category, cat['is_multiple'].get('cache_to_list', None), False)
        elif (cat['datatype'] == 'composite' and
              cat['display'].get('make_category', False)):
            yield (category, cat['is_multiple'].get('cache_to_list', None), True)


def create_tag_class(category, fm):
    cat = fm[category]
    dt = cat['datatype']
    is_editable = category not in {'news', 'rating', 'languages', 'formats',
                                   'identifiers'} and dt != 'composite'

    if (tweaks['categories_use_field_for_author_name'] == 'author_sort' and
            (category == 'authors' or
                (cat['display'].get('is_names', False) and
                cat['is_custom'] and cat['is_multiple'] and
                dt == 'text'))):
        use_sort_as_name = True
    else:
        use_sort_as_name = False

    return partial(Tag, use_sort_as_name=use_sort_as_name,
                   is_editable=is_editable, category=category)


def clean_user_categories(dbcache):
    user_cats = dbcache.pref('user_categories', {})
    new_cats = {}
    for k in user_cats:
        comps = [c.strip() for c in k.split('.') if c.strip()]
        if len(comps) == 0:
            i = 1
            while True:
                if str(i) not in user_cats:
                    new_cats[str(i)] = user_cats[k]
                    break
                i += 1
        else:
            new_cats['.'.join(comps)] = user_cats[k]
    try:
        if new_cats != user_cats:
            dbcache.set_pref('user_categories', new_cats)
    except:
        pass
    return new_cats


numeric_collation = prefs['numeric_collation']


def sort_key_for_name_and_first_letter(x):
    v1 = icu_upper(x.sort or x.name)
    v2 = v1 or ' '
    # The idea is that '9999999999' is larger than any digit so all digits
    # will sort in front. Non-digits will sort according to their ICU first letter
    c = v2[0]
    return (c if numeric_collation and c.isdigit() else '9999999999',
            collation_order(v2), sort_key(v1))


category_sort_keys = {True:{}, False: {}}
category_sort_keys[True]['popularity'] = category_sort_keys[False]['popularity'] = \
    lambda x:(-getattr(x, 'count', 0), sort_key(x.sort or x.name))
category_sort_keys[True]['rating'] = category_sort_keys[False]['rating'] = \
    lambda x:(-getattr(x, 'avg_rating', 0.0), sort_key(x.sort or x.name))
category_sort_keys[True]['name'] = \
    sort_key_for_name_and_first_letter
category_sort_keys[False]['name'] = \
    lambda x:sort_key(x.sort or x.name)


def get_categories(dbcache, sort='name', book_ids=None, first_letter_sort=False):
    if sort not in CATEGORY_SORTS:
        raise ValueError('sort ' + sort + ' not a valid value')

    fm = dbcache.field_metadata
    book_rating_map = dbcache.fields['rating'].book_value_map
    lang_map = dbcache.fields['languages'].book_value_map

    categories = {}
    book_ids = frozenset(book_ids) if book_ids else book_ids
    pm_cache = {}

    def get_metadata(book_id):
        ans = pm_cache.get(book_id)
        if ans is None:
            ans = pm_cache[book_id] = dbcache._get_proxy_metadata(book_id)
        return ans

    bids = None
    first_letter_sort = bool(first_letter_sort)

    for category, is_multiple, is_composite in find_categories(fm):
        tag_class = create_tag_class(category, fm)
        sort_on, reverse = sort, False
        if is_composite:
            if bids is None:
                bids = dbcache._all_book_ids() if book_ids is None else book_ids
            cats = dbcache.fields[category].get_composite_categories(
                tag_class, book_rating_map, bids, is_multiple, get_metadata)
        elif category == 'news':
            cats = dbcache.fields['tags'].get_news_category(tag_class, book_ids)
        else:
            cat = fm[category]
            brm = book_rating_map
            dt = cat['datatype']
            if dt == 'rating':
                if category != 'rating':
                    brm = dbcache.fields[category].book_value_map
                if sort_on == 'name':
                    sort_on, reverse = 'rating', True
            cats = dbcache.fields[category].get_categories(
                tag_class, brm, lang_map, book_ids)
            if (category != 'authors' and dt == 'text' and
                cat['is_multiple'] and cat['display'].get('is_names', False)):
                for item in cats:
                    item.sort = author_to_author_sort(item.sort)
        cats.sort(key=category_sort_keys[first_letter_sort][sort_on], reverse=reverse)
        categories[category] = cats

    # Needed for legacy databases that have multiple ratings that
    # map to n stars
    for r in categories['rating']:
        for x in tuple(categories['rating']):
            if r.name == x.name and r.id != x.id:
                r.id_set |= x.id_set
                r.count = len(r.id_set)
                categories['rating'].remove(x)
                break

    # User categories
    user_categories = clean_user_categories(dbcache).copy()

    # First add any grouped search terms to the user categories
    muc = dbcache.pref('grouped_search_make_user_categories', [])
    gst = dbcache.pref('grouped_search_terms', {})
    for c in gst:
        if c not in muc:
            continue
        uc = []
        for sc in gst[c]:
            for t in categories.get(sc, ()):
                uc.append([t.name, sc, 0])
        user_categories[c] = uc

    if user_categories:
        # We want to use same node in the user category as in the source
        # category. To do that, we need to find the original Tag node. There is
        # a time/space tradeoff here. By converting the tags into a map, we can
        # do the verification in the category loop much faster, at the cost of
        # temporarily duplicating the categories lists.
        taglist = {}
        for c, items in iteritems(categories):
            taglist[c] = dict(map(lambda t:(icu_lower(t.name), t), items))

        # Add the category values to the user categories
        for user_cat in sorted(user_categories, key=sort_key):
            items = []
            names_seen = {}
            user_cat_is_gst = user_cat in gst
            for name, label, ign in user_categories[user_cat]:
                n = icu_lower(name)
                if label in taglist and n in taglist[label]:
                    if user_cat_is_gst:
                        # for gst items, make copy and consolidate the tags by name.
                        if n in names_seen:
                            # We must combine this node into a previous one with
                            # the same name ignoring case. As part of the process,
                            # remember the source categories and correct the
                            # average rating
                            t = names_seen[n]
                            other_tag = taglist[label][n]
                            t.id_set |= other_tag.id_set
                            t.count = len(t.id_set)
                            t.original_categories.add(other_tag.category)

                            total_rating = 0
                            count = 0
                            for id_ in t.id_set:
                                rating = book_rating_map.get(id_, 0)
                                if rating:
                                    total_rating += rating/2
                                    count += 1
                            if total_rating and count:
                                t.avg_rating = total_rating/count
                        else:
                            # Must deepcopy so we don't share the id_set between nodes
                            t = copy.deepcopy(taglist[label][n])
                            t.original_categories = {t.category}
                            names_seen[n] = t
                            items.append(t)
                    else:
                        items.append(taglist[label][n])
                # else: do nothing, to not include nodes w zero counts
            cat_name = '@' + user_cat  # add the '@' to avoid name collision
            items.sort(key=category_sort_keys[False][sort])
            categories[cat_name] = items

    # ### Finally, the saved searches category ####
    items = []
    queries = dbcache._search_api.saved_searches.queries
    for srch in sorted(queries, key=sort_key):
        items.append(Tag(srch, sort=srch, search_expression=queries[srch],
                         category='search', is_editable=False))
    if len(items):
        categories['search'] = items

    return categories
