from __future__ import annotations

import copy
import threading
from typing import List
from typing import Optional
from typing import Set
from typing import Tuple
from typing import TYPE_CHECKING

from optuna.distributions import BaseDistribution
from optuna.trial import FrozenTrial
from optuna.trial import TrialState


#  In-memory cache
cached_extra_study_property_cache_lock = threading.Lock()
cached_extra_study_property_cache: dict[int, "_CachedExtraStudyProperty"] = {}


if TYPE_CHECKING:
    SearchSpaceSetT = Set[Tuple[str, BaseDistribution]]
    SearchSpaceListT = List[Tuple[str, BaseDistribution]]


def get_cached_extra_study_property(
    study_id: int, trials: list[FrozenTrial]
) -> tuple[SearchSpaceListT, SearchSpaceListT, list[tuple[str, bool]], bool]:
    with cached_extra_study_property_cache_lock:
        cached_extra_study_property = cached_extra_study_property_cache.get(study_id, None)
        if cached_extra_study_property is None:
            cached_extra_study_property = _CachedExtraStudyProperty()
        cached_extra_study_property.update(trials)
        cached_extra_study_property_cache[study_id] = cached_extra_study_property
        return (
            cached_extra_study_property.intersection_search_space,
            cached_extra_study_property.union_search_space,
            cached_extra_study_property.union_user_attrs,
            cached_extra_study_property.has_intermediate_values,
        )


class _CachedExtraStudyProperty:
    def __init__(self) -> None:
        self._cursor: int = -1
        self._intersection_search_space: Optional[SearchSpaceSetT] = None
        self._union_search_space: SearchSpaceSetT = set()
        self._union_user_attrs: dict[str, bool] = {}  # attr_name: is_sortable (= is_number)
        self.has_intermediate_values: bool = False

    @property
    def intersection_search_space(self) -> SearchSpaceListT:
        if self._intersection_search_space is None:
            return []
        intersection = list(self._intersection_search_space)
        intersection.sort(key=lambda x: x[0])
        return intersection

    @property
    def union_search_space(self) -> SearchSpaceListT:
        union = list(self._union_search_space)
        union.sort(key=lambda x: x[0])
        return union

    @property
    def union_user_attrs(self) -> list[tuple[str, bool]]:
        union = [(name, is_sortable) for name, is_sortable in self._union_user_attrs.items()]
        sorted(union, key=lambda x: x[0])
        return union

    def update(self, trials: list[FrozenTrial]) -> None:
        next_cursor = self._cursor
        for trial in reversed(trials):
            if self._cursor > trial.number:
                break

            if not trial.state.is_finished():
                next_cursor = trial.number

            self._update_user_attrs(trial)
            if trial.state != TrialState.FAIL:
                self._update_intermediate_values(trial)
                self._update_search_space(trial)

        self._cursor = next_cursor

    def _update_user_attrs(self, trial: FrozenTrial) -> None:
        # TODO(c-bata): Support numpy-specific number types.
        current_user_attrs = {
            k: not isinstance(v, bool) and isinstance(v, (int, float))
            for k, v in trial.user_attrs.items()
        }
        for attr_name, current_is_sortable in current_user_attrs.items():
            is_sortable = self._union_user_attrs.get(attr_name)
            if is_sortable is None:
                self._union_user_attrs[attr_name] = current_is_sortable
            elif is_sortable and not current_is_sortable:
                self._union_user_attrs[attr_name] = False

    def _update_intermediate_values(self, trial: FrozenTrial) -> None:
        if not self.has_intermediate_values and len(trial.intermediate_values) > 0:
            self.has_intermediate_values = True

    def _update_search_space(self, trial: FrozenTrial) -> None:
        current = set([(n, d) for n, d in trial.distributions.items()])
        self._union_search_space = self._union_search_space.union(current)

        if self._intersection_search_space is None:
            self._intersection_search_space = copy.copy(current)
        else:
            self._intersection_search_space = self._intersection_search_space.intersection(current)