import hashlib
import logging
import uuid
from datetime import timedelta
from decimal import Decimal

from django.core.cache import cache
from django.utils import timezone
from prometheus_client import Counter

from apps.conflicts.models import ExperimentConflictDomain
from apps.conflicts.services import resolve_domain_conflict
from apps.events.models import Decision
from apps.events.services import decision_create
from apps.experiments.models import Experiment, ExperimentStatus, Variant
from apps.experiments.selectors import active_experiment_for_flag
from apps.flags.models import FeatureFlag
from apps.flags.selectors import feature_flag_get_by_key
from libs.dsl import evaluate
from libs.dsl.exceptions import EvaluationError, LexerError, ParserError

logger = logging.getLogger("lotty")

DECIDE_REQUESTS = Counter(
    "lotty_decide_requests_total",
    "Total number of flag decision requests",
    ["reason"],
)

FLAG_CACHE_TTL = 300
EXPERIMENT_CACHE_TTL = 60
MAX_CONCURRENT_EXPERIMENTS = 3
COOLDOWN_DAYS = 7


def _hash_subject(subject_id: str, experiment_id: str, salt: str) -> float:
    hash_input = f"{subject_id}:{experiment_id}:{salt}".encode()
    hash_bytes = hashlib.sha256(hash_input).digest()
    hash_int = int.from_bytes(hash_bytes[:8], byteorder="big")
    return (hash_int % 10000) / Decimal(100)


def _select_variant(
    variants: list[Variant], hash_value: float
) -> Variant | None:
    cumulative = Decimal(0)
    for variant in sorted(variants, key=lambda v: v.name):
        cumulative += variant.weight
        if hash_value < cumulative:
            return variant
    return variants[-1] if variants else None


def _persist_decision(result: dict, subject_id: str) -> None:
    decision_create(
        decision_id=result["decision_id"],
        flag_key=result["flag"],
        subject_id=subject_id,
        experiment_id=result.get("experiment_id"),
        variant_id=result.get("variant_id"),
        value=str(result["value"]) if result["value"] is not None else "",
        reason=result["reason"],
    )


def _cached_flag_get(flag_key: str) -> FeatureFlag | None:
    cache_key = f"flag:{flag_key}"
    cached = cache.get(cache_key)
    if cached is not None:
        return cached if cached != "__none__" else None
    flag = feature_flag_get_by_key(flag_key)
    cache.set(cache_key, flag or "__none__", FLAG_CACHE_TTL)
    return flag


def _cached_active_experiment(flag_pk):
    cache_key = f"active_exp:{flag_pk}"
    cached = cache.get(cache_key)
    if cached is not None:
        return cached if cached != "__none__" else None
    experiment = active_experiment_for_flag(flag_pk)
    cache.set(
        cache_key,
        experiment or "__none__",
        EXPERIMENT_CACHE_TTL,
    )
    return experiment


def _check_targeting(
    targeting_rules: str,
    subject_attributes: dict,
) -> bool:
    if not targeting_rules or not targeting_rules.strip():
        return True
    try:
        return evaluate(targeting_rules, subject_attributes)
    except (EvaluationError, LexerError, ParserError):
        logger.warning(
            "targeting_rules_evaluation_error",
            extra={"rules": targeting_rules},
        )
        return False


def _check_participation_limits(
    subject_id: str,
    experiment_pk: object,
) -> bool:
    active_count = (
        Decision.objects.filter(
            subject_id=subject_id,
            reason="experiment_assigned",
            experiment_id__isnull=False,
        )
        .exclude(experiment_id=experiment_pk)
        .values("experiment_id")
        .distinct()
        .count()
    )
    if active_count >= MAX_CONCURRENT_EXPERIMENTS:
        return False

    cutoff = timezone.now() - timedelta(days=COOLDOWN_DAYS)
    recent_completed = (
        Decision.objects.filter(
            subject_id=subject_id,
            reason="experiment_assigned",
            experiment_id__isnull=False,
            created_at__gte=cutoff,
        )
        .filter(
            experiment_id__in=Experiment.objects.filter(
                status__in=(
                    ExperimentStatus.COMPLETED,
                    ExperimentStatus.ARCHIVED,
                ),
            ).values("pk"),
        )
        .exclude(experiment_id=experiment_pk)
        .values("experiment_id")
        .distinct()
        .exists()
    )
    return not recent_completed


def _check_domain_conflicts(
    experiment: Experiment,
    subject_id: str,
) -> bool:
    memberships = ExperimentConflictDomain.objects.filter(
        experiment=experiment,
    ).select_related("conflict_domain")

    for membership in memberships:
        if not resolve_domain_conflict(
            experiment_id=experiment.pk,
            domain_id=membership.conflict_domain_id,
            subject_id=subject_id,
        ):
            return False
    return True


def decide_for_flag(
    flag_key: str,
    subject_id: str,
    subject_attributes: dict,
) -> dict:
    flag = _cached_flag_get(flag_key)
    if not flag:
        DECIDE_REQUESTS.labels(reason="flag_not_found").inc()
        result = {
            "flag": flag_key,
            "value": None,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": None,
            "variant_id": None,
            "reason": "flag_not_found",
        }
        _persist_decision(result, subject_id)
        return result

    experiment = _cached_active_experiment(flag.pk)
    if not experiment or experiment.status != ExperimentStatus.RUNNING:
        DECIDE_REQUESTS.labels(reason="no_active_experiment").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": None,
            "variant_id": None,
            "reason": "no_active_experiment",
        }
        _persist_decision(result, subject_id)
        return result

    if not _check_targeting(experiment.targeting_rules, subject_attributes):
        DECIDE_REQUESTS.labels(reason="targeting_mismatch").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": str(experiment.pk),
            "variant_id": None,
            "reason": "targeting_mismatch",
        }
        _persist_decision(result, subject_id)
        return result

    if not _check_participation_limits(subject_id, experiment.pk):
        DECIDE_REQUESTS.labels(reason="participation_limit").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": str(experiment.pk),
            "variant_id": None,
            "reason": "participation_limit",
        }
        _persist_decision(result, subject_id)
        return result

    if not _check_domain_conflicts(experiment, subject_id):
        DECIDE_REQUESTS.labels(reason="domain_conflict").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": str(experiment.pk),
            "variant_id": None,
            "reason": "domain_conflict",
        }
        _persist_decision(result, subject_id)
        return result

    allocation_hash = _hash_subject(
        subject_id,
        str(experiment.pk),
        "allocation",
    )
    if allocation_hash >= experiment.traffic_allocation:
        DECIDE_REQUESTS.labels(reason="outside_traffic_allocation").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": str(experiment.pk),
            "variant_id": None,
            "reason": "outside_traffic_allocation",
        }
        _persist_decision(result, subject_id)
        return result

    variants = list(experiment.variants.all())
    if not variants:
        DECIDE_REQUESTS.labels(reason="no_variants").inc()
        result = {
            "flag": flag_key,
            "value": flag.default_value,
            "decision_id": str(uuid.uuid4()),
            "experiment_id": str(experiment.pk),
            "variant_id": None,
            "reason": "no_variants",
        }
        _persist_decision(result, subject_id)
        return result

    variant_hash = _hash_subject(
        subject_id,
        str(experiment.pk),
        "variant",
    )
    total_weight = sum(v.weight for v in variants)
    normalized_hash = variant_hash * total_weight / Decimal(100)
    selected = _select_variant(variants, normalized_hash)

    DECIDE_REQUESTS.labels(reason="experiment_assigned").inc()
    result = {
        "flag": flag_key,
        "value": selected.value if selected else flag.default_value,
        "decision_id": str(uuid.uuid4()),
        "experiment_id": str(experiment.pk),
        "variant_id": str(selected.pk) if selected else None,
        "reason": "experiment_assigned",
    }
    _persist_decision(result, subject_id)
    return result