from __future__ import annotations

from datetime import datetime, timezone
from enum import StrEnum

from pydantic import BaseModel, Field


class HandoffMode(StrEnum):
    SHADOW = "shadow"
    CANDIDATE = "candidate"
    CONTROLLED = "controlled"
    CONFLICT = "conflict"
    ROLLBACK = "rollback"


class SafetyStage(StrEnum):
    OBSERVE = "observe"
    DRY_RUN = "dry_run"
    ACTIVE = "active"
    BLOCKED = "blocked"


class ProposalStatus(StrEnum):
    DRAFT = "draft"
    APPROVED = "approved"
    REJECTED = "rejected"


class JobStatus(StrEnum):
    QUEUED = "queued"
    RUNNING = "running"
    SUCCEEDED = "succeeded"
    FAILED = "failed"


class CandidateStatus(StrEnum):
    PROPOSED = "proposed"
    ACCEPTED = "accepted"
    DISMISSED = "dismissed"


class EntityState(BaseModel):
    entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
    domain: str
    state: str | None = None
    changed_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    area_name: str | None = None
    device_id: str | None = None
    friendly_name: str | None = None


class StateEvent(BaseModel):
    entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
    new_state: str | None = None
    changed_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    attributes: dict[str, str] = Field(default_factory=dict)


class BehaviorPatternV2(BaseModel):
    actuator_entity_id: str
    target_state: str
    trigger_entity_id: str | None = None
    trigger_state: str | None = None
    context: dict[str, str] = Field(default_factory=dict)
    support: int = Field(default=1, ge=1)
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    source: str = Field(default="observed", max_length=40)
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class LearningProfile(BaseModel):
    actuator_entity_id: str
    patterns: list[BehaviorPatternV2] = Field(default_factory=list)
    feedback_positive: int = Field(default=0, ge=0)
    feedback_negative: int = Field(default=0, ge=0)
    model_version: str = "empty"


class ControlProfile(BaseModel):
    actuator_entity_id: str
    stage: SafetyStage = SafetyStage.OBSERVE
    min_confidence: float = Field(default=0.82, ge=0.0, le=1.0)
    manual_block: bool = False
    cooldown_seconds: int = Field(default=900, ge=0)
    handoff_mode: HandoffMode = HandoffMode.SHADOW
    related_automation_ids: list[str] = Field(default_factory=list)
    paused_automation_ids: list[str] = Field(default_factory=list)
    dry_run_events: int = Field(default=0, ge=0)
    dry_run_successes: int = Field(default=0, ge=0)
    dry_run_failures: int = Field(default=0, ge=0)
    active_ready: bool = False
    active_readiness_reason: str = "Noch nicht bewertet."


class RoomProfile(BaseModel):
    room_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
    name: str
    actuator_entity_ids: list[str] = Field(default_factory=list)
    context_entity_ids: list[str] = Field(default_factory=list)


class SceneProfile(BaseModel):
    scene_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
    name: str
    actuator_entity_ids: list[str] = Field(default_factory=list)
    trigger_entity_id: str | None = None
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)


class AutomationProposal(BaseModel):
    proposal_id: str
    name: str
    trigger_entity_id: str
    trigger_state: str | None = None
    actuator_entity_id: str
    target_state: str
    status: ProposalStatus = ProposalStatus.DRAFT
    revision: int = 1
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    decided_at: datetime | None = None


class ModelRecord(BaseModel):
    model_id: str
    actuator_entity_id: str
    pattern_count: int
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    trained_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class ModelEvaluation(BaseModel):
    evaluation_id: str
    model_id: str
    actuator_entity_id: str
    score: float = Field(default=0.0, ge=0.0, le=1.0)
    coverage: float = Field(default=0.0, ge=0.0, le=1.0)
    dry_run_success_rate: float = Field(default=0.0, ge=0.0, le=1.0)
    feedback_score: float = Field(default=0.0, ge=0.0, le=1.0)
    verdict: str
    reasons: list[str] = Field(default_factory=list)
    evaluated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class JobQueueItem(BaseModel):
    job_id: str
    kind: str
    status: JobStatus = JobStatus.QUEUED
    message: str = ""
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    finished_at: datetime | None = None


class SensorWeightOverride(BaseModel):
    actuator_entity_id: str
    sensor_weights: dict[str, float] = Field(default_factory=dict)
    note: str | None = Field(default=None, max_length=500)
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class AutopilotSettings(BaseModel):
    enabled: bool = True
    interval_seconds: int = Field(default=3600, ge=300)
    min_candidate_confidence: float = Field(default=0.65, ge=0.0, le=1.0)
    auto_train: bool = True
    auto_evaluate: bool = True
    auto_activate: bool = False
    last_run_at: datetime | None = None


class CandidateRecommendation(BaseModel):
    candidate_id: str
    actuator_entity_id: str
    trigger_entity_id: str
    trigger_state: str | None = None
    target_state: str
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    reason: str
    status: CandidateStatus = CandidateStatus.PROPOSED
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class HistoryAnalysis(BaseModel):
    entity_id: str
    samples: int
    last_state: str | None = None
    changed_at: datetime | None = None
    unique_states: int = 0
    transitions: int = 0
    recommendation: str


class Decision(BaseModel):
    actuator_entity_id: str
    target_state: str | None = None
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    allowed: bool = False
    executed: bool = False
    dry_run: bool = False
    reason: str
    blockers: list[str] = Field(default_factory=list)
    trigger_entity_id: str | None = None
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class AuditEvent(BaseModel):
    event_id: str
    kind: str = Field(max_length=40)
    entity_id: str | None = None
    message: str = Field(max_length=700)
    decision: Decision | None = None
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class RuntimeState(BaseModel):
    entities: dict[str, EntityState] = Field(default_factory=dict)
    audit: list[AuditEvent] = Field(default_factory=list)
    websocket_status: str = "unavailable"


class LearningState(BaseModel):
    profiles: dict[str, LearningProfile] = Field(default_factory=dict)
    rooms: dict[str, RoomProfile] = Field(default_factory=dict)
    scenes: dict[str, SceneProfile] = Field(default_factory=dict)
    automation_proposals: dict[str, AutomationProposal] = Field(default_factory=dict)
    models: dict[str, ModelRecord] = Field(default_factory=dict)
    model_evaluations: dict[str, ModelEvaluation] = Field(default_factory=dict)
    jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
    weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)
    candidates: dict[str, CandidateRecommendation] = Field(default_factory=dict)


class ControlState(BaseModel):
    profiles: dict[str, ControlProfile] = Field(default_factory=dict)
    global_enabled: bool = True
    autopilot: AutopilotSettings = Field(default_factory=AutopilotSettings)


class BackupBundle(BaseModel):
    exported_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    runtime: RuntimeState
    learning: LearningState
    control: ControlState