from __future__ import annotations

from datetime import datetime, timezone
from enum import StrEnum

from pydantic import BaseModel, Field

from app.ha.discovery import EntityRole


class AssignmentSource(StrEnum):
    NONE = "none"
    AUTOMATIC = "automatic"
    MANUAL = "manual"


class LifecycleStatus(StrEnum):
    PENDING_ASSIGNMENT = "pending_assignment"
    REVIEW_REQUIRED = "review_required"
    PENDING_HISTORY = "pending_history"
    TRAINED = "trained"
    STALE = "stale"
    INVALID = "invalid"
    ORPHANED = "orphaned"
    ARCHIVED = "archived"


class BehaviorMode(StrEnum):
    SHADOW = "shadow"
    ACTIVE = "active"
    PAUSED = "paused"


class BehaviorStatus(StrEnum):
    COLLECTING = "collecting"
    TRAINED = "trained"
    BLOCKED = "blocked"


class AssignmentCandidate(BaseModel):
    entity_id: str
    domain: str
    role: EntityRole
    device_class: str | None = None
    state_class: str | None = None
    unit_of_measurement: str | None = None
    friendly_name: str | None = None
    area_name: str | None = None
    device_name: str | None = None
    score: float = Field(ge=0.0)
    confidence: float = Field(ge=0.0, le=1.0)
    auto_accepted: bool = False
    evidence: list[str] = Field(default_factory=list)


class AssignmentSelection(BaseModel):
    selected_numeric_entity_id: str | None = None
    selected_context_entity_ids: list[str] = Field(default_factory=list)
    source: AssignmentSource = AssignmentSource.NONE
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    review_required: bool = True
    reason: str = "Noch keine Zuordnung vorhanden."


class ManualOverride(BaseModel):
    numeric_entity_id: str | None = None
    context_entity_ids: list[str] = Field(default_factory=list)
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    note: str | None = None


class LifecycleAuditEntry(BaseModel):
    at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    action: str = Field(min_length=1, max_length=120)
    reason: str = Field(min_length=1, max_length=500)


class ModelLifecycleState(BaseModel):
    model_id: str
    status: LifecycleStatus = LifecycleStatus.PENDING_ASSIGNMENT
    last_reconciled_at: datetime | None = None
    last_trained_at: datetime | None = None
    last_history_signature: str | None = None
    last_history_point_count: int = Field(default=0, ge=0)
    reason: str = "Noch keine Trainingsdaten ausgewertet."
    next_action: str = "Aktor auswählen; Kontext und Historie werden automatisch geprüft."
    audit: list[LifecycleAuditEntry] = Field(default_factory=list)


class BehaviorPattern(BaseModel):
    target_state: str = Field(min_length=1, max_length=100)
    minute_of_day: int = Field(ge=0, le=1439)
    weekday: int = Field(ge=0, le=6)
    context_states: dict[str, str] = Field(default_factory=dict)
    source: str = Field(default="observed", max_length=40)
    weight: float = Field(default=1.0, ge=0.1, le=1.0)
    observed_at: datetime


class BehaviorPrediction(BaseModel):
    target_state: str
    confidence: float = Field(ge=0.0, le=1.0)
    generated_at: datetime
    reason: str
    matching_patterns: int = Field(default=0, ge=0)
    executed: bool = False


class ExecutionEvent(BaseModel):
    target_state: str
    executed_at: datetime


class BehaviorState(BaseModel):
    mode: BehaviorMode = BehaviorMode.SHADOW
    status: BehaviorStatus = BehaviorStatus.COLLECTING
    approved_at: datetime | None = None
    sample_count: int = Field(default=0, ge=0)
    high_confidence_sample_count: int = Field(default=0, ge=0)
    patterns: list[BehaviorPattern] = Field(default_factory=list)
    prediction: BehaviorPrediction | None = None
    last_trained_at: datetime | None = None
    last_evaluated_at: datetime | None = None
    last_executed_at: datetime | None = None
    execution_events: list[ExecutionEvent] = Field(default_factory=list)
    reason: str = "Historische Aktorhandlungen werden analysiert."


class ActuatorRecord(BaseModel):
    actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
    enabled: bool = True
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    assignment: AssignmentSelection = Field(default_factory=AssignmentSelection)
    manual_override: ManualOverride | None = None
    numeric_candidates: list[AssignmentCandidate] = Field(default_factory=list)
    context_candidates: list[AssignmentCandidate] = Field(default_factory=list)
    lifecycle: ModelLifecycleState
    behavior: BehaviorState = Field(default_factory=BehaviorState)


class ReconciliationState(BaseModel):
    last_started_at: datetime | None = None
    last_completed_at: datetime | None = None
    last_trigger: str | None = None
    running: bool = False
    configured_actuators: int = Field(default=0, ge=0)
    review_required: int = Field(default=0, ge=0)
    trained_models: int = Field(default=0, ge=0)
    last_summary: str = "Noch keine Reconciliation ausgeführt."


def model_id_for_actuator(actuator_entity_id: str) -> str:
    return f"actuator.{actuator_entity_id}"