"""
Job, Timeline, and Checkpoint Schema Definitions

Source of truth for pipeline jobs, timelines, and checkpoints.
Generates: SQLModel (core/db/models.py), TypeScript via modelgen.
"""

from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional
from uuid import UUID


class JobStatus(str, Enum):
    PENDING = "pending"
    RUNNING = "running"
    PAUSED = "paused"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELLED = "cancelled"


class RunType(str, Enum):
    INITIAL = "initial"
    REPLAY = "replay"
    RETRY = "retry"


@dataclass
class Job:
    """
    A pipeline job.

    Each invocation (initial run, replay, retry) creates a Job.
    Jobs for the same source are linked via parent_id.
    """

    id: UUID

    # Input
    source_asset_id: UUID
    video_path: str
    profile_name: str = "soccer_broadcast"

    # Lineage
    parent_id: Optional[UUID] = None
    run_type: RunType = RunType.INITIAL
    config_overrides: Dict[str, Any] = field(default_factory=dict)

    # Status
    status: JobStatus = JobStatus.PENDING
    current_stage: Optional[str] = None
    progress: float = 0.0
    error_message: Optional[str] = None

    # Results summary
    total_detections: int = 0
    brands_found: int = 0
    cloud_llm_calls: int = 0
    estimated_cost_usd: float = 0.0

    # Worker tracking
    celery_task_id: Optional[str] = None
    priority: int = 0

    # Timestamps
    created_at: Optional[datetime] = None
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None


@dataclass
class Timeline:
    """
    The frame sequence from a source video.

    Independent of stages — exists before any stage runs.
    Frames stored in MinIO as JPEGs, metadata here.
    One timeline per job.
    """

    id: UUID
    source_asset_id: Optional[UUID] = None
    source_video: str = ""
    profile_name: str = ""
    fps: float = 2.0

    frames_prefix: str = ""  # s3: timeline/{id}/frames/
    frames_manifest: Dict[int, str] = field(default_factory=dict)  # seq → s3 key
    frames_meta: List[Dict[str, Any]] = field(default_factory=list)

    created_at: Optional[datetime] = None


@dataclass
class Checkpoint:
    """
    A snapshot of pipeline state on a timeline.

    Stage outputs stored as JSONB — each stage serializes to JSON,
    the checkpoint stores it without knowing the shape.

    parent_id forms a tree: multiple children from the same parent
    = different config tries from the same starting point.
    """

    id: UUID
    timeline_id: UUID
    parent_id: Optional[UUID] = None  # null = root checkpoint

    # Stage outputs — JSONB per stage, opaque to the checkpoint layer
    stage_outputs: Dict[str, Any] = field(default_factory=dict)

    # Config that produced this checkpoint
    config_overrides: Dict[str, Any] = field(default_factory=dict)

    # Pipeline state
    stats: Dict[str, Any] = field(default_factory=dict)

    # Scenario bookmark
    is_scenario: bool = False
    scenario_label: str = ""

    created_at: Optional[datetime] = None


# --- Brands ---

class BrandSource(str, Enum):
    OCR = "ocr"
    VLM = "local_vlm"
    CLOUD = "cloud_llm"
    MANUAL = "manual"


@dataclass
class Brand:
    """
    A brand discovered or registered in the system.

    Airings track where/when the brand appeared — each airing
    references a timeline and a frame range.
    """

    id: UUID
    canonical_name: str
    aliases: List[str] = field(default_factory=list)
    source: BrandSource = BrandSource.OCR  # how first discovered
    confirmed: bool = False

    # Airings — JSONB array of appearances
    # [{timeline_id, frame_start, frame_end, confidence, source, timestamp}]
    airings: List[Dict[str, Any]] = field(default_factory=list)
    total_airings: int = 0

    created_at: Optional[datetime] = None
    updated_at: Optional[datetime] = None