"""
ArcisVLM API — Pydantic request/response schemas.

All API contracts are defined here for type safety and auto-generated OpenAPI docs.
"""

from __future__ import annotations

from pydantic import BaseModel, Field
from typing import Optional


# ---------------------------------------------------------------------------
# Inference
# ---------------------------------------------------------------------------
class QueryRequest(BaseModel):
    """Submit a visual question to the VLM."""
    image_path: Optional[str] = Field(None, description="Local file path to the image")
    image_base64: Optional[str] = Field(None, description="Base64-encoded image bytes")
    question: str = Field(..., min_length=1, description="Natural-language question or instruction")
    task_type: str = Field("vqa", description="Task hint: vqa, detect, alert, caption, count, ocr, reason")
    max_tokens: int = Field(256, ge=1, le=4096)
    temperature: float = Field(0.7, ge=0.0, le=2.0)
    camera_id: Optional[str] = Field(None, description="Camera ID to pull latest frame from")


class QueryResponse(BaseModel):
    """Response from a VLM inference query."""
    answer: str
    confidence: float
    expert_used: str
    processing_time_ms: float
    task_id: str = ""
    metadata: dict = {}
    # Multimodal output fields
    output_type: str = "text"
    detections: list[dict] = []
    counts: dict = {}
    text_regions: list[dict] = []
    alert: dict = {}
    analysis: dict = {}
    tracks: list[dict] = []
    scene_attributes: dict = {}
    annotated_frame_base64: Optional[str] = None
    clip_frames_base64: list[str] = []


class EmbeddingRequest(BaseModel):
    """Get the raw JEPA embedding for an image (+ optional query)."""
    image_path: Optional[str] = None
    image_base64: Optional[str] = None
    query: Optional[str] = None


class EmbeddingResponse(BaseModel):
    """Raw embedding vector."""
    embedding: list[float]
    dimension: int


# ---------------------------------------------------------------------------
# Streams (camera management)
# ---------------------------------------------------------------------------
class StreamStartRequest(BaseModel):
    """Start ingesting from an RTSP camera."""
    camera_id: str = Field(..., min_length=1)
    rtsp_url: str = Field(..., min_length=1)
    target_fps: float = Field(2.0, ge=0.1, le=30.0)
    tasks: list[str] = Field(default=["detect", "alert"], description="Auto-inference task types")


class StreamStopRequest(BaseModel):
    """Stop ingesting from a camera."""
    camera_id: str


class StreamStatusResponse(BaseModel):
    """Status of a single camera stream."""
    camera_id: str
    state: str
    frames_captured: int = 0
    frames_dropped: int = 0
    actual_fps: float = 0.0
    reconnect_count: int = 0


# ---------------------------------------------------------------------------
# Alerts
# ---------------------------------------------------------------------------
class AlertRuleCreate(BaseModel):
    """Create an alert rule."""
    rule_id: str = Field(..., min_length=1)
    condition_type: str = Field(..., description="presence, absence, count_above, count_below")
    target_object: str = Field(..., min_length=1)
    threshold: Optional[int] = None
    action: str = Field("log", description="webhook, log, escalate")
    webhook_url: Optional[str] = None


class AlertRuleResponse(BaseModel):
    """An alert rule."""
    rule_id: str
    condition_type: str
    target_object: str
    threshold: Optional[int]
    action: str
    webhook_url: Optional[str]
    enabled: bool = True


class AlertHistoryItem(BaseModel):
    """A fired alert event."""
    rule_id: str
    timestamp: float
    camera_id: str = ""
    description: str = ""
    actions_taken: list[str] = []


# ---------------------------------------------------------------------------
# Agents
# ---------------------------------------------------------------------------
class AgentInfo(BaseModel):
    """Status of a single agent."""
    agent_id: str
    expert_type: str
    status: str
    tasks_processed: int
    avg_latency_ms: float
    healthy: bool


class AgentPoolStatus(BaseModel):
    """Full agent pool status."""
    agents: dict[str, list[AgentInfo]] = {}
    total_agents: int = 0
    metrics: dict = {}


# ---------------------------------------------------------------------------
# Health
# ---------------------------------------------------------------------------
class HealthResponse(BaseModel):
    model: str = "arcisvlm-1.6b"
    version: str = "1.0.0"
    status: str = "ok"
    model_loaded: bool = False
    agents_ready: bool = False


# ---------------------------------------------------------------------------
# Metrics
# ---------------------------------------------------------------------------
class GPUStats(BaseModel):
    name: str = ""
    utilization_pct: float = 0.0
    memory_used_mb: float = 0.0
    memory_total_mb: float = 0.0
    temperature_c: float = 0.0


class MetricsResponse(BaseModel):
    gpu: list[GPUStats] = []
    inference_count: int = 0
    avg_latency_ms: float = 0.0
    p95_latency_ms: float = 0.0
    p99_latency_ms: float = 0.0
    queries_per_sec: float = 0.0
    uptime_seconds: float = 0.0
    model_params: int = 0


# ---------------------------------------------------------------------------
# HyperMother
# ---------------------------------------------------------------------------
class AdapterCacheEntry(BaseModel):
    camera_id: str
    scene_hash: str
    rank: int = 16
    sigma: float = 0.0
    confidence: float = 0.0
    age_seconds: float = 0.0


class HyperMotherStatus(BaseModel):
    enabled: bool = False
    cache_size: int = 0
    cache_max: int = 500
    cache_hit_rate: float = 0.0
    adapters: list[AdapterCacheEntry] = []
    dynamic_route_count: int = 0
    static_fallback_count: int = 0
    confidence_threshold: float = 0.7


# ---------------------------------------------------------------------------
# Dreamer
# ---------------------------------------------------------------------------
class DreamPrediction(BaseModel):
    step: int
    cosine_similarity: float = 0.0
    mse: float = 0.0
    confidence: float = 0.0


class DreamerStatus(BaseModel):
    enabled: bool = False
    total_dreams: int = 0
    avg_cosine_sim: float = 0.0
    avg_confidence: float = 0.0
    recent_predictions: list[DreamPrediction] = []
    rl_reward_avg: float = 0.0