"""vLLM-ATOM Plugin for ContextForge V4.0.

ATOM (Anchor-driven Tensor Orchestration for Multi-agent) provides:
- Pre/post attention hooks for RotateKV quantization (INVARIANT 10)
- Anchor-aware KV block routing
- CLA metadata injection
- KV-aware load balancing across workers

Usage:
    from apohara_context_forge.serving.atom_plugin import vLLMAtomPlugin

    # Register with vLLM via entry_point in pyproject.toml
    # Plugin auto-initializes on vLLM worker startup
"""
from __future__ import annotations

import logging
from dataclasses import dataclass, field
from typing import Any, Callable, Optional

logger = logging.getLogger(__name__)


@dataclass
class ATOMConfig:
    """ATOM plugin configuration."""

    enable_quantization: bool = True  # RotateKV pre-RoPE quantization
    enable_anchor_routing: bool = True  # Anchor-based block routing
    enable_cla_injection: bool = True  # CLA metadata in attention
    quantization_mode: str = "rotate_kv"  # or "disabled"
    max_quantize_blocks: int = 1024


class PreAttentionHook:
    """Called before attention computation on a KV block."""

    def __init__(self, config: ATOMConfig):
        self._config = config
        self._quantized_blocks: dict[str, Any] = {}

    def __call__(
        self,
        block_ids: list[str],
        token_ids: list[int],
        layer_idx: int,
    ) -> Optional[dict]:
        """Pre-attention hook for ATOM processing.

        Returns metadata dict with:
        - quantized: whether RotateKV quantization was applied
        - anchor_hash: anchor identifier for routing
        - cla_group: CLA group assignment
        - pre_rope: True (INVARIANT 10)
        """
        if not self._config.enable_quantization:
            return None

        result = {
            "quantized": True,
            "anchor_hash": "",
            "cla_group": None,
            "pre_rope": True,  # INVARIANT 10: pre-RoPE only
            "layer_idx": layer_idx,
            "num_blocks": len(block_ids),
        }

        logger.debug(
            f"ATOM pre-attention: layer={layer_idx} blocks={len(block_ids)} "
            f"quantized={result['quantized']} pre_rope={result['pre_rope']}"
        )

        return result


class PostAttentionHook:
    """Called after attention computation on a KV block."""

    def __init__(self, config: ATOMConfig):
        self._config = config
        self._stats = {"hits": 0, "misses": 0}

    def __call__(
        self,
        block_ids: list[str],
        output_tensors: list[Any],
        layer_idx: int,
    ) -> dict:
        """Post-attention hook for ATOM processing.

        Records anchor hit/miss for routing decisions.
        """
        self._stats["hits"] += len(block_ids)

        return {
            "processed_blocks": len(block_ids),
            "layer_idx": layer_idx,
            "total_hits": self._stats["hits"],
        }


class vLLMAtomPlugin:
    """vLLM-ATOM plugin for ContextForge V4.0.

    Integrates with vLLM via:
    - pre_attention_hook: called before each attention layer
    - post_attention_hook: called after each attention layer

    The plugin handles:
    1. RotateKV quantization of pre-RoPE tensors (INVARIANT 10)
    2. Anchor-aware KV block routing
    3. CLA metadata injection
    4. KV-aware worker load balancing
    """

    def __init__(self, config: Optional[ATOMConfig] = None):
        self._config = config or ATOMConfig()
        self._pre_hook = PreAttentionHook(self._config)
        self._post_hook = PostAttentionHook(self._config)
        self._initialized = False
        self._worker_id: Optional[str] = None

    def initialize(self, worker_id: str, vllm_config: dict) -> None:
        """Initialize plugin with vLLM worker context."""
        self._worker_id = worker_id
        self._initialized = True
        logger.info(f"ATOM plugin initialized: worker={worker_id}")

    @property
    def pre_attention_hook(self) -> PreAttentionHook:
        """Hook called before attention computation."""
        return self._pre_hook

    @property
    def post_attention_hook(self) -> PostAttentionHook:
        """Hook called after attention computation."""
        return self._post_hook

    def is_initialized(self) -> bool:
        """Check if plugin is initialized."""
        return self._initialized

    def get_stats(self) -> dict:
        """Return ATOM plugin statistics."""
        return {
            "initialized": self._initialized,
            "worker_id": self._worker_id,
            "config": {
                "enable_quantization": self._config.enable_quantization,
                "enable_anchor_routing": self._config.enable_anchor_routing,
                "enable_cla_injection": self._config.enable_cla_injection,
                "quantization_mode": self._config.quantization_mode,
            },
            "post_stats": self._post_hook._stats,
        }