"""VLM2Vec-V2.0 编码封装（Q3 决策：帧级 image emb + clip 级 video emb 两级）。

复用 MAGIC-video 的 VLM2VecV2EmbeddingModel wrapper（权重已在本地 HF 缓存）：
  - encode_frames: 关键帧 JPEG 批量图像编码（wrapper.encode_image 内部真批量）
  - encode_clip:   一个 clip 的关键帧序列当作视频编码 → 1 个 clip 级向量
                   （复用已保存的关键帧 JPEG，不重复解码视频；帧数补齐为偶数
                    以满足 Qwen2-VL temporal patch=2）
  - encode_texts:  检索 query 文本编码（推理阶段 memory_store 调用）
"""

import logging
import sys

import numpy as np
import torch

from config import MAGIC_SRC, setup_hf_env

logger = logging.getLogger(__name__)

setup_hf_env()
if MAGIC_SRC not in sys.path:
    sys.path.insert(0, MAGIC_SRC)


class MemoryEncoder:
    def __init__(self, model_name: str = "VLM2Vec/VLM2Vec-V2.0",
                 device: str = "cuda", clip_max_pixels: int = 360 * 420):
        from worldmm.embedding.vlm2vecv2 import VLM2VecV2EmbeddingModel
        self.model = VLM2VecV2EmbeddingModel(model_name=model_name, device=device)
        self.device = device
        self.clip_max_pixels = clip_max_pixels
        # 视频/图像的 instruction 常量（与 MMEB 协议一致）
        from worldmm.embedding.VLM2Vec.src.model.processor import (
            QWEN2_VL, VLM_IMAGE_TOKENS, VLM_VIDEO_TOKENS,
        )
        self._img_token = VLM_IMAGE_TOKENS[QWEN2_VL]
        self._vid_token = VLM_VIDEO_TOKENS[QWEN2_VL]

    # ---------------------------------------------------------- 帧级编码

    def encode_frames(self, image_paths: list, batch_size: int = 16) -> np.ndarray:
        """关键帧 JPEG → (n, D) 归一化向量。"""
        embs = []
        for i in range(0, len(image_paths), batch_size):
            batch = image_paths[i:i + batch_size]
            out = self.model.encode_image(
                batch,
                query_text=f"{self._img_token} Represent the given video frame.",
            )
            embs.append(out)
        return np.concatenate(embs, axis=0) if embs else np.zeros((0, 1536), np.float32)

    # --------------------------------------------------------- clip 级编码

    def encode_clip(self, frames: list, clip_seconds: float) -> np.ndarray:
        """一个 clip 的帧序列 → (1, D) 视频级向量。

        frames: 图片路径列表 或 PIL.Image 列表 或 JPEG bytes 列表
        （v2 用 clip 内均匀 8 帧的内存 JPEG，qwen_vl_utils.fetch_image 原生支持 PIL）。
        """
        import io
        from PIL import Image
        from worldmm.embedding.VLM2Vec.src.model.vlm_backbone.qwen2_vl.qwen_vl_utils import (
            process_vision_info,
        )
        items = []
        for f in frames:
            if isinstance(f, bytes):
                items.append(Image.open(io.BytesIO(f)).convert("RGB"))
            elif isinstance(f, str):
                items.append(f"file://{f}")
            else:
                items.append(f)   # PIL.Image
        if len(items) == 1:
            items = items * 2  # temporal patch=2 需要至少 2 帧
        if len(items) % 2 == 1:
            items.append(items[-1])

        messages = [{
            "role": "user",
            "content": [
                {
                    "type": "video",
                    "video": items,
                    "max_pixels": self.clip_max_pixels,
                    "fps": max(len(items) / max(clip_seconds, 1.0), 0.1),
                },
                {"type": "text", "text": "Describe this video."},
            ],
        }]
        _, video_inputs = process_vision_info(messages)
        inputs = self.model.processor(
            text=f"{self._vid_token} Represent the given video.",
            videos=video_inputs,
            return_tensors="pt",
        )
        inputs = {k: v.to(self.device) for k, v in inputs.items()}
        if "pixel_values_videos" in inputs:
            inputs["pixel_values_videos"] = inputs["pixel_values_videos"].unsqueeze(0)
        if "video_grid_thw" in inputs:
            inputs["video_grid_thw"] = inputs["video_grid_thw"].unsqueeze(0)
        with torch.no_grad():
            with torch.autocast(device_type=self.device.split(":")[0], dtype=torch.bfloat16):
                out = self.model.model(qry=inputs)["qry_reps"]
        return out.float().cpu().numpy()

    # ------------------------------------------------------------ 文本编码

    def encode_texts(self, texts: list) -> np.ndarray:
        """检索 query → (n, D)。推理阶段 search_vis 用。"""
        return self.model.encode_text(texts)