eval_test_time_cut_layer.py

# import datetime
# import logging
# import json
# import random
# import time
# import numpy as np
# import os
# import pickle
# import sys
# import torch
# import torch.distributed as dist
# import torch.nn.functional as F
# import yaml
# import transformers

# from torch.utils.data import DataLoader
# from tqdm import tqdm
# from transformers import HfArgumentParser, AutoConfig, AutoTokenizer
# from datasets import Dataset, concatenate_datasets
# from datasets.distributed import split_dataset_by_node
# from src.model.vlm_backbone.qwen2_vl.modeling_qwen2_vl import Qwen2VLForConditionalGeneration as _Qwen2VLForConditionalGeneration_src

# from src.arguments import ModelArguments, DataArguments, TrainingArguments
# from src.data.collator.eval_collator import MultimodalEvalDataCollator
# from src.data.eval_dataset.base_eval_dataset import AutoEvalPairDataset, generate_cand_dataset
# from src.eval_utils.metrics import RankingMetrics
# from src.model.model_cut_layer import MMEBModel
# from src.model.processor import get_backbone_name, load_processor, COLPALI
# from src.utils import batch_to_device, print_rank, print_master

# logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s')
# logger = logging.getLogger(__name__)

# # --- Global Dictionaries for Hooks (will be cleared before each encode_embeddings call) ---
# timing_info = {}
# token_info = {
#     "vision_tokens": 0,
#     "text_input_tokens": 0,  # Refers to the original text token count
#     "text_output_tokens": 0, # Not directly applicable here as we are encoding, not generating. Will be 0.
#     "total_llm_input_tokens": 0, # Refers to the total tokens LLM receives (visual + formatted text)
# }

# # --- Hook Functions Definition ---
# def timing_pre_hook(module, input):
#     module_id = id(module)
#     if module_id not in timing_info:
#         timing_info[module_id] = []
#     timing_info[module_id].append((time.time(), 'pre', module.__class__.__name__))

# def timing_post_hook(module, input, output):
#     module_id = id(module)
#     if module_id not in timing_info:
#         # print(f"Warning: No pre-hook data for module {module.__class__.__name__} ({module_id})")
#         return

#     timing_info[module_id].append((time.time(), 'post', module.__class__.__name__))

#     # Collect vision token count (only from Vision Transformer module's post hook)
#     module_name = module.__class__.__name__
#     if "vision" in module_name.lower() and "transformer" in module_name.lower():
#         if isinstance(output, torch.Tensor):
#             token_info["vision_tokens"] = output.shape[0] # For visual features, usually (batch_size, num_tokens, hidden_dim)
#         elif hasattr(output, 'last_hidden_state'):
#             token_info["vision_tokens"] = output.last_hidden_state.shape[1]


# def register_model_hooks(model):
#     registered_modules = []
    
#     core_model = model
#     # print_master(f"DEBUG: Initial model type in register_model_hooks: {type(model)}")
    
#     if hasattr(model, 'encoder') and model.encoder is not None:
#         # print_master(f"DEBUG: model has 'encoder' attribute. Type of model.encoder: {type(model.encoder)}")
        
#         # 使用从 'src' 路径导入的 Qwen2VLForConditionalGeneration 进行检查
#         if isinstance(model.encoder, _Qwen2VLForConditionalGeneration_src):
#             # print_master("Detected MMEBModel structure, registering hooks on model.encoder's sub-modules.")
#             core_model = model.encoder
#         else:
#             print_master(f"WARNING: model.encoder is not an instance of _Qwen2VLForConditionalGeneration_src. Its type is {type(model.encoder)}. Hooks will be registered on top-level model if applicable.")
#     else:
#         print_master("WARNING: Model structure does not have an 'encoder' attribute. Registering hooks directly on top-level modules.")

#     # Vision module
#     if hasattr(core_model, 'visual') and core_model.visual is not None:
#         vision_module = core_model.visual
#         vision_module.register_forward_pre_hook(timing_pre_hook)
#         vision_module.register_forward_hook(timing_post_hook)
#         registered_modules.append(vision_module)
#         print_master(f"Registered hooks for vision module: {vision_module.__class__.__name__}")
#     else:
#         print_master(f"WARNING: No 'visual' attribute found on core_model ({type(core_model)}).")


#     # Merger module (if inside visual) - it's part of the vision component
#     if hasattr(core_model, 'visual') and hasattr(core_model.visual, 'merger') and core_model.visual.merger is not None:
#         merger_module = core_model.visual.merger
#         merger_module.register_forward_pre_hook(timing_pre_hook)
#         merger_module.register_forward_hook(timing_post_hook)
#         registered_modules.append(merger_module)
#         print_master(f"Registered hooks for merger module: {merger_module.__class__.__name__}")
#     else:
#         print_master(f"WARNING: No 'merger' attribute found on core_model.visual ({type(getattr(core_model, 'visual', 'N/A'))}).")

#     # Language model body
#     if hasattr(core_model, 'model') and core_model.model is not None:
#         llm_main_module = core_model.model
#         llm_main_module.register_forward_pre_hook(timing_pre_hook)
#         llm_main_module.register_forward_hook(timing_post_hook)
#         registered_modules.append(llm_main_module)
#         print_master(f"Registered hooks for LLM main module: {llm_main_module.__class__.__name__}")
#     else:
#         print_master(f"WARNING: No 'model' attribute found on core_model ({type(core_model)}).")


#     # LM Head
#     if hasattr(core_model, 'lm_head') and core_model.lm_head is not None:
#         lm_head_module = core_model.lm_head
#         lm_head_module.register_forward_pre_hook(timing_pre_hook)
#         lm_head_module.register_forward_hook(timing_post_hook)
#         registered_modules.append(lm_head_module)
#         print_master(f"Registered hooks for LM head module: {lm_head_module.__class__.__name__}")
#     else:
#         print_master(f"WARNING: No 'lm_head' attribute found on core_model ({type(core_model)}).")


#     if not registered_modules:
#         print_master("Warning: No major modules found for hook registration. Check model architecture.")
#     return registered_modules


# def pad_dataset_to_divisible(dataset, world_size):
#     num_samples = len(dataset)
#     if num_samples % world_size == 0:
#         return dataset, num_samples

#     num_to_add = world_size - (num_samples % world_size)
#     padded_size = num_samples + num_to_add

#     padding_data = dataset.select([i % len(dataset) for i in range(num_to_add)])
#     padded_dataset = concatenate_datasets([dataset, padding_data])
#     return padded_dataset, padded_size

# def encode_embeddings(
#     model: MMEBModel,
#     loader: DataLoader,
#     training_args: TrainingArguments,
#     model_args: ModelArguments,
#     full_dataset: Dataset,
#     encode_side: str,
#     description: str = "Encoding"
# ) -> tuple[np.ndarray, list, list, list]:  # CHANGED: + list for img_token_masks
#     """
#     Encodes embeddings for a given dataset using the model, handling both standard and
#     late-interaction models in a DDP-safe manner.
#     Returns:
#         - embeddings: np.ndarray
#         - infos_or_ids: list
#         - batch_stats_list: list
#         - img_token_masks: list[None | list[bool]]  # NEW
#     """
#     local_rank = dist.get_rank() if dist.is_initialized() else 0
#     world_size = dist.get_world_size() if dist.is_initialized() else 1

#     # Check if the model is a late-interaction type
#     is_late_interaction = (model_args.model_backbone == COLPALI)

#     local_embeds = []
#     local_gt_infos = []
#     local_max_len = 0
    
#     # --- New: List to store statistics for each batch ---
#     batch_stats_list = []

#     # --- NEW: Collect image token masks locally ---
#     local_img_token_masks = []  # 每个样本一个元素：None 或 [bool, ...]

#     model.eval()

#     # Register hooks for the model once per encode_embeddings call
#     registered_hooks = register_model_hooks(model)

#     # --- NEW: helpers to取mask并序列化 ---
#     def _search_key(obj, key: str):
#         # 递归搜索 dict/list/tuple，找到指定 key
#         if isinstance(obj, dict):
#             if key in obj:
#                 return obj[key]
#             for v in obj.values():
#                 r = _search_key(v, key)
#                 if r is not None:
#                     return r
#         elif isinstance(obj, (list, tuple)):
#             for v in obj:
#                 r = _search_key(v, key)
#                 if r is not None:
#                     return r
#         return None

#     def _to_serializable_mask_list(mask_list, batch_size: int):
#         # 将模型返回的 mask（list/tensor/ndarray/None）转成 [None | list[bool]] * B
#         if mask_list is None:
#             return [None] * batch_size

#         out = []
#         if isinstance(mask_list, (list, tuple)):
#             for m in mask_list:
#                 if m is None:
#                     out.append(None)
#                 elif torch.is_tensor(m):
#                     out.append(m.detach().cpu().tolist())
#                 elif isinstance(m, np.ndarray):
#                     out.append(m.tolist())
#                 else:
#                     # already python list/bool
#                     out.append(m)
#         elif torch.is_tensor(mask_list):
#             # 若是 2D 张量（B, L），直接 tolist() -> list[list[bool/int]]
#             out = mask_list.detach().cpu().tolist()
#         elif isinstance(mask_list, np.ndarray):
#             out = mask_list.tolist()
#         else:
#             # 未知类型，保守返回 None 占位
#             out = [None] * batch_size

#         # 长度对齐 batch_size
#         if isinstance(out, list):
#             if len(out) < batch_size:
#                 out = out + [None] * (batch_size - len(out))
#             elif len(out) > batch_size:
#                 out = out[:batch_size]
#         return out

#     with torch.no_grad():
#         for inputs, dataset_info in tqdm(loader, desc=f"{description} (rank {local_rank})", disable=local_rank > 0):
#             # --- Reset statistics for each inference pass ---
#             timing_info.clear()
#             token_info["vision_tokens"] = 0
#             token_info["text_input_tokens"] = 0
#             token_info["text_output_tokens"] = 0
#             token_info["total_llm_input_tokens"] = 0

#             inputs = batch_to_device(inputs, training_args.device)
#             current_batch_size = inputs['input_ids'].shape[0] if 'input_ids' in inputs and inputs['input_ids'] is not None else 1

#             with torch.autocast(enabled=True, dtype=torch.bfloat16, device_type="cuda"):
#                 start_inference_time = time.time()
#                 if encode_side == "qry":
#                     output = model(qry=inputs)
#                     reps = output["qry_reps"].detach()
#                     local_gt_infos.extend(dataset_info)
#                 else:
#                     output = model(tgt=inputs)
#                     reps = output["tgt_reps"].detach()
#                     local_gt_infos.extend([info["cand_name"] for info in dataset_info])
#                 end_inference_time = time.time()

#             # --- NEW: 提取并保存本 batch 的 image_token_bool_masks ---
#             # 期望 MMEBModel 的 output 中直接或间接包含 'image_token_bool_masks'
#             img_masks_raw = None
#             if isinstance(output, dict):
#                 img_masks_raw = _search_key(output, "image_token_bool_masks")
#             # 可选：若你在 MMEBModel 上挂了属性，也可以尝试读取
#             if img_masks_raw is None and hasattr(model, "image_token_bool_masks"):
#                 img_masks_raw = getattr(model, "image_token_bool_masks")

#             img_masks_serializable = _to_serializable_mask_list(img_masks_raw, current_batch_size)
#             local_img_token_masks.extend(img_masks_serializable)

#             # --- Update total LLM input tokens after the model call ---
#             if 'input_ids' in inputs and inputs['input_ids'] is not None:
#                 token_info["total_llm_input_tokens"] = inputs['input_ids'].shape[1]
#                 token_info["text_input_tokens"] = token_info["total_llm_input_tokens"] - token_info["vision_tokens"]
#                 token_info["text_input_tokens"] = max(0, token_info["text_input_tokens"])

#             # --- Collect and Store Batch Statistics ---
#             batch_inference_time = end_inference_time - start_inference_time
            
#             current_batch_stats = {
#                 "batch_size": current_batch_size,
#                 "total_inference_time_seconds": batch_inference_time,
#                 "module_inference_times": {},
#                 "token_counts": {
#                     "visual_tokens": token_info["vision_tokens"],
#                     "language_input_tokens_raw": token_info["text_input_tokens"],
#                     "llm_total_input_tokens": token_info["total_llm_input_tokens"],
#                     "language_output_tokens": token_info["text_output_tokens"],
#                 }
#             }

#             # Calculate and store module timings for the current batch
#             for module_obj in registered_hooks:
#                 module_id = id(module_obj)
#                 module_name = module_obj.__class__.__name__
#                 times = timing_info.get(module_id, [])
#                 durations = []
#                 pre_times = {} 
#                 for t, event_type, _ in times:
#                     if event_type == 'pre':
#                         pre_times[module_id] = t
#                     elif event_type == 'post' and module_id in pre_times:
#                         duration = t - pre_times.pop(module_id)
#                         durations.append(duration)
                
#                 if durations:
#                     current_batch_stats["module_inference_times"][module_name] = {
#                         "total": sum(durations),
#                         "count": len(durations),
#                         "avg": sum(durations) / len(durations)
#                     }
#                 else:
#                     current_batch_stats["module_inference_times"][module_name] = {
#                         "total": 0.0,
#                         "count": 0,
#                         "avg": 0.0
#                     }
            
#             batch_stats_list.append(current_batch_stats)

#             # --- Debug prints (optional) ---
#             print_rank(f"\n--- Inference Statistics for {encode_side} batch (Rank {local_rank}) ---")
#             print_rank(f"Batch Inference took: {batch_inference_time:.4f} seconds")
#             print_rank("--- Module Inference Timing Statistics ---")
#             for module_name, stats in current_batch_stats["module_inference_times"].items():
#                 print_rank(f"**{module_name}**: Total: {stats['total']:.6f}s, Count: {stats['count']}, Avg: {stats['avg']:.6f}s")
#             print_rank("--- Token Count Statistics ---")
#             print_rank(f"**视觉 token 数量**: {current_batch_stats['token_counts']['visual_tokens']}")
#             print_rank(f"**语言输入 token 数量 (仅原始文本)**: {current_batch_stats['token_counts']['language_input_tokens_raw']}")
#             print_rank(f"**LLM总输入 token 数量 (包含视觉 + 格式化文本)**: {current_batch_stats['token_counts']['llm_total_input_tokens']}")
#             print_rank(f"**语言输出 token 数量**: {current_batch_stats['token_counts']['language_output_tokens']}")

#             if is_late_interaction and reps.dim() == 3:
#                 local_max_len = max(local_max_len, reps.shape[1])

#             local_embeds.append(reps)

#     if not local_embeds:
#         # Handle cases where a rank gets no data
#         return np.array([]), [], [], []  # CHANGED: 4个返回值

#     # === DDP Synchronization and Padding for Late-Interaction Models ===
#     if is_late_interaction:
#         if dist.is_initialized():
#             # 1: global max length
#             local_max_len_tensor = torch.tensor(local_max_len, device=training_args.device)
#             dist.all_reduce(local_max_len_tensor, op=dist.ReduceOp.MAX)
#             global_max_len = local_max_len_tensor.item()
#         else:
#             global_max_len = local_max_len

#         # 2: pad to global max length
#         padded_embeds = []
#         for reps_batch in local_embeds:
#             if reps_batch.dim() == 3:
#                 B, L, H = reps_batch.shape
#                 padding_size = global_max_len - L
#                 padded_batch = F.pad(reps_batch, (0, 0, 0, padding_size), "constant", 0)
#                 padded_embeds.append(padded_batch)
#             else:
#                 padded_embeds.append(reps_batch)

#         embeds_tensor = torch.cat(padded_embeds, dim=0).contiguous()
#     else:
#         embeds_tensor = torch.cat(local_embeds, dim=0).contiguous()

#     # === Gather embeddings and keys from all ranks ===
#     if dist.is_initialized() and full_dataset.num_rows >= world_size:
#         print_master(f"Gathering {encode_side} embeddings across all ranks...")

#         # tensor gather
#         output_shape = list(embeds_tensor.shape)
#         output_shape[0] = full_dataset.num_rows
#         embeds_tensor = embeds_tensor.to(training_args.device)
#         gathered_embeds_tensor = torch.empty(output_shape, dtype=embeds_tensor.dtype, device=training_args.device)
#         dist.all_gather_into_tensor(gathered_embeds_tensor, embeds_tensor)
#         final_embeddings = gathered_embeds_tensor.cpu().float().numpy()

#         # object gather for infos and stats
#         gathered_gt_infos = [None for _ in range(world_size)]
#         dist.all_gather_object(gathered_gt_infos, local_gt_infos)
#         all_gt_infos = [key for rank_keys in gathered_gt_infos for key in rank_keys]

#         gathered_batch_stats = [None for _ in range(world_size)]
#         dist.all_gather_object(gathered_batch_stats, batch_stats_list)
#         all_batch_stats = [stats for rank_stats in gathered_batch_stats for stats in rank_stats]

#         # --- NEW: gather masks ---
#         gathered_masks = [None for _ in range(world_size)]
#         dist.all_gather_object(gathered_masks, local_img_token_masks)
#         all_img_token_masks = [m for rank_list in gathered_masks for m in rank_list]
#     else:
#         all_gt_infos = local_gt_infos
#         final_embeddings = embeds_tensor.cpu().float().numpy()
#         all_batch_stats = batch_stats_list
#         all_img_token_masks = local_img_token_masks  # NEW

#     return final_embeddings, all_gt_infos, all_batch_stats, all_img_token_masks  # CHANGED


# def main():
#     if "RANK" in os.environ and dist.is_available() and not dist.is_initialized():
#         dist.init_process_group(backend="nccl", timeout=datetime.timedelta(minutes=60))
#     local_rank = dist.get_rank() if dist.is_initialized() else 0
#     world_size = dist.get_world_size() if dist.is_initialized() else 1
#     # DEBUG PRINTS for Distributed Setup
#     print_master("Distributed init debug info:")
#     print_master(f"RANK: {os.environ.get('RANK')}")
#     print_master(f"LOCAL_RANK: {os.environ.get('LOCAL_RANK')}")
#     print_master(f"WORLD_SIZE: {os.environ.get('WORLD_SIZE')}")
#     print_master(f"MASTER_ADDR: {os.environ.get('MASTER_ADDR')}")
#     print_master(f"MASTER_PORT: {os.environ.get('MASTER_PORT')}")
#     if dist.is_initialized():
#         print_rank(f"dist.get_rank(): {dist.get_rank()}")
#         print_rank(f"dist.get_world_size(): {dist.get_world_size()}")

#     for arg in sys.argv:
#         if arg.startswith("--local-rank="):
#             rank = arg.split("=")[1]
#             sys.argv.remove(arg)
#             sys.argv.append('--local_rank')
#             sys.argv.append(rank)
#     parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
#     model_args, data_args, training_args = parser.parse_args_into_dataclasses()
#     model_args: ModelArguments
#     data_args: DataArguments
#     training_args: TrainingArguments
#     os.makedirs(data_args.encode_output_path, exist_ok=True)

#     # --- Model Loading ---
#     hf_config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
#     if not getattr(model_args, "model_backbone", None):
#         model_backbone = get_backbone_name(hf_config=hf_config, model_type=model_args.model_type)
#         setattr(model_args, 'model_backbone', model_backbone)
#         setattr(training_args, 'model_backbone', model_backbone)
#     print_master(f'Model Backbone: {model_args.model_backbone}')
#     # --- DDP-Safe Model Loading ---
#     # Step 1: Only the master process (rank 0) downloads the model.
#     if local_rank == 0:
#         processor = load_processor(model_args, data_args)
#         model = MMEBModel.load(model_args, is_trainable=False, processor=processor)
#         print_master(f"[rank=0] Loading the model from Huggingface: {model_args.model_name}...")
#     # Step 2: All processes wait here. The non-master processes will pause
#     # until the master process (rank 0) finishes downloading and exits this barrier.
#     if torch.distributed.is_initialized():
#         torch.distributed.barrier()
#     # Step 3: Now that the model is cached, the non-master processes load it from the local cache.
#     if local_rank != 0:
#         print_rank(f"Loading the model from cache...")
#         processor = load_processor(model_args, data_args)
#         time.sleep(random.randint(2 * local_rank, 3 * local_rank))
#         model = MMEBModel.load(model_args, is_trainable=False, processor=processor)
#     model.eval()
#     model = model.to(training_args.device, dtype=torch.bfloat16)
#     with open(data_args.dataset_config, 'r') as yaml_file:
#         dataset_configs = yaml.safe_load(yaml_file)


#     # --- Main Evaluation Loop ---
#     for dataset_idx, (dataset_name, task_config) in enumerate(dataset_configs.items()):
#         # Initialize task-level statistics accumulators for QUERY
#         query_total_stats = {
#             "total_inference_time_seconds": 0.0,
#             "module_inference_times": {
#                 "Qwen2VisionTransformerPretrainedModel": {"total": 0.0, "count": 0},
#                 "PatchMerger": {"total": 0.0, "count": 0},
#                 "Qwen2VLModel": {"total": 0.0, "count": 0},
#                 "Linear": {"total": 0.0, "count": 0},
#             },
#             "token_counts": {
#                 "visual_tokens": 0,
#                 "language_input_tokens_raw": 0,
#                 "llm_total_input_tokens": 0,
#                 "language_output_tokens": 0,
#             },
#             "data_point_count": 0 # Number of image-text pairs processed
#         }

#         # Initialize task-level statistics accumulators for CANDIDATE
#         cand_total_stats = {
#             "total_inference_time_seconds": 0.0,
#             "module_inference_times": {
#                 "Qwen2VisionTransformerPretrainedModel": {"total": 0.0, "count": 0},
#                 "PatchMerger": {"total": 0.0, "count": 0},
#                 "Qwen2VLModel": {"total": 0.0, "count": 0},
#                 "Linear": {"total": 0.0, "count": 0},
#             },
#             "token_counts": {
#                 "visual_tokens": 0,
#                 "language_input_tokens_raw": 0,
#                 "llm_total_input_tokens": 0,
#                 "language_output_tokens": 0,
#             },
#             "data_point_count": 0 # Number of image-text pairs processed
#         }

#         if dist.is_initialized():
#             dist.barrier()
#         print_master(f"\n--- Evaluating {dataset_name} ---")

#         query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry")
#         cand_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt")
#         dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
        
#         # New: Define distinct paths for query and candidate inference statistics output
#         query_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_inference_stats.json")
#         cand_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_inference_stats.json")


#         do_query = not os.path.exists(query_embed_path) or not os.path.exists(dataset_info_path)
#         do_cand = not os.path.exists(cand_embed_path)

#         if do_query or do_cand:
#             if data_args.data_basedir is not None:
#                 # Construct full paths for data files if --data_basedir is provided
#                 for key in ["image_root", "video_root", "frame_root", "clip_root", "data_path"]:
#                     if data_args.data_basedir and task_config.get(key):
#                         task_config[key] = os.path.join(data_args.data_basedir, task_config[key])

#             full_eval_qry_dataset, corpus = AutoEvalPairDataset.instantiate(model_args=model_args, data_args=data_args, **task_config)
#             full_eval_cand_dataset = generate_cand_dataset(full_eval_qry_dataset, corpus)
#             eval_qry_dataset, eval_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset
#             # Pad datasets to be divisible by world_size before splitting
#             if dist.is_initialized():
#                 padded_qry_dataset, _ = pad_dataset_to_divisible(full_eval_qry_dataset, world_size)
#                 padded_cand_dataset, _ = pad_dataset_to_divisible(full_eval_cand_dataset, world_size)
#                 eval_qry_dataset = split_dataset_by_node(padded_qry_dataset, rank=local_rank, world_size=world_size)
#                 eval_cand_dataset = split_dataset_by_node(padded_cand_dataset, rank=local_rank, world_size=world_size)
#             else:
#                 padded_qry_dataset, padded_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset

#         # --- 1. Compute Query Embeddings ---
#         if do_query:
#             print_master("Encoding queries...")
#             eval_qry_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "qry")
#             eval_qry_loader = DataLoader(eval_qry_dataset, batch_size=training_args.per_device_eval_batch_size, collate_fn=eval_qry_collator, num_workers=training_args.dataloader_num_workers)
            
#             # Modified: capture batch_stats_list
#             query_embeds, gt_infos, qry_batch_stats, qry_img_masks = encode_embeddings(model, eval_qry_loader, training_args, model_args, padded_qry_dataset, encode_side="qry", description=f"Queries for {dataset_name}")
            
#             # Accumulate query statistics
#             for batch_stat in qry_batch_stats:
#                 batch_size = batch_stat["batch_size"]
#                 query_total_stats["total_inference_time_seconds"] += batch_stat["total_inference_time_seconds"]
#                 for module_name, module_stats in batch_stat["module_inference_times"].items():
#                     if module_name in query_total_stats["module_inference_times"]:
#                         query_total_stats["module_inference_times"][module_name]["total"] += module_stats["total"]
#                         query_total_stats["module_inference_times"][module_name]["count"] += module_stats["count"]
                
#                 query_total_stats["token_counts"]["visual_tokens"] += batch_stat["token_counts"]["visual_tokens"] * batch_size
#                 query_total_stats["token_counts"]["language_input_tokens_raw"] += batch_stat["token_counts"]["language_input_tokens_raw"] * batch_size
#                 query_total_stats["token_counts"]["llm_total_input_tokens"] += batch_stat["token_counts"]["llm_total_input_tokens"] * batch_size
#                 query_total_stats["token_counts"]["language_output_tokens"] += batch_stat["token_counts"]["language_output_tokens"] * batch_size
                
#                 query_total_stats["data_point_count"] += batch_size # Accumulate the number of processed items

#             query_embeds = query_embeds[:len(full_eval_qry_dataset)]
#             gt_infos = gt_infos[:len(full_eval_qry_dataset)]
#             if local_rank == 0:
#                 with open(query_embed_path, 'wb') as f:
#                     pickle.dump(query_embeds, f)
#                 with open(dataset_info_path, 'w') as f:
#                     for info in gt_infos:
#                         f.write(json.dumps(info) + '\n')
#                 print_master(f"Saved query embeddings to {query_embed_path}")

#                 qry_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_img_token_masks.jsonl")
#                 with open(qry_img_masks_path, 'w', encoding='utf-8') as f:
#                     for i, m in enumerate(qry_img_masks[:len(full_eval_qry_dataset)]):
#                         f.write(json.dumps({"index": i, "mask": m}, ensure_ascii=False) + "\n")
#                 print_master(f"Saved query image token masks to {qry_img_masks_path}")
                
#                 # Save query-specific inference statistics
#                 if query_total_stats["data_point_count"] > 0:
#                     final_query_stats = {
#                         "task_name": dataset_name,
#                         "encode_side": "query",
#                         "data_point_count": query_total_stats["data_point_count"],
#                         "inference_times": {
#                             "total_inference_time_seconds": query_total_stats["total_inference_time_seconds"],
#                             "avg_inference_time_per_item_seconds": query_total_stats["total_inference_time_seconds"] / query_total_stats["data_point_count"],
#                             "module_average_times_per_call": {},
#                             "module_total_times_seconds": {},
#                             "module_calls_count": {},
#                         },
#                         "token_counts": {
#                             "total_visual_tokens": query_total_stats["token_counts"]["visual_tokens"],
#                             "avg_visual_tokens_per_item": query_total_stats["token_counts"]["visual_tokens"] / query_total_stats["data_point_count"],
#                             "total_language_input_tokens_raw": query_total_stats["token_counts"]["language_input_tokens_raw"],
#                             "avg_language_input_tokens_raw_per_item": query_total_stats["token_counts"]["language_input_tokens_raw"] / query_total_stats["data_point_count"],
#                             "total_llm_total_input_tokens": query_total_stats["token_counts"]["llm_total_input_tokens"],
#                             "avg_llm_total_input_tokens_per_item": query_total_stats["token_counts"]["llm_total_input_tokens"] / query_total_stats["data_point_count"],
#                             "total_language_output_tokens": query_total_stats["token_counts"]["language_output_tokens"],
#                             "avg_language_output_tokens_per_item": query_total_stats["token_counts"]["language_output_tokens"] / query_total_stats["data_point_count"],
#                         }
#                     }
#                     for module_name, stats in query_total_stats["module_inference_times"].items():
#                         final_query_stats["inference_times"]["module_total_times_seconds"][module_name] = stats["total"]
#                         final_query_stats["inference_times"]["module_calls_count"][module_name] = stats["count"]
#                         if stats["count"] > 0:
#                             final_query_stats["inference_times"]["module_average_times_per_call"][module_name] = stats["total"] / stats["count"]
#                         else:
#                             final_query_stats["inference_times"]["module_average_times_per_call"][module_name] = 0.0

#                     with open(query_inference_stats_path, 'w', encoding='utf-8') as f:
#                         json.dump(final_query_stats, f, ensure_ascii=False, indent=4)
#                     print_master(f"Query inference statistics for {dataset_name} saved to: {query_inference_stats_path}")
#                 else:
#                     print_master(f"No query data processed for {dataset_name}, skipping query inference statistics output.")

#             if dist.is_initialized():
#                 dist.barrier()


#         # --- 2. Compute Candidate Embeddings ---
#         if do_cand:
#             print_master("Encoding candidates...")
#             eval_cand_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "cand")
#             eval_cand_loader = DataLoader(eval_cand_dataset, batch_size=training_args.per_device_eval_batch_size, collate_fn=eval_cand_collator, num_workers=training_args.dataloader_num_workers)

#             # Modified: capture batch_stats_list
#             cand_embeds, all_cand_ids, cand_batch_stats, cand_img_masks = encode_embeddings(model, eval_cand_loader, training_args, model_args, padded_cand_dataset, encode_side="cand", description=f"Candidates for {dataset_name}")
            
#             # Accumulate candidate statistics (similar logic as query)
#             for batch_stat in cand_batch_stats:
#                 batch_size = batch_stat["batch_size"]
#                 cand_total_stats["total_inference_time_seconds"] += batch_stat["total_inference_time_seconds"]
#                 for module_name, module_stats in batch_stat["module_inference_times"].items():
#                     if module_name in cand_total_stats["module_inference_times"]:
#                         cand_total_stats["module_inference_times"][module_name]["total"] += module_stats["total"]
#                         cand_total_stats["module_inference_times"][module_name]["count"] += module_stats["count"]
                
#                 cand_total_stats["token_counts"]["visual_tokens"] += batch_stat["token_counts"]["visual_tokens"] * batch_size
#                 cand_total_stats["token_counts"]["language_input_tokens_raw"] += batch_stat["token_counts"]["language_input_tokens_raw"] * batch_size
#                 cand_total_stats["token_counts"]["llm_total_input_tokens"] += batch_stat["token_counts"]["llm_total_input_tokens"] * batch_size
#                 cand_total_stats["token_counts"]["language_output_tokens"] += batch_stat["token_counts"]["language_output_tokens"] * batch_size
                
#                 cand_total_stats["data_point_count"] += batch_size # Accumulate the number of processed items

#             cand_embeds = cand_embeds[:len(full_eval_cand_dataset)]
#             all_cand_ids = all_cand_ids[:len(full_eval_cand_dataset)]

#             if local_rank == 0:
#                 cand_embed_dict = {cand_id: embed for cand_id, embed in zip(all_cand_ids, cand_embeds)}
#                 with open(cand_embed_path, 'wb') as f: pickle.dump(cand_embed_dict, f)
#                 print_master(f"Saved candidate embeddings to {cand_embed_path}")

#                 cand_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_img_token_masks.jsonl")
#                 with open(cand_img_masks_path, 'w', encoding='utf-8') as f:
#                     for cid, m in zip(all_cand_ids[:len(full_eval_cand_dataset)], cand_img_masks[:len(full_eval_cand_dataset)]):
#                         f.write(json.dumps({"cand_id": str(cid), "mask": m}, ensure_ascii=False) + "\n")
#                 print_master(f"Saved candidate image token masks to {cand_img_masks_path}")

#                 # Save candidate-specific inference statistics
#                 if cand_total_stats["data_point_count"] > 0:
#                     final_cand_stats = {
#                         "task_name": dataset_name,
#                         "encode_side": "candidate",
#                         "data_point_count": cand_total_stats["data_point_count"],
#                         "inference_times": {
#                             "total_inference_time_seconds": cand_total_stats["total_inference_time_seconds"],
#                             "avg_inference_time_per_item_seconds": cand_total_stats["total_inference_time_seconds"] / cand_total_stats["data_point_count"],
#                             "module_average_times_per_call": {},
#                             "module_total_times_seconds": {},
#                             "module_calls_count": {},
#                         },
#                         "token_counts": {
#                             "total_visual_tokens": cand_total_stats["token_counts"]["visual_tokens"],
#                             "avg_visual_tokens_per_item": cand_total_stats["token_counts"]["visual_tokens"] / cand_total_stats["data_point_count"],
#                             "total_language_input_tokens_raw": cand_total_stats["token_counts"]["language_input_tokens_raw"],
#                             "avg_language_input_tokens_raw_per_item": cand_total_stats["token_counts"]["language_input_tokens_raw"] / cand_total_stats["data_point_count"],
#                             "total_llm_total_input_tokens": cand_total_stats["token_counts"]["llm_total_input_tokens"],
#                             "avg_llm_total_input_tokens_per_item": cand_total_stats["token_counts"]["llm_total_input_tokens"] / cand_total_stats["data_point_count"],
#                             "total_language_output_tokens": cand_total_stats["token_counts"]["language_output_tokens"],
#                             "avg_language_output_tokens_per_item": cand_total_stats["token_counts"]["language_output_tokens"] / cand_total_stats["data_point_count"],
#                         }
#                     }
#                     for module_name, stats in cand_total_stats["module_inference_times"].items():
#                         final_cand_stats["inference_times"]["module_total_times_seconds"][module_name] = stats["total"]
#                         final_cand_stats["inference_times"]["module_calls_count"][module_name] = stats["count"]
#                         if stats["count"] > 0:
#                             final_cand_stats["inference_times"]["module_average_times_per_call"][module_name] = stats["total"] / stats["count"]
#                         else:
#                             final_cand_stats["inference_times"]["module_average_times_per_call"][module_name] = 0.0

#                     with open(cand_inference_stats_path, 'w', encoding='utf-8') as f:
#                         json.dump(final_cand_stats, f, ensure_ascii=False, indent=4)
#                     print_master(f"Candidate inference statistics for {dataset_name} saved to: {cand_inference_stats_path}")
#                 else:
#                     print_master(f"No candidate data processed for {dataset_name}, skipping candidate inference statistics output.")

#         if dist.is_initialized():
#             dist.barrier()

#         # --- 3. Compute Scores (on master rank only) ---
#         score_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score.json")
#         ####################################################################################
#         pred_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred.jsonl")
#         score_detail_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details.jsonl")  # 新文件，存相似度分数
#         def append_score_detail(score_detail_list, qid, ranked_indices, score_vector, cand_ids, labels):
#             """追加一个 query 的候选分数详情"""
#             score_detail_list.append({
#                 "qid": int(qid),
#                 "cand_scores": [
#                     {"cand_id": str(cand_ids[i]), "score": float(score_vector[i])}
#                     for i in ranked_indices
#                 ],
#                 "label": labels
#             })
#         ####################################################################################
#         if local_rank == 0:
#             if os.path.exists(score_path):
#                 try:
#                     with open(score_path, "r") as f:
#                         score_dict = json.load(f)
#                     print_master(f"Score of {dataset_name} (loaded from previous run): {score_path}")
#                     formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
#                     print_master(formatted)
#                     # No `continue` here, as we want to ensure other files are processed/generated
#                 except Exception as e:
#                     print_master(f"Failed to load score for {dataset_name}, proceeding to recompute. Error: {e}")
#             # Proceed with score computation if not loaded or failed to load
#             with open(query_embed_path, 'rb') as f: qry_embeds = pickle.load(f)
#             with open(cand_embed_path, 'rb') as f: cand_embed_dict = pickle.load(f)
#             gt_infos = [json.loads(l) for l in open(dataset_info_path)]
#             pred_dicts = []
#             score_detail_dicts = []###################################

#             rank_against_all_candidates = task_config.get("eval_type", "global") == "global"
#             # if rank_against_all_candidates:
#             #     cand_keys = list(cand_embed_dict.keys())
#             #     cand_embeds = np.stack([cand_embed_dict[key] for key in cand_keys])
#             #     # Handle late-interaction scoring
#             #     if qry_embeds.ndim == 3: # Query: [N_q, L_q, H] | Candidate: [N_c, L_c, H]
#             #         qry_embed = torch.from_numpy(qry_embeds)
#             #         cand_embeds = [torch.from_numpy(np.array(t)) for t in cand_embeds]
#             #         scores = processor.score(qry_embed, cand_embeds, batch_size=64)  # use ColPali score function
#             #         ranked_candids = torch.argsort(-scores, dim=1).cpu().numpy().tolist()
#             #         scores = scores.cpu().numpy()
#             #     else: # Dense
#             #         cosine_scores = np.dot(qry_embeds, cand_embeds.T)
#             #         ranked_candids = np.argsort(-cosine_scores, axis=1)
#             #####################################################
#             if rank_against_all_candidates:
#                 cand_keys = list(cand_embed_dict.keys())
#                 cand_embeds = np.stack([cand_embed_dict[key] for key in cand_keys])

#                 if qry_embeds.ndim == 3:  # Late-interaction
#                     qry_embed_t = torch.from_numpy(qry_embeds)
#                     cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
#                     sim_matrix = processor.score(qry_embed_t, cand_embeds_t, batch_size=64).cpu().numpy()  # [N_q, N_c]
#                 else:  # Dense
#                     sim_matrix = np.dot(qry_embeds, cand_embeds.T)  # [N_q, N_c]

#                 ranked_all = np.argsort(-sim_matrix, axis=1)
#             #########################################################
#                 for qid, (ranked_candid, gt_info) in tqdm(enumerate(zip(ranked_candids, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
#                     rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
#                     rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None
#                     assert rel_scores is None or len(rel_docids) == len(rel_scores)
#                     pred_dicts.append({
#                         "prediction": [cand_keys[i] for i in ranked_candid],
#                         "label": rel_docids,
#                         "rel_scores": rel_scores,
#                     })
#                     ################################# 新增：详细相似度字典
#                     append_score_detail(score_detail_dicts, qid, ranked_indices, sim_matrix[qid], cand_keys, rel_docids)
#                     ########################################
#             # else:
#             #     for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
#             #         cand_embeds = np.stack([cand_embed_dict[key] for key in gt_info["cand_names"]])
#             #         if qry_embeds.ndim == 3: # Query: [N_q, L_q, H] | Candidate: [N_c, L_c, H]
#             #             qry_embed = torch.from_numpy(np.array(qry_embed)).unsqueeze(0)
#             #             cand_embeds = [torch.from_numpy(np.array(t)) for t in cand_embeds]
#             #             scores = processor.score(qry_embed, cand_embeds, batch_size=1024)  # use ColPali score function
#             #             ranked_candids = torch.argsort(-scores, dim=1).cpu().numpy().tolist()[0]
#             #         else:
#             #             cosine_score = np.dot(qry_embed, cand_embeds.T)
#             #             ranked_candids = np.argsort(-cosine_score)
#             #         rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
#             #         rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None

#             #         assert rel_scores is None or len(rel_docids) == len(rel_scores)
#             #         pred_dicts.append({
#             #             "prediction": [gt_info["cand_names"][i] for i in ranked_candids],
#             #             "label": rel_docids,
#             #             "rel_scores": rel_scores,
#             #         })
#             #######################################################################
#             else:  # 非全局
#                 for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
#                     cand_ids_local = gt_info["cand_names"]
#                     cand_embeds = np.stack([cand_embed_dict[key] for key in cand_ids_local])

#                     if qry_embeds.ndim == 3:  # Late-interaction
#                         qry_embed_t = torch.from_numpy(np.array(qry_embed)).unsqueeze(0)  # [1, Lq, H]
#                         cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
#                         sim_vec = processor.score(qry_embed_t, cand_embeds_t, batch_size=1024).cpu().numpy()[0]  # [N_c]
#                     else:  # Dense
#                         sim_vec = np.dot(qry_embed, cand_embeds.T)  # [N_c]

#                     ranked_indices = np.argsort(-sim_vec)
#                     rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
#                     rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None
#                     assert rel_scores is None or len(rel_docids) == len(rel_scores)

#                     pred_dicts.append({
#                         "prediction": [cand_ids_local[i] for i in ranked_indices],
#                         "label": rel_docids,
#                         "rel_scores": rel_scores,
#                     })

#                     # 新增：分数详情
#                     append_score_detail(score_detail_dicts, qid, ranked_indices, sim_vec, cand_ids_local, rel_docids)

#             ########################################## 保存预测和分数
#             with open(score_detail_path, "w") as f:  # 新增
#                 for detail in score_detail_dicts:
#                     f.write(json.dumps(detail) + '\n')
#             print_master(f"Detailed score file saved to: {score_detail_path}")

#             metrics_to_report = task_config["metrics"] if task_config.get("metrics", None) is not None else ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"]
#             metrics = RankingMetrics(metrics_to_report)
#             score_dict = metrics.evaluate(pred_dicts)
#             formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
#             score_dict["num_pred"] = len(pred_dicts)
#             score_dict["num_data"] = len(gt_infos)
#             print_master(f"Score of {dataset_name}:")
#             print_master(formatted)
#             print_master(f"Outputting final score to: {score_path}")
#             with open(score_path, "w") as f:
#                 json.dump(score_dict, f, indent=4)
#             with open(pred_path, "w") as f:
#                 for pred in pred_dicts:
#                     f.write(json.dumps(pred) + '\n')
#             ####################################################################
#             score_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score.json")
#             pred_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred.jsonl")

#             metrics_to_report = task_config["metrics"] if task_config.get("metrics", None) is not None else ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"]
#             metrics = RankingMetrics(metrics_to_report)
#             score_dict = metrics.evaluate(pred_dicts)
#             formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
#             score_dict["num_pred"] = len(pred_dicts)
#             score_dict["num_data"] = len(gt_infos)
#             print_master(f"Score of {dataset_name}:")
#             print_master(formatted)
#             print_master(f"Outputting final score to: {score_path}")
#             with open(score_path, "w") as f:
#                 json.dump(score_dict, f, indent=4)
#             with open(pred_path, "w") as f:
#                 for pred in pred_dicts:
#                     f.write(json.dumps(pred) + '\n')            


# if __name__ == '__main__':
#     main()


############################################################################################

import datetime
import logging
import json
import random
import time
import numpy as np
import os
import pickle
import sys
import torch
import torch.distributed as dist
import torch.nn.functional as F
import yaml
import transformers

from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import HfArgumentParser, AutoConfig, AutoTokenizer
from datasets import Dataset, concatenate_datasets
from datasets.distributed import split_dataset_by_node
from src.model.vlm_backbone.qwen2_vl.modeling_qwen2_vl_train_tokrnpooling import Qwen2VLForConditionalGeneration as _Qwen2VLForConditionalGeneration_src

from src.arguments import ModelArguments, DataArguments, TrainingArguments
from src.data.collator.eval_collator import MultimodalEvalDataCollator
from src.data.eval_dataset.base_eval_dataset import AutoEvalPairDataset, generate_cand_dataset
from src.eval_utils.metrics import RankingMetrics
from src.model.model_cut_layer import MMEBModel
from src.model.processor import get_backbone_name, load_processor, COLPALI
from src.utils import batch_to_device, print_rank, print_master

import math

def get_env_mid_layer():
    v = os.environ.get("MID_LM_LAYER", "").strip()
    if v == "" or v.lower() in {"none", "null"}:
        return None
    try:
        return int(v)
    except:
        logger.warning(f"Invalid MID_LM_LAYER={v}, ignore.")
        return None

def get_env_eval_layers():
    """
    解析环境变量 LM_LAYERS（优先）或兼容旧的 MID_LM_LAYER。
    - LM_LAYERS 示例："4,8,12,last"；可包含 'last'/'none'/'null'/'-1' 表示最后一层(None)。
    - 若未设置 LM_LAYERS，则回落到旧逻辑：MID_LM_LAYER=None -> [None]；否则 [mid, None]
    返回: list[ int | None ]，例如 [4, 8, 12, None]；None 代表最后一层。
    """
    v = os.environ.get("LM_LAYERS", None)
    if v is not None:
        v = v.strip()

    if v:
        toks = [t.strip() for t in v.split(',') if t.strip() != ""]
        layers = []
        for tok in toks:
            tl = tok.lower()
            if tl in {"last", "none", "null", "-1"}:
                layers.append(None)
            else:
                try:
                    val = int(tok)
                    if val > 0:
                        layers.append(val)
                    else:
                        logger.warning(f"Ignoring non-positive layer '{tok}' in LM_LAYERS.")
                except Exception:
                    logger.warning(f"Invalid token '{tok}' in LM_LAYERS; must be int or 'last'/'none'.")
        # 去重但保持顺序
        seen = set()
        uniq = []
        for l in layers:
            key = -1 if l is None else l
            if key in seen:
                continue
            seen.add(key)
            uniq.append(l)
        if not uniq:
            return [None]
        return uniq
    else:
        # 兼容旧逻辑
        mid = get_env_mid_layer()
        return [None] if mid is None else [mid, None]

def make_layer_tag(keep_layers: int | None):
    return f"layer{keep_layers}" if keep_layers and keep_layers > 0 else "layerlast"

def dot_sim(a: np.ndarray, b: np.ndarray) -> np.ndarray:
    # a: [Nq, D], b: [Nc, D], both L2-normalized already if normalize=true
    return a @ b.T

def build_score_details(qid: int, cand_ids: list, score_vec: np.ndarray, ranked_indices: np.ndarray):
    return {
        "qid": int(qid),
        "cand_scores": [
            {"cand_id": str(cand_ids[i]), "score": float(score_vec[i])}
            for i in ranked_indices
        ]
    }

def top1_top2_margin(score_vec: np.ndarray) -> float:
    if len(score_vec) < 2:
        return float("inf")  # 只有一个候选时视作极大margin
    top2 = np.partition(score_vec, -2)[-2:]
    top2.sort()
    return float(top2[-1] - top2[-2])

def simulate_early_exit_by_margin(
    sims_mid: list[dict], sims_last: list[dict], labels: list[list[str]], metrics_to_report: list[str],
    taus: list[float], rank_global: bool
):
    """
    sims_mid / sims_last: 每个query一个dict: {cand_id: score}
    labels: 每个query的正样本cand_id列表
    返回：不同tau下的覆盖率、指标
    """
    assert len(sims_mid) == len(sims_last) == len(labels)
    N = len(labels)
    results = []

    from src.eval_utils.metrics import RankingMetrics
    metrics = RankingMetrics(metrics_to_report)

    # 预构造 用于metrics.evaluate 的pred_dict
    def to_pred_dicts(use_mid_mask: list[bool]) -> list[dict]:
        pred_dicts = []
        for qid in range(N):
            sims_use = sims_mid[qid] if use_mid_mask[qid] else sims_last[qid]
            # 排序
            ranked = sorted(sims_use.items(), key=lambda x: -x[1])
            pred_dicts.append({
                "prediction": [cid for cid, _ in ranked],
                "label": labels[qid],
                "rel_scores": None
            })
        return pred_dicts

    # 计算中间层margin
    margins = []
    for qid in range(N):
        # 取前两大分数的margin
        if len(sims_mid[qid]) == 0:
            margins.append(0.0)
            continue
        scores = np.array(list(sims_mid[qid].values()), dtype=np.float32)
        margins.append(top1_top2_margin(scores))

    margins = np.array(margins, dtype=np.float32)

    for tau in taus:
        use_mid_mask = (margins >= tau).tolist()
        pred_dicts = to_pred_dicts(use_mid_mask)
        score_dict = metrics.evaluate(pred_dicts)
        coverage = float(np.mean(use_mid_mask))  # 早停覆盖率
        results.append({
            "tau": tau,
            "coverage": coverage,
            **score_dict
        })
    return results

def top1_top2_margin_from_array(score_vec: np.ndarray) -> float:
    if score_vec is None or len(score_vec) == 0:
        return 0.0
    if len(score_vec) == 1:
        return float('inf')
    # 取前两大
    top2 = np.partition(score_vec, -2)[-2:]
    top2.sort()
    return float(top2[-1] - top2[-2])

logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s')
logger = logging.getLogger(__name__)

# --- Global Dictionaries for Hooks (will be cleared before each encode_embeddings call) ---
timing_info = {}
token_info = {
    "vision_tokens": 0,
    "text_input_tokens": 0,  # Refers to the original text token count
    "text_output_tokens": 0, # Not directly applicable here as we are encoding, not generating. Will be 0.
    "total_llm_input_tokens": 0, # Refers to the total tokens LLM receives (visual + formatted text)
}

# --- Hook Functions Definition ---
def timing_pre_hook(module, input):
    module_id = id(module)
    if module_id not in timing_info:
        timing_info[module_id] = []
    timing_info[module_id].append((time.time(), 'pre', module.__class__.__name__))

def timing_post_hook(module, input, output):
    module_id = id(module)
    if module_id not in timing_info:
        # print(f"Warning: No pre-hook data for module {module.__class__.__name__} ({module_id})")
        return

    timing_info[module_id].append((time.time(), 'post', module.__class__.__name__))

    # Collect vision token count (only from Vision Transformer module's post hook)
    module_name = module.__class__.__name__
    if "vision" in module_name.lower() and "transformer" in module_name.lower():
        if isinstance(output, torch.Tensor):
            token_info["vision_tokens"] = output.shape[0] # For visual features, usually (batch_size, num_tokens, hidden_dim)
        elif hasattr(output, 'last_hidden_state'):
            token_info["vision_tokens"] = output.last_hidden_state.shape[1]


def register_model_hooks(model):
    registered_modules = []
    
    core_model = model
    # print_master(f"DEBUG: Initial model type in register_model_hooks: {type(model)}")
    
    if hasattr(model, 'encoder') and model.encoder is not None:
        # print_master(f"DEBUG: model has 'encoder' attribute. Type of model.encoder: {type(model.encoder)}")
        
        # 使用从 'src' 路径导入的 Qwen2VLForConditionalGeneration 进行检查
        if isinstance(model.encoder, _Qwen2VLForConditionalGeneration_src):
            # print_master("Detected MMEBModel structure, registering hooks on model.encoder's sub-modules.")
            core_model = model.encoder
        else:
            print_master(f"WARNING: model.encoder is not an instance of _Qwen2VLForConditionalGeneration_src. Its type is {type(model.encoder)}. Hooks will be registered on top-level model if applicable.")
    else:
        print_master("WARNING: Model structure does not have an 'encoder' attribute. Registering hooks directly on top-level modules.")

    # Vision module
    if hasattr(core_model, 'visual') and core_model.visual is not None:
        vision_module = core_model.visual
        vision_module.register_forward_pre_hook(timing_pre_hook)
        vision_module.register_forward_hook(timing_post_hook)
        registered_modules.append(vision_module)
        print_master(f"Registered hooks for vision module: {vision_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'visual' attribute found on core_model ({type(core_model)}).")


    # Merger module (if inside visual) - it's part of the vision component
    if hasattr(core_model, 'visual') and hasattr(core_model.visual, 'merger') and core_model.visual.merger is not None:
        merger_module = core_model.visual.merger
        merger_module.register_forward_pre_hook(timing_pre_hook)
        merger_module.register_forward_hook(timing_post_hook)
        registered_modules.append(merger_module)
        print_master(f"Registered hooks for merger module: {merger_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'merger' attribute found on core_model.visual ({type(getattr(core_model, 'visual', 'N/A'))}).")

    # Language model body
    if hasattr(core_model, 'model') and core_model.model is not None:
        llm_main_module = core_model.model
        llm_main_module.register_forward_pre_hook(timing_pre_hook)
        llm_main_module.register_forward_hook(timing_post_hook)
        registered_modules.append(llm_main_module)
        print_master(f"Registered hooks for LLM main module: {llm_main_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'model' attribute found on core_model ({type(core_model)}).")


    # LM Head
    if hasattr(core_model, 'lm_head') and core_model.lm_head is not None:
        lm_head_module = core_model.lm_head
        lm_head_module.register_forward_pre_hook(timing_pre_hook)
        lm_head_module.register_forward_hook(timing_post_hook)
        registered_modules.append(lm_head_module)
        print_master(f"Registered hooks for LM head module: {lm_head_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'lm_head' attribute found on core_model ({type(core_model)}).")


    if not registered_modules:
        print_master("Warning: No major modules found for hook registration. Check model architecture.")
    return registered_modules


def pad_dataset_to_divisible(dataset, world_size):
    num_samples = len(dataset)
    if num_samples % world_size == 0:
        return dataset, num_samples

    num_to_add = world_size - (num_samples % world_size)
    padded_size = num_samples + num_to_add

    padding_data = dataset.select([i % len(dataset) for i in range(num_to_add)])
    padded_dataset = concatenate_datasets([dataset, padding_data])
    return padded_dataset, padded_size

def encode_embeddings(
    model: MMEBModel,
    loader: DataLoader,
    training_args: TrainingArguments,
    model_args: ModelArguments,
    full_dataset: Dataset,
    encode_side: str,
    description: str = "Encoding"
) -> tuple[np.ndarray, list, list, list]:  # CHANGED: + list for img_token_masks
    """
    Encodes embeddings for a given dataset using the model, handling both standard and
    late-interaction models in a DDP-safe manner.
    Returns:
        - embeddings: np.ndarray
        - infos_or_ids: list
        - batch_stats_list: list
        - img_token_masks: list[None | list[bool]]  # NEW
    """
    local_rank = dist.get_rank() if dist.is_initialized() else 0
    world_size = dist.get_world_size() if dist.is_initialized() else 1

    # Check if the model is a late-interaction type
    is_late_interaction = (model_args.model_backbone == COLPALI)

    local_embeds = []
    local_gt_infos = []
    local_max_len = 0
    
    # --- New: List to store statistics for each batch ---
    batch_stats_list = []

    # --- NEW: Collect image token masks locally ---
    local_img_token_masks = []  # 每个样本一个元素：None 或 [bool, ...]

    model.eval()

    # Register hooks for the model once per encode_embeddings call
    registered_hooks = register_model_hooks(model)

    # --- NEW: helpers to取mask并序列化 ---
    def _search_key(obj, key: str):
        # 递归搜索 dict/list/tuple，找到指定 key
        if isinstance(obj, dict):
            if key in obj:
                return obj[key]
            for v in obj.values():
                r = _search_key(v, key)
                if r is not None:
                    return r
        elif isinstance(obj, (list, tuple)):
            for v in obj:
                r = _search_key(v, key)
                if r is not None:
                    return r
        return None

    def _to_serializable_mask_list(mask_list, batch_size: int):
        # 将模型返回的 mask（list/tensor/ndarray/None）转成 [None | list[bool]] * B
        if mask_list is None:
            return [None] * batch_size

        out = []
        if isinstance(mask_list, (list, tuple)):
            for m in mask_list:
                if m is None:
                    out.append(None)
                elif torch.is_tensor(m):
                    out.append(m.detach().cpu().tolist())
                elif isinstance(m, np.ndarray):
                    out.append(m.tolist())
                else:
                    # already python list/bool
                    out.append(m)
        elif torch.is_tensor(mask_list):
            # 若是 2D 张量（B, L），直接 tolist() -> list[list[bool/int]]
            out = mask_list.detach().cpu().tolist()
        elif isinstance(mask_list, np.ndarray):
            out = mask_list.tolist()
        else:
            # 未知类型，保守返回 None 占位
            out = [None] * batch_size

        # 长度对齐 batch_size
        if isinstance(out, list):
            if len(out) < batch_size:
                out = out + [None] * (batch_size - len(out))
            elif len(out) > batch_size:
                out = out[:batch_size]
        return out

    with torch.no_grad():
        for inputs, dataset_info in tqdm(loader, desc=f"{description} (rank {local_rank})", disable=local_rank > 0):
            # --- Reset statistics for each inference pass ---
            timing_info.clear()
            token_info["vision_tokens"] = 0
            token_info["text_input_tokens"] = 0
            token_info["text_output_tokens"] = 0
            token_info["total_llm_input_tokens"] = 0

            inputs = batch_to_device(inputs, training_args.device)
            current_batch_size = inputs['input_ids'].shape[0] if 'input_ids' in inputs and inputs['input_ids'] is not None else 1

            with torch.autocast(enabled=True, dtype=torch.bfloat16, device_type="cuda"):
                start_inference_time = time.time()
                if encode_side == "qry":
                    output = model(qry=inputs)
                    reps = output["qry_reps"].detach()
                    local_gt_infos.extend(dataset_info)
                else:
                    output = model(tgt=inputs)
                    reps = output["tgt_reps"].detach()
                    local_gt_infos.extend([info["cand_name"] for info in dataset_info])
                end_inference_time = time.time()

            # --- NEW: 提取并保存本 batch 的 image_token_bool_masks ---
            # 期望 MMEBModel 的 output 中直接或间接包含 'image_token_bool_masks'
            img_masks_raw = None
            if isinstance(output, dict):
                img_masks_raw = _search_key(output, "image_token_bool_masks")
            # 可选：若你在 MMEBModel 上挂了属性，也可以尝试读取
            if img_masks_raw is None and hasattr(model, "image_token_bool_masks"):
                img_masks_raw = getattr(model, "image_token_bool_masks")

            img_masks_serializable = _to_serializable_mask_list(img_masks_raw, current_batch_size)
            local_img_token_masks.extend(img_masks_serializable)

            # --- Update total LLM input tokens after the model call ---
            if 'input_ids' in inputs and inputs['input_ids'] is not None:
                token_info["total_llm_input_tokens"] = inputs['input_ids'].shape[1]
                token_info["text_input_tokens"] = token_info["total_llm_input_tokens"] - token_info["vision_tokens"]
                token_info["text_input_tokens"] = max(0, token_info["text_input_tokens"])

            # --- Collect and Store Batch Statistics ---
            batch_inference_time = end_inference_time - start_inference_time
            
            current_batch_stats = {
                "batch_size": current_batch_size,
                "total_inference_time_seconds": batch_inference_time,
                "module_inference_times": {},
                "token_counts": {
                    "visual_tokens": token_info["vision_tokens"],
                    "language_input_tokens_raw": token_info["text_input_tokens"],
                    "llm_total_input_tokens": token_info["total_llm_input_tokens"],
                    "language_output_tokens": token_info["text_output_tokens"],
                }
            }

            # Calculate and store module timings for the current batch
            for module_obj in registered_hooks:
                module_id = id(module_obj)
                module_name = module_obj.__class__.__name__
                times = timing_info.get(module_id, [])
                durations = []
                pre_times = {} 
                for t, event_type, _ in times:
                    if event_type == 'pre':
                        pre_times[module_id] = t
                    elif event_type == 'post' and module_id in pre_times:
                        duration = t - pre_times.pop(module_id)
                        durations.append(duration)
                
                if durations:
                    current_batch_stats["module_inference_times"][module_name] = {
                        "total": sum(durations),
                        "count": len(durations),
                        "avg": sum(durations) / len(durations)
                    }
                else:
                    current_batch_stats["module_inference_times"][module_name] = {
                        "total": 0.0,
                        "count": 0,
                        "avg": 0.0
                    }
            
            batch_stats_list.append(current_batch_stats)

            # --- Debug prints (optional) ---
            print_rank(f"\n--- Inference Statistics for {encode_side} batch (Rank {local_rank}) ---")
            print_rank(f"Batch Inference took: {batch_inference_time:.4f} seconds")
            print_rank("--- Module Inference Timing Statistics ---")
            for module_name, stats in current_batch_stats["module_inference_times"].items():
                print_rank(f"**{module_name}**: Total: {stats['total']:.6f}s, Count: {stats['count']}, Avg: {stats['avg']:.6f}s")
            print_rank("--- Token Count Statistics ---")
            print_rank(f"**视觉 token 数量**: {current_batch_stats['token_counts']['visual_tokens']}")
            print_rank(f"**语言输入 token 数量 (仅原始文本)**: {current_batch_stats['token_counts']['language_input_tokens_raw']}")
            print_rank(f"**LLM总输入 token 数量 (包含视觉 + 格式化文本)**: {current_batch_stats['token_counts']['llm_total_input_tokens']}")
            print_rank(f"**语言输出 token 数量**: {current_batch_stats['token_counts']['language_output_tokens']}")

            if is_late_interaction and reps.dim() == 3:
                local_max_len = max(local_max_len, reps.shape[1])

            local_embeds.append(reps)

    if not local_embeds:
        # Handle cases where a rank gets no data
        return np.array([]), [], [], []  # CHANGED: 4个返回值

    # === DDP Synchronization and Padding for Late-Interaction Models ===
    if is_late_interaction:
        if dist.is_initialized():
            # 1: global max length
            local_max_len_tensor = torch.tensor(local_max_len, device=training_args.device)
            dist.all_reduce(local_max_len_tensor, op=dist.ReduceOp.MAX)
            global_max_len = local_max_len_tensor.item()
        else:
            global_max_len = local_max_len

        # 2: pad to global max length
        padded_embeds = []
        for reps_batch in local_embeds:
            if reps_batch.dim() == 3:
                B, L, H = reps_batch.shape
                padding_size = global_max_len - L
                padded_batch = F.pad(reps_batch, (0, 0, 0, padding_size), "constant", 0)
                padded_embeds.append(padded_batch)
            else:
                padded_embeds.append(reps_batch)

        embeds_tensor = torch.cat(padded_embeds, dim=0).contiguous()
    else:
        embeds_tensor = torch.cat(local_embeds, dim=0).contiguous()

    # === Gather embeddings and keys from all ranks ===
    if dist.is_initialized() and full_dataset.num_rows >= world_size:
        print_master(f"Gathering {encode_side} embeddings across all ranks...")

        # tensor gather
        output_shape = list(embeds_tensor.shape)
        output_shape[0] = full_dataset.num_rows
        embeds_tensor = embeds_tensor.to(training_args.device)
        gathered_embeds_tensor = torch.empty(output_shape, dtype=embeds_tensor.dtype, device=training_args.device)
        dist.all_gather_into_tensor(gathered_embeds_tensor, embeds_tensor)
        final_embeddings = gathered_embeds_tensor.cpu().float().numpy()

        # object gather for infos and stats
        gathered_gt_infos = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_gt_infos, local_gt_infos)
        all_gt_infos = [key for rank_keys in gathered_gt_infos for key in rank_keys]

        gathered_batch_stats = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_batch_stats, batch_stats_list)
        all_batch_stats = [stats for rank_stats in gathered_batch_stats for stats in rank_stats]

        # --- NEW: gather masks ---
        gathered_masks = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_masks, local_img_token_masks)
        all_img_token_masks = [m for rank_list in gathered_masks for m in rank_list]
    else:
        all_gt_infos = local_gt_infos
        final_embeddings = embeds_tensor.cpu().float().numpy()
        all_batch_stats = batch_stats_list
        all_img_token_masks = local_img_token_masks  # NEW

    return final_embeddings, all_gt_infos, all_batch_stats, all_img_token_masks  # CHANGED


def main():
    # ----------------------- Distributed init -----------------------
    if "RANK" in os.environ and dist.is_available() and not dist.is_initialized():
        dist.init_process_group(backend="nccl", timeout=datetime.timedelta(minutes=60))
    local_rank = dist.get_rank() if dist.is_initialized() else 0
    world_size = dist.get_world_size() if dist.is_initialized() else 1

    print_master("Distributed init debug info:")
    print_master(f"RANK: {os.environ.get('RANK')}")
    print_master(f"LOCAL_RANK: {os.environ.get('LOCAL_RANK')}")
    print_master(f"WORLD_SIZE: {os.environ.get('WORLD_SIZE')}")
    print_master(f"MASTER_ADDR: {os.environ.get('MASTER_ADDR')}")
    print_master(f"MASTER_PORT: {os.environ.get('MASTER_PORT')}")
    if dist.is_initialized():
        print_rank(f"dist.get_rank(): {dist.get_rank()}")
        print_rank(f"dist.get_world_size(): {dist.get_world_size()}")

    # 兼容 torchrun 参数
    for arg in sys.argv:
        if arg.startswith("--local-rank="):
            rank = arg.split("=")[1]
            sys.argv.remove(arg)
            sys.argv.append('--local_rank')
            sys.argv.append(rank)

    # ----------------------- Parse args -----------------------
    parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    model_args: ModelArguments
    data_args: DataArguments
    training_args: TrainingArguments
    os.makedirs(data_args.encode_output_path, exist_ok=True)

    # 支持多层评测（优先 LM_LAYERS，兼容 MID_LM_LAYER）
    layers_to_eval = get_env_eval_layers()
    print_master(f"Eval layers (qry/tgt): {layers_to_eval}")

    # ----------------------- Model loading -----------------------
    hf_config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
    if not getattr(model_args, "model_backbone", None):
        model_backbone = get_backbone_name(hf_config=hf_config, model_type=model_args.model_type)
        setattr(model_args, 'model_backbone', model_backbone)
        setattr(training_args, 'model_backbone', model_backbone)
    print_master(f'Model Backbone: {model_args.model_backbone}')

    # 仅 rank0 下载，其他rank等待缓存
    if local_rank == 0:
        processor = load_processor(model_args, data_args)
        model = MMEBModel.load(model_args, is_trainable=False, processor=processor)
        print_master(f"[rank=0] Loading the model from Huggingface: {model_args.model_name}...")
    if torch.distributed.is_initialized():
        torch.distributed.barrier()
    if local_rank != 0:
        print_rank(f"Loading the model from cache...")
        processor = load_processor(model_args, data_args)
        time.sleep(random.randint(2 * local_rank, 3 * local_rank))
        model = MMEBModel.load(model_args, is_trainable=False, processor=processor)

    model.eval()
    model = model.to(training_args.device, dtype=torch.bfloat16)
    # 确保“最后一层”时不裁层（避免类里默认20层的坑）
    model.set_inference_layers(qry_layers=None, tgt_layers=None)

    with open(data_args.dataset_config, 'r') as yaml_file:
        dataset_configs = yaml.safe_load(yaml_file)

    # ----------------------- Main evaluation loop -----------------------
    for dataset_idx, (dataset_name, task_config) in enumerate(dataset_configs.items()):
        if dist.is_initialized():
            dist.barrier()
        print_master(f"\n--- Evaluating {dataset_name} ---")

        # 根据 data_basedir 修正路径
        if data_args.data_basedir is not None:
            for key in ["image_root", "video_root", "frame_root", "clip_root", "data_path"]:
                if data_args.data_basedir and task_config.get(key):
                    task_config[key] = os.path.join(data_args.data_basedir, task_config[key])

        # 构建数据集
        full_eval_qry_dataset, corpus = AutoEvalPairDataset.instantiate(model_args=model_args, data_args=data_args, **task_config)
        full_eval_cand_dataset = generate_cand_dataset(full_eval_qry_dataset, corpus)
        eval_qry_dataset, eval_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset

        if dist.is_initialized():
            world_size = dist.get_world_size()
            padded_qry_dataset, _ = pad_dataset_to_divisible(full_eval_qry_dataset, world_size)
            padded_cand_dataset, _ = pad_dataset_to_divisible(full_eval_cand_dataset, world_size)
            eval_qry_dataset = split_dataset_by_node(padded_qry_dataset, rank=local_rank, world_size=world_size)
            eval_cand_dataset = split_dataset_by_node(padded_cand_dataset, rank=local_rank, world_size=world_size)
        else:
            padded_qry_dataset, padded_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset

        # 路径索引
        saved_paths = {}  # {(side, tag): path}

        # --------- 针对每个层设置（中间层/最后一层）分别编码与保存 ---------
        for keep_layers in layers_to_eval:
            tag = make_layer_tag(keep_layers)
            print_master(f"[{dataset_name}] Start encoding for tag={tag} (keep_layers={keep_layers})")
            # 设置模型层数
            model.set_inference_layers(qry_layers=keep_layers, tgt_layers=keep_layers)

            # 路径
            query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_{tag}")
            cand_embed_path  = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt_{tag}")
            dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
            query_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_inference_stats_{tag}.json")
            cand_inference_stats_path  = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_inference_stats_{tag}.json")
            qry_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_img_token_masks_{tag}.jsonl")
            cand_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_img_token_masks_{tag}.jsonl")

            saved_paths[("qry", tag)] = query_embed_path
            saved_paths[("tgt", tag)] = cand_embed_path

            do_query = not os.path.exists(query_embed_path) or not os.path.exists(dataset_info_path)
            do_cand  = not os.path.exists(cand_embed_path)

            # 动态累计统计
            def init_total_stats():
                return {
                    "total_inference_time_seconds": 0.0,
                    "module_inference_times": {},  # 动态模块名 -> {"total": float, "count": int}
                    "token_counts": {
                        "visual_tokens": 0,
                        "language_input_tokens_raw": 0,
                        "llm_total_input_tokens": 0,
                        "language_output_tokens": 0,
                    },
                    "data_point_count": 0
                }

            def accumulate_stats(total_stats, batch_stats):
                batch_size = batch_stats["batch_size"]
                total_stats["total_inference_time_seconds"] += batch_stats["total_inference_time_seconds"]
                # 模块时间
                for mname, mstats in batch_stats["module_inference_times"].items():
                    if mname not in total_stats["module_inference_times"]:
                        total_stats["module_inference_times"][mname] = {"total": 0.0, "count": 0}
                    total_stats["module_inference_times"][mname]["total"] += mstats.get("total", 0.0)
                    total_stats["module_inference_times"][mname]["count"] += mstats.get("count", 0)
                # token 统计（按样本乘 batch_size 再累积）
                total_stats["token_counts"]["visual_tokens"] += batch_stats["token_counts"]["visual_tokens"] * batch_size
                total_stats["token_counts"]["language_input_tokens_raw"] += batch_stats["token_counts"]["language_input_tokens_raw"] * batch_size
                total_stats["token_counts"]["llm_total_input_tokens"] += batch_stats["token_counts"]["llm_total_input_tokens"] * batch_size
                total_stats["token_counts"]["language_output_tokens"] += batch_stats["token_counts"]["language_output_tokens"] * batch_size
                total_stats["data_point_count"] += batch_size

            def finalize_and_save_stats(total_stats, out_path, task_name, encode_side):
                if local_rank != 0:
                    return
                if total_stats["data_point_count"] <= 0:
                    print_master(f"No data processed for {task_name} [{encode_side}], skip saving stats.")
                    return
                final_stats = {
                    "task_name": task_name,
                    "encode_side": encode_side,
                    "data_point_count": total_stats["data_point_count"],
                    "inference_times": {
                        "total_inference_time_seconds": total_stats["total_inference_time_seconds"],
                        "avg_inference_time_per_item_seconds": total_stats["total_inference_time_seconds"] / max(1, total_stats["data_point_count"]),
                        "module_average_times_per_call": {},
                        "module_total_times_seconds": {},
                        "module_calls_count": {},
                    },
                    "token_counts": {
                        "total_visual_tokens": total_stats["token_counts"]["visual_tokens"],
                        "avg_visual_tokens_per_item": total_stats["token_counts"]["visual_tokens"] / max(1, total_stats["data_point_count"]),
                        "total_language_input_tokens_raw": total_stats["token_counts"]["language_input_tokens_raw"],
                        "avg_language_input_tokens_raw_per_item": total_stats["token_counts"]["language_input_tokens_raw"] / max(1, total_stats["data_point_count"]),
                        "total_llm_total_input_tokens": total_stats["token_counts"]["llm_total_input_tokens"],
                        "avg_llm_total_input_tokens_per_item": total_stats["token_counts"]["llm_total_input_tokens"] / max(1, total_stats["data_point_count"]),
                        "total_language_output_tokens": total_stats["token_counts"]["language_output_tokens"],
                        "avg_language_output_tokens_per_item": total_stats["token_counts"]["language_output_tokens"] / max(1, total_stats["data_point_count"]),
                    }
                }
                for mname, mstats in total_stats["module_inference_times"].items():
                    total = mstats.get("total", 0.0)
                    count = mstats.get("count", 0)
                    final_stats["inference_times"]["module_total_times_seconds"][mname] = total
                    final_stats["inference_times"]["module_calls_count"][mname] = count
                    final_stats["inference_times"]["module_average_times_per_call"][mname] = (total / count) if count > 0 else 0.0

                with open(out_path, 'w', encoding='utf-8') as f:
                    json.dump(final_stats, f, ensure_ascii=False, indent=4)
                print_master(f"[{task_name}] {encode_side} inference statistics saved to: {out_path}")

            # ------- Encode queries -------
            if do_query:
                print_master(f"[{tag}] Encoding queries...")
                eval_qry_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "qry")
                eval_qry_loader = DataLoader(
                    eval_qry_dataset,
                    batch_size=training_args.per_device_eval_batch_size,
                    collate_fn=eval_qry_collator,
                    num_workers=training_args.dataloader_num_workers
                )
                query_embeds, gt_infos, qry_batch_stats, qry_img_masks = encode_embeddings(
                    model, eval_qry_loader, training_args, model_args, padded_qry_dataset,
                    encode_side="qry", description=f"Queries[{tag}] for {dataset_name}"
                )
                # 截断到真实长度
                query_embeds = query_embeds[:len(full_eval_qry_dataset)]
                gt_infos = gt_infos[:len(full_eval_qry_dataset)]
                qry_img_masks = qry_img_masks[:len(full_eval_qry_dataset)]

                # 累计统计并保存
                qry_total_stats = init_total_stats()
                for bs in qry_batch_stats:
                    accumulate_stats(qry_total_stats, bs)

                if local_rank == 0:
                    with open(query_embed_path, 'wb') as f:
                        pickle.dump(query_embeds, f)
                    # dataset_info 只需写一次；若第一次就写
                    if not os.path.exists(dataset_info_path):
                        with open(dataset_info_path, 'w') as f:
                            for info in gt_infos:
                                f.write(json.dumps(info) + '\n')
                    # 保存 masks
                    with open(qry_img_masks_path, 'w', encoding='utf-8') as f:
                        for i, m in enumerate(qry_img_masks):
                            f.write(json.dumps({"index": i, "mask": m}, ensure_ascii=False) + "\n")
                    print_master(f"Saved query embeddings to {query_embed_path}")
                    print_master(f"Saved query image token masks to {qry_img_masks_path}")

                finalize_and_save_stats(qry_total_stats, query_inference_stats_path, dataset_name, f"query[{tag}]")

            if dist.is_initialized():
                dist.barrier()

            # ------- Encode candidates -------
            if do_cand:
                print_master(f"[{tag}] Encoding candidates...")
                eval_cand_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "cand")
                eval_cand_loader = DataLoader(
                    eval_cand_dataset,
                    batch_size=training_args.per_device_eval_batch_size,
                    collate_fn=eval_cand_collator,
                    num_workers=training_args.dataloader_num_workers
                )
                cand_embeds, all_cand_ids, cand_batch_stats, cand_img_masks = encode_embeddings(
                    model, eval_cand_loader, training_args, model_args, padded_cand_dataset,
                    encode_side="cand", description=f"Candidates[{tag}] for {dataset_name}"
                )
                cand_embeds = cand_embeds[:len(full_eval_cand_dataset)]
                all_cand_ids = all_cand_ids[:len(full_eval_cand_dataset)]
                cand_img_masks = cand_img_masks[:len(full_eval_cand_dataset)]

                cand_total_stats = init_total_stats()
                for bs in cand_batch_stats:
                    accumulate_stats(cand_total_stats, bs)

                if local_rank == 0:
                    cand_embed_dict = {cid: emb for cid, emb in zip(all_cand_ids, cand_embeds)}
                    with open(cand_embed_path, 'wb') as f:
                        pickle.dump(cand_embed_dict, f)
                    with open(cand_img_masks_path, 'w', encoding='utf-8') as f:
                        for cid, m in zip(all_cand_ids, cand_img_masks):
                            f.write(json.dumps({"cand_id": str(cid), "mask": m}, ensure_ascii=False) + "\n")
                    print_master(f"Saved candidate embeddings to {cand_embed_path}")
                    print_master(f"Saved candidate image token masks to {cand_img_masks_path}")

                finalize_and_save_stats(cand_total_stats, cand_inference_stats_path, dataset_name, f"candidate[{tag}]")

            if dist.is_initialized():
                dist.barrier()

        # --------- Scoring per layer + combined + early-exit curve ---------
        if local_rank == 0:
            dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
            gt_infos = [json.loads(l) for l in open(dataset_info_path)]
            rank_against_all_candidates = task_config.get("eval_type", "global") == "global"
            metrics_to_report = task_config.get("metrics", ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"])

            layer_tags = [make_layer_tag(l) for l in layers_to_eval]
            sims_by_layer = {}  # tag -> list[ dict(cand_id->score) ]

            for tag in layer_tags:
                query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_{tag}")
                cand_embed_path  = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt_{tag}")

                with open(query_embed_path, 'rb') as f:
                    qry_embeds = pickle.load(f)
                with open(cand_embed_path, 'rb') as f:
                    cand_embed_dict = pickle.load(f)

                pred_dicts = []
                score_detail_dicts = []
                sims_for_exit = []

                if rank_against_all_candidates:
                    cand_keys = list(cand_embed_dict.keys())
                    cand_embeds = np.stack([cand_embed_dict[key] for key in cand_keys])

                    if isinstance(qry_embeds, np.ndarray) and qry_embeds.ndim == 3:
                        # Late-interaction
                        qry_embed_t = torch.from_numpy(qry_embeds)
                        cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
                        sim_matrix = processor.score(qry_embed_t, cand_embeds_t, batch_size=64).cpu().numpy()
                    else:
                        sim_matrix = np.dot(qry_embeds, cand_embeds.T)

                    ranked_all = np.argsort(-sim_matrix, axis=1)
                    for qid, gt_info in tqdm(enumerate(gt_infos), total=len(gt_infos), desc=f"[{tag}] scoring(all) {dataset_name}"):
                        ranked_indices = ranked_all[qid]
                        rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
                        rel_scores = gt_info.get("rel_scores")
                        pred_dicts.append({
                            "prediction": [cand_keys[i] for i in ranked_indices],
                            "label": rel_docids,
                            "rel_scores": rel_scores,
                        })
                        score_detail_dicts.append(build_score_details(qid, cand_keys, sim_matrix[qid], ranked_indices))
                        sims_for_exit.append({cand_keys[i]: float(sim_matrix[qid][i]) for i in range(len(cand_keys))})
                else:
                    # 非全局：每个query用 gt_info["cand_names"] 的子集进行评分
                    for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), total=len(gt_infos), desc=f"[{tag}] scoring(local) {dataset_name}"):
                        cand_ids_local = gt_info["cand_names"]
                        cand_embeds = np.stack([cand_embed_dict[key] for key in cand_ids_local])

                        if isinstance(qry_embeds, np.ndarray) and qry_embeds.ndim == 3:
                            qry_embed_t = torch.from_numpy(np.array(qry_embed)).unsqueeze(0)  # [1, Lq, H]
                            cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
                            sim_vec = processor.score(qry_embed_t, cand_embeds_t, batch_size=1024).cpu().numpy()[0]
                        else:
                            sim_vec = np.dot(qry_embed, cand_embeds.T)

                        ranked_indices = np.argsort(-sim_vec)
                        rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
                        rel_scores = gt_info.get("rel_scores")
                        pred_dicts.append({
                            "prediction": [cand_ids_local[i] for i in ranked_indices],
                            "label": rel_docids,
                            "rel_scores": rel_scores,
                        })
                        score_detail_dicts.append(build_score_details(qid, cand_ids_local, sim_vec, ranked_indices))
                        sims_for_exit.append({cid: float(s) for cid, s in zip(cand_ids_local, sim_vec.tolist())})

                # 保存每层指标与详情
                layer_score_path  = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_{tag}.json")
                layer_pred_path   = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred_{tag}.jsonl")
                layer_detail_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details_{tag}.jsonl")
                metrics = RankingMetrics(metrics_to_report)
                score_dict = metrics.evaluate(pred_dicts)
                score_dict["num_pred"] = len(pred_dicts)
                score_dict["num_data"] = len(gt_infos)
                with open(layer_score_path, "w") as f:
                    json.dump(score_dict, f, indent=4)
                with open(layer_pred_path, "w") as f:
                    for pred in pred_dicts:
                        f.write(json.dumps(pred) + '\n')
                with open(layer_detail_path, "w") as f:
                    for detail in score_detail_dicts:
                        f.write(json.dumps(detail) + "\n")
                print_master(f"[{dataset_name}] {tag} score: " + json.dumps({k: (f"{v:.4f}" if isinstance(v, (int, float)) else v) for k, v in score_dict.items()}))

                sims_by_layer[tag] = sims_for_exit

            # 合并对比文件 + 早停曲线（仅在存在中间层时）
            if len(layer_tags) == 2 and "layerlast" in layer_tags:
                mid_tag = [t for t in layer_tags if t != "layerlast"][0]
                last_tag = "layerlast"

                # 合并详情：每个query包含 mid/last 的cand_scores、top1、margin
                combined_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details_both_layers.jsonl")
                with open(combined_path, "w", encoding='utf-8') as f:
                    for qid in range(len(gt_infos)):
                        sims_mid = sims_by_layer[mid_tag][qid]
                        sims_last = sims_by_layer[last_tag][qid]

                        def top1_cid(sims: dict):
                            return max(sims.items(), key=lambda x: x[1])[0] if sims else None
                        def margin_of(sims: dict):
                            vals = np.array(list(sims.values()), dtype=np.float32)
                            return top1_top2_margin_from_array(vals)

                        row = {
                            "qid": int(qid),
                            "label": gt_infos[qid]["label_name"] if isinstance(gt_infos[qid]["label_name"], list) else [gt_infos[qid]["label_name"]],
                            "mid": {
                                "top1": top1_cid(sims_mid),
                                "margin": margin_of(sims_mid),
                                "cand_scores": sims_mid
                            },
                            "last": {
                                "top1": top1_cid(sims_last),
                                "margin": margin_of(sims_last),
                                "cand_scores": sims_last
                            }
                        }
                        f.write(json.dumps(row, ensure_ascii=False) + "\n")
                print_master(f"[{dataset_name}] combined details saved to {combined_path}")

                # 早停曲线（margin 阈值）
                taus = [round(x, 3) for x in np.linspace(0.0, 0.6, 31).tolist()]
                labels = [
                    gi["label_name"] if isinstance(gi["label_name"], list) else [gi["label_name"]]
                    for gi in gt_infos
                ]
                exit_curve = simulate_early_exit_by_margin(
                    sims_by_layer[mid_tag], sims_by_layer[last_tag], labels, metrics_to_report, taus, rank_against_all_candidates
                )
                curve_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_early_exit_curve_margin.json")
                with open(curve_path, "w") as f:
                    json.dump(exit_curve, f, indent=4)
                print_master(f"[{dataset_name}] early-exit curve saved to {curve_path}")

            # # 合并对比文件 + 早停曲线（与 last 对比），如果存在 last
            # last_tag = "layerlast" if "layerlast" in layer_tags else None
            # if last_tag is not None:
            #     # 准备 labels 一次即可
            #     labels = [
            #         gi["label_name"] if isinstance(gi["label_name"], list) else [gi["label_name"]]
            #         for gi in gt_infos
            #     ]
            #     taus = [round(x, 3) for x in np.linspace(0.0, 0.6, 31).tolist()]

            #     # 对每个中间层分别与 last 做对比
            #     for mid_tag in [t for t in layer_tags if t != last_tag]:
            #         # 合并详情：每个query包含 mid/last 的cand_scores、top1、margin
            #         combined_path = os.path.join(
            #             data_args.encode_output_path,
            #             f"{dataset_name}_score_details_{mid_tag}_vs_last.jsonl"
            #         )
            #         with open(combined_path, "w", encoding="utf-8") as f:
            #             for qid in range(len(gt_infos)):
            #                 sims_mid = sims_by_layer[mid_tag][qid]
            #                 sims_last = sims_by_layer[last_tag][qid]

            #                 def top1_cid(sims: dict):
            #                     return max(sims.items(), key=lambda x: x[1])[0] if sims else None
            #                 def margin_of(sims: dict):
            #                     vals = np.array(list(sims.values()), dtype=np.float32)
            #                     return top1_top2_margin_from_array(vals)

            #                 row = {
            #                     "qid": int(qid),
            #                     "label": labels[qid],
            #                     "mid": {
            #                         "top1": top1_cid(sims_mid),
            #                         "margin": margin_of(sims_mid),
            #                         "cand_scores": sims_mid
            #                     },
            #                     "last": {
            #                         "top1": top1_cid(sims_last),
            #                         "margin": margin_of(sims_last),
            #                         "cand_scores": sims_last
            #                     }
            #                 }
            #                 f.write(json.dumps(row, ensure_ascii=False) + "\n")
            #         print_master(f"[{dataset_name}] combined details saved to {combined_path} (mid={mid_tag} vs last)")

            #         # 早停曲线（margin 阈值）
            #         exit_curve = simulate_early_exit_by_margin(
            #             sims_by_layer[mid_tag], sims_by_layer[last_tag], labels, metrics_to_report, taus, rank_against_all_candidates
            #         )
            #         curve_path = os.path.join(
            #             data_args.encode_output_path,
            #             f"{dataset_name}_early_exit_curve_margin_{mid_tag}_vs_last.json"
            #         )
            #         with open(curve_path, "w") as f:
            #             json.dump(exit_curve, f, indent=4)
            #         print_master(f"[{dataset_name}] early-exit curve saved to {curve_path} (mid={mid_tag} vs last)")

        if dist.is_initialized():
            dist.barrier()

if __name__ == '__main__':
    main()