#!/usr/bin/env python3
import os
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
import glob
import socket
import argparse
import inspect
from typing import Dict, List, Iterable, Iterator, Tuple, Optional

import torch
from torch.utils.data import IterableDataset, Dataset

from datasets import load_dataset
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer,
    set_seed
)
from transformers.trainer_callback import TrainerCallback


# ----------------- 进程工具 -----------------
def is_main_process():
    return int(os.environ.get("RANK", "0")) == 0

def print_once(*args, **kwargs):
    if is_main_process():
        print(*args, **kwargs, flush=True)


# ----------------- 日志回调 -----------------
class CsvLossLogger(TrainerCallback):
    def __init__(self, csv_path: str):
        self.csv_path = csv_path
        if is_main_process():
            os.makedirs(os.path.dirname(csv_path), exist_ok=True)
            with open(self.csv_path, "w", encoding="utf-8") as f:
                f.write("step,loss,lr,total_flos\n")

    def on_log(self, args, state, control, logs=None, **kwargs):
        if not is_main_process() or logs is None:
            return
        with open(self.csv_path, "a", encoding="utf-8") as f:
            f.write(f"{state.global_step},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n")


# ----------------- 仅监督 assistant 的数据集 -----------------
def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
    """
    在 apply_chat_template 渲染后的文本中，返回所有 assistant 内容的字符区间 [start, end)。
    """
    spans: List[Tuple[int, int]] = []
    open_tag = "<|im_start|>assistant\n"
    close_tag = "<|im_end|>\n"
    pos = 0
    while True:
        a = rendered.find(open_tag, pos)
        if a == -1:
            break
        start = a + len(open_tag)
        b = rendered.find(close_tag, start)
        if b == -1:
            break
        spans.append((start, b))
        pos = b + len(close_tag)
    return spans

class QwenChatSFTDataset(IterableDataset):
    """
    期望 jsonl 每行形如：
      {"messages":[{"role":"system","content":"..."},{"role":"user","content":"..."},{"role":"assistant","content":"..."}]}
    可选包含工具：
      {"messages":[...], "tools":[{...}]}

    工作流：
      - 使用 tokenizer.apply_chat_template 渲染
      - 仅对 assistant 片段计损失（其他 token 的 label = -100）
      - 超长序列保留尾部（通常包含回答）
    """
    def __init__(self,
                 ex_iter: Iterable[dict],
                 tokenizer: AutoTokenizer,
                 seq_len: int = 4096):
        self.ex_iter = ex_iter
        self.tok = tokenizer
        self.seq_len = seq_len

    def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
        for ex in self.ex_iter:
            msgs = ex.get("messages", None)
            if not msgs or not isinstance(msgs, list):
                # 严格要求 messages 格式；发现旧的 "text" 数据直接跳过
                continue

            # 可选：过滤掉带有非空 <think>…</think> 的样本（避免训练真实 COT）
            bad = False
            for m in msgs:
                if m.get("role") == "assistant" and isinstance(m.get("content"), str):
                    c = m["content"]
                    if "<think>" in c and "</think>" in c:
                        inner = c.split("<think>")[-1].split("</think>")[0].strip()
                        if inner:
                            bad = True; break
            if bad:
                continue

            tools = ex.get("tools", None)

            # 1) 按模型自带模板渲染（不要手写）
            rendered: str = self.tok.apply_chat_template(
                msgs,
                tools=tools,
                add_generation_prompt=False,   # 训练包含 assistant 答案
                tokenize=False
            )
            if not isinstance(rendered, str) or not rendered.strip():
                continue

            # 2) 找出 assistant 片段的字符区间
            spans = _assistant_char_spans(rendered)
            if not spans:
                continue

            # 3) 分词 + 字符/Token 对齐
            enc = self.tok(
                rendered,
                add_special_tokens=False,
                return_offsets_mapping=True
            )
            input_ids: List[int] = enc["input_ids"]
            offsets: List[Tuple[int, int]] = enc["offset_mapping"]

            # 4) 仅 assistant 计损失
            labels = [-100] * len(input_ids)

            def in_any_span(lo: int, hi: int) -> bool:
                for s, e in spans:
                    if not (hi <= s or lo >= e):
                        return True
                return False

            for i, (lo, hi) in enumerate(offsets):
                if in_any_span(lo, hi):
                    labels[i] = input_ids[i]

            # 5) 超长裁剪（保留尾部）
            if len(input_ids) > self.seq_len:
                input_ids = input_ids[-self.seq_len:]
                labels    = labels[-self.seq_len:]

            yield {
                "input_ids": torch.tensor(input_ids, dtype=torch.long),
                "attention_mask": torch.ones(len(input_ids), dtype=torch.long),
                "labels": torch.tensor(labels, dtype=torch.long)
            }


# ----------------- 专用 Collator：pad inputs, pad labels=-100 -----------------
class SFTDataCollator:
    def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
        self.tok = tokenizer
        self.pad_to_length = pad_to_length
        assert self.tok.pad_token_id is not None

    def __call__(self, features):
        def _to_list(x): return x.tolist() if isinstance(x, torch.Tensor) else list(x)
        input_ids   = [_to_list(f["input_ids"])      for f in features]
        attn_masks  = [_to_list(f["attention_mask"]) for f in features]
        labels_list = [_to_list(f["labels"])         for f in features]

        max_len_in_batch = max(len(x) for x in input_ids)
        target_len = self.pad_to_length if self.pad_to_length is not None else max_len_in_batch

        pad_id = self.tok.pad_token_id
        batch_inp, batch_attn, batch_lab = [], [], []
        for inp, msk, lab in zip(input_ids, attn_masks, labels_list):
            pad_len = target_len - len(inp)
            if pad_len < 0:
                inp, msk, lab = inp[:target_len], msk[:target_len], lab[:target_len]
                pad_len = 0
            batch_inp.append(torch.tensor(inp + [pad_id]*pad_len, dtype=torch.long))
            batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
            batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
        return {
            "input_ids": torch.stack(batch_inp, dim=0),
            "attention_mask": torch.stack(batch_attn, dim=0),
            "labels": torch.stack(batch_lab, dim=0),
        }

# ----------------- 参数 -----------------
def parse_args():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model_name_or_path", type=str, required=True,
                    help="本地权重目录或 HF 名称（如 /home/test/Qwen3-8B）")
    ap.add_argument("--data_glob", type=str, required=True,
                    help="本地 jsonl 通配符（每台机器都需有同路径数据；每行应含 messages/可选 tools）")
    ap.add_argument("--output_dir", type=str, required=True,
                    help="本地输出目录（各节点各自本地写）")
    ap.add_argument("--seq_len", type=int, default=4096)
    ap.add_argument("--learning_rate", type=float, default=2e-5)
    ap.add_argument("--weight_decay", type=float, default=0.1)
    ap.add_argument("--warmup_ratio", type=float, default=0.02)
    ap.add_argument("--num_train_epochs", type=float, default=1.0)
    ap.add_argument("--max_steps", type=int, default=-1)
    ap.add_argument("--log_interval", type=int, default=10)
    ap.add_argument("--save_steps", type=int, default=500)
    ap.add_argument("--eval_ratio", type=float, default=0.0)  # 兜底抽样评估
    ap.add_argument("--seed", type=int, default=1337)
    ap.add_argument("--deepspeed", type=str, default="ds_config_zero3.json")
    ap.add_argument("--gradient_checkpointing", action="store_true")
    ap.add_argument("--bf16", action="store_true",
                    help="3090/A100/H100 等可开 bf16；同时在 DS 配置里也要开")
    ap.add_argument("--per_device_train_batch_size", type=int, default=1)
    ap.add_argument("--gradient_accumulation_steps", type=int, default=64)
    ap.add_argument("--report_to", type=str, default="tensorboard",
                    choices=["none","tensorboard","wandb"])
    ap.add_argument("--wandb_project", type=str, default="ds-qwen3")
    ap.add_argument("--eval_data_glob", type=str, default=None,
                    help="(可选) 测试/验证集 jsonl 通配符；如提供则优先使用")
    ap.add_argument("--local_rank", type=int, default=-1,
                    help="for deepspeed/torchrun launcher; ignored by user code")
    return ap.parse_args()


# ----------------- 主函数 -----------------
def main():
    args = parse_args()
    set_seed(args.seed)

    # 1) 先补 tokenizer 的 pad
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True, trust_remote_code=True)
    
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token  # 供 padding 使用
    
    # 可选：让警告更少
    tokenizer.model_max_length = args.seq_len

    # 2) 再加载模型
    model = AutoModelForCausalLM.from_pretrained(
        args.model_name_or_path,
        torch_dtype=(torch.bfloat16 if args.bf16 else torch.float16),
        low_cpu_mem_usage=True,
        trust_remote_code=True
    )

    # 3) 最后对齐模型的 pad_token_id
    model.config.pad_token_id = tokenizer.pad_token_id
    model.config.use_cache = False  # 训练时禁用 cache
    
    if args.gradient_checkpointing:
        model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})

        try:
            torch.backends.cuda.enable_flash_sdp(False)
            torch.backends.cuda.enable_mem_efficient_sdp(False)
            torch.backends.cuda.enable_math_sdp(True)  # 走 math 实现
        except Exception:
            pass

    # ===== 数据鲁棒性检查（多机各自执行）=====
    host = socket.gethostname()
    rank = int(os.environ.get("RANK", "0"))

    files = sorted(glob.glob(args.data_glob))
    if len(files) == 0:
        raise FileNotFoundError(
            f"[host={host} rank={rank}] No files matched DATA_GLOB={args.data_glob}\n"
            "每台机器都必须在相同本地路径下放置数据；"
            "可通过 DATA_GLOB=<your_glob> ./run_ds.sh 覆写。"
        )
    if is_main_process():
        print(f"[data] matched {len(files)} files on host={host}, example[0]={files[0]}", flush=True)

    # streaming 逐行读取（messages/tools 结构）
    ds_stream = load_dataset(
        "json",
        data_files={"train": files},
        split="train",
        streaming=True
    )

    def ex_iter():
        for ex in ds_stream:
            yield ex

    train_stream_probe = QwenChatSFTDataset(ex_iter(), tokenizer, seq_len=args.seq_len)
    # 探针：确保能产出至少一个样本
    _probe_it = iter(train_stream_probe)
    try:
        _ = next(_probe_it)
    except StopIteration:
        raise RuntimeError(
            f"[host={host} rank={rank}] 数据文件匹配到了，但没有产生任何可训练样本。\n"
            "请确认每行 JSON 至少包含 'messages'（列表，含 user/assistant）字段；"
            "若含 <think>…</think> 请确保不包含真实思维文本，或移除。\n"
            "另外检查 seq_len 是否过小导致全部被裁。"
        )

    # 探针已消耗流；为正式训练重建一次
    ds_stream2 = load_dataset(
        "json",
        data_files={"train": files},
        split="train",
        streaming=True
    )

    # 多机/多卡分片（让每个全局 rank 读不同子流）
    world_size = int(os.environ.get("WORLD_SIZE", "1"))
    ds_stream2 = ds_stream2.shard(num_shards=world_size, index=rank, contiguous=True)
    def ex_iter2():
        for ex in ds_stream2:
            yield ex
    train_stream = QwenChatSFTDataset(ex_iter2(), tokenizer, seq_len=args.seq_len)

    # ---- Eval 构造：优先使用 --eval_data_glob；否则才用 eval_ratio 抽样 ----
    eval_dataset: Optional[Dataset] = None

    class ListDataset(Dataset):
        def __init__(self, items): self.items = items
        def __len__(self): return len(self.items)
        def __getitem__(self, idx): return self.items[idx]

    if args.eval_data_glob:
        eval_files = sorted(glob.glob(args.eval_data_glob))
        if len(eval_files) == 0:
            raise FileNotFoundError(f"[host={host} rank={rank}] No eval files matched EVAL_DATA_GLOB={args.eval_data_glob}")
        if is_main_process():
            print(f"[eval] matched {len(eval_files)} files, example[0]={eval_files[0]}", flush=True)

        ds_eval_stream = load_dataset("json", data_files={"eval": eval_files}, split="eval", streaming=True)
        def ex_iter_eval():
            for ex in ds_eval_stream:
                yield ex

        eval_iterable = QwenChatSFTDataset(ex_iter_eval(), tokenizer, seq_len=args.seq_len)
        eval_items: List[Dict[str, torch.Tensor]] = []
        for sample in eval_iterable:
            eval_items.append(sample)

        if len(eval_items) == 0:
            raise RuntimeError("[eval] eval_data_glob 读到了 0 条有效样本，请检查 messages 结构。")

        eval_dataset = ListDataset(eval_items)

    elif args.eval_ratio and args.eval_ratio > 0:
        # 简易头部抽样（流式下仅作粗评）
        desired_eval_batches = 200
        tmp_stream = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
        def ex_iter_eval2():
            for ex in tmp_stream:
                yield ex
        eval_stream = QwenChatSFTDataset(ex_iter_eval2(), tokenizer, seq_len=args.seq_len)
        eval_samples = []
        it = iter(eval_stream)
        for _ in range(desired_eval_batches):
            try:
                eval_samples.append(next(it))
            except StopIteration:
                break
        if len(eval_samples) > 0:
            eval_dataset = ListDataset(eval_samples)

    data_collator = SFTDataCollator(tokenizer, pad_to_length=args.seq_len)

    os.makedirs(args.output_dir, exist_ok=True)
    logging_dir = os.path.join(args.output_dir, "logs")
    os.makedirs(logging_dir, exist_ok=True)

    # ---- 兼容 4.51（eval_strategy）与旧版（evaluation_strategy） ----
    ta_kwargs = {}
    sig = inspect.signature(TrainingArguments.__init__).parameters
    if eval_dataset is not None:
        if "eval_strategy" in sig:
            ta_kwargs["eval_strategy"] = "steps"
        elif "evaluation_strategy" in sig:
            ta_kwargs["evaluation_strategy"] = "steps"
    else:
        if "eval_strategy" in sig:
            ta_kwargs["eval_strategy"] = "no"
        elif "evaluation_strategy" in sig:
            ta_kwargs["evaluation_strategy"] = "no"

    training_args = TrainingArguments(
        output_dir=args.output_dir,
        logging_dir=logging_dir,
        do_train=True,
        do_eval=(eval_dataset is not None),
        eval_steps=max(50, args.save_steps // 5) if eval_dataset is not None else None,
        per_device_train_batch_size=args.per_device_train_batch_size,
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        learning_rate=args.learning_rate,
        weight_decay=args.weight_decay,
        warmup_ratio=args.warmup_ratio,
        num_train_epochs=args.num_train_epochs if args.max_steps < 0 else 1.0,
        max_steps=args.max_steps if args.max_steps > 0 else -1,
        lr_scheduler_type="cosine",
        logging_steps=args.log_interval,
        save_steps=args.save_steps,
        save_total_limit=2,
        deepspeed=args.deepspeed,
        dataloader_drop_last=True,
        dataloader_num_workers=0,
        dataloader_prefetch_factor=None,
        dataloader_pin_memory=False,
        report_to=([] if args.report_to == "none" else [args.report_to]),
        bf16=args.bf16,
        fp16=(not args.bf16),
        gradient_checkpointing=args.gradient_checkpointing,
        remove_unused_columns=False,
        torch_compile=False,
        save_on_each_node=False,
        logging_first_step=True,
        **ta_kwargs,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_stream,
        eval_dataset=eval_dataset,
        processing_class=tokenizer,
        data_collator=data_collator
    )
    trainer.add_callback(CsvLossLogger(csv_path=os.path.join(args.output_dir, "loss.csv")))

    # 无共享盘：各节点本地 output_dir 下是否已有 checkpoint-*
    ckpt_exists = (os.path.isdir(args.output_dir)
                   and any(n.startswith("checkpoint-") for n in os.listdir(args.output_dir)))
    resume_flag = True if ckpt_exists else None

    print_once(f"[host={socket.gethostname()}] Resume = {resume_flag is True}")
    print_once("***** Starting training *****")
    train_result = trainer.train(resume_from_checkpoint=resume_flag)
    trainer.save_model()  # DeepSpeed stage3_gather_16bit_weights_on_model_save=true 时，在 rank0 聚合整模型

    metrics = train_result.metrics
    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

    if eval_dataset is not None:
        print_once("***** Running eval *****")
        eval_metrics = trainer.evaluate()
        trainer.log_metrics("eval", eval_metrics)
        trainer.save_metrics("eval", eval_metrics)

    print_once("Done.")


if __name__ == "__main__":
    main()