import os
import sys

__package__ = "trainer"
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

import argparse
import time
import warnings
import torch
import torch.distributed as dist
from contextlib import nullcontext
from torch import optim, nn
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler
from model.model_minimind import MiniMindConfig
from dataset.lm_dataset import SFTDataset
from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler

warnings.filterwarnings('ignore')


def train_epoch(epoch, loader, iters, tokenizer, lm_config, start_step=0, wandb=None):
    # 思考标签占位符
    start_of_think_ids = tokenizer('<think>').input_ids
    end_of_think_ids = tokenizer('</think>').input_ids
    start_of_answer_ids = tokenizer('<answer>').input_ids
    end_of_answer_ids = tokenizer('</answer>').input_ids
    loss_fct = nn.CrossEntropyLoss(reduction='none')
    start_time = time.time()
    
    for step, (X, Y, loss_mask) in enumerate(loader, start=start_step + 1):
        X = X.to(args.device)
        Y = Y.to(args.device)
        loss_mask = loss_mask.to(args.device)
        lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate)
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr

        with autocast_ctx:
            res = model(X)
            loss = loss_fct(
                res.logits.view(-1, res.logits.size(-1)),
                Y.view(-1)
            ).view(Y.size())

            # 特殊标签位置增加权重（推理蒸馏特有）
            sp_ids = torch.isin(Y.view(-1),
                                torch.tensor(start_of_think_ids + end_of_think_ids
                                             + start_of_answer_ids + end_of_answer_ids
                                             ).to(args.device))
            loss_mask = loss_mask.view(-1)
            loss_mask_sum = loss_mask.sum()
            loss_mask[sp_ids] = 10  # 对思考标签增加10倍权重
            loss_mask = loss_mask.view(Y.size())
            loss = (loss * loss_mask).sum() / loss_mask_sum
            loss += res.aux_loss
            loss = loss / args.accumulation_steps

        scaler.scale(loss).backward()

        if (step + 1) % args.accumulation_steps == 0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
            scaler.step(optimizer)
            scaler.update()
            optimizer.zero_grad(set_to_none=True)

        if step % args.log_interval == 0 or step == iters - 1:
            spend_time = time.time() - start_time
            current_loss = loss.item() * args.accumulation_steps
            current_lr = optimizer.param_groups[-1]['lr']
            eta_min = spend_time / (step + 1) * iters // 60 - spend_time // 60
            
            Logger(f'Epoch:[{epoch+1}/{args.epochs}]({step}/{iters}) loss:{current_loss:.6f} lr:{current_lr:.12f} epoch_Time:{eta_min}min:')
            
            if wandb: wandb.log({"loss": current_loss, "lr": current_lr, "epoch_Time": eta_min})

        if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
            model.eval()
            moe_suffix = '_moe' if lm_config.use_moe else ''
            ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
            if isinstance(model, torch.nn.parallel.DistributedDataParallel):
                state_dict = model.module.state_dict()
            else:
                state_dict = model.state_dict()
            state_dict = {k: v.half() for k, v in state_dict.items()}  # 半精度保存
            torch.save(state_dict, ckp)
            lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer, scaler=scaler, epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints')
            model.train()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="MiniMind Reasoning Distillation")
    parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
    parser.add_argument('--save_weight', default='reason', type=str, help="保存权重的前缀名")
    parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
    parser.add_argument("--batch_size", type=int, default=8, help="batch size")
    parser.add_argument("--learning_rate", type=float, default=1e-6, help="初始学习率")
    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
    parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
    parser.add_argument("--num_workers", type=int, default=1, help="数据加载线程数")
    parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
    parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
    parser.add_argument("--log_interval", type=int, default=100, help="日志打印间隔")
    parser.add_argument("--save_interval", type=int, default=100, help="模型保存间隔")
    parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
    parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
    parser.add_argument('--max_seq_len', default=1024, type=int, help="训练的最大截断长度")
    parser.add_argument('--use_moe', default=False, type=bool, help="是否使用MoE")
    parser.add_argument("--data_path", type=str, default="../dataset/r1_mix_1024.jsonl", help="推理蒸馏数据路径")
    parser.add_argument('--from_weight', default='dpo', type=str, help="基于哪个权重训练，默认dpo")
    parser.add_argument('--from_resume', default=0, type=int, help="是否自动检测&续训，0否1是")
    parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
    parser.add_argument("--wandb_project", type=str, default="MiniMind-Reasoning", help="wandb项目名")
    args = parser.parse_args()

    # ========== 1. 初始化环境和随机种子 ==========
    local_rank = init_distributed_mode()
    if dist.is_initialized(): args.device = f"cuda:{local_rank}"
    setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
    
    # ========== 2. 配置目录、模型参数、检查ckp ==========
    os.makedirs(args.save_dir, exist_ok=True)
    lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe)
    ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
    
    # ========== 3. 设置混合精度 ==========
    device_type = "cuda" if "cuda" in args.device else "cpu"
    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
    
    # ========== 4. 配wandb ==========
    wandb = None
    if args.use_wandb and is_main_process():
        import swanlab as wandb
        wandb_id = ckp_data.get('wandb_id') if ckp_data else None
        resume = 'must' if wandb_id else None
        wandb_run_name = f"MiniMind-Reasoning-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LR-{args.learning_rate}"
        wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
    
    # ========== 5. 定义模型、数据、优化器 ==========
    model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
    train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
    train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
    optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
    
    # ========== 6. 从ckp恢复状态 ==========
    start_epoch, start_step = 0, 0
    if ckp_data:
        model.load_state_dict(ckp_data['model'])
        optimizer.load_state_dict(ckp_data['optimizer'])
        scaler.load_state_dict(ckp_data['scaler'])
        start_epoch = ckp_data['epoch']
        start_step = ckp_data.get('step', 0)
    
    # ========== 7. DDP包模型 ==========
    if dist.is_initialized():
        model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
        model = DistributedDataParallel(model, device_ids=[local_rank])
    
    # ========== 8. 开始训练 ==========
    for epoch in range(start_epoch, args.epochs):
        train_sampler and train_sampler.set_epoch(epoch)
        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
            Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
            train_epoch(epoch, loader, len(loader) + start_step + 1, tokenizer, lm_config, start_step, wandb)
        else: # 默认从头开始
            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
            train_epoch(epoch, loader, len(loader), tokenizer, lm_config, 0, wandb)