import math
import pickle
from importlib.resources import files
from pathlib import Path
from typing import Optional, Union

import torch
import torch.amp as amp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from loguru import logger
from modelscope import AutoModel, AutoTokenizer
from tqdm.autonotebook import tqdm

from suinput.dataset import PG

from .monitor import TrainingMonitor, send_serverchan_message


def eval_dataloader(path: Union[str, Path] = (files(__package__) / "eval_dataset")):
    return [pickle.load(file.open("rb")) for file in Path(path).glob("*.pkl")]


# ---------------------------- 注意力池化模块（新增）----------------------------
class AttentionPooling(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.attn = nn.Linear(hidden_size, 1)
        # 三个可学习偏置：文本、拼音、个性化
        self.bias = nn.Parameter(torch.zeros(3))  # [text_bias, pinyin_bias, user_bias]

    def forward(self, x, mask=None, token_type_ids=None):
        scores = self.attn(x).squeeze(-1)  # [batch, seq_len]
        if token_type_ids is not None:
            # 根据 token_type_ids 添加对应偏置
            # bias 形状 [3]，通过索引扩展为 [batch, seq_len]
            bias_per_token = self.bias[token_type_ids]  # [batch, seq_len]
            scores = scores + bias_per_token
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        weights = torch.softmax(scores, dim=-1)
        pooled = torch.sum(weights.unsqueeze(-1) * x, dim=1)
        return pooled


# ---------------------------- 残差块 ----------------------------
class ResidualBlock(nn.Module):
    def __init__(self, dim, dropout_prob=0.3):
        super().__init__()
        self.linear1 = nn.Linear(dim, dim)
        self.ln1 = nn.LayerNorm(dim)
        self.linear2 = nn.Linear(dim, dim)
        self.ln2 = nn.LayerNorm(dim)
        self.gelu = nn.GELU()
        self.dropout = nn.Dropout(dropout_prob)

    def forward(self, x):
        residual = x
        # 修复：使用 self.gelu 而不是未定义的 self.relu
        x = self.gelu(self.linear1(x))
        x = self.ln1(x)
        x = self.linear2(x)
        x = self.ln2(x)
        x = self.dropout(x)
        x = x + residual
        return self.gelu(x)


# ---------------------------- 专家网络 ----------------------------
class Expert(nn.Module):
    def __init__(
        self,
        input_dim,
        d_model=768,
        num_resblocks=4,
        output_multiplier=2,
        dropout_prob=0.3,
    ):
        super().__init__()
        self.output_dim = input_dim * output_multiplier
        self.linear_in = nn.Linear(input_dim, d_model)
        self.res_blocks = nn.ModuleList(
            [ResidualBlock(d_model, dropout_prob) for _ in range(num_resblocks)]
        )
        self.output = nn.Sequential(
            nn.Linear(d_model, d_model),
            nn.GELU(),
            nn.Dropout(dropout_prob),
            nn.Linear(d_model, self.output_dim),
        )

    def forward(self, x):
        x = self.linear_in(x)
        for block in self.res_blocks:
            x = block(x)
        return self.output(x)


# ---------------------------- 主模型（MoE + 硬路由）------------------------
class MoEModel(nn.Module):
    def __init__(
        self,
        pretrained_model_name="iic/nlp_structbert_backbone_lite_std",
        num_classes=10018,
        output_multiplier=2,
        d_model=768,
        num_resblocks=4,
        num_domain_experts=12,
        num_shared_experts=1,
    ):
        super().__init__()
        self.output_multiplier = output_multiplier

        # 1. 加载预训练 Embedding (Lite: hidden_size=512)
        logger.info(f"Loading backbone: {pretrained_model_name}")
        bert = AutoModel.from_pretrained(pretrained_model_name)
        self.embedding = bert.embeddings
        self.bert_config = bert.config
        self.hidden_size = self.bert_config.hidden_size  # 512
        self.device = None

        # 2. Transformer Encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=self.hidden_size,
            nhead=8,
            dim_feedforward=self.bert_config.intermediate_size,
            dropout=self.bert_config.hidden_dropout_prob,
            activation="gelu",
            batch_first=True,
        )
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)

        # 3. 注意力池化（新增）
        self.attn_pool = AttentionPooling(self.hidden_size)

        # 4. 专家系统
        total_experts = num_domain_experts + num_shared_experts
        self.experts = nn.ModuleList()
        for i in range(total_experts):
            dropout = 0.3 if i < num_domain_experts else 0.4
            self.experts.append(
                Expert(
                    input_dim=self.hidden_size,
                    d_model=d_model,
                    num_resblocks=num_resblocks,
                    output_multiplier=self.output_multiplier,
                    dropout_prob=dropout,
                )
            )

        self.expert_bias = nn.Embedding(
            total_experts, self.hidden_size * self.output_multiplier
        )

        # 5. 分类头
        self.classifier = nn.Sequential(
            nn.LayerNorm(self.hidden_size * self.output_multiplier),
            nn.Dropout(0.4),
            nn.Linear(self.hidden_size * self.output_multiplier, self.hidden_size * self.output_multiplier * 2),
            nn.GELU(),
            nn.Linear(self.hidden_size * self.output_multiplier * 2, num_classes),
        )

    def to(self, device):
        """重写 to 方法，记录设备"""
        self.device = device
        return super().to(device)

    def forward(self, input_ids, attention_mask, token_type_ids, pg):
        """
        新版 Forward 函数，不再需要 p_start，改用 token_type_ids。
        Args:
            input_ids: [B, L]
            attention_mask: [B, L]
            token_type_ids: [B, L]  (0=文本, 1=拼音)
            pg: [B] 拼音组 ID
        """
        # ----- 1. Embeddings -----
        # 注意：预训练的 embedding 层本身可能已经包含了 token_type_ids 的处理，
        # 但这里我们直接使用它的 embedding，并手动将 token_type_ids 的嵌入加到上面。
        # 由于 bert.embeddings 通常包含 token_type_embeddings，我们可以利用它。
        # 但为简化，我们直接使用 bert.embeddings(input_ids, token_type_ids=token_type_ids)
        # 如果当前 embedding 不支持传入 token_type_ids，可以手动相加：
        # embeddings = self.embedding(input_ids) + self.embedding.token_type_embeddings(token_type_ids)
        # 这里采用更通用的方式：假设 self.embedding 有 token_type_ids 参数
        embeddings = self.embedding(input_ids, token_type_ids=token_type_ids)

        # ----- 2. Transformer Encoder -----
        padding_mask = attention_mask == 0
        encoded = self.encoder(
            embeddings, src_key_padding_mask=padding_mask
        )  # [B, S, H]

        # ----- 3. 注意力池化（代替原来的 Span Pooling）-----
        # 使用 attention_mask 忽略 padding 位置
        pooled = self.attn_pool(encoded, attention_mask, token_type_ids)  # [B, H]

        # ----- 4. 专家路由（硬路由）-----
        if torch.jit.is_tracing():
            # ONNX 导出模式：batch=1，根据 pg 选择专家
            group_id = pg.item() if torch.is_tensor(pg) else pg
            # 注意：专家索引从 0 开始，确保所有 case 都覆盖且偏置正确
            # 使用字典映射或 if-elif（ONNX 需要静态图，此处保持原样但修正索引错误）
            if group_id == 0:
                expert_out = self.experts[0](pooled) + self.expert_bias(
                    torch.tensor(0, device=pooled.device)
                )
            elif group_id == 1:
                expert_out = self.experts[1](pooled) + self.expert_bias(
                    torch.tensor(1, device=pooled.device)
                )
            elif group_id == 2:
                expert_out = self.experts[2](pooled) + self.expert_bias(
                    torch.tensor(2, device=pooled.device)
                )
            elif group_id == 3:
                expert_out = self.experts[3](pooled) + self.expert_bias(
                    torch.tensor(3, device=pooled.device)
                )
            elif group_id == 4:
                expert_out = self.experts[4](pooled) + self.expert_bias(
                    torch.tensor(4, device=pooled.device)
                )
            elif group_id == 5:
                expert_out = self.experts[5](pooled) + self.expert_bias(
                    torch.tensor(5, device=pooled.device)
                )
            elif group_id == 6:
                expert_out = self.experts[6](pooled) + self.expert_bias(
                    torch.tensor(6, device=pooled.device)
                )
            elif group_id == 7:
                expert_out = self.experts[7](pooled) + self.expert_bias(
                    torch.tensor(7, device=pooled.device)
                )
            elif group_id == 8:
                expert_out = self.experts[8](pooled) + self.expert_bias(
                    torch.tensor(8, device=pooled.device)
                )
            elif group_id == 9:
                expert_out = self.experts[9](pooled) + self.expert_bias(
                    torch.tensor(9, device=pooled.device)
                )
            elif group_id == 10:
                expert_out = self.experts[10](pooled) + self.expert_bias(
                    torch.tensor(10, device=pooled.device)
                )
            elif group_id == 11:
                expert_out = self.experts[11](pooled) + self.expert_bias(
                    torch.tensor(11, device=pooled.device)
                )
            else:  # group_id == 12
                expert_out = self.experts[12](pooled) + self.expert_bias(
                    torch.tensor(12, device=pooled.device)
                )
        else:
            batch_size = pooled.size(0)
            expert_outputs = torch.stack(
                [e(pooled) for e in self.experts], dim=0
            )  # [E, B, D]
            expert_out = expert_outputs[pg, torch.arange(batch_size)]  # [B, D]
            bias = self.expert_bias(pg)  # [B, D]
            expert_out = expert_out + bias

        # ----- 5. 分类头 -----
        return self.classifier(expert_out)  # [batch, num_classes]

    def model_eval(self, eval_dataloader, criterion):
        """
        评估模型在验证集上的性能。

        Args:
            eval_dataloader (DataLoader): 验证集的数据加载器，每个batch包含以下字段：
                - hint: 包含input_ids、attention_mask和token_type_ids的字典
                - pg: 程序图数据
                - char_id: 字符ID标签
            criterion (callable): 损失函数，用于计算模型输出与标签之间的损失

        Returns:
            tuple: 包含两个浮点数的元组 (accuracy, avg_loss)
                - accuracy (float): 模型在验证集上的准确率
                - avg_loss (float): 模型在验证集上的平均损失

        Note:
            该方法会自动将模型切换到评估模式(self.eval())，
            并使用torch.no_grad()上下文管理器来禁用梯度计算，
            以节省内存和计算资源。
        """
        self.eval()
        total_loss = 0.0
        correct = 0
        total = 0

        with torch.no_grad():
            for batch in eval_dataloader:
                input_ids = batch["hint"]["input_ids"].to(self.device)
                attention_mask = batch["hint"]["attention_mask"].to(self.device)
                token_type_ids = batch["hint"]["token_type_ids"].to(self.device)  # 新增
                pg = batch["pg"].to(self.device)
                labels = batch["char_id"].to(self.device)

                logits = self(input_ids, attention_mask, token_type_ids, pg)
                loss = criterion(logits, labels)

                total_loss += loss.item() * labels.size(0)

                preds = torch.softmax(logits, dim=-1).argmax(dim=-1)
                correct += (preds == labels).sum().item()
                total += labels.size(0)

        avg_loss = total_loss / total if total > 0 else 0.0
        accuracy = correct / total if total > 0 else 0.0
        return accuracy, avg_loss

    def gen_predict_sample(self, text, py, tokenizer=None):
        """
        生成用于预测的样本数据。

        该方法将文本和拼音转换为模型所需的输入格式，包括input_ids、attention_mask和token_type_ids。
        如果没有提供tokenizer，会使用默认的AutoTokenizer。

        Args:
            text (str): 输入文本，作为第一句输入。
            py (str): 拼音字符串，作为第二句输入。
            tokenizer (AutoTokenizer, optional): 分词器实例。如果为None且self.tokenizer不存在，
                                               则会创建默认的分词器。默认为None。

        Returns:
            dict: 包含模型输入的字典，格式为：
                {
                    "hint": {
                        "input_ids": tensor,      # 文本和拼音的token IDs
                        "attention_mask": tensor, # 注意力掩码
                        "token_type_ids": tensor  # 句子类型ID
                    },
                    "pg": tensor                  # 拼音组ID，根据拼音首字母生成
                }

        Notes:
            - 使用text_pair参数让分词器自动生成token_type_ids
            - 确保分词器支持return_token_type_ids=True
            - 最大长度(max_length)设置为88
            - 会自动进行padding和truncation处理
            - 拼音组ID当前根据拼音首字母生成，可根据实际需要改进
        """
        if tokenizer is None and not hasattr(self, "tokenizer"):
            self.tokenizer = AutoTokenizer.from_pretrained(
                "iic/nlp_structbert_backbone_lite_std"
            )
        else:
            self.tokenizer = tokenizer or self.tokenizer

        # 使用 text_pair 参数让分词器自动生成 token_type_ids
        # 注意：确保分词器支持 return_token_type_ids=True
        encoded = self.tokenizer(
            text,  # 文本作为第一句
            py,  # 拼音作为第二句
            max_length=88,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
            return_token_type_ids=True,  # 显式要求返回 token_type_ids
        )

        sample = {
            "hint": {
                "input_ids": encoded["input_ids"],
                "attention_mask": encoded["attention_mask"],
                "token_type_ids": encoded["token_type_ids"],  # 新增
            },
            "pg": torch.tensor(
                [PG[py[0]] if py != "" else 12]
            ),  # 拼音组 ID 仍根据首字母生成（可根据实际需要改进）
        }
        return sample

    def predict(self, text, py, tokenizer=None):
        """
        预测函数，自动处理 batch 维度

        Args:
            text (str or List[str]): 输入文本或文本列表
            py (int or List[int]): 拼音特征，可以是单个值或列表
            tokenizer (object, optional): 分词器对象，用于文本预处理。默认为None

        Returns:
            torch.Tensor: 预测结果，如果是单个输入则返回一维张量，
                         如果是批量输入则返回二维张量
        """
        self.eval()
        sample = self.gen_predict_sample(text, py, tokenizer)
        input_ids = sample["hint"]["input_ids"]
        attention_mask = sample["hint"]["attention_mask"]
        token_type_ids = sample["hint"]["token_type_ids"]
        pg = sample["pg"]

        has_batch_dim = input_ids.dim() > 1
        if not has_batch_dim:
            input_ids = input_ids.unsqueeze(0)
            attention_mask = attention_mask.unsqueeze(0)
            token_type_ids = token_type_ids.unsqueeze(0)
        if pg.dim() == 0:
            pg = pg.unsqueeze(0).expand(input_ids.size(0))

        input_ids = input_ids.to(self.device)
        attention_mask = attention_mask.to(self.device)
        token_type_ids = token_type_ids.to(self.device)
        pg = pg.to(self.device)

        with torch.no_grad():
            logits = self(input_ids, attention_mask, token_type_ids, pg)
            preds = torch.softmax(logits, dim=-1).argmax(dim=-1)

        if not has_batch_dim:
            return preds.squeeze(0)
        return preds

    def fit(
        self,
        train_dataloader,
        eval_dataloader=None,
        monitor: Optional[TrainingMonitor] = None,
        criterion=None,
        optimizer=None,
        num_epochs=1,
        stop_batch=2e5,
        eval_frequency=500,
        grad_accum_steps=1,
        clip_grad_norm=1.0,
        loss_weight=None,
        mixed_precision=True,
        weight_decay=0.1,
        warmup_ratio=0.1,
        label_smoothing=0.15,
        lr=1e-4,
    ):
        """训练函数，调整了输入参数"""
        if self.device is None:
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            self.to(self.device)

        self.train()

        if optimizer is None:
            optimizer = optim.AdamW(self.parameters(), lr=lr, weight_decay=weight_decay)

        if criterion is None:
            if loss_weight is not None:
                criterion = nn.CrossEntropyLoss(
                    weight=loss_weight, label_smoothing=label_smoothing
                )
            else:
                criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)

        scaler = amp.GradScaler(enabled=mixed_precision)

        total_steps = max(stop_batch, 2e5)
        warmup_steps = int(total_steps * warmup_ratio)
        logger.info(f"Training Start: Steps={total_steps}, Warmup={warmup_steps}")
        processed_batches = 0
        global_step = 0  # 初始化
        batch_loss_sum = 0.0
        optimizer.zero_grad()

        for epoch in range(num_epochs):
            for batch_idx, batch in enumerate(
                tqdm(train_dataloader, total=int(stop_batch))
            ):
                # LR Schedule
                if processed_batches < warmup_steps:
                    current_lr = lr * (processed_batches/ warmup_steps)
                else:
                    progress = (processed_batches - warmup_steps) / (
                        total_steps - warmup_steps
                    )
                    current_lr = lr * (0.5 * (1.0 + math.cos(math.pi * progress)))
                for param_group in optimizer.param_groups:
                    param_group["lr"] = current_lr

                # 移动数据（注意：batch 中现在包含 token_type_ids）
                input_ids = batch["hint"]["input_ids"].to(self.device)
                attention_mask = batch["hint"]["attention_mask"].to(self.device)
                token_type_ids = batch["hint"]["token_type_ids"].to(self.device)  # 新增
                pg = batch["pg"].to(self.device)
                labels = batch["char_id"].to(self.device)

                with torch.amp.autocast(
                    device_type=self.device.type, enabled=mixed_precision
                ):
                    logits = self(input_ids, attention_mask, token_type_ids, pg)
                    loss = criterion(logits, labels)
                    loss = loss / grad_accum_steps

                scaler.scale(loss).backward()

                if (processed_batches) % grad_accum_steps == 0:
                    scaler.unscale_(optimizer)
                    torch.nn.utils.clip_grad_norm_(self.parameters(), clip_grad_norm)

                    scaler.step(optimizer)
                    scaler.update()
                    optimizer.zero_grad()
                    batch_loss_sum += loss.item() * grad_accum_steps
                    if global_step % eval_frequency == 0:
                        if eval_dataloader:
                            self.eval()
                            acc, eval_loss = self.model_eval(eval_dataloader, criterion)
                            self.train()
                            if monitor:
                                # 使用 eval_loss 作为监控指标
                                monitor.add_step(
                                    global_step, {"loss": batch_loss_sum / (eval_frequency if global_step > 0 else 1), "acc": acc}
                                )
                            logger.info(
                                f"step: {global_step}, eval_loss: {eval_loss:.4f}, acc: {acc:.4f}, 'batch_loss_sum': {batch_loss_sum / (eval_frequency if global_step > 0 else 1):.4f}, current_lr: {current_lr}"
                            )
                        else:
                            logger.info(f"step: {global_step}, 'batch_loss_sum': {batch_loss_sum / (eval_frequency if global_step > 0 else 1):.4f}, current_lr: {current_lr}")
                        batch_loss_sum = 0.0
                    if processed_batches >= stop_batch:
                        break
                    processed_batches += 1
                    global_step += 1

        # 训练结束发送通知
        try:
            res_acc, res_loss = self.model_eval(eval_dataloader, criterion)
            send_serverchan_message(
                title="训练完成",
                content=f"acc: {res_acc:.4f}, loss: {res_loss:.4f}",
            )
            logger.info(f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}")
        except Exception as e:
            logger.error(f"发送消息失败: {e}")

    def load_from_state_dict(self, state_dict_path: Union[str, Path]):
        state_dict = torch.load(
            state_dict_path, weights_only=True, map_location=self.device
        )
        self.load_state_dict(state_dict)

    def load_from_pretrained_base_model(
        self,
        BaseModel,
        snapshot_path: Union[str, Path],
        device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
        *args,
        **kwargs,
    ):
        base_model = BaseModel(*args, **kwargs)
        base_model.load_state_dict(torch.load(snapshot_path, map_location=device))
        self_static_dict = self.state_dict()
        pretrained_dict = base_model.state_dict()

        freeze_layers = []

        for key in self_static_dict.keys():
            if key in pretrained_dict.keys():
                if self_static_dict[key].shape == pretrained_dict[key].shape:
                    self_static_dict[key] = pretrained_dict[key].to(self.device)
                    freeze_layers.append(key)
        self.load_state_dict(self_static_dict)
        for name, param in self.named_parameters():
            if name in freeze_layers:
                param.requires_grad = False

    # --- ONNX 导出辅助函数 ---
    def export_onnx(self, output_path, dummy_input):
        """
        dummy_input 应该是一个元组，包含:
        (input_ids, attention_mask, token_type_ids, pg)
        """
        self.eval()
        input_names = ["input_ids", "attention_mask", "token_type_ids", "pg"]
        output_names = ["logits"]

        torch.onnx.export(
            self,
            dummy_input,
            output_path,
            input_names=input_names,
            output_names=output_names,
            dynamic_axes={
                "input_ids": {0: "batch_size", 1: "seq_len"},
                "attention_mask": {0: "batch_size", 1: "seq_len"},
                "token_type_ids": {0: "batch_size", 1: "seq_len"},
                "pg": {0: "batch_size"},
                "logits": {0: "batch_size"},
            },
            opset_version=14,
            do_constant_folding=True,
        )
        logger.info(f"ONNX model exported to {output_path}")