"""
Titans Neural Memory 与 Qwen 模型集成示例

本文件展示了如何将 Titans 的 NeuralMemory 模块集成到 Qwen 模型中，
以增强其长期记忆能力。

主要集成方案：
1. 作为独立的记忆增强模块（Memory Augmented）
2. 替换/增强特定层的注意力机制
3. Memory-as-Context 方式（类似 MAC Transformer）
"""

import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional, Tuple
from einops import rearrange, repeat
from copy import deepcopy

# 导入 Titans 的核心组件
from titans_pytorch import NeuralMemory, MemoryMLP, NeuralMemState


# ============================================================================
# 方案 1: 简单的记忆增强包装器 (Memory Augmented Wrapper)
# ============================================================================

class TitansMemoryWrapper(nn.Module):
    """
    最简单的集成方式：在 Qwen 模型外部添加 Titans 记忆模块
    
    工作原理：
    1. 使用 NeuralMemory 存储和检索长期信息
    2. 将检索到的记忆与 Qwen 的输出融合
    
    适用场景：
    - 不想修改 Qwen 内部结构
    - 需要快速验证 Titans 记忆的效果
    """
    
    def __init__(
        self,
        qwen_model,
        hidden_size: int = 896,  # Qwen2-0.5B 的隐藏层大小
        chunk_size: int = 64,
        memory_batch_size: int = 128,
        num_heads: int = 4,
        dim_head: int = 64,
        memory_depth: int = 2,
    ):
        super().__init__()
        self.qwen = qwen_model
        
        # 投影层：将 Qwen 的隐藏状态投影到记忆维度
        self.mem_dim = dim_head * num_heads
        self.to_mem_input = nn.Linear(hidden_size, self.mem_dim)
        self.from_mem_output = nn.Linear(self.mem_dim, hidden_size)
        
        # 创建 Titans 记忆模块
        memory_model = MemoryMLP(
            dim=dim_head,
            depth=memory_depth,
            expansion_factor=2.0
        )
        
        self.neural_memory = NeuralMemory(
            dim=self.mem_dim,
            chunk_size=chunk_size,
            batch_size=memory_batch_size,
            dim_head=dim_head,
            heads=num_heads,
            model=memory_model,
            momentum=True,
            momentum_order=1,
            qk_rmsnorm=True,
        )
        
        # 融合门控
        self.fusion_gate = nn.Sequential(
            nn.Linear(hidden_size * 2, hidden_size),
            nn.Sigmoid()
        )
        
    def forward(
        self,
        input_ids: Tensor,
        attention_mask: Optional[Tensor] = None,
        memory_state: Optional[NeuralMemState] = None,
        **kwargs
    ):
        # 获取 Qwen 的隐藏状态
        qwen_outputs = self.qwen(
            input_ids=input_ids,
            attention_mask=attention_mask,
            output_hidden_states=True,
            **kwargs
        )
        
        hidden_states = qwen_outputs.hidden_states[-1]  # 最后一层隐藏状态
        
        # 投影到记忆空间
        mem_input = self.to_mem_input(hidden_states)
        
        # 使用 Titans 记忆模块存储和检索
        retrieved, next_memory_state = self.neural_memory(
            mem_input,
            state=memory_state
        )
        
        # 投影回原始维度
        retrieved_hidden = self.from_mem_output(retrieved)
        
        # 门控融合
        gate = self.fusion_gate(torch.cat([hidden_states, retrieved_hidden], dim=-1))
        enhanced_hidden = hidden_states + gate * retrieved_hidden
        
        # 使用增强的隐藏状态计算 logits
        # 注意：这里需要访问 Qwen 的 lm_head
        if hasattr(self.qwen, 'lm_head'):
            logits = self.qwen.lm_head(enhanced_hidden)
        else:
            logits = qwen_outputs.logits
            
        return {
            'logits': logits,
            'hidden_states': enhanced_hidden,
            'memory_state': next_memory_state,
            'qwen_outputs': qwen_outputs
        }


# ============================================================================
# 方案 2: 将 Titans 记忆嵌入到 Qwen 的特定层中
# ============================================================================

class QwenDecoderLayerWithMemory(nn.Module):
    """
    修改后的 Qwen Decoder 层，集成了 Titans 记忆模块
    
    在每个 attention 层后添加记忆检索和更新
    """
    
    def __init__(
        self,
        original_layer,
        hidden_size: int,
        chunk_size: int = 64,
        memory_batch_size: int = 128,
        num_heads: int = 4,
        dim_head: int = 64,
    ):
        super().__init__()
        
        # 保留原始层的组件
        self.self_attn = original_layer.self_attn
        self.mlp = original_layer.mlp
        self.input_layernorm = original_layer.input_layernorm
        self.post_attention_layernorm = original_layer.post_attention_layernorm
        
        # 添加 Titans 记忆模块
        self.mem_dim = dim_head * num_heads
        self.to_mem = nn.Linear(hidden_size, self.mem_dim)
        self.from_mem = nn.Linear(self.mem_dim, hidden_size)
        
        memory_model = MemoryMLP(dim=dim_head, depth=2)
        
        self.neural_memory = NeuralMemory(
            dim=self.mem_dim,
            chunk_size=chunk_size,
            batch_size=memory_batch_size,
            dim_head=dim_head,
            heads=num_heads,
            model=memory_model,
            momentum=True,
        )
        
        # 记忆输出的门控
        self.mem_gate = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.Sigmoid()
        )
        
    def forward(
        self,
        hidden_states: Tensor,
        attention_mask: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        memory_state: Optional[NeuralMemState] = None,
        **kwargs
    ):
        # 标准的 attention 前向传播
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        
        attn_output, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            **kwargs
        )
        hidden_states = residual + attn_output
        
        # === Titans 记忆增强 ===
        mem_input = self.to_mem(hidden_states)
        retrieved, next_memory_state = self.neural_memory(
            mem_input,
            state=memory_state
        )
        mem_output = self.from_mem(retrieved)
        
        # 门控融合记忆
        gate = self.mem_gate(hidden_states)
        hidden_states = hidden_states + gate * mem_output
        # ========================
        
        # 标准的 FFN 前向传播
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        
        return hidden_states, next_memory_state


# ============================================================================
# 方案 3: Memory-as-Context 方式（最接近原论文）
# ============================================================================

class QwenWithMAC(nn.Module):
    """
    Memory-as-Context 方式集成 Titans 到 Qwen
    
    核心思想：
    1. 将长序列分成多个 segment
    2. 每个 segment 的开头添加 longterm memory tokens
    3. 使用 NeuralMemory 来更新这些 memory tokens
    
    这种方式最接近 Titans 论文中的 MAC 配置
    """
    
    def __init__(
        self,
        qwen_model,
        hidden_size: int = 896,
        segment_len: int = 128,
        num_longterm_mem_tokens: int = 16,
        num_persist_mem_tokens: int = 4,
        memory_layers: Tuple[int, ...] = (2, 4, 6),
        chunk_size: int = 64,
        memory_batch_size: int = 128,
    ):
        super().__init__()
        
        self.qwen = qwen_model
        self.hidden_size = hidden_size
        self.segment_len = segment_len
        self.num_longterm_mem_tokens = num_longterm_mem_tokens
        
        # 持久记忆 tokens（全局共享）
        self.persist_mem = nn.Parameter(
            torch.randn(num_persist_mem_tokens, hidden_size) * 0.02
        )
        
        # 长期记忆 tokens（每个 segment 独立）
        self.longterm_mem = nn.Parameter(
            torch.randn(num_longterm_mem_tokens, hidden_size) * 0.02
        )
        
        # 为指定层创建 NeuralMemory 模块
        self.memory_layers = memory_layers
        self.neural_memories = nn.ModuleDict()
        
        memory_model = MemoryMLP(dim=64, depth=2)
        
        for layer_idx in memory_layers:
            self.neural_memories[str(layer_idx)] = NeuralMemory(
                dim=hidden_size,
                chunk_size=chunk_size,
                batch_size=memory_batch_size,
                dim_head=64,
                heads=hidden_size // 64,
                model=deepcopy(memory_model),
                momentum=True,
                qk_rmsnorm=True,
            )
            
    def prepare_inputs_with_memory(
        self,
        hidden_states: Tensor,
        batch_size: int,
    ) -> Tensor:
        """
        在每个 segment 开头插入 memory tokens
        """
        seq_len = hidden_states.shape[1]
        num_segments = (seq_len + self.segment_len - 1) // self.segment_len
        
        # 扩展 longterm memory
        longterm = repeat(
            self.longterm_mem,
            'n d -> b s n d',
            b=batch_size,
            s=num_segments
        )
        
        # 将序列分成 segments
        padded_len = num_segments * self.segment_len
        if seq_len < padded_len:
            hidden_states = nn.functional.pad(
                hidden_states,
                (0, 0, 0, padded_len - seq_len)
            )
            
        hidden_states = rearrange(
            hidden_states,
            'b (s n) d -> b s n d',
            n=self.segment_len
        )
        
        # 在每个 segment 前添加 memory tokens
        hidden_states = torch.cat([longterm, hidden_states], dim=2)
        
        # 合并回完整序列
        hidden_states = rearrange(hidden_states, 'b s n d -> b (s n) d')
        
        # 添加持久记忆 tokens 在最前面
        persist = repeat(self.persist_mem, 'n d -> b n d', b=batch_size)
        hidden_states = torch.cat([persist, hidden_states], dim=1)
        
        return hidden_states
    
    def forward(
        self,
        input_ids: Tensor,
        attention_mask: Optional[Tensor] = None,
        memory_states: Optional[dict] = None,
        **kwargs
    ):
        batch_size = input_ids.shape[0]
        
        # 获取 token embeddings
        if hasattr(self.qwen.model, 'embed_tokens'):
            hidden_states = self.qwen.model.embed_tokens(input_ids)
        else:
            hidden_states = self.qwen.get_input_embeddings()(input_ids)
        
        # 添加 memory tokens
        hidden_states = self.prepare_inputs_with_memory(hidden_states, batch_size)
        
        # 初始化记忆状态
        if memory_states is None:
            memory_states = {}
            
        next_memory_states = {}
        
        # 遍历 Qwen 的层
        for layer_idx, layer in enumerate(self.qwen.model.layers):
            # 标准的 transformer 层前向传播
            layer_outputs = layer(
                hidden_states,
                attention_mask=None,  # 需要修改 attention mask 来处理 memory tokens
                **kwargs
            )
            hidden_states = layer_outputs[0]
            
            # 在指定层应用 Titans 记忆
            if str(layer_idx) in self.neural_memories:
                neural_mem = self.neural_memories[str(layer_idx)]
                mem_state = memory_states.get(str(layer_idx))
                
                retrieved, next_state = neural_mem(
                    hidden_states,
                    state=mem_state
                )
                
                # 融合检索到的记忆
                hidden_states = hidden_states + retrieved * 0.1  # 可学习的权重
                next_memory_states[str(layer_idx)] = next_state
        
        # 最终的 layer norm
        hidden_states = self.qwen.model.norm(hidden_states)
        
        # 计算 logits
        logits = self.qwen.lm_head(hidden_states)
        
        return {
            'logits': logits,
            'hidden_states': hidden_states,
            'memory_states': next_memory_states
        }


# ============================================================================
# 使用示例
# ============================================================================

def example_usage():
    """展示如何使用上述集成方案"""
    
    print("=" * 60)
    print("Titans Neural Memory 与 Qwen 集成示例")
    print("=" * 60)
    
    # 注意：需要先安装 transformers 和 qwen 相关依赖
    # pip install transformers torch titans-pytorch
    
    try:
        from transformers import AutoModelForCausalLM, AutoTokenizer
        
        # 加载 Qwen 模型（以 Qwen2-0.5B 为例）
        model_name = "Qwen/Qwen2-0.5B"
        
        print(f"\n加载模型: {model_name}")
        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
        qwen_model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto",
            trust_remote_code=True
        )
        
        # 获取隐藏层大小
        hidden_size = qwen_model.config.hidden_size
        print(f"模型隐藏层大小: {hidden_size}")
        
        # 方案 1: 简单包装器
        print("\n--- 方案 1: TitansMemoryWrapper ---")
        wrapped_model = TitansMemoryWrapper(
            qwen_model=qwen_model,
            hidden_size=hidden_size,
            chunk_size=64,
            memory_batch_size=128,
        )
        
        # 测试输入
        text = "人工智能的发展历程"
        inputs = tokenizer(text, return_tensors="pt")
        
        with torch.no_grad():
            outputs = wrapped_model(
                input_ids=inputs.input_ids.to(qwen_model.device),
            )
            print(f"输出 logits 形状: {outputs['logits'].shape}")
            print(f"记忆状态: {type(outputs['memory_state'])}")
        
    except ImportError as e:
        print(f"\n注意: 需要安装相关依赖")
        print(f"pip install transformers torch titans-pytorch")
        print(f"错误: {e}")
    
    # 独立测试 NeuralMemory
    print("\n--- 独立测试 NeuralMemory ---")
    
    mem = NeuralMemory(
        dim=384,
        chunk_size=64,
        batch_size=128,
        dim_head=64,
        heads=4,
        model=MemoryMLP(dim=64, depth=2),
        momentum=True,
    ).cuda() if torch.cuda.is_available() else NeuralMemory(
        dim=384,
        chunk_size=64,
        batch_size=128,
        dim_head=64,
        heads=4,
        model=MemoryMLP(dim=64, depth=2),
        momentum=True,
    )
    
    # 模拟输入
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    seq = torch.randn(2, 256, 384).to(device)
    
    retrieved, mem_state = mem(seq)
    print(f"输入形状: {seq.shape}")
    print(f"检索输出形状: {retrieved.shape}")
    print(f"记忆状态序列索引: {mem_state.seq_index}")
    
    print("\n" + "=" * 60)
    print("集成完成!")
    print("=" * 60)


if __name__ == "__main__":
    example_usage()