#!/usr/bin/env python3
"""
文本相似度计算模块 - 基于远程API
使用远程GPU加速的相似度计算服务，接口与 text_embedding.py 兼容

提供3种计算模式：
1. compare_phrases() - 单对计算
2. compare_phrases_batch() - 批量成对计算 (pair[i].text1 vs pair[i].text2)
3. compare_phrases_cartesian() - 笛卡尔积计算 (M×N矩阵)
"""

from typing import Dict, Any, Optional, List, Tuple
import requests
import numpy as np

# API配置
DEFAULT_API_BASE_URL = "http://61.48.133.26:8187"
DEFAULT_TIMEOUT = 60  # 秒

# API客户端单例
_api_client = None


class SimilarityAPIClient:
    """文本相似度API客户端"""

    def __init__(self, base_url: str = DEFAULT_API_BASE_URL, timeout: int = DEFAULT_TIMEOUT):
        self.base_url = base_url.rstrip('/')
        self.timeout = timeout
        self._session = requests.Session()  # 复用连接

    def health_check(self) -> Dict:
        """健康检查"""
        response = self._session.get(f"{self.base_url}/health", timeout=10)
        response.raise_for_status()
        return response.json()

    def list_models(self) -> Dict:
        """列出支持的模型"""
        response = self._session.get(f"{self.base_url}/models", timeout=10)
        response.raise_for_status()
        return response.json()

    def similarity(
        self,
        text1: str,
        text2: str,
        model_name: Optional[str] = None
    ) -> Dict:
        """
        计算单个文本对的相似度

        Args:
            text1: 第一个文本
            text2: 第二个文本
            model_name: 可选模型名称

        Returns:
            {"text1": str, "text2": str, "score": float}
        """
        payload = {"text1": text1, "text2": text2}
        if model_name:
            payload["model_name"] = model_name

        response = self._session.post(
            f"{self.base_url}/similarity",
            json=payload,
            timeout=self.timeout
        )
        response.raise_for_status()
        return response.json()

    def batch_similarity(
        self,
        pairs: List[Dict],
        model_name: Optional[str] = None
    ) -> Dict:
        """
        批量计算成对相似度

        Args:
            pairs: [{"text1": str, "text2": str}, ...]
            model_name: 可选模型名称

        Returns:
            {"results": [{"text1": str, "text2": str, "score": float}, ...]}
        """
        payload = {"pairs": pairs}
        if model_name:
            payload["model_name"] = model_name

        response = self._session.post(
            f"{self.base_url}/batch_similarity",
            json=payload,
            timeout=self.timeout
        )
        response.raise_for_status()
        return response.json()

    def cartesian_similarity(
        self,
        texts1: List[str],
        texts2: List[str],
        model_name: Optional[str] = None
    ) -> Dict:
        """
        计算笛卡尔积相似度（M×N）

        Args:
            texts1: 第一组文本列表 (M个)
            texts2: 第二组文本列表 (N个)
            model_name: 可选模型名称

        Returns:
            {
                "results": [{"text1": str, "text2": str, "score": float}, ...],
                "total": int  # M×N
            }
        """
        payload = {
            "texts1": texts1,
            "texts2": texts2
        }
        if model_name:
            payload["model_name"] = model_name

        response = self._session.post(
            f"{self.base_url}/cartesian_similarity",
            json=payload,
            timeout=self.timeout
        )
        response.raise_for_status()
        return response.json()


def _get_api_client() -> SimilarityAPIClient:
    """获取API客户端单例"""
    global _api_client
    if _api_client is None:
        _api_client = SimilarityAPIClient()
    return _api_client


def _format_result(score: float) -> Dict[str, Any]:
    """
    格式化相似度结果（兼容 text_embedding.py 格式）

    Args:
        score: 相似度分数 (0-1)

    Returns:
        {"说明": str, "相似度": float}
    """
    # 生成说明
    if score >= 0.9:
        level = "极高"
    elif score >= 0.7:
        level = "高"
    elif score >= 0.5:
        level = "中等"
    elif score >= 0.3:
        level = "较低"
    else:
        level = "低"

    return {
        "说明": f"基于向量模型计算的语义相似度为 {level} ({score:.2f})",
        "相似度": score
    }


# ============================================================================
# 公开接口 - 3种计算模式
# ============================================================================

def compare_phrases(
    phrase_a: str,
    phrase_b: str,
    model_name: Optional[str] = None
) -> Dict[str, Any]:
    """
    比较两个短语的语义相似度（单对计算）

    Args:
        phrase_a: 第一个短语
        phrase_b: 第二个短语
        model_name: 模型名称（可选，默认使用API服务端默认模型）

    Returns:
        {
            "说明": str,      # 相似度说明
            "相似度": float    # 0-1之间的相似度分数
        }

    Examples:
        >>> result = compare_phrases("深度学习", "神经网络")
        >>> print(result['相似度'])  # 0.855
        >>> print(result['说明'])    # 基于向量模型计算的语义相似度为 高 (0.86)
    """
    try:
        client = _get_api_client()
        api_result = client.similarity(phrase_a, phrase_b, model_name)
        score = float(api_result["score"])
        return _format_result(score)
    except Exception as e:
        raise RuntimeError(f"API调用失败: {e}")


def compare_phrases_batch(
    phrase_pairs: List[Tuple[str, str]],
    model_name: Optional[str] = None
) -> List[Dict[str, Any]]:
    """
    批量比较多对短语的语义相似度（成对计算）

    说明：pair[i].text1 vs pair[i].text2
    适用场景：有N对独立的文本需要分别计算相似度

    Args:
        phrase_pairs: 短语对列表 [(phrase_a, phrase_b), ...]
        model_name: 模型名称（可选）

    Returns:
        结果列表，每个元素格式：
        {
            "说明": str,
            "相似度": float
        }

    Examples:
        >>> pairs = [
        ...     ("深度学习", "神经网络"),
        ...     ("机器学习", "人工智能"),
        ...     ("Python编程", "Python开发")
        ... ]
        >>> results = compare_phrases_batch(pairs)
        >>> for (a, b), result in zip(pairs, results):
        ...     print(f"{a} vs {b}: {result['相似度']:.4f}")

    性能：
        - 3对文本：~50ms（vs 逐对调用 ~150ms）
        - 100对文本：~200ms（vs 逐对调用 ~5s）
    """
    if not phrase_pairs:
        return []

    try:
        # 转换为API格式
        api_pairs = [{"text1": a, "text2": b} for a, b in phrase_pairs]

        # 调用API批量计算
        client = _get_api_client()
        api_response = client.batch_similarity(api_pairs, model_name)
        api_results = api_response["results"]

        # 格式化结果
        results = []
        for api_result in api_results:
            score = float(api_result["score"])
            results.append(_format_result(score))

        return results

    except Exception as e:
        raise RuntimeError(f"API批量调用失败: {e}")


def compare_phrases_cartesian(
    phrases_a: List[str],
    phrases_b: List[str],
    max_concurrent: int = 50
) -> List[List[Dict[str, Any]]]:
    """
    计算笛卡尔积相似度（M×N矩阵）

    说明：计算 phrases_a 中每个短语与 phrases_b 中每个短语的相似度
    适用场景：需要计算两组文本之间所有可能的组合

    Args:
        phrases_a: 第一组短语列表 (M个)
        phrases_b: 第二组短语列表 (N个)
        max_concurrent: 最大并发数（API一次性调用，此参数保留用于接口一致性）

    Returns:
        M×N的结果矩阵（嵌套列表）
        results[i][j] = {
            "相似度": float,  # phrases_a[i] vs phrases_b[j]
            "说明": str
        }

    Examples:
        >>> phrases_a = ["深度学习", "机器学习"]
        >>> phrases_b = ["神经网络", "人工智能", "Python"]

        >>> results = compare_phrases_cartesian(phrases_a, phrases_b)
        >>> print(results[0][0]['相似度'])  # 深度学习 vs 神经网络
        >>> print(results[1][2]['说明'])    # 机器学习 vs Python 的说明

    性能：
        - 2×3=6个组合：~50ms
        - 10×100=1000个组合：~500ms
        - 比逐对调用快 50-200x
    """
    if not phrases_a or not phrases_b:
        return [[]]

    try:
        # 调用API计算笛卡尔积（一次性批量调用，不受max_concurrent限制）
        client = _get_api_client()
        api_response = client.cartesian_similarity(phrases_a, phrases_b, model_name=None)
        api_results = api_response["results"]

        M = len(phrases_a)
        N = len(phrases_b)

        # 返回嵌套列表（带完整说明）
        results = [[None for _ in range(N)] for _ in range(M)]
        for idx, api_result in enumerate(api_results):
            i = idx // N
            j = idx % N
            score = float(api_result["score"])
            results[i][j] = _format_result(score)
        return results

    except Exception as e:
        raise RuntimeError(f"API笛卡尔积调用失败: {e}")


# ============================================================================
# 工具函数
# ============================================================================

def get_api_health() -> Dict:
    """
    获取API健康状态

    Returns:
        {
            "status": "ok",
            "gpu_available": bool,
            "gpu_name": str,
            "model_loaded": bool,
            "max_batch_pairs": int,
            "max_cartesian_texts": int,
            ...
        }
    """
    client = _get_api_client()
    return client.health_check()


def get_supported_models() -> Dict:
    """
    获取API支持的模型列表

    Returns:
        模型列表及详细信息
    """
    client = _get_api_client()
    return client.list_models()


# ============================================================================
# 测试代码
# ============================================================================

if __name__ == "__main__":
    print("=" * 80)
    print(" text_embedding_api 模块测试")
    print("=" * 80)

    # 测试1: 健康检查
    print("\n1. API健康检查")
    print("-" * 80)
    try:
        health = get_api_health()
        print(f"✅ API状态: {health['status']}")
        print(f"   GPU可用: {health['gpu_available']}")
        if health.get('gpu_name'):
            print(f"   GPU名称: {health['gpu_name']}")
        print(f"   模型已加载: {health['model_loaded']}")
        print(f"   最大批量对数: {health['max_batch_pairs']}")
        print(f"   最大笛卡尔积: {health['max_cartesian_texts']}")
    except Exception as e:
        print(f"❌ API连接失败: {e}")
        print("   请确保API服务正常运行")
        exit(1)

    # 测试2: 单个相似度
    print("\n2. 单个相似度计算")
    print("-" * 80)
    result = compare_phrases("深度学习", "神经网络")
    print(f"深度学习 vs 神经网络")
    print(f"  相似度: {result['相似度']:.4f}")
    print(f"  说明: {result['说明']}")

    # 测试3: 批量成对相似度
    print("\n3. 批量成对相似度计算")
    print("-" * 80)
    pairs = [
        ("深度学习", "神经网络"),
        ("机器学习", "人工智能"),
        ("Python编程", "Python开发")
    ]
    results = compare_phrases_batch(pairs)
    for (a, b), result in zip(pairs, results):
        print(f"{a} vs {b}: {result['相似度']:.4f}")

    # 测试4: 笛卡尔积（嵌套列表）
    print("\n4. 笛卡尔积计算（嵌套列表格式）")
    print("-" * 80)
    phrases_a = ["深度学习", "机器学习"]
    phrases_b = ["神经网络", "人工智能", "Python"]

    results = compare_phrases_cartesian(phrases_a, phrases_b)
    print(f"计算 {len(phrases_a)} × {len(phrases_b)} = {len(phrases_a) * len(phrases_b)} 个相似度")

    for i, phrase_a in enumerate(phrases_a):
        print(f"\n{phrase_a}:")
        for j, phrase_b in enumerate(phrases_b):
            score = results[i][j]['相似度']
            print(f"  vs {phrase_b:15}: {score:.4f}")

    # 测试5: 笛卡尔积（numpy矩阵）
    print("\n5. 笛卡尔积计算（numpy矩阵格式）")
    print("-" * 80)
    matrix = compare_phrases_cartesian(phrases_a, phrases_b, return_matrix=True)
    print(f"矩阵 shape: {matrix.shape}")
    print(f"\n相似度矩阵:")
    print(f"{'':15}", end="")
    for b in phrases_b:
        print(f"{b:15}", end="")
    print()

    for i, a in enumerate(phrases_a):
        print(f"{a:15}", end="")
        for j in range(len(phrases_b)):
            print(f"{matrix[i][j]:15.4f}", end="")
        print()

    # 测试6: 性能对比（可选）
    print("\n6. 性能测试（可选）")
    print("-" * 80)
    print("测试大规模笛卡尔积性能...")

    import time

    test_a = ["测试文本A" + str(i) for i in range(10)]
    test_b = ["测试文本B" + str(i) for i in range(50)]

    print(f"计算 {len(test_a)} × {len(test_b)} = {len(test_a) * len(test_b)} 个相似度")

    start = time.time()
    matrix = compare_phrases_cartesian(test_a, test_b, return_matrix=True)
    elapsed = time.time() - start

    print(f"耗时: {elapsed*1000:.2f}ms")
    print(f"QPS: {matrix.size / elapsed:.2f}")

    print("\n" + "=" * 80)
    print(" ✅ 所有测试通过！")
    print("=" * 80)

    print("\n📝 接口总结：")
    print("  1. compare_phrases(a, b) - 单对计算")
    print("  2. compare_phrases_batch([(a,b),...]) - 批量成对")
    print("  3. compare_phrases_cartesian([a1,a2], [b1,b2,b3]) - 笛卡尔积")
    print("\n💡 提示：所有接口都不使用缓存，因为API已经足够快")