from __future__ import annotations

import logging
import os

import requests
try:
    from openai import OpenAI
except ImportError:  # pragma: no cover - depends on deployment environment
    OpenAI = None
import time
from typing import List

try:
    import numpy as np
except ImportError:  # pragma: no cover - depends on deployment environment
    np = None

from code_parser import FUNCTION_CALL_GRAPH, CALLED_BY_GRAPH, FILE_DEPENDENCIES
from config import QWEN_API_KEY, QWEN_API_URL, QWEN_CHAT_MODEL, QWEN_EMBEDDING_MODEL

MAX_CODE_LENGTH = 800
logger = logging.getLogger(__name__)


def _get_qwen_api_key() -> str:
    api_key = (
        os.getenv("DASHSCOPE_API_KEY")
        or os.getenv("DASH_SCOPE_API_KEY")
        or os.getenv("QWEN_API_KEY")
        or QWEN_API_KEY
    )
    if not api_key:
        raise RuntimeError(
            "Qwen/DashScope API key is not configured. Set DASHSCOPE_API_KEY, "
            "DASH_SCOPE_API_KEY, or QWEN_API_KEY."
        )
    return api_key


def _chat_completions_url() -> str:
    return QWEN_API_URL.rstrip("/") + "/chat/completions"

def generate_code_summary(func_name, comment, logic, code_snippet, file_path):
    called_functions = FUNCTION_CALL_GRAPH.get(func_name, [])
    caller_functions = CALLED_BY_GRAPH.get(func_name, [])
    included_headers = FILE_DEPENDENCIES.get(file_path, [])

    prompt = f"""
你是一名资深的航天软件工程师，请总结以下C++函数的核心功能，**必须严格包含以下6点**：

1. 函数流程与逻辑:
{logic}

2. 函数目的:
→ 结合航天术语总结，必须根据上述“函数流程与逻辑”推导生成，不得依赖假设！

3. 输入参数（名称、类型、作用，用括号列出）

4. 返回值（类型和含义）

5. 与其他函数的关联关系：
   - 被调用的函数: {', '.join(called_functions) or '无'}
   - 调用此函数的函数: {', '.join(caller_functions) or '无'}

6. 与跨文件关联的函数（头文件）: {', '.join(included_headers) or '无'}

---
函数名: {func_name}
注释: {comment or '无'}
代码片段（截断至{MAX_CODE_LENGTH}字符）:
{code_snippet[:MAX_CODE_LENGTH]}

输出格式: "功能: [按上述6点组织的连贯段落，不要编号]"
"""
    headers = {"Authorization": f"Bearer {_get_qwen_api_key()}", "Content-Type": "application/json"}
    payload = {
        "model": QWEN_CHAT_MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.1
    }
    response = requests.post(
        _chat_completions_url(),
        headers=headers,
        json=payload
    )
    response.raise_for_status()
    summary = response.json()['choices'][0]['message']['content'].strip()
    return summary.replace("功能: ", "")


def generate_code_logic(func_name, comment, code_snippet, file_path):
    prompt = f"""
    你是一名资深的航天软件工程师，请分析以下C++函数，并生成其markdown格式的**核心流程逻辑总结**。

    **函数名**: {func_name}
    **所在文件**: {file_path}
    **前置注释**: {comment or '无'}
    **函数代码片段**:{code_snippet}请严格按照以下步骤执行：
1.  **步骤分解**：逐行或按代码块分析函数体，用简洁的步骤描述其执行流程。例如：“1. 检查输入参数x是否有效。2. 调用内部函数compute()进行计算。3. 对结果进行格式化处理。4. 返回格式化后的字符串。”
2.  **逻辑归纳**：基于上述步骤，用1-3句话概括这个函数的核心功能与逻辑主线。

**输出格式**：只需输出纯粹的、不带Markdown标题的流程逻辑描述文本。
    """
    headers = {
        "Authorization": f"Bearer {_get_qwen_api_key()}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": QWEN_CHAT_MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.1
    }
    response = requests.post(
        _chat_completions_url(),
        headers=headers,
        json=payload
    )
    response.raise_for_status()
    result = response.json()
    logic = result['choices'][0]['message']['content'].strip()
    return logic


def get_qwen_embedding(text: str) -> np.ndarray:
    try:
        if OpenAI is None:
            raise RuntimeError("openai package is not installed.")
        client = OpenAI(
            api_key=_get_qwen_api_key(),
            base_url=QWEN_API_URL
        )
        response = client.embeddings.create(
            model=QWEN_EMBEDDING_MODEL,
            input=text
        )
        embedding = response.data[0].embedding
        if np is not None:
            return np.array(embedding, dtype='float32')
        return [float(value) for value in embedding]
    except Exception as e:
        logger.error(f"获取嵌入失败: {e}")
        raise RuntimeError(f"Failed to get embedding: {e}") from e


def call_qwen_max(prompt: str, max_tokens: int = 1500, temperature: float = 0.1) -> str:
    """
    调用 Qwen-Max 进行推理

    Args:
        prompt: 输入的提示文本
        max_tokens: 最大输出token数
        temperature: 温度参数，控制随机性

    Returns:
        模型生成的文本
    """
    try:
        # 使用 OpenAI 兼容格式调用通义千问
        if OpenAI is None:
            raise RuntimeError("openai package is not installed.")
        client = OpenAI(
            api_key=_get_qwen_api_key(),
            base_url=QWEN_API_URL
        )

        response = client.chat.completions.create(
            model=QWEN_CHAT_MODEL,
            messages=[
                {"role": "user", "content": prompt}
            ],
            max_tokens=max_tokens,
            temperature=temperature
        )

        return response.choices[0].message.content.strip()

    except Exception as e:
        logger.error(f"调用Qwen-Max失败: {e}")
        return f"API调用失败: {str(e)}"