import axios, { type AxiosRequestConfig } from "axios";

import { buildApiUrl, type AiConfig, type ModelCapability } from "@/stores/use-config-store";

type RequestOptions = { signal?: AbortSignal };

export type PluginHttpOptions = {
    headers?: Record<string, string>;
    params?: Record<string, unknown>;
    responseType?: "json" | "blob" | "text" | "arraybuffer";
};

export type PluginHttp = {
    url: (path: string) => string;
    post: (path: string, body?: unknown, options?: PluginHttpOptions) => Promise<unknown>;
    get: (path: string, options?: PluginHttpOptions) => Promise<unknown>;
};

export type PluginPollOptions = { intervalMs?: number; timeoutMs?: number };

export type RunPluginArgs = {
    capability: ModelCapability;
    script: string;
    config: AiConfig;
    prompt?: string;
    images?: string[];
    messages?: unknown[];
    params?: Record<string, unknown>;
    signal?: AbortSignal;
    onDelta?: (text: string) => void;
};

function pluginHeaders(extra?: Record<string, string>, hasJsonBody = false): Record<string, string> {
    const headers: Record<string, string> = {};
    if (hasJsonBody) headers["Content-Type"] = "application/json";
    return { ...headers, ...extra };
}

function pluginUrl(config: AiConfig, path: string) {
    if (/^https?:/i.test(path)) return path;
    return buildApiUrl(config.baseUrl, path.startsWith("/") ? path : `/${path}`);
}

function createPluginHttp(config: AiConfig, options?: RequestOptions): PluginHttp {
    const run = async (method: "get" | "post", path: string, body: unknown, opts?: PluginHttpOptions) => {
        const isForm = typeof FormData !== "undefined" && body instanceof FormData;
        const response = await axios.request({
            method,
            url: pluginUrl(config, path),
            data: method === "post" ? body : undefined,
            params: opts?.params,
            headers: pluginHeaders({ Authorization: `Bearer ${config.apiKey}`, ...opts?.headers }, method === "post" && !isForm && body !== undefined),
            responseType: opts?.responseType || "json",
            signal: options?.signal,
        });
        return response.data;
    };
    return {
        url: (path) => pluginUrl(config, path),
        post: (path, body, opts) => run("post", path, body, opts),
        get: (path, opts) => run("get", path, undefined, opts),
    };
}

/** Raw request with no automatic auth header — the script controls method, url, headers, body entirely. */
function createPluginRequest(config: AiConfig, options?: RequestOptions) {
    return async (requestConfig: AxiosRequestConfig & { url: string }) => {
        const response = await axios.request({ ...requestConfig, url: pluginUrl(config, requestConfig.url), signal: options?.signal });
        return response.data;
    };
}

function sleep(ms: number, signal?: AbortSignal) {
    return new Promise<void>((resolve, reject) => {
        if (signal?.aborted) {
            reject(new DOMException("Aborted", "AbortError"));
            return;
        }
        const timer = setTimeout(resolve, ms);
        signal?.addEventListener(
            "abort",
            () => {
                clearTimeout(timer);
                reject(new DOMException("Aborted", "AbortError"));
            },
            { once: true },
        );
    });
}

function createPoll(signal?: AbortSignal) {
    return async function poll<T, R>(request: () => Promise<T>, extract: (value: T) => R | null | undefined | false, options?: PluginPollOptions): Promise<R> {
        const intervalMs = options?.intervalMs ?? 2500;
        const timeoutMs = options?.timeoutMs ?? 300000;
        const deadline = performance.now() + timeoutMs;
        for (;;) {
            if (signal?.aborted) throw new DOMException("Aborted", "AbortError");
            const result = extract(await request());
            if (result !== null && result !== undefined && result !== false) return result;
            if (performance.now() >= deadline) throw new Error("插件轮询超时，请检查调用脚本或稍后重试");
            await sleep(intervalMs, signal);
        }
    };
}

/**
 * Run a user-authored model call script as an async function body with flat locals (see PLUGIN_VARIABLES):
 *   prompt / images / messages / params        —— 本次请求的输入
 *   model / baseUrl / apiKey / systemPrompt / reasoningEffort     —— 当前渠道与文本设置
 *   http / request / poll / sleep / signal / onDelta    —— 调用辅助
 * The script must `return` the result; each caller normalizes it to its capability's shape.
 */
export async function runModelPlugin<T = unknown>(args: RunPluginArgs): Promise<T> {
    const { config } = args;
    const http = createPluginHttp(config, { signal: args.signal });
    const request = createPluginRequest(config, { signal: args.signal });
    const poll = createPoll(args.signal);
    const runner = new Function(
        "prompt",
        "images",
        "messages",
        "params",
        "model",
        "baseUrl",
        "apiKey",
        "systemPrompt",
        "reasoningEffort",
        "http",
        "request",
        "poll",
        "sleep",
        "signal",
        "onDelta",
        `"use strict"; return (async () => {\n${args.script}\n})();`,
    ) as (...fnArgs: unknown[]) => Promise<T>;
    try {
        return await runner(
            args.prompt || "",
            args.images || [],
            args.messages || [],
            args.params || {},
            config.model,
            config.baseUrl,
            config.apiKey,
            config.systemPrompt || "",
            config.reasoningEffort,
            http,
            request,
            poll,
            (ms: number) => sleep(ms, args.signal),
            args.signal,
            args.onDelta,
        );
    } catch (error) {
        if (error instanceof DOMException && error.name === "AbortError") throw error;
        if (axios.isCancel(error)) throw error;
        const message = error instanceof Error ? error.message : String(error);
        throw new Error(`模型调用脚本执行失败：${message}`);
    }
}

export type PluginVariable = { name: string; type: string; desc: string; capabilities?: ModelCapability[] };

/** Documentation surface shown in the script editor. */
export const PLUGIN_VARIABLES: PluginVariable[] = [
    { name: "prompt", type: "string", desc: "用户输入的提示词（已拼接系统提示词）", capabilities: ["image", "video", "audio"] },
    { name: "images", type: "string[]", desc: "参考图，dataURL 数组（改图 / 图生视频时有值）", capabilities: ["image", "video"] },
    { name: "messages", type: "{ role, content }[]", desc: "对话消息数组，含系统消息", capabilities: ["text"] },
    { name: "params", type: "object", desc: "生成参数：生图 {size,quality,count}、视频 {seconds,size,resolution,ratio,generateAudio,watermark}、音频 {voice,format,speed,instructions}" },
    { name: "model", type: "string", desc: "模型名称（不含渠道前缀）" },
    { name: "baseUrl", type: "string", desc: "渠道接口地址（原样，未拼 /v1）" },
    { name: "apiKey", type: "string", desc: "渠道 API Key，请求头里自己带上" },
    { name: "systemPrompt", type: "string", desc: "系统提示词原文" },
    { name: "reasoningEffort", type: '"auto" | "low" | "medium" | "high" | "xhigh"', desc: "文本推理强度；auto 表示由脚本决定是否传递", capabilities: ["text"] },
    { name: "http", type: "object", desc: "便捷请求：http.post(path, body, {headers,params,responseType})、http.get(path, opts)、http.url(path)；默认带 Authorization: Bearer apiKey，可用 headers 覆盖；path 相对时按 baseUrl 拼 /v1" },
    { name: "request", type: "function", desc: "原始请求 request({ method, url, headers, params, data, responseType })，不加任何默认头，鉴权头自己写；url 相对时按 baseUrl 拼接（不加 /v1）" },
    { name: "poll", type: "function", desc: "轮询 poll(request, extract, {intervalMs,timeoutMs})，extract 返回真值即结束" },
    { name: "sleep", type: "function", desc: "sleep(ms) 延时" },
    { name: "signal", type: "AbortSignal", desc: "取消信号，可透传给 http/request" },
    { name: "onDelta", type: "function", desc: "onDelta(text) 推送流式文本（文本模型）", capabilities: ["text"] },
];

export const PLUGIN_RETURNS: Record<ModelCapability, string> = {
    image: "文生图（images 为空）和图生图（images 有参考图）接口不同，脚本需自行区分；返回图片 URL 或 dataURL 字符串，也可返回它们的数组，或 [{ dataUrl }] / [{ url }] / [{ b64_json }]",
    video: "脚本内部完成轮询，返回 { url } 或 { blob } 或视频 URL 字符串",
    audio: "返回 Blob，或 base64 / dataURL 字符串，或 { b64_json } / { data } / { url }",
    text: "用 onDelta(text) 推送流式，最终 return 完整文本字符串",
};

export type PluginTemplate = { label: string; script: string };

export const PLUGIN_TEMPLATES: Record<ModelCapability, PluginTemplate[]> = {
    image: [
        {
            label: "OpenAI 规范",
            script: `// 生图 / 改图：两者接口不同，用 images 是否为空来区分。
// 可用：prompt、images(dataURL[])、params{size,quality,count}、model、baseUrl、apiKey
if (images.length === 0) {
  // 文生图：/images/generations（JSON）
  const data = await request({
    method: "post",
    url: \`\${baseUrl}/v1/images/generations\`,
    headers: { "Content-Type": "application/json", Authorization: \`Bearer \${apiKey}\` },
    data: { model, prompt, n: params.count, size: params.size, response_format: "b64_json" },
  });
  return (data.data || []).map((item) => item.b64_json ? \`data:image/png;base64,\${item.b64_json}\` : item.url);
}

// 图生图：/images/edits（multipart/form-data，参考图作为文件上传）
const form = new FormData();
form.set("model", model);
form.set("prompt", prompt);
form.set("n", String(params.count));
form.set("response_format", "b64_json");
for (const dataUrl of images) {
  form.append("image", await (await fetch(dataUrl)).blob(), "ref.png");
}
const edited = await request({
  method: "post",
  url: \`\${baseUrl}/v1/images/edits\`,
  headers: { Authorization: \`Bearer \${apiKey}\` }, // 不要手动设 Content-Type，交给浏览器带 boundary
  data: form,
});
return (edited.data || []).map((item) => item.b64_json ? \`data:image/png;base64,\${item.b64_json}\` : item.url);`,
        },
        {
            label: "Gemini 规范",
            script: `// Gemini 文生图 / 图生图：都走 generateContent，参考图放进 parts 的 inline_data。
// 可用：prompt、images(dataURL[])、model、baseUrl、apiKey
const parts = [{ text: prompt }];
for (const dataUrl of images) {
  const match = dataUrl.match(/^data:([^;]+);base64,(.*)$/);
  if (match) parts.push({ inline_data: { mime_type: match[1], data: match[2] } });
}
const data = await request({
  method: "post",
  url: \`\${baseUrl}/v1beta/models/\${model}:generateContent\`,
  headers: { "Content-Type": "application/json", "x-goog-api-key": apiKey },
  data: { contents: [{ role: "user", parts }], generationConfig: { responseModalities: ["IMAGE"] } },
});
return (data.candidates || [])
  .flatMap((c) => c.content?.parts || [])
  .map((p) => p.inlineData || p.inline_data)
  .filter(Boolean)
  .map((img) => \`data:\${img.mimeType || img.mime_type || "image/png"};base64,\${img.data}\`);`,
        },
    ],
    video: [
        {
            label: "OpenAI 规范",
            script: `// 视频（脚本内部自行轮询）。可用：prompt、images(dataURL[])、params{seconds,size,resolution,ratio}
const headers = { "Content-Type": "application/json", Authorization: \`Bearer \${apiKey}\` };
const task = await request({
  method: "post",
  url: \`\${baseUrl}/v1/videos\`,
  headers,
  data: { model, prompt, seconds: params.seconds },
});
return await poll(
  () => request({ method: "get", url: \`\${baseUrl}/v1/videos/\${task.id}\`, headers }),
  (state) => state.status === "completed" ? { url: state.video_url || state.url } : null,
  { intervalMs: 2500, timeoutMs: 300000 },
);`,
        },
        {
            label: "Gemini 规范",
            script: `// Gemini(Veo) 视频：predictLongRunning 提交，轮询 operation 拿视频 URI。
// 可用：prompt、images(dataURL[])、params、model、baseUrl、apiKey
const headers = { "Content-Type": "application/json", "x-goog-api-key": apiKey };
const instance = { prompt };
const first = images[0] && images[0].match(/^data:([^;]+);base64,(.*)$/);
if (first) instance.image = { bytesBase64Encoded: first[2], mimeType: first[1] };
const op = await request({
  method: "post",
  url: \`\${baseUrl}/v1beta/models/\${model}:predictLongRunning\`,
  headers,
  data: { instances: [instance], parameters: { aspectRatio: params.ratio } },
});
return await poll(
  () => request({ method: "get", url: \`\${baseUrl}/v1beta/\${op.name}\`, headers }),
  (state) => {
    if (!state.done) return null;
    const uri = state.response?.generateVideoResponse?.generatedSamples?.[0]?.video?.uri;
    if (!uri) throw new Error("Gemini 未返回视频 URI");
    return { url: uri.includes("key=") ? uri : \`\${uri}\${uri.includes("?") ? "&" : "?"}key=\${apiKey}\` };
  },
  { intervalMs: 5000, timeoutMs: 300000 },
);`,
        },
    ],
    audio: [
        {
            label: "OpenAI 规范",
            script: `// 音频 TTS。可用：prompt、params{voice,format,speed,instructions}、model
return await request({
  method: "post",
  url: \`\${baseUrl}/v1/audio/speech\`,
  headers: { "Content-Type": "application/json", Authorization: \`Bearer \${apiKey}\` },
  responseType: "blob",
  data: { model, input: prompt, voice: params.voice, response_format: params.format, speed: Number(params.speed) },
});`,
        },
        {
            label: "Gemini 规范",
            script: `// Gemini TTS：generateContent + AUDIO 模态，返回 base64 PCM（音频数据在 inlineData.data）。
// 可用：prompt、params{voice}、model、baseUrl、apiKey
const data = await request({
  method: "post",
  url: \`\${baseUrl}/v1beta/models/\${model}:generateContent\`,
  headers: { "Content-Type": "application/json", "x-goog-api-key": apiKey },
  data: {
    contents: [{ role: "user", parts: [{ text: prompt }] }],
    generationConfig: {
      responseModalities: ["AUDIO"],
      speechConfig: { voiceConfig: { prebuiltVoiceConfig: { voiceName: params.voice } } },
    },
  },
});
const audio = data.candidates?.[0]?.content?.parts?.map((p) => p.inlineData || p.inline_data).find(Boolean);
if (!audio?.data) throw new Error("Gemini 未返回音频");
return { data: audio.data };`,
        },
    ],
    text: [
        {
            label: "OpenAI 规范",
            script: `// 文本对话（OpenAI Responses 接口）。可用：messages([{role,content}])、systemPrompt、model、reasoningEffort
const data = await request({
  method: "post",
  url: \`\${baseUrl}/v1/responses\`,
  headers: { "Content-Type": "application/json", Authorization: \`Bearer \${apiKey}\` },
  data: {
    model,
    input: messages,
    ...(reasoningEffort === "auto" ? {} : { reasoning: { effort: reasoningEffort } }),
  },
});
const text = data.output_text
  || (data.output || []).flatMap((o) => o.content || []).map((c) => c.text || "").join("")
  || "";
onDelta(text);
return text;`,
        },
        {
            label: "Gemini 规范",
            script: `// Gemini 文本：generateContent，system 消息放 systemInstruction。
// 可用：messages([{role,content}])、systemPrompt、model、baseUrl、apiKey
const contents = messages
  .filter((m) => m.role !== "system")
  .map((m) => ({ role: m.role === "assistant" ? "model" : "user", parts: [{ text: m.content }] }));
const data = await request({
  method: "post",
  url: \`\${baseUrl}/v1beta/models/\${model}:generateContent\`,
  headers: { "Content-Type": "application/json", "x-goog-api-key": apiKey },
  data: { contents, ...(systemPrompt ? { systemInstruction: { parts: [{ text: systemPrompt }] } } : {}) },
});
const text = data.candidates?.[0]?.content?.parts?.map((p) => p.text || "").join("") || "";
onDelta(text);
return text;`,
        },
    ],
};

/** Normalize whatever an image script returns into the app's generated-image shape. */
export function normalizePluginImages(result: unknown): string[] {
    const items = Array.isArray(result) ? result : [result];
    const urls = items
        .map((item) => {
            if (typeof item === "string") return item;
            if (item && typeof item === "object") {
                const record = item as Record<string, unknown>;
                if (typeof record.dataUrl === "string") return record.dataUrl;
                if (typeof record.url === "string") return record.url;
                if (typeof record.b64_json === "string") return `data:image/png;base64,${record.b64_json}`;
            }
            return "";
        })
        .filter(Boolean);
    if (!urls.length) throw new Error("模型调用脚本没有返回图片");
    return urls;
}