import axios from "axios";

import { buildApiUrl, resolveModelRequestConfig, type AiConfig, type ModelChannel } from "@/stores/use-config-store";
import { nanoid } from "nanoid";
import { dataUrlToFile } from "@/lib/image-utils";
import { buildImageReferencePromptText } from "@/lib/image-reference-prompt";
import { imageToDataUrl } from "@/services/image-storage";
import type { ReferenceImage } from "@/types/image";

export type AiTextMessage = {
    role: "system" | "user" | "assistant";
    content: string | Array<{ type: "text"; text: string } | { type: "image_url"; image_url: { url: string } }>;
};

export type ResponseToolCall = {
    id: string;
    type: "function";
    function: { name: string; arguments: string };
};

export type ResponseInputMessage =
    | AiTextMessage
    | { type: "function_call"; call_id: string; name: string; arguments: string }
    | { role: "tool"; tool_call_id: string; content: string };

export type ResponseFunctionTool = {
    type: "function";
    function: {
        name: string;
        description?: string;
        parameters: Record<string, unknown>;
        strict?: boolean;
    };
};

export type ToolResponseResult = {
    content: string;
    toolCalls: ResponseToolCall[];
};

type ToolChoice = "auto" | "required" | { type: "function"; name: string };
type ResponseMessageContent = AiTextMessage["content"] | string;
type ResponseInputContent = { type: "input_text"; text: string } | { type: "input_image"; image_url: string };
type ResponseInputItem =
    | { role: "system" | "user" | "assistant"; content: string | ResponseInputContent[] }
    | { type: "function_call"; call_id: string; name: string; arguments: string }
    | { type: "function_call_output"; call_id: string; output: string };
type ResponseApiToolDefinition = {
    type: "function";
    name: string;
    description?: string;
    parameters: Record<string, unknown>;
    strict?: boolean;
};
type ResponseApiOutputItem =
    | { type?: "message"; content?: Array<{ type?: string; text?: string }> }
    | { type?: "function_call"; id?: string; call_id?: string; name?: string; arguments?: string };
type ResponseApiPayload = {
    id?: string;
    output?: ResponseApiOutputItem[];
    output_text?: string;
    error?: { message?: string };
    code?: number;
    msg?: string;
};

type ImageApiResponse = {
    data?: Array<Record<string, unknown>>;
    error?: { message?: string };
    code?: number;
    msg?: string;
};

const QUALITY_BASE: Record<string, number> = {
    low: 1024,
    medium: 2048,
    high: 2880,
    standard: 1024,
    hd: 2048,
};
const QUALITY_ALIASES: Record<string, string> = {
    "1k": "low",
    "2k": "medium",
    "4k": "high",
};
const DEFAULT_IMAGE_SHORT_SIDE = 1024;
const IMAGE_SIZE_STEP = 16;
const IMAGE_MIN_PIXELS = 655360;
const IMAGE_MAX_PIXELS = 8294400;
const IMAGE_MAX_EDGE = 3840;
const IMAGE_MAX_RATIO = 3;
const IMAGE_OUTPUT_FORMAT = "png";

function normalizeQuality(quality: string) {
    const value = quality.trim().toLowerCase();
    const normalized = QUALITY_ALIASES[value] || value;
    return QUALITY_BASE[normalized] ? normalized : undefined;
}

/** Map "quality + ratio" to an explicit pixel dimension like "3840x2160". */
function resolveSize(quality: string | undefined, ratio: string): string {
    const parsedRatio = parseImageRatio(ratio);
    const basePixels = quality ? QUALITY_BASE[quality] : undefined;
    const isLandscape = parsedRatio.width >= parsedRatio.height;
    const longRatio = isLandscape ? parsedRatio.width / parsedRatio.height : parsedRatio.height / parsedRatio.width;
    let longSide: number;
    let shortSide: number;

    if (basePixels) {
        const targetPixels = basePixels * basePixels;
        const longSideRaw = Math.sqrt(targetPixels * longRatio);
        longSide = Math.floor(longSideRaw / IMAGE_SIZE_STEP) * IMAGE_SIZE_STEP;
        shortSide = Math.round(longSide / longRatio / IMAGE_SIZE_STEP) * IMAGE_SIZE_STEP;
    } else {
        shortSide = DEFAULT_IMAGE_SHORT_SIDE;
        longSide = Math.round((shortSide * longRatio) / IMAGE_SIZE_STEP) * IMAGE_SIZE_STEP;
    }

    const width = isLandscape ? longSide : shortSide;
    const height = isLandscape ? shortSide : longSide;
    validateImageSize(width, height);
    return `${width}x${height}`;
}

function parseImageRatio(value: string) {
    const parts = value.split(":");
    if (parts.length !== 2) throw new Error("图像尺寸格式不支持，请使用 auto、9:16 或 1024x1024");
    const w = Number(parts[0]);
    const h = Number(parts[1]);
    if (!Number.isFinite(w) || !Number.isFinite(h) || w <= 0 || h <= 0) throw new Error("图像比例必须是正数，例如 9:16");
    if (Math.max(w, h) / Math.min(w, h) > IMAGE_MAX_RATIO) throw new Error("图像宽高比不能超过 3:1，请调整尺寸");
    return { width: w, height: h };
}

function parseImageDimensions(value: string) {
    const match = value.match(/^(\d+)x(\d+)$/i);
    if (!match) return null;
    return { width: Number(match[1]), height: Number(match[2]) };
}

function validateImageSize(width: number, height: number) {
    if (!Number.isInteger(width) || !Number.isInteger(height) || width <= 0 || height <= 0) throw new Error("图像尺寸必须是正整数，例如 1024x1024");
    if (width % IMAGE_SIZE_STEP !== 0 || height % IMAGE_SIZE_STEP !== 0) throw new Error("图像尺寸的宽高必须是 16 的倍数，请调整尺寸");
    if (Math.max(width, height) > IMAGE_MAX_EDGE) throw new Error("图像尺寸最长边不能超过 3840px，请调整尺寸");
    if (Math.max(width, height) / Math.min(width, height) > IMAGE_MAX_RATIO) throw new Error("图像宽高比不能超过 3:1，请调整尺寸");
    const pixels = width * height;
    if (pixels < IMAGE_MIN_PIXELS || pixels > IMAGE_MAX_PIXELS) throw new Error("图像总像素需在 655360 到 8294400 之间，请调整尺寸");
}

function resolveRequestSize(quality: string | undefined, size: string) {
    const value = size.trim();
    if (!value || value.toLowerCase() === "auto") return undefined;
    const dimensions = parseImageDimensions(value);
    if (dimensions) {
        validateImageSize(dimensions.width, dimensions.height);
        return `${dimensions.width}x${dimensions.height}`;
    }
    if (value.includes(":")) return resolveSize(quality, value);
    throw new Error("图像尺寸格式不支持，请使用 auto、9:16 或 1024x1024");
}

function resolveImageDataUrl(item: Record<string, unknown>) {
    if (typeof item.b64_json === "string" && item.b64_json) {
        return `data:image/png;base64,${item.b64_json}`;
    }
    if (typeof item.url === "string" && item.url) {
        return item.url;
    }
    return null;
}

function parseImagePayload(payload: ImageApiResponse) {
    if (typeof payload.code === "number" && payload.code !== 0) {
        throw new Error(payload.msg || "请求失败");
    }
    const images =
        payload.data
            ?.map(resolveImageDataUrl)
            .filter((value): value is string => Boolean(value))
            .map((dataUrl) => ({ id: nanoid(), dataUrl })) || [];

    if (images.length === 0) {
        throw new Error("接口没有返回图片");
    }

    return images;
}

function readAxiosError(error: unknown, fallback: string) {
    if (axios.isAxiosError<{ error?: { message?: string }; msg?: string; code?: number }>(error)) {
        const responseData = error.response?.data;
        return responseData?.msg || responseData?.error?.message || readStatusError(error.response?.status, fallback);
    }
    return error instanceof Error ? error.message : fallback;
}

function readStatusError(status: number | undefined, fallback: string) {
    if (status === 401 || status === 403) return "鉴权失败，请检查 API Key、套餐权限或模型权限";
    if (status === 429) return "请求被限流或额度不足，请稍后重试";
    return status ? `${fallback}：${status}` : fallback;
}

function withSystemPrompt(config: AiConfig, prompt: string) {
    const systemPrompt = config.systemPrompt.trim();
    return systemPrompt ? `${systemPrompt}\n\n${prompt}` : prompt;
}

function aiApiUrl(config: AiConfig, path: string) {
    return buildApiUrl(config.baseUrl, path);
}

function aiHeaders(config: AiConfig, contentType?: string) {
    return {
        Authorization: `Bearer ${config.apiKey}`,
        ...(contentType ? { "Content-Type": contentType } : {}),
    };
}

function withSystemMessage<T extends ResponseInputMessage>(config: AiConfig, messages: T[]): ResponseInputMessage[] {
    const systemPrompt = config.systemPrompt.trim();
    return systemPrompt ? [{ role: "system" as const, content: systemPrompt }, ...messages] : messages;
}

function toResponseInput(messages: ResponseInputMessage[]): ResponseInputItem[] {
    return messages.flatMap((message): ResponseInputItem[] => {
        if ("type" in message) return [message];
        if (message.role === "tool") return [{ type: "function_call_output", call_id: message.tool_call_id, output: message.content }];
        return [{ role: message.role, content: toResponseContent(message.content || "") }];
    });
}

function toResponseContent(content: ResponseMessageContent): string | ResponseInputContent[] {
    if (!Array.isArray(content)) return String(content || "");
    return content.map((item) => (item.type === "text" ? { type: "input_text" as const, text: item.text } : { type: "input_image" as const, image_url: item.image_url.url }));
}

function toResponseTool(tool: ResponseFunctionTool): ResponseApiToolDefinition {
    return {
        type: "function",
        name: tool.function.name,
        description: tool.function.description,
        parameters: tool.function.parameters,
        strict: tool.function.strict,
    };
}

function parseToolResponse(payload: ResponseApiPayload): ToolResponseResult {
    const output = payload.output || [];
    const content =
        payload.output_text ||
        output
            .flatMap((item) => (item.type === "message" ? item.content || [] : []))
            .map((item) => item.text || "")
            .join("");
    const toolCalls = output
        .filter((item): item is Extract<ResponseApiOutputItem, { type?: "function_call" }> => item.type === "function_call")
        .map((item) => ({
            id: item.call_id || item.id || "",
            type: "function" as const,
            function: { name: item.name || "", arguments: item.arguments || "{}" },
        }))
        .filter((item) => item.id && item.function.name);
    return { content, toolCalls };
}

export async function requestGeneration(config: AiConfig, prompt: string) {
    const requestConfig = resolveModelRequestConfig(config, config.model || config.imageModel);
    const n = Math.max(1, Math.min(15, Math.floor(Math.abs(Number(config.count)) || 1)));
    const quality = normalizeQuality(config.quality);
    const requestSize = resolveRequestSize(quality, config.size);
    try {
        const response = await axios.post<ImageApiResponse>(
            aiApiUrl(requestConfig, "/images/generations"),
            {
                model: requestConfig.model,
                prompt: withSystemPrompt(requestConfig, prompt),
                n,
                ...(quality ? { quality } : {}),
                ...(requestSize ? { size: requestSize } : {}),
                response_format: "b64_json",
                output_format: IMAGE_OUTPUT_FORMAT,
            },
            {
                headers: aiHeaders(requestConfig, "application/json"),
            },
        );
        const images = parseImagePayload(response.data);
        return images;
    } catch (error) {
        throw new Error(readAxiosError(error, "请求失败"));
    }
}

export async function requestEdit(config: AiConfig, prompt: string, references: ReferenceImage[], mask?: ReferenceImage) {
    const requestConfig = resolveModelRequestConfig(config, config.model || config.imageModel);
    const n = Math.max(1, Math.min(15, Math.floor(Math.abs(Number(config.count)) || 1)));
    const quality = normalizeQuality(config.quality);
    const requestSize = resolveRequestSize(quality, config.size);
    const requestPrompt = buildImageReferencePromptText(prompt, references);
    const formData = new FormData();
    formData.set("model", requestConfig.model);
    formData.set("prompt", withSystemPrompt(requestConfig, requestPrompt));
    formData.set("n", String(n));
    formData.set("response_format", "b64_json");
    formData.set("output_format", IMAGE_OUTPUT_FORMAT);
    if (quality) {
        formData.set("quality", quality);
    }
    if (requestSize) {
        formData.set("size", requestSize);
    }
    const files = await Promise.all(references.map(async (image) => dataUrlToFile({ ...image, dataUrl: await imageToDataUrl(image) })));
    files.forEach((file) => formData.append("image", file));
    if (mask) formData.set("mask", dataUrlToFile(mask));

    try {
        const response = await axios.post<ImageApiResponse>(aiApiUrl(requestConfig, "/images/edits"), formData, { headers: aiHeaders(requestConfig) });
        const images = parseImagePayload(response.data);
        return images;
    } catch (error) {
        throw new Error(readAxiosError(error, "请求失败"));
    }
}

export async function requestImageQuestion(config: AiConfig, messages: AiTextMessage[], onDelta: (text: string) => void) {
    const requestConfig = resolveModelRequestConfig(config, config.model || config.textModel);
    try {
        const response = await axios.post<ResponseApiPayload>(
            aiApiUrl(requestConfig, "/responses"),
            {
                model: requestConfig.model,
                input: toResponseInput(withSystemMessage(requestConfig, messages)),
            },
            {
                headers: aiHeaders(requestConfig, "application/json"),
            },
        );
        if (typeof response.data.code === "number" && response.data.code !== 0) throw new Error(response.data.msg || "请求失败");
        if (response.data.error?.message) throw new Error(response.data.error.message);
        const answer = parseToolResponse(response.data).content || "没有返回内容";
        onDelta(answer);
        return answer;
    } catch (error) {
        throw new Error(readAxiosError(error, "请求失败"));
    }
}

export async function requestToolResponse(config: AiConfig, messages: ResponseInputMessage[], tools: ResponseFunctionTool[], toolChoice: ToolChoice = "auto"): Promise<ToolResponseResult> {
    const requestConfig = resolveModelRequestConfig(config, config.model || config.textModel);
    try {
        const response = await axios.post<ResponseApiPayload>(
            aiApiUrl(requestConfig, "/responses"),
            {
                model: requestConfig.model,
                input: toResponseInput(withSystemMessage(requestConfig, messages)),
                tools: tools.map(toResponseTool),
                tool_choice: toolChoice,
                parallel_tool_calls: false,
            },
            {
                headers: aiHeaders(requestConfig, "application/json"),
            },
        );
        if (typeof response.data.code === "number" && response.data.code !== 0) throw new Error(response.data.msg || "请求失败");
        if (response.data.error?.message) throw new Error(response.data.error.message);
        return parseToolResponse(response.data);
    } catch (error) {
        throw new Error(readAxiosError(error, "请求失败"));
    }
}

export async function fetchImageModels(config: Pick<AiConfig, "baseUrl" | "apiKey">) {
    try {
        const response = await axios.get<{ data?: Array<{ id?: string }>; error?: { message?: string } }>(buildApiUrl(config.baseUrl, "/models"), {
            headers: {
                Authorization: `Bearer ${config.apiKey}`,
            },
        });
        return (response.data.data || [])
            .map((model) => model.id)
            .filter((id): id is string => Boolean(id))
            .sort((a, b) => a.localeCompare(b));
    } catch (error) {
        throw new Error(readAxiosError(error, "读取模型失败"));
    }
}

export async function fetchChannelModels(channel: ModelChannel) {
    return fetchImageModels({ baseUrl: channel.baseUrl, apiKey: channel.apiKey });
}