import * as readline from "readline";
import process from "process";
import path from "path";
import chalk from "chalk";
import fs from "fs-extra";
import { getLlama } from "../../bindings/getLlama.js";
import { LlamaLogLevel, LlamaLogLevelGreaterThan, nodeLlamaCppGpuOptions, parseNodeLlamaCppGpuOption } from "../../bindings/types.js";
import { LlamaCompletion } from "../../evaluator/LlamaCompletion.js";
import withOra from "../../utils/withOra.js";
import { TokenMeter } from "../../evaluator/TokenMeter.js";
import { printInfoLine } from "../utils/printInfoLine.js";
import { printCommonInfoLines } from "../utils/printCommonInfoLines.js";
import { resolveCommandGgufPath } from "../utils/resolveCommandGgufPath.js";
import { withProgressLog } from "../../utils/withProgressLog.js";
import { resolveHeaderFlag } from "../utils/resolveHeaderFlag.js";
import { withCliCommandDescriptionDocsUrl } from "../utils/withCliCommandDescriptionDocsUrl.js";
import { documentationPageUrls } from "../../config.js";
import { ConsoleInteraction, ConsoleInteractionKey } from "../utils/ConsoleInteraction.js";
export const CompleteCommand = {
    command: "complete [modelPath]",
    describe: withCliCommandDescriptionDocsUrl("Generate a completion for a given text", documentationPageUrls.CLI.Complete),
    builder(yargs) {
        return yargs
            .option("modelPath", {
            alias: ["m", "model", "path", "url", "uri"],
            type: "string",
            description: "Model file to use for the completion. Can be a path to a local file or a URI of a model file to download. Leave empty to choose from a list of recommended models"
        })
            .option("header", {
            alias: ["H"],
            type: "string",
            array: true,
            description: "Headers to use when downloading a model from a URL, in the format `key: value`. You can pass this option multiple times to add multiple headers."
        })
            .option("gpu", {
            type: "string",
            // yargs types don't support passing `false` as a choice, although it is supported by yargs
            choices: nodeLlamaCppGpuOptions,
            coerce: (value) => {
                if (value == null || value == "")
                    return undefined;
                return parseNodeLlamaCppGpuOption(value);
            },
            defaultDescription: "Uses the latest local build, and fallbacks to \"auto\"",
            description: "Compute layer implementation type to use for llama.cpp. If omitted, uses the latest local build, and fallbacks to \"auto\""
        })
            .option("systemInfo", {
            alias: "i",
            type: "boolean",
            default: false,
            description: "Print llama.cpp system info"
        })
            .option("text", {
            type: "string",
            description: "First text to automatically start generating completion for"
        })
            .option("textFile", {
            type: "string",
            description: "Path to a file to load text from and use as the first text to automatically start generating completion for"
        })
            .option("contextSize", {
            alias: "c",
            type: "number",
            description: "Context size to use for the model context",
            default: -1,
            defaultDescription: "Automatically determined based on the available VRAM"
        })
            .option("batchSize", {
            alias: "b",
            type: "number",
            description: "Batch size to use for the model context. The default value is the context size"
        })
            .option("flashAttention", {
            alias: "fa",
            type: "boolean",
            default: false,
            description: "Enable flash attention"
        })
            .option("threads", {
            type: "number",
            defaultDescription: "Number of cores that are useful for math on the current machine",
            description: "Number of threads to use for the evaluation of tokens"
        })
            .option("temperature", {
            alias: "t",
            type: "number",
            default: 0,
            description: "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The suggested temperature is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run. Set to `0` to disable."
        })
            .option("minP", {
            alias: "mp",
            type: "number",
            default: 0,
            description: "From the next token candidates, discard the percentage of tokens with the lowest probability. For example, if set to `0.05`, 5% of the lowest probability tokens will be discarded. This is useful for generating more high-quality results when using a high temperature. Set to a value between `0` and `1` to enable. Only relevant when `temperature` is set to a value greater than `0`."
        })
            .option("topK", {
            alias: "k",
            type: "number",
            default: 40,
            description: "Limits the model to consider only the K most likely next tokens for sampling at each step of sequence generation. An integer number between `1` and the size of the vocabulary. Set to `0` to disable (which uses the full vocabulary). Only relevant when `temperature` is set to a value greater than 0."
        })
            .option("topP", {
            alias: "p",
            type: "number",
            default: 0.95,
            description: "Dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold P, and samples the next token only from this set. A float number between `0` and `1`. Set to `1` to disable. Only relevant when `temperature` is set to a value greater than `0`."
        })
            .option("seed", {
            type: "number",
            description: "Used to control the randomness of the generated text. Only relevant when using `temperature`.",
            defaultDescription: "The current epoch time"
        })
            .option("gpuLayers", {
            alias: "gl",
            type: "number",
            description: "number of layers to store in VRAM",
            default: -1,
            defaultDescription: "Automatically determined based on the available VRAM"
        })
            .option("repeatPenalty", {
            alias: "rp",
            type: "number",
            default: 1.1,
            description: "Prevent the model from repeating the same token too much. Set to `1` to disable."
        })
            .option("lastTokensRepeatPenalty", {
            alias: "rpn",
            type: "number",
            default: 64,
            description: "Number of recent tokens generated by the model to apply penalties to repetition of"
        })
            .option("penalizeRepeatingNewLine", {
            alias: "rpnl",
            type: "boolean",
            default: true,
            description: "Penalize new line tokens. set `--no-penalizeRepeatingNewLine` or `--no-rpnl` to disable"
        })
            .option("repeatFrequencyPenalty", {
            alias: "rfp",
            type: "number",
            description: "For n time a token is in the `punishTokens` array, lower its probability by `n * repeatFrequencyPenalty`. Set to a value between `0` and `1` to enable."
        })
            .option("repeatPresencePenalty", {
            alias: "rpp",
            type: "number",
            description: "Lower the probability of all the tokens in the `punishTokens` array by `repeatPresencePenalty`. Set to a value between `0` and `1` to enable."
        })
            .option("maxTokens", {
            alias: "mt",
            type: "number",
            default: 0,
            description: "Maximum number of tokens to generate in responses. Set to `0` to disable. Set to `-1` to set to the context size"
        })
            .option("debug", {
            alias: "d",
            type: "boolean",
            default: false,
            description: "Print llama.cpp info and debug logs"
        })
            .option("meter", {
            type: "boolean",
            default: false,
            description: "Log how many tokens were used as input and output for each response"
        })
            .option("printTimings", {
            alias: "pt",
            type: "boolean",
            default: false,
            description: "Print llama.cpp timings after each response"
        });
    },
    async handler({ modelPath, header, gpu, systemInfo, text, textFile, contextSize, batchSize, flashAttention, threads, temperature, minP, topK, topP, seed, gpuLayers, repeatPenalty, lastTokensRepeatPenalty, penalizeRepeatingNewLine, repeatFrequencyPenalty, repeatPresencePenalty, maxTokens, debug, meter, printTimings }) {
        try {
            await RunCompletion({
                modelPath, header, gpu, systemInfo, text, textFile, contextSize, batchSize, flashAttention,
                threads, temperature, minP, topK, topP, seed, gpuLayers, lastTokensRepeatPenalty,
                repeatPenalty, penalizeRepeatingNewLine, repeatFrequencyPenalty, repeatPresencePenalty, maxTokens,
                debug, meter, printTimings
            });
        }
        catch (err) {
            await new Promise((accept) => setTimeout(accept, 0)); // wait for logs to finish printing
            console.error(err);
            process.exit(1);
        }
    }
};
async function RunCompletion({ modelPath: modelArg, header: headerArg, gpu, systemInfo, text, textFile, contextSize, batchSize, flashAttention, threads, temperature, minP, topK, topP, seed, gpuLayers, lastTokensRepeatPenalty, repeatPenalty, penalizeRepeatingNewLine, repeatFrequencyPenalty, repeatPresencePenalty, maxTokens, debug, meter, printTimings }) {
    if (contextSize === -1)
        contextSize = undefined;
    if (gpuLayers === -1)
        gpuLayers = undefined;
    const headers = resolveHeaderFlag(headerArg);
    if (debug)
        console.info(`${chalk.yellow("Log level:")} debug`);
    const llamaLogLevel = debug
        ? LlamaLogLevel.debug
        : LlamaLogLevel.warn;
    const llama = gpu == null
        ? await getLlama("lastBuild", {
            logLevel: llamaLogLevel
        })
        : await getLlama({
            gpu,
            logLevel: llamaLogLevel
        });
    const logBatchSize = batchSize != null;
    const resolvedModelPath = await resolveCommandGgufPath(modelArg, llama, headers, {
        flashAttention
    });
    if (systemInfo)
        console.log(llama.systemInfo);
    if (textFile != null && textFile !== "") {
        if (text != null && text !== "")
            console.warn(chalk.yellow("Both `text` and `textFile` were specified. `textFile` will be used."));
        text = await fs.readFile(path.resolve(process.cwd(), textFile), "utf8");
    }
    if (batchSize != null && contextSize != null && batchSize > contextSize) {
        console.warn(chalk.yellow("Batch size is greater than the context size. Batch size will be set to the context size."));
        batchSize = contextSize;
    }
    let initialText = text ?? null;
    const model = await withProgressLog({
        loadingText: chalk.blue.bold("Loading model"),
        successText: chalk.blue("Model loaded"),
        failText: chalk.blue("Failed to load model"),
        liveUpdates: !debug,
        noProgress: debug,
        liveCtrlCSendsAbortSignal: true
    }, async (progressUpdater) => {
        try {
            return await llama.loadModel({
                modelPath: resolvedModelPath,
                gpuLayers: gpuLayers != null
                    ? gpuLayers
                    : contextSize != null
                        ? { fitContext: { contextSize } }
                        : undefined,
                defaultContextFlashAttention: flashAttention,
                ignoreMemorySafetyChecks: gpuLayers != null,
                onLoadProgress(loadProgress) {
                    progressUpdater.setProgress(loadProgress);
                },
                loadSignal: progressUpdater.abortSignal
            });
        }
        catch (err) {
            if (err === progressUpdater.abortSignal?.reason)
                process.exit(0);
            throw err;
        }
        finally {
            if (llama.logLevel === LlamaLogLevel.debug) {
                await new Promise((accept) => setTimeout(accept, 0)); // wait for logs to finish printing
                console.info();
            }
        }
    });
    const context = await withOra({
        loading: chalk.blue("Creating context"),
        success: chalk.blue("Context created"),
        fail: chalk.blue("Failed to create context"),
        useStatusLogs: debug
    }, async () => {
        try {
            return await model.createContext({
                contextSize: contextSize != null ? contextSize : undefined,
                batchSize: batchSize != null ? batchSize : undefined,
                threads: threads === null ? undefined : threads,
                ignoreMemorySafetyChecks: gpuLayers != null || contextSize != null,
                performanceTracking: printTimings
            });
        }
        finally {
            if (llama.logLevel === LlamaLogLevel.debug) {
                await new Promise((accept) => setTimeout(accept, 0)); // wait for logs to finish printing
                console.info();
            }
        }
    });
    const contextSequence = context.getSequence();
    const completion = new LlamaCompletion({
        contextSequence
    });
    let lastTokenMeterState = contextSequence.tokenMeter.getState();
    await new Promise((accept) => setTimeout(accept, 0)); // wait for logs to finish printing
    const padTitle = "Complete".length + 1;
    await printCommonInfoLines({
        context,
        minTitleLength: padTitle,
        logBatchSize,
        tokenMeterEnabled: meter
    });
    printInfoLine({
        title: "Complete",
        padTitle: padTitle,
        info: [{
                title: "Repeat penalty",
                value: `${repeatPenalty} (apply to last ${lastTokensRepeatPenalty} tokens)`
            }, {
                show: repeatFrequencyPenalty != null,
                title: "Repeat frequency penalty",
                value: String(repeatFrequencyPenalty)
            }, {
                show: repeatPresencePenalty != null,
                title: "Repeat presence penalty",
                value: String(repeatPresencePenalty)
            }, {
                show: !penalizeRepeatingNewLine,
                title: "Penalize repeating new line",
                value: "disabled"
            }]
    });
    // this is for ora to not interfere with readline
    await new Promise((resolve) => setTimeout(resolve, 1));
    const replHistory = [];
    async function getPrompt() {
        const rl = readline.createInterface({
            input: process.stdin,
            output: process.stdout,
            history: replHistory.slice()
        });
        const res = await new Promise((accept) => rl.question(chalk.yellow("> "), accept));
        rl.close();
        return res;
    }
    while (true) {
        const input = initialText != null
            ? initialText
            : await getPrompt();
        if (initialText != null) {
            console.log(chalk.green("> ") + initialText);
            initialText = null;
        }
        else
            await replHistory.push(input);
        if (input === ".exit")
            break;
        process.stdout.write(chalk.yellow("Completion: "));
        const [startColor, endColor] = chalk.blue("MIDDLE").split("MIDDLE");
        const abortController = new AbortController();
        const consoleInteraction = new ConsoleInteraction();
        consoleInteraction.onKey(ConsoleInteractionKey.ctrlC, async () => {
            abortController.abort();
            consoleInteraction.stop();
        });
        try {
            process.stdout.write(startColor);
            consoleInteraction.start();
            await completion.generateCompletion(input, {
                temperature,
                minP,
                topK,
                topP,
                seed: seed ?? undefined,
                signal: abortController.signal,
                repeatPenalty: {
                    penalty: repeatPenalty,
                    frequencyPenalty: repeatFrequencyPenalty != null ? repeatFrequencyPenalty : undefined,
                    presencePenalty: repeatPresencePenalty != null ? repeatPresencePenalty : undefined,
                    penalizeNewLine: penalizeRepeatingNewLine,
                    lastTokens: lastTokensRepeatPenalty
                },
                maxTokens: maxTokens === -1
                    ? context.contextSize
                    : maxTokens <= 0
                        ? undefined
                        : maxTokens,
                onTextChunk(chunk) {
                    process.stdout.write(chunk);
                }
            });
        }
        catch (err) {
            if (!(abortController.signal.aborted && err === abortController.signal.reason))
                throw err;
        }
        finally {
            consoleInteraction.stop();
            if (abortController.signal.aborted)
                process.stdout.write(endColor + chalk.yellow("[generation aborted by user]"));
            else
                process.stdout.write(endColor);
            console.log();
        }
        if (printTimings) {
            if (LlamaLogLevelGreaterThan(llama.logLevel, LlamaLogLevel.info))
                llama.logLevel = LlamaLogLevel.info;
            await context.printTimings();
            await new Promise((accept) => setTimeout(accept, 0)); // wait for logs to finish printing
            llama.logLevel = llamaLogLevel;
        }
        if (meter) {
            const newTokenMeterState = contextSequence.tokenMeter.getState();
            const tokenMeterDiff = TokenMeter.diff(newTokenMeterState, lastTokenMeterState);
            lastTokenMeterState = newTokenMeterState;
            console.info(`${chalk.dim("Input tokens:")} ${String(tokenMeterDiff.usedInputTokens).padEnd(5, " ")}  ${chalk.dim("Output tokens:")} ${tokenMeterDiff.usedOutputTokens}`);
        }
    }
}
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