import type { ILMStudioClient } from './client';
import { LMStudioLifecycleError } from './client';
import { logError, logInfo } from '../utils';

export interface ChatStreamMessage {
    role: 'user' | 'assistant' | 'system';
    content: string;
}

export interface ChatStreamRequest {
    modelName: string;
    messages: ChatStreamMessage[];
    temperature: number;
    /** Upper bound on tokens to generate. Omit to fall back to a conservative default. */
    maxTokens?: number;
    /** LM Studio context-overflow safety net used only if the prompt still exceeds the window. */
    contextOverflowPolicy?: 'stopAtLimit' | 'truncateMiddle' | 'rollingWindow';
    signal?: AbortSignal;
}

/**
 * One stream event. `token` carries generated text (possibly empty for the final event);
 * `stopReason` is set on the *last* event only and is the SDK's `stats.stopReason`
 * (e.g. `eosFound`, `maxPredictedTokensReached`, `contextLengthReached`, `userStopped`).
 */
export interface ChatStreamEvent {
    token: string;
    stopReason?: string;
}

export interface IChatStreamer {
    /** Token-level streaming for an LM Studio chat completion via the WebSocket SDK. */
    stream(req: ChatStreamRequest): AsyncIterable<ChatStreamEvent>;
    /**
     * Drop the SDK's cached handle for `modelName`. Callers invoke this when
     * the previous stream returned zero tokens with no error — a symptom of a
     * silently-disposed handle that needs a fresh WebSocket round-trip.
     */
    resetHandle?(modelName: string): Promise<void>;
}

/**
 * Adapter that streams LM Studio chat completions via @lmstudio/sdk's `model.respond()`,
 * replacing the manual fetch + SSE parser path used for the OpenAI-compatible REST endpoint.
 *
 * Benefits over the REST path:
 *  - No SSE parsing (no `data: [DONE]` / partial-chunk fragility).
 *  - Reuses the same WebSocket the lifecycle manager already opened — handle lookup is cheap
 *    if the model is already loaded, and load-on-first-use is implicit when it isn't.
 *  - First-class `signal` support for user-cancel and abort propagation.
 */
export class LMStudioStreamer implements IChatStreamer {
    constructor(private readonly client: ILMStudioClient) {}

    async *stream(req: ChatStreamRequest): AsyncIterable<ChatStreamEvent> {
        const trimmedModel = (req.modelName || '').trim();
        if (!trimmedModel) {
            throw new LMStudioLifecycleError('LMStudioStreamer.stream called without a model name.');
        }

        // One automatic retry path: when the first attempt blows up with a
        // "Model is disposed!" / "lock() request could not be registered"
        // error before any tokens have been yielded, we drop the cached SDK
        // handle and try once more. These errors are caused by a previous
        // aborted prediction leaving the SDK's internal handle map pointing
        // at a dead WebSocket binding — a fresh client.model() lookup minted
        // from a recreated SDK fixes it. We only retry when zero tokens have
        // streamed: if the consumer already saw partial output, restarting
        // would duplicate tokens.
        for (let attempt = 1; attempt <= 2; attempt++) {
            const refresh = attempt > 1;
            const model = await this.client.getModelHandle(trimmedModel, refresh ? { refresh: true } : undefined);
            logInfo('LM Studio SDK chat stream started.', { model: trimmedModel, messageCount: req.messages.length, attempt });

            const prediction = (model as any).respond(req.messages, {
                temperature: req.temperature,
                maxTokens: req.maxTokens ?? 4096,
                // Safety net: if our own token budgeting still underestimated and the prompt
                // exceeds the model's context window, decide whether the SDK should fail
                // loudly (stopAtLimit — default) or silently drop content.
                contextOverflowPolicy: req.contextOverflowPolicy ?? 'stopAtLimit',
                signal: req.signal,
            });

            // Bridge AbortSignal → prediction.cancel(): without this, an
            // aborted request keeps generating on the LM Studio server. The
            // orphaned prediction holds locks on the model handle, which is
            // a known cause of "lock() request could not be registered" on
            // the very next request — the reused handle is still bound to a
            // dead prediction.
            const onAbort = () => {
                try { (prediction as any)?.cancel?.(); } catch { /* swallow — best effort */ }
            };
            if (req.signal) {
                if (req.signal.aborted) onAbort();
                else req.signal.addEventListener('abort', onAbort, { once: true });
            }

            let yielded = 0;
            let caught: any = null;
            try {
                for await (const fragment of prediction as AsyncIterable<{ content: string }>) {
                    if (req.signal?.aborted) return;
                    const token = fragment?.content ?? '';
                    if (token) {
                        yielded++;
                        yield { token };
                    }
                }
            } catch (err: any) {
                if (req.signal?.aborted) return;
                if (err?.name === 'AbortError') return;
                caught = err;
            } finally {
                req.signal?.removeEventListener?.('abort', onAbort);
            }

            if (!caught) {
                if (req.signal?.aborted) return;
                // The prediction object is also a Promise<PredictionResult>; awaiting it after
                // the stream drains gives us stats.stopReason so callers can tell a truncated
                // answer (maxPredictedTokensReached / contextLengthReached) from a normal one.
                let stopReason: string | undefined;
                try {
                    const result: any = await prediction;
                    stopReason = result?.stats?.stopReason;
                    if (stopReason) {
                        logInfo('LM Studio SDK chat stream finished.', { model: trimmedModel, stopReason, tokensYielded: yielded });
                    }
                } catch { /* result unavailable on some SDK versions — non-fatal */ }
                // Don't claim `eosFound` if we couldn't actually read the stop reason — leave it
                // undefined so the caller treats it as 'unknown' (and its mid-sentence heuristics kick in).
                yield { token: '', stopReason };
                return;
            }

            const errMsg = String(caught?.message ?? caught);
            const handleDead = /\bdisposed\b/i.test(errMsg)
                || /lock\(\) request could not be registered/i.test(errMsg);

            if (handleDead && yielded === 0 && attempt === 1) {
                logInfo('Dead LM Studio handle detected — retrying with a fresh SDK.', { model: trimmedModel, error: errMsg });
                continue;
            }

            logError('LM Studio SDK chat stream failed.', { model: trimmedModel, error: errMsg, attempt });
            throw caught;
        }
    }

    async resetHandle(modelName: string): Promise<void> {
        const trimmed = (modelName || '').trim();
        if (!trimmed) return;
        try {
            await this.client.getModelHandle(trimmed, { refresh: true });
        } catch (err: any) {
            // Best effort — caller will see the next stream() attempt fail
            // with a normal error path if the refresh itself was broken.
            logError('LM Studio handle reset failed.', { model: trimmed, error: err?.message ?? String(err) });
        }
    }
}