import { type Api, completeSimple, type Model } from "@mariozechner/pi-ai";
import { discoverAuthStorage, discoverModels } from "@mariozechner/pi-coding-agent";
import { Type } from "@sinclair/typebox";
import { describe, expect, it } from "vitest";
import { loadConfig } from "../config/config.js";
import { isTruthyEnvValue } from "../infra/env.js";
import { resolveClawdbotAgentDir } from "./agent-paths.js";
import {
  collectAnthropicApiKeys,
  isAnthropicBillingError,
  isAnthropicRateLimitError,
} from "./live-auth-keys.js";
import { isModernModelRef } from "./live-model-filter.js";
import { getApiKeyForModel, requireApiKey } from "./model-auth.js";
import { ensureClawdbotModelsJson } from "./models-config.js";
import { isRateLimitErrorMessage } from "./pi-embedded-helpers/errors.js";

const LIVE = isTruthyEnvValue(process.env.LIVE) || isTruthyEnvValue(process.env.CLAWDBOT_LIVE_TEST);
const DIRECT_ENABLED = Boolean(process.env.CLAWDBOT_LIVE_MODELS?.trim());
const REQUIRE_PROFILE_KEYS = isTruthyEnvValue(process.env.CLAWDBOT_LIVE_REQUIRE_PROFILE_KEYS);

const describeLive = LIVE ? describe : describe.skip;

function parseProviderFilter(raw?: string): Set<string> | null {
  const trimmed = raw?.trim();
  if (!trimmed || trimmed === "all") return null;
  const ids = trimmed
    .split(",")
    .map((s) => s.trim())
    .filter(Boolean);
  return ids.length ? new Set(ids) : null;
}

function parseModelFilter(raw?: string): Set<string> | null {
  const trimmed = raw?.trim();
  if (!trimmed || trimmed === "all") return null;
  const ids = trimmed
    .split(",")
    .map((s) => s.trim())
    .filter(Boolean);
  return ids.length ? new Set(ids) : null;
}

function logProgress(message: string): void {
  console.log(`[live] ${message}`);
}

function isGoogleModelNotFoundError(err: unknown): boolean {
  const msg = String(err);
  if (!/not found/i.test(msg)) return false;
  if (/models\/.+ is not found for api version/i.test(msg)) return true;
  if (/"status"\\s*:\\s*"NOT_FOUND"/.test(msg)) return true;
  if (/"code"\\s*:\\s*404/.test(msg)) return true;
  return false;
}

function isModelNotFoundErrorMessage(raw: string): boolean {
  const msg = raw.trim();
  if (!msg) return false;
  if (/\b404\b/.test(msg) && /not[_-]?found/i.test(msg)) return true;
  if (/not_found_error/i.test(msg)) return true;
  if (/model:\s*[a-z0-9._-]+/i.test(msg) && /not[_-]?found/i.test(msg)) return true;
  return false;
}

function isChatGPTUsageLimitErrorMessage(raw: string): boolean {
  const msg = raw.toLowerCase();
  return msg.includes("hit your chatgpt usage limit") && msg.includes("try again in");
}

function toInt(value: string | undefined, fallback: number): number {
  const trimmed = value?.trim();
  if (!trimmed) return fallback;
  const parsed = Number.parseInt(trimmed, 10);
  return Number.isFinite(parsed) ? parsed : fallback;
}

function resolveTestReasoning(
  model: Model<Api>,
): "minimal" | "low" | "medium" | "high" | "xhigh" | undefined {
  if (!model.reasoning) return undefined;
  const id = model.id.toLowerCase();
  if (model.provider === "openai" || model.provider === "openai-codex") {
    if (id.includes("pro")) return "high";
    return "medium";
  }
  return "low";
}

async function completeSimpleWithTimeout<TApi extends Api>(
  model: Model<TApi>,
  context: Parameters<typeof completeSimple<TApi>>[1],
  options: Parameters<typeof completeSimple<TApi>>[2],
  timeoutMs: number,
) {
  const controller = new AbortController();
  const timer = setTimeout(() => controller.abort(), Math.max(1, timeoutMs));
  timer.unref?.();
  try {
    return await completeSimple(model, context, {
      ...options,
      signal: controller.signal,
    });
  } finally {
    clearTimeout(timer);
  }
}

async function completeOkWithRetry(params: {
  model: Model<Api>;
  apiKey: string;
  timeoutMs: number;
}) {
  const runOnce = async () => {
    const res = await completeSimpleWithTimeout(
      params.model,
      {
        messages: [
          {
            role: "user",
            content: "Reply with the word ok.",
            timestamp: Date.now(),
          },
        ],
      },
      {
        apiKey: params.apiKey,
        reasoning: resolveTestReasoning(params.model),
        maxTokens: 64,
      },
      params.timeoutMs,
    );
    const text = res.content
      .filter((block) => block.type === "text")
      .map((block) => block.text.trim())
      .join(" ");
    return { res, text };
  };

  const first = await runOnce();
  if (first.text.length > 0) return first;
  return await runOnce();
}

describeLive("live models (profile keys)", () => {
  it(
    "completes across selected models",
    async () => {
      const cfg = loadConfig();
      await ensureClawdbotModelsJson(cfg);
      if (!DIRECT_ENABLED) {
        logProgress(
          "[live-models] skipping (set CLAWDBOT_LIVE_MODELS=modern|all|<list>; all=modern)",
        );
        return;
      }
      const anthropicKeys = collectAnthropicApiKeys();
      if (anthropicKeys.length > 0) {
        process.env.ANTHROPIC_API_KEY = anthropicKeys[0];
        logProgress(`[live-models] anthropic keys loaded: ${anthropicKeys.length}`);
      }

      const agentDir = resolveClawdbotAgentDir();
      const authStorage = discoverAuthStorage(agentDir);
      const modelRegistry = discoverModels(authStorage, agentDir);
      const models = modelRegistry.getAll() as Array<Model<Api>>;

      const rawModels = process.env.CLAWDBOT_LIVE_MODELS?.trim();
      const useModern = rawModels === "modern" || rawModels === "all";
      const useExplicit = Boolean(rawModels) && !useModern;
      const filter = useExplicit ? parseModelFilter(rawModels) : null;
      const allowNotFoundSkip = useModern;
      const providers = parseProviderFilter(process.env.CLAWDBOT_LIVE_PROVIDERS);
      const perModelTimeoutMs = toInt(process.env.CLAWDBOT_LIVE_MODEL_TIMEOUT_MS, 30_000);

      const failures: Array<{ model: string; error: string }> = [];
      const skipped: Array<{ model: string; reason: string }> = [];
      const candidates: Array<{
        model: Model<Api>;
        apiKeyInfo: Awaited<ReturnType<typeof getApiKeyForModel>>;
      }> = [];

      for (const model of models) {
        if (providers && !providers.has(model.provider)) continue;
        const id = `${model.provider}/${model.id}`;
        if (filter && !filter.has(id)) continue;
        if (!filter && useModern) {
          if (!isModernModelRef({ provider: model.provider, id: model.id })) {
            continue;
          }
        }
        try {
          const apiKeyInfo = await getApiKeyForModel({ model, cfg });
          if (REQUIRE_PROFILE_KEYS && !apiKeyInfo.source.startsWith("profile:")) {
            skipped.push({
              model: id,
              reason: `non-profile credential source: ${apiKeyInfo.source}`,
            });
            continue;
          }
          candidates.push({ model, apiKeyInfo });
        } catch (err) {
          skipped.push({ model: id, reason: String(err) });
        }
      }

      if (candidates.length === 0) {
        logProgress("[live-models] no API keys found; skipping");
        return;
      }

      logProgress(`[live-models] selection=${useExplicit ? "explicit" : "modern"}`);
      logProgress(`[live-models] running ${candidates.length} models`);
      const total = candidates.length;

      for (const [index, entry] of candidates.entries()) {
        const { model, apiKeyInfo } = entry;
        const id = `${model.provider}/${model.id}`;
        const progressLabel = `[live-models] ${index + 1}/${total} ${id}`;
        const attemptMax =
          model.provider === "anthropic" && anthropicKeys.length > 0 ? anthropicKeys.length : 1;
        for (let attempt = 0; attempt < attemptMax; attempt += 1) {
          if (model.provider === "anthropic" && anthropicKeys.length > 0) {
            process.env.ANTHROPIC_API_KEY = anthropicKeys[attempt];
          }
          const apiKey =
            model.provider === "anthropic" && anthropicKeys.length > 0
              ? anthropicKeys[attempt]
              : requireApiKey(apiKeyInfo, model.provider);
          try {
            // Special regression: OpenAI requires replayed `reasoning` items for tool-only turns.
            if (
              model.provider === "openai" &&
              model.api === "openai-responses" &&
              model.id === "gpt-5.2"
            ) {
              logProgress(`${progressLabel}: tool-only regression`);
              const noopTool = {
                name: "noop",
                description: "Return ok.",
                parameters: Type.Object({}, { additionalProperties: false }),
              };

              let firstUserContent = "Call the tool `noop` with {}. Do not write any other text.";
              let firstUser = {
                role: "user" as const,
                content: firstUserContent,
                timestamp: Date.now(),
              };

              let first = await completeSimpleWithTimeout(
                model,
                { messages: [firstUser], tools: [noopTool] },
                {
                  apiKey,
                  reasoning: resolveTestReasoning(model),
                  maxTokens: 128,
                },
                perModelTimeoutMs,
              );

              let toolCall = first.content.find((b) => b.type === "toolCall");
              let firstText = first.content
                .filter((b) => b.type === "text")
                .map((b) => b.text.trim())
                .join(" ")
                .trim();

              // Occasional flake: model answers in text instead of tool call (or adds text).
              // Retry a couple times with a stronger instruction so we still exercise the tool-only replay path.
              for (let i = 0; i < 2 && (!toolCall || firstText.length > 0); i += 1) {
                firstUserContent =
                  "Call the tool `noop` with {}. IMPORTANT: respond ONLY with the tool call; no other text.";
                firstUser = {
                  role: "user" as const,
                  content: firstUserContent,
                  timestamp: Date.now(),
                };

                first = await completeSimpleWithTimeout(
                  model,
                  { messages: [firstUser], tools: [noopTool] },
                  {
                    apiKey,
                    reasoning: resolveTestReasoning(model),
                    maxTokens: 128,
                  },
                  perModelTimeoutMs,
                );

                toolCall = first.content.find((b) => b.type === "toolCall");
                firstText = first.content
                  .filter((b) => b.type === "text")
                  .map((b) => b.text.trim())
                  .join(" ")
                  .trim();
              }

              expect(toolCall).toBeTruthy();
              expect(firstText.length).toBe(0);
              if (!toolCall || toolCall.type !== "toolCall") {
                throw new Error("expected tool call");
              }

              const second = await completeSimpleWithTimeout(
                model,
                {
                  messages: [
                    firstUser,
                    first,
                    {
                      role: "toolResult",
                      toolCallId: toolCall.id,
                      toolName: "noop",
                      content: [{ type: "text", text: "ok" }],
                      isError: false,
                      timestamp: Date.now(),
                    },
                    {
                      role: "user",
                      content: "Reply with the word ok.",
                      timestamp: Date.now(),
                    },
                  ],
                },
                {
                  apiKey,
                  reasoning: resolveTestReasoning(model),
                  // Headroom: reasoning summary can consume most of the output budget.
                  maxTokens: 256,
                },
                perModelTimeoutMs,
              );

              const secondText = second.content
                .filter((b) => b.type === "text")
                .map((b) => b.text.trim())
                .join(" ");
              expect(secondText.length).toBeGreaterThan(0);
              logProgress(`${progressLabel}: done`);
              break;
            }

            logProgress(`${progressLabel}: prompt`);
            const ok = await completeOkWithRetry({
              model,
              apiKey,
              timeoutMs: perModelTimeoutMs,
            });

            if (ok.res.stopReason === "error") {
              const msg = ok.res.errorMessage ?? "";
              if (allowNotFoundSkip && isModelNotFoundErrorMessage(msg)) {
                skipped.push({ model: id, reason: msg });
                logProgress(`${progressLabel}: skip (model not found)`);
                break;
              }
              throw new Error(msg || "model returned error with no message");
            }

            if (ok.text.length === 0 && model.provider === "google") {
              skipped.push({
                model: id,
                reason: "no text returned (likely unavailable model id)",
              });
              logProgress(`${progressLabel}: skip (google model not found)`);
              break;
            }
            if (
              ok.text.length === 0 &&
              (model.provider === "openrouter" || model.provider === "opencode")
            ) {
              skipped.push({
                model: id,
                reason: "no text returned (provider returned empty content)",
              });
              logProgress(`${progressLabel}: skip (empty response)`);
              break;
            }
            if (
              ok.text.length === 0 &&
              allowNotFoundSkip &&
              (model.provider === "google-antigravity" || model.provider === "openai-codex")
            ) {
              skipped.push({
                model: id,
                reason: "no text returned (provider returned empty content)",
              });
              logProgress(`${progressLabel}: skip (empty response)`);
              break;
            }
            expect(ok.text.length).toBeGreaterThan(0);
            logProgress(`${progressLabel}: done`);
            break;
          } catch (err) {
            const message = String(err);
            if (
              model.provider === "anthropic" &&
              isAnthropicRateLimitError(message) &&
              attempt + 1 < attemptMax
            ) {
              logProgress(`${progressLabel}: rate limit, retrying with next key`);
              continue;
            }
            if (model.provider === "anthropic" && isAnthropicBillingError(message)) {
              if (attempt + 1 < attemptMax) {
                logProgress(`${progressLabel}: billing issue, retrying with next key`);
                continue;
              }
              skipped.push({ model: id, reason: message });
              logProgress(`${progressLabel}: skip (anthropic billing)`);
              break;
            }
            if (model.provider === "google" && isGoogleModelNotFoundError(err)) {
              skipped.push({ model: id, reason: message });
              logProgress(`${progressLabel}: skip (google model not found)`);
              break;
            }
            if (
              allowNotFoundSkip &&
              model.provider === "minimax" &&
              message.includes("request ended without sending any chunks")
            ) {
              skipped.push({ model: id, reason: message });
              logProgress(`${progressLabel}: skip (minimax empty response)`);
              break;
            }
            if (
              allowNotFoundSkip &&
              model.provider === "opencode" &&
              isRateLimitErrorMessage(message)
            ) {
              skipped.push({ model: id, reason: message });
              logProgress(`${progressLabel}: skip (rate limit)`);
              break;
            }
            if (
              allowNotFoundSkip &&
              model.provider === "openai-codex" &&
              isChatGPTUsageLimitErrorMessage(message)
            ) {
              skipped.push({ model: id, reason: message });
              logProgress(`${progressLabel}: skip (chatgpt usage limit)`);
              break;
            }
            logProgress(`${progressLabel}: failed`);
            failures.push({ model: id, error: message });
            break;
          }
        }
      }

      if (failures.length > 0) {
        const preview = failures
          .slice(0, 10)
          .map((f) => `- ${f.model}: ${f.error}`)
          .join("\n");
        throw new Error(`live model failures (${failures.length}):\n${preview}`);
      }

      void skipped;
    },
    15 * 60 * 1000,
  );
});