const { z } = require('zod');
const { tool } = require('@langchain/core/tools');
const { ChatGenerationChunk } = require('@langchain/core/outputs');
const { HumanMessage, AIMessageChunk } = require('@langchain/core/messages');
const {
  Run,
  Providers,
  GraphEvents,
  FakeChatModel,
  createContentAggregator,
} = require('@librechat/agents');
const { GenerationJobManager } = require('@librechat/api');
const { getDefaultHandlers } = require('~/server/controllers/agents/callbacks');

jest.mock('nanoid', () => ({
  nanoid: jest.fn(() => 'mock-nanoid'),
}));

jest.mock('~/server/services/Files/Citations', () => ({
  processFileCitations: jest.fn(),
}));

jest.mock('~/server/services/Files/Code/process', () => ({
  processCodeOutput: jest.fn(),
  runPreviewFinalize: jest.fn(),
}));

jest.mock('~/server/services/Files/process', () => ({
  saveBase64Image: jest.fn(),
}));

/** Real pipeline guard: published lib versions without the event skip its assertions */
const hasContextUsageEvent = GraphEvents.ON_CONTEXT_USAGE != null;

/**
 * FakeChatModel that attaches provider-style usage_metadata on a final
 * empty chunk (the OpenAI streaming pattern), so CHAT_MODEL_END carries
 * aggregated usage through the real @librechat/agents pipeline.
 */
class UsageFakeModel extends FakeChatModel {
  constructor(options, usagePerCall) {
    super(options);
    this.usagePerCall = usagePerCall;
    this.usageCallIndex = 0;
  }

  async *_streamResponseChunks(messages, options, runManager) {
    yield* super._streamResponseChunks(messages, options, runManager);
    const index = Math.min(this.usageCallIndex, this.usagePerCall.length - 1);
    this.usageCallIndex += 1;
    yield new ChatGenerationChunk({
      text: '',
      message: new AIMessageChunk({ content: '', usage_metadata: this.usagePerCall[index] }),
    });
  }
}

const addTool = tool(async ({ a, b }) => String(a + b), {
  name: 'add',
  description: 'Add two numbers',
  schema: z.object({ a: z.number(), b: z.number() }),
});

const charCounter = (msg) => {
  const content = msg.content;
  if (typeof content === 'string') {
    return content.length + 3;
  }
  if (Array.isArray(content)) {
    let length = 3;
    for (const part of content) {
      if (typeof part === 'string') {
        length += part.length;
      } else if (typeof part?.text === 'string') {
        length += part.text.length;
      }
    }
    return length;
  }
  return 3;
};

function createMockRes() {
  const events = [];
  return {
    events,
    headersSent: true,
    writableEnded: false,
    write(payload) {
      for (const line of String(payload).split('\n')) {
        if (line.startsWith('data: ')) {
          events.push(JSON.parse(line.slice(6)));
        }
      }
      return true;
    },
  };
}

const FIRST_CALL_USAGE = {
  input_tokens: 100,
  output_tokens: 20,
  total_tokens: 120,
};

const SECOND_CALL_USAGE = {
  input_tokens: 150,
  output_tokens: 10,
  total_tokens: 160,
  input_token_details: { cache_creation: 30, cache_read: 50 },
};

const MAX_CONTEXT_TOKENS = 8000;

async function runToolLoop({ res, streamId = null, collectedUsage }) {
  const { contentParts, aggregateContent } = createContentAggregator();
  const handlers = getDefaultHandlers({
    res,
    aggregateContent,
    toolEndCallback: () => {},
    collectedUsage,
    streamId,
  });

  const run = await Run.create({
    runId: 'usage-e2e-response',
    graphConfig: {
      type: 'standard',
      llmConfig: {
        provider: Providers.OPENAI,
        model: 'gpt-4o-mini',
        streaming: true,
        streamUsage: false,
      },
      instructions: 'You are a helpful assistant.',
      maxContextTokens: MAX_CONTEXT_TOKENS,
      tools: [addTool],
    },
    returnContent: true,
    customHandlers: handlers,
    tokenCounter: charCounter,
    indexTokenCountMap: {},
  });

  run.Graph.overrideModel = new UsageFakeModel(
    {
      responses: ['Let me calculate that.', 'The answer is 4.'],
      toolCalls: [{ name: 'add', args: { a: 2, b: 2 }, id: 'tc_1', type: 'tool_call' }],
    },
    [FIRST_CALL_USAGE, SECOND_CALL_USAGE],
  );

  await run.processStream(
    { messages: [new HumanMessage('What is 2+2?')] },
    {
      configurable: { thread_id: 'usage-e2e-thread', user_id: 'user-1' },
      streamMode: 'values',
      version: 'v2',
    },
  );

  return { run, contentParts };
}

describe('usage events through the real agents pipeline', () => {
  jest.setTimeout(30000);

  afterAll(async () => {
    await GenerationJobManager.destroy();
  });

  test('emits on_token_usage per model call with collectedUsage parity', async () => {
    const res = createMockRes();
    const collectedUsage = [];
    const { contentParts } = await runToolLoop({ res, collectedUsage });

    const usageEvents = res.events.filter((e) => e.event === 'on_token_usage');
    expect(usageEvents).toHaveLength(2);

    expect(usageEvents[0].data).toMatchObject(FIRST_CALL_USAGE);
    expect(usageEvents[1].data).toMatchObject(SECOND_CALL_USAGE);
    expect(usageEvents[0].data.provider).toBe(Providers.OPENAI);
    expect(usageEvents[0].data.model).toBeTruthy();
    expect(usageEvents[0].data.usage_type).toBeUndefined();

    expect(collectedUsage).toHaveLength(2);
    expect(collectedUsage[0]).toMatchObject(FIRST_CALL_USAGE);
    expect(collectedUsage[1]).toMatchObject(SECOND_CALL_USAGE);

    const text = contentParts
      .filter((part) => part?.type === 'text')
      .map((part) => part.text)
      .join('');
    expect(text).toContain('The answer is 4.');
  });

  test('emits a context snapshot before each model call', async () => {
    if (!hasContextUsageEvent) {
      console.warn('Skipping: installed @librechat/agents predates ON_CONTEXT_USAGE');
      return;
    }
    const res = createMockRes();
    const { run } = await runToolLoop({ res, collectedUsage: [] });
    expect(run).toBeDefined();

    const contextEvents = res.events.filter((e) => e.event === 'on_context_usage');
    expect(contextEvents).toHaveLength(2);

    for (const event of contextEvents) {
      const { breakdown, contextBudget, remainingContextTokens, effectiveInstructionTokens } =
        event.data;
      expect(breakdown.maxContextTokens).toBe(MAX_CONTEXT_TOKENS);
      expect(contextBudget).toBeGreaterThan(0);
      expect(contextBudget).toBeLessThanOrEqual(MAX_CONTEXT_TOKENS);
      expect(effectiveInstructionTokens).toBeGreaterThan(0);
      expect(remainingContextTokens).toBeGreaterThan(0);
      expect(remainingContextTokens).toBeLessThan(contextBudget);
    }

    /** Tool loop grows the context between calls */
    expect(contextEvents[1].data.prePruneContextTokens).toBeGreaterThan(
      contextEvents[0].data.prePruneContextTokens,
    );

    /** Snapshot precedes the call's usage event */
    const firstContextIndex = res.events.findIndex((e) => e.event === 'on_context_usage');
    const firstUsageIndex = res.events.findIndex((e) => e.event === 'on_token_usage');
    expect(firstContextIndex).toBeGreaterThanOrEqual(0);
    expect(firstContextIndex).toBeLessThan(firstUsageIndex);
  });

  test('persists usage and context snapshot for resume via GenerationJobManager', async () => {
    const streamId = `usage-e2e-stream-${Date.now()}`;
    await GenerationJobManager.createJob(streamId, 'user-1', 'convo-1');

    const res = createMockRes();
    await runToolLoop({ res, streamId, collectedUsage: [] });

    const resumeState = await GenerationJobManager.getResumeState(streamId);
    expect(resumeState).not.toBeNull();

    expect(resumeState.collectedUsage).toHaveLength(2);
    expect(resumeState.collectedUsage[0]).toMatchObject(FIRST_CALL_USAGE);
    expect(resumeState.collectedUsage[1]).toMatchObject(SECOND_CALL_USAGE);

    if (hasContextUsageEvent) {
      expect(resumeState.contextUsage.breakdown.maxContextTokens).toBe(MAX_CONTEXT_TOKENS);
      /** Latest-wins: the persisted snapshot is the second call's */
      expect(resumeState.contextUsage.prePruneContextTokens).toBeGreaterThan(0);
    }
  });
});