import { PreTrainedTokenizer, ArceeForCausalLM } from "../../../src/transformers.js";

import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";

export default () => {
  describe("ArceeForCausalLM", () => {
    const model_id = "onnx-internal-testing/tiny-random-ArceeForCausalLM";
    /** @type {ArceeForCausalLM} */
    let model;
    /** @type {PreTrainedTokenizer} */
    let tokenizer;
    beforeAll(async () => {
      model = await ArceeForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
      tokenizer = await PreTrainedTokenizer.from_pretrained(model_id);
      tokenizer.padding_side = "left";
    }, MAX_MODEL_LOAD_TIME);

    it(
      "batch_size=1",
      async () => {
        const inputs = tokenizer("hello");
        const outputs = await model.generate({
          ...inputs,
          max_length: 10,
        });
        expect(outputs.tolist()).toEqual([[1n, 22172n, 1316n, 11038n, 25378n, 11619n, 7959n, 15231n, 15231n, 23659n]]);
      },
      MAX_TEST_EXECUTION_TIME,
    );

    it(
      "batch_size>1",
      async () => {
        const inputs = tokenizer(["hello", "hello world"], { padding: true });
        const outputs = await model.generate({
          ...inputs,
          max_length: 10,
        });
        expect(outputs.tolist()).toEqual([
          [2n, 1n, 22172n, 5706n, 3803n, 11619n, 28763n, 4015n, 18904n, 7959n],
          [1n, 22172n, 3186n, 1316n, 11038n, 22918n, 9469n, 25671n, 22918n, 2687n],
        ]);
      },
      MAX_TEST_EXECUTION_TIME,
    );

    afterAll(async () => {
      await model?.dispose();
    }, MAX_MODEL_DISPOSE_TIME);
  });
};