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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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"""Testing suite for the PyTorch Helium model."""

import unittest

from transformers import AutoModelForCausalLM, AutoTokenizer, is_torch_available
from transformers.testing_utils import (
    Expectations,
    require_read_token,
    require_torch,
    slow,
    torch_device,
)

from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester


if is_torch_available():
    import torch

    from transformers import (
        HeliumModel,
    )


class HeliumModelTester(CausalLMModelTester):
    if is_torch_available():
        base_model_class = HeliumModel


@require_torch
class HeliumModelTest(CausalLMModelTest, unittest.TestCase):
    _is_stateful = True
    model_split_percents = [0.5, 0.6]
    model_tester_class = HeliumModelTester


@slow
class HeliumIntegrationTest(unittest.TestCase):
    input_text = ["Hello, today is a great day to"]

    @require_read_token
    def test_model_2b(self):
        model_id = "kyutai/helium-1-preview"
        expected_texts = Expectations(
            {
                ("rocm", (9, 5)): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I"],
                (None, None): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now and I have"],
                ("cuda", 8): ['Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I'],
            }
        )  # fmt: skip
        EXPECTED_TEXTS = expected_texts.get_expectation()

        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, revision="refs/pr/1").to(
            torch_device
        )
        tokenizer = AutoTokenizer.from_pretrained(model_id, revision="refs/pr/1")
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

        self.assertEqual(output_text, EXPECTED_TEXTS)