# Copyright 2025 the LlamaFactory team.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import random

import pytest
from datasets import load_dataset
from transformers import AutoTokenizer

from llamafactory.train.test_utils import load_dataset_module


DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")

TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")

TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")

TRAIN_ARGS = {
    "model_name_or_path": TINY_LLAMA,
    "stage": "ppo",
    "do_train": True,
    "finetuning_type": "full",
    "reward_model": "",
    "reward_model_type": "full",
    "dataset": "system_chat",
    "dataset_dir": "REMOTE:" + DEMO_DATA,
    "template": "llama3",
    "cutoff_len": 8192,
    "output_dir": "dummy_dir",
    "overwrite_output_dir": True,
    "fp16": True,
}


@pytest.mark.parametrize("num_samples", [16])
def test_unsupervised_data(num_samples: int):
    train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
    ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
    original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
    indexes = random.choices(range(len(original_data)), k=num_samples)
    for index in indexes:
        messages = original_data["messages"][index]
        ref_ids = ref_tokenizer.apply_chat_template(messages)
        ref_input_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
        ref_labels = ref_ids[len(ref_input_ids) :]
        assert train_dataset["input_ids"][index] == ref_input_ids
        assert train_dataset["labels"][index] == ref_labels
