# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import annotations

import shutil
import sys
import tempfile
import time
import unittest

from parameterized import parameterized_class

from paddlenlp.utils.downloader import get_path_from_url
from tests.testing_utils import argv_context_guard, load_test_config

from .testing_utils import LLMTest


@parameterized_class(
    ["model_dir"],
    [
        # ["llama"], @skip("Skip and wait to fix.")
        # ["qwen"], @skip("Skip and wait to fix.")
        ["qwen2"],
        ["gpt"],
    ],
)
class PretrainTest(LLMTest, unittest.TestCase):
    config_path: str = "./tests/fixtures/llm/pretrain.yaml"
    model_dir: str = None

    def setUp(self) -> None:
        LLMTest.setUp(self)

        self.dataset_dir = tempfile.mkdtemp()
        self.model_codes_dir = self.root_path

    def tearDown(self) -> None:
        LLMTest.tearDown(self)
        shutil.rmtree(self.dataset_dir)

    def test_pretrain(self):

        pretrain_flag = False
        for key, value in sys.modules.items():
            if "run_pretrain" in key:
                pretrain_flag = True
                break
        if pretrain_flag:
            del sys.modules["run_pretrain"]

        # Run pretrain
        URL = "https://bj.bcebos.com/paddlenlp/models/transformers/llama/data/llama_openwebtext_100k.bin"
        URL2 = "https://bj.bcebos.com/paddlenlp/models/transformers/llama/data/llama_openwebtext_100k.idx"
        get_path_from_url(URL, root_dir=self.dataset_dir)
        time.sleep(5)
        get_path_from_url(URL2, root_dir=self.dataset_dir)

        pretrain_config = load_test_config(self.config_path, "pretrain", self.model_dir)

        pretrain_config["input_dir"] = self.dataset_dir
        pretrain_config["output_dir"] = self.output_dir

        with argv_context_guard(pretrain_config):
            from run_pretrain import main

            main()

        # Now, only work for llama, not gpt or qwen
        if self.model_dir == "llama":
            self.run_predictor({"inference_model": True})

        self.run_predictor({"inference_model": False})