# coding=utf-8
# Copyright (c) 2024, HUAWEI CORPORATION.  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.


import modellink
from megatron import get_args
from megatron.model import GPTModel
from megatron.initialize import initialize_megatron
from megatron.arguments import core_transformer_config_from_args
from modellink.tasks.inference.text_generation.infer_base import task_factory, add_text_generate_args


def model_provider(pre_process=True, post_process=True):
    """Build the model."""
    config = core_transformer_config_from_args(get_args())
    init_model = GPTModel(
        config,
        parallel_output=False,
        pre_process=pre_process,
        post_process=post_process
    )
    return init_model


if __name__ == "__main__":
    initialize_megatron(extra_args_provider=add_text_generate_args,
                        args_defaults={'no_load_rng': True,
                                       'no_load_optim': True})

    args = get_args()

    model = GPTModel.from_pretrained(
        model_provider=model_provider,
        pretrained_model_name_or_path=args.load
    )

    system_template = ""
    dialog_template = "{instruction}"

    if args.inference_prompt_type == 'alpaca':
        system_template = "Below is an instruction that describes a task, paired with an input that provides further " \
                          "context. Write a response that appropriately completes the request. " \
                          "Please note that you need to think through your response logically and step by step.\n\n"
        dialog_template = "### Instruction:\n{instruction}\n\n### Response:"

    elif args.inference_prompt_type == 'mixtral':
        system_template = "<s>"
        dialog_template = "[INST] {instruction} [/INST] "

    task_factory(args, model, system_template=system_template, dialog_template=dialog_template)