# Copyright (c) 2025, NVIDIA 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 argparse
import os
from dataclasses import dataclass

import torch
from megatron.core.optimizer import OptimizerConfig

from nemo import lightning as nl
from nemo.collections import llm
from nemo.collections.llm.gpt.data.core import get_dataset_root
from nemo.collections.llm.gpt.data.packed_sequence import PackedSequenceSpecs
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from tests.collections.llm.common import Llama3ConfigCI

## NOTE: This script is present for github-actions testing only.
## CI tests that call this script should set max_steps=3 for initial training
## and max_steps=6 for resume testing


def get_args():
    parser = argparse.ArgumentParser(description='Finetune a small GPT model using NeMo 2.0')
    parser.add_argument('--restore_path', type=str, help="Path to model to be finetuned")
    parser.add_argument('--experiment_dir', type=str, help="directory to write results and checkpoints to")
    parser.add_argument('--peft', type=str, default='none', help="none | lora")
    parser.add_argument('--devices', type=int, default=1, help="number of devices")
    parser.add_argument('--max_steps', type=int, default=1, help="number of devices")
    parser.add_argument('--mbs', type=int, default=1, help="micro batch size")
    parser.add_argument('--tp_size', type=int, default=1, help="tensor parallel size")
    parser.add_argument('--pp_size', type=int, default=1, help="pipeline parallel size")
    parser.add_argument('--packed', action='store_true', help="use packed sequence dataset")
    parser.add_argument('--dataset', type=str, default="dolly", choices=['dolly', 'chat'], help="Dataset to use")
    parser.add_argument('--te_op_fuser', action='store_true', help="Enable Transformer Engine operation fuser")

    return parser.parse_args()


if __name__ == '__main__':
    args = get_args()

    strategy = nl.MegatronStrategy(
        tensor_model_parallel_size=args.tp_size,
        pipeline_model_parallel_size=args.pp_size,
        # Pipeline dtype is coupled with the bf16 mixed precision plugin
        pipeline_dtype=torch.bfloat16,
        ckpt_load_strictness="log_all",  # Only for CI tests to use older versions of checkpoint
    )

    trainer = nl.Trainer(
        devices=args.devices,
        max_steps=args.max_steps,
        accelerator="gpu",
        strategy=strategy,
        plugins=nl.MegatronMixedPrecision(precision="bf16-mixed"),
        log_every_n_steps=1,
        limit_val_batches=2,
        val_check_interval=2,
        num_sanity_val_steps=0,
    )

    ckpt = nl.ModelCheckpoint(
        save_last=True,
        monitor="reduced_train_loss",
        save_top_k=1,
        save_on_train_epoch_end=True,
        save_optim_on_train_end=True,
    )

    logger = nl.NeMoLogger(
        log_dir=args.experiment_dir,
        use_datetime_version=False,  # must be false if using auto resume
        ckpt=ckpt,
    )

    adam = nl.MegatronOptimizerModule(
        config=OptimizerConfig(
            optimizer="adam",
            lr=0.0001,
            adam_beta2=0.98,
            use_distributed_optimizer=True,
            clip_grad=1.0,
            bf16=True,
        ),
    )

    if args.peft in llm.peft.PEFT_STR2CLS:
        peft = llm.peft.PEFT_STR2CLS[args.peft]()
    else:
        peft = None
    if args.te_op_fuser:
        # TE op fuser replaces MLP, so only finetune qkv and proj layers
        peft.target_modules = ["linear_proj", "linear_qkv"]

    packed_sequence_specs = (
        PackedSequenceSpecs(packed_sequence_size=2048, tokenizer_model_name="dummy_tokenizer") if args.packed else None
    )

    if args.dataset == 'chat':
        assert not args.packed
        data = llm.ChatDataModule(
            dataset_root=get_dataset_root("chat"),
            seq_length=2048,
            micro_batch_size=args.mbs,
            global_batch_size=8,
            num_workers=0,
            packed_sequence_specs=packed_sequence_specs,
        )
    else:
        data = llm.DollyDataModule(
            seq_length=2048,
            micro_batch_size=args.mbs,
            global_batch_size=8,
            num_workers=0,
            packed_sequence_specs=packed_sequence_specs,
        )

    # ensure using cached dir
    assert str(data.dataset_root).startswith(os.environ.get("NEMO_HOME"))

    tokenizer = get_nmt_tokenizer(tokenizer_model=os.path.join(args.restore_path, "dummy_tokenizer.model"))
    model_config = Llama3ConfigCI(use_transformer_engine_op_fuser=args.te_op_fuser)
    llama3_8b = llm.LlamaModel(model_config, tokenizer=tokenizer)

    resume = nl.AutoResume(
        restore_config=nl.RestoreConfig(path=args.restore_path),
        resume_if_exists=True,
    )

    llm.finetune(
        model=llama3_8b,
        data=data,
        trainer=trainer,
        peft=peft,
        log=logger,
        optim=adam,
        resume=resume,
    )

    if args.max_steps == 3:
        print("Initial Training Succeeded")
    if args.max_steps == 6:
        # assert a resume has happened for CI tests
        msg = (
            "Resume did not happen in this resume test.\n"
            "Hint: Scroll up and see whether 'Initial Training Succeeded' is printed out.\n"
            "If not, then the issue is not with ckpt resume."
        )
        assert 'reduced_train_loss=' in str(trainer.ckpt_path), msg
