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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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import torch
from lightning.pytorch.loggers import TensorBoardLogger
from megatron.core.distributed import DistributedDataParallelConfig
from megatron.core.optimizer import OptimizerConfig

from nemo import lightning as nl
from nemo.collections import llm, vlm

from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
from nemo.collections.vlm.gemma3vl.data.mock import Gemma3VLMockDataModule
from nemo.lightning.pytorch.optim import CosineAnnealingScheduler
from nemo.lightning.pytorch.optim.megatron import MegatronOptimizerModule
from nemo.utils.exp_manager import TimingCallback


NAME = "gemma3vl_4b"

HF_MODEL_NAME = "google/gemma-3-4b-it"


def finetune_recipe(
    log_dir: str,
    num_nodes: int = 1,
    num_gpus_per_node: int = 1,
):
    """Gemma3 VL finetune"""

    max_sequence_length = 512

    tokenizer = AutoTokenizer(HF_MODEL_NAME)
    language_transformer_config = llm.Gemma3Config4B(seq_length=max_sequence_length)
    vision_transformer_config = vlm.Gemma3VLVisionConfig()
    vision_projection_config = vlm.Gemma3VLMultimodalProjectorConfig(
        input_size=vision_transformer_config.hidden_size,
        hidden_size=language_transformer_config.hidden_size,
    )
    gemma3vl_config = vlm.Gemma3VLConfig(
        language_transformer_config=language_transformer_config,
        vision_transformer_config=vision_transformer_config,
        vision_projection_config=vision_projection_config,
        freeze_language_model=False,
        freeze_vision_model=True,
        freeze_vision_projection=True,
    )
    model = vlm.Gemma3VLModel(gemma3vl_config, tokenizer=tokenizer)
    llm.import_ckpt(model=model, source=f"hf://{HF_MODEL_NAME}")

    strategy = nl.MegatronStrategy(
        tensor_model_parallel_size=2,
        pipeline_model_parallel_size=1,
        pipeline_dtype=torch.bfloat16,
        virtual_pipeline_model_parallel_size=None,
        encoder_tensor_model_parallel_size=0,
        encoder_pipeline_model_parallel_size=0,
        context_parallel_size=1,
        sequence_parallel=True,
        ckpt_async_save=True,
        ckpt_parallel_save=True,
        ckpt_parallel_load=True,
        ckpt_parallel_save_optim=True,
        ckpt_load_strictness="log_all",
        gradient_as_bucket_view=True,
        ddp=DistributedDataParallelConfig(
            check_for_nan_in_grad=True,
            grad_reduce_in_fp32=True,
            overlap_grad_reduce=True,
            overlap_param_gather=True,
            average_in_collective=True,
        ),
    )

    trainer = nl.Trainer(
        accelerator="gpu",
        devices=num_gpus_per_node,
        num_nodes=num_nodes,
        max_steps=5,
        limit_val_batches=0,
        val_check_interval=10,
        num_sanity_val_steps=0,
        log_every_n_steps=1,
        strategy=strategy,
        accumulate_grad_batches=1,
        use_distributed_sampler=False,
        plugins=nl.MegatronMixedPrecision(precision="bf16-mixed"),
        enable_checkpointing=False,
        callbacks=[TimingCallback()],
    )

    opt_config = OptimizerConfig(
        optimizer="adam",
        lr=3e-4,
        weight_decay=0.1,
        bf16=True,
        fp16=False,
        adam_beta1=0.9,
        adam_beta2=0.95,
        adam_eps=1e-5,
        use_distributed_optimizer=True,
        clip_grad=1.0,
    )
    lr_scheduler = CosineAnnealingScheduler(
        warmup_steps=2000,
        constant_steps=0,
        min_lr=3e-5,
    )
    opt = MegatronOptimizerModule(config=opt_config, lr_scheduler=lr_scheduler)

    data = Gemma3VLMockDataModule(
        seq_length=max_sequence_length,
        global_batch_size=4,
        micro_batch_size=1,
        tokenizer=tokenizer,
        num_workers=4,
    )

    ckpt = nl.ModelCheckpoint(
        save_top_k=1,
        save_last=True,
        save_optim_on_train_end=False,
        filename="{val_loss:.2f}-{step}-{consumed_samples}",
    )
    tb = TensorBoardLogger(
        save_dir="tensorboard",  # The name of tfevents folder
        name="",  # No need further subfolder
    )
    logger = nl.NeMoLogger(
        explicit_log_dir=log_dir,
        log_global_rank_0_only=True,
        update_logger_directory=True,
        ckpt=ckpt,
        tensorboard=tb,
    )

    resume = nl.AutoResume(
        resume_if_exists=True,
        resume_ignore_no_checkpoint=True,
    )

    llm.finetune(
        model=model,
        data=data,
        trainer=trainer,
        log=logger,
        optim=opt,
        resume=resume,
    )


if __name__ == "__main__":
    finetune_recipe(f"/tmp/{NAME}", num_gpus_per_node=2)