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from typing import Optional

import lightning.pytorch as pl
import nemo_run as run
import torch
from lightning.pytorch.callbacks.callback import Callback
from megatron.core.distributed import DistributedDataParallelConfig

from nemo import lightning as nl
from nemo.collections import llm
from nemo.collections.common.tokenizers.tokenizer_utils import get_nmt_tokenizer
from nemo.collections.llm.api import finetune, pretrain
from nemo.collections.llm.gpt.data.mock import MockDataModule
from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
from nemo.utils.exp_manager import TimingCallback

NAME = "mamba2_2_7b"


@run.cli.factory(name=NAME)
def tokenizer(tokenizer_model: str = None) -> run.Config[pl.LightningModule]:
    """
    Factory function to create a tokenizer configuration.
    """
    return run.Config(
        get_nmt_tokenizer,
        library='huggingface',
        model_name="EleutherAI/gpt-neox-20b",
        tokenizer_model=tokenizer_model,
        use_fast=True,
    )


@run.cli.factory(name=NAME)
def model(tokenizer_model: str = None) -> run.Config[pl.LightningModule]:
    """
    Factory function to create a Mamba2 2.7B model configuration.

    Returns:
        run.Config[pl.LightningModule]: Configuration for the Mamba2 2.7B model.

    Examples:
        CLI usage:
            $ nemo llm pretrain model=mamba2_2_7B ...

        Python API usage:
            >>> model_config = model()
            >>> print(model_config)
    """
    return run.Config(
        llm.MambaModel,
        config=run.Config(llm.BaseMambaConfig2_7B),
        tokenizer=tokenizer(tokenizer_model=tokenizer_model),
    )


@run.cli.factory(target=finetune, name=NAME)
def trainer(
    tensor_parallelism: int = 1,
    pipeline_parallelism: int = 1,
    pipeline_parallelism_type: Optional[torch.dtype] = None,
    virtual_pipeline_parallelism: Optional[int] = None,
    context_parallelism: int = 1,
    sequence_parallelism: bool = False,
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    max_steps: int = 100,
    val_check_interval: int = 100,
    limit_test_batches: int = 50,
    limit_val_batches: int = 32,
    log_every_n_steps: int = 10,
    callbacks: Optional[list[run.Config[Callback]]] = None,
) -> run.Config[nl.Trainer]:
    """
    Configure the NeMo Lightning Trainer for Mamba2 2.7B model.

    This function sets up the distributed training strategy and other training parameters.

    Args:
        tensor_parallelism (int): Degree of tensor model parallelism.
        pipeline_parallelism (int): Degree of pipeline model parallelism.
        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.
        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.
        context_parallelism (int): Degree of context parallelism.
        sequence_parallelism (bool): Whether to use sequence parallelism.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        max_steps (int): Maximum number of training steps.
        callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.

    Returns:
        run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.

    Examples:
        CLI usage:
            $ nemo llm pretrain trainer=mamba2_2_7b ...

        Python API usage:
            >>> trainer_config = trainer(num_nodes=1, num_gpus_per_node=1)
            >>> print(trainer_config)

    Note:
        For more information on distributed training strategies, refer to the
        NeMo documentation on multi-GPU and multi-node training.
    """
    strategy = run.Config(
        nl.MegatronStrategy,
        tensor_model_parallel_size=tensor_parallelism,
        pipeline_model_parallel_size=pipeline_parallelism,
        pipeline_dtype=pipeline_parallelism_type,
        virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,
        context_parallel_size=context_parallelism,
        sequence_parallel=sequence_parallelism,
        gradient_as_bucket_view=True,
        ckpt_async_save=False,
        ckpt_parallel_load=True,
        ddp=run.Config(
            DistributedDataParallelConfig,
            check_for_nan_in_grad=True,
            grad_reduce_in_fp32=True,
            overlap_grad_reduce=True,
            overlap_param_gather=True,
        ),
    )

    trainer = run.Config(
        nl.Trainer,
        accelerator="gpu",
        accumulate_grad_batches=1,
        callbacks=callbacks,
        devices=num_gpus_per_node,
        max_steps=max_steps,
        num_nodes=num_nodes,
        plugins=bf16_mixed(),
        strategy=strategy,
        use_distributed_sampler=False,
        val_check_interval=val_check_interval,
        limit_test_batches=limit_test_batches,
        limit_val_batches=limit_val_batches,
        log_every_n_steps=log_every_n_steps,
    )

    return trainer


@run.cli.factory(target=pretrain, name=NAME)
def pretrain_recipe(
    dir: Optional[str] = None,
    name: str = "default",
    tokenizer_model: str = None,
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    tensor_parallelism: int = 1,
    pipeline_parallelism: int = 1,
    max_steps: int = 100,
    val_check_interval: int = 100,
    limit_test_batches: int = 50,
    limit_val_batches: int = 32,
    log_every_n_steps: int = 10,
    seq_length: int = 4096,
    gbs: int = 8,
    mbs: int = 1,
    fn=pretrain,
) -> run.Partial:
    """
    Create a pre-training recipe for Mamba2 2.7B model.

    This function sets up a complete configuration for pre-training, including
    model, trainer, data, logging, optimization, and resumption settings.

    Args:
        dir (Optional[str]): Directory for saving logs and checkpoints.
        name (str): Name of the pre-training run.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        fn (Callable): The pre-training function to use.

    Returns:
        run.Partial: Partial configuration for pre-training.

    Examples:
        CLI usage:
            $ nemo llm pretrain --factory mamba2_2_7b
            $ nemo llm pretrain --factory "mamba2_2_7b(num_nodes=1, name='my_pretrain')"

        Python API usage:
            >>> recipe = pretrain_recipe(name="mamba2_2_7b_pretrain", num_nodes=1)
            >>> print(recipe)
    """
    return run.Partial(
        fn,
        model=model(),
        trainer=trainer(
            max_steps=max_steps,
            num_nodes=num_nodes,
            tensor_parallelism=tensor_parallelism,
            pipeline_parallelism=pipeline_parallelism,
            num_gpus_per_node=num_gpus_per_node,
            val_check_interval=val_check_interval,
            limit_test_batches=limit_test_batches,
            limit_val_batches=limit_val_batches,
            log_every_n_steps=log_every_n_steps,
            callbacks=[run.Config(TimingCallback)],
        ),
        data=run.Config(
            MockDataModule,
            seq_length=seq_length,
            global_batch_size=gbs,
            micro_batch_size=mbs,
            tokenizer=tokenizer(),
        ),
        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
        optim=distributed_fused_adam_with_cosine_annealing(max_lr=3e-4),
        resume=default_resume(),
    )


@run.cli.factory(target=finetune, name=NAME)
def finetune_recipe(
    dir: Optional[str] = None,
    name: str = "default",
    resume_path: str = None,
    tokenizer_model: str = None,
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    seq_length: int = 4096,
    max_steps: int = 100,
    val_check_interval: int = 100,
    limit_test_batches: int = 50,
    limit_val_batches: int = 32,
    log_every_n_steps: int = 10,
    gbs: int = 8,
    mbs: int = 1,
    peft_scheme: Optional[str] = 'none',
) -> run.Partial:
    """
    Create a fine-tuning recipe for Mamba2 2.7B model.

    This function sets up a complete configuration for fine-tuning, including
    model, trainer, data, logging, optimization, and resumption settings.

    Args:
        dir (Optional[str]): Directory for saving logs and checkpoints.
        name (str): Name of the fine-tuning run.
        resume_path (str): Path to the NeMo checkpoint (refer to notes below
                            on how to convert a pytorch checkpoint to NeMo)
        tokenizer_model (str): Path to tokenizer model (defaults to None)
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
    Returns:
        run.Partial: Partial configuration for fine-tuning.

    Examples:
        CLI usage:
            $ nemo llm finetune --factory mamba2_2_7b

        Python API usage:
            >>> recipe = finetune_recipe(name="mamba2_2_7b_finetune", num_nodes=1)
            >>> print(recipe)

    Note:
        This recipe uses the SQuAD dataset for fine-tuning.
        For converting an SSM pytorch checkpoint, use the following line of python code:

        llm.MambaModel(llm.BaseMambaConfig2_7B(), tokenizer=tokenizer()).import_ckpt(
            path="pytorch://ABSOLUTE_PATH_TO_CKPT/your_pytorch_state_dict_file",
            model_config=llm.BaseMambaConfig2_7B())
        This line will cache the nemo checkpoint to following directory:
            /root/.cache/nemo/models/your_pytorch_state_dict_file

    """
    nemo_resume = run.Config(
        nl.AutoResume,
        restore_config=run.Config(nl.RestoreConfig, path=resume_path),
    )
    strategy = run.Config(
        nl.MegatronStrategy,
        tensor_model_parallel_size=tensor_model_parallel_size,
        pipeline_model_parallel_size=pipeline_model_parallel_size,
        gradient_as_bucket_view=True,
        ckpt_load_optimizer=False,
        ckpt_save_optimizer=False,
        ckpt_async_save=False,
    )
    checkpoint_callback = run.Config(
        nl.ModelCheckpoint,
        every_n_train_steps=10,
        dirpath=dir,
    )
    trainer = run.Config(
        nl.Trainer,
        accelerator="gpu",
        accumulate_grad_batches=1,
        devices=num_gpus_per_node,
        max_steps=max_steps,
        val_check_interval=val_check_interval,
        limit_test_batches=limit_test_batches,
        limit_val_batches=limit_val_batches,
        log_every_n_steps=log_every_n_steps,
        num_nodes=num_nodes,
        plugins=run.Config(
            nl.MegatronMixedPrecision,
            precision="bf16-mixed",
            params_dtype=torch.bfloat16,
        ),
        callbacks=[checkpoint_callback],
        strategy=strategy,
        use_distributed_sampler=False,
    )
    recipe = run.Partial(
        llm.finetune,
        model=model(tokenizer_model=tokenizer_model),
        trainer=trainer,
        data=run.Config(
            llm.SquadDataModule,
            seq_length=seq_length,
            global_batch_size=gbs,
            micro_batch_size=mbs,
            tokenizer=tokenizer(tokenizer_model=tokenizer_model),
        ),
        log=llm.default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
        optim=distributed_fused_adam_with_cosine_annealing(max_lr=1e-4, min_lr=0, warmup_steps=50),
        resume=nemo_resume,
    )
    if peft_scheme is None or peft_scheme.lower() == 'none':
        recipe.trainer.strategy.tensor_model_parallel_size = 1
        recipe.optim.config.lr = 5e-6
    else:
        raise ValueError(f"Unrecognized peft scheme: {peft_scheme}")
    return recipe