# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.

import logging
from pathlib import Path
from typing import Optional, Tuple, Union

import modelopt.torch.opt as mto
import torch.nn as nn
from modelopt.torch.opt.plugins import restore_sharded_modelopt_state

from megatron.core import dist_checkpointing
from megatron.core.utils import get_torch_version, is_torch_min_version
from megatron.training import get_args
from megatron.training.checkpointing import _load_base_checkpoint, load_checkpoint
from megatron.training.utils import print_rank_0, unwrap_model
from .utils import print_distributed_quant_summary

logger = logging.getLogger(__name__)

NEMO_WEIGHT_DIR_NAMES = {"model_weights": "model.", "weights": "module."}


def has_modelopt_state(checkpoint_path: str) -> bool:
    """Check if modelopt_state folder exists inside the checkpoint.
    Args:
        checkpoint_path: Path to the checkpoint directory

    Returns:
        True if modelopt_state exists, False otherwise
    """
    args = get_args()

    try:
        if args.ckpt_format == "torch":
            # Non-sharded
            state_dict, _, _ = _load_base_checkpoint(checkpoint_path, rank0=False)
            if state_dict is None:
                return False
            if "modelopt_state" not in state_dict:
                return False
            return True
        else:
            # Sharded
            load_dir, _ = get_sharded_load_dir(checkpoint_path)
            if load_dir is None:
                return False
            if not (load_dir / "modelopt_state").is_dir():
                return False
            return True
    except Exception as e:
        print_rank_0(f"Failed to inspect checkpoint in {checkpoint_path}: {e}")
        return False


def get_sharded_load_dir(load_dir: str) -> Tuple[Union[Path, None], str]:
    """Helper to retrieve the sharded load directory and its prefix, if any."""
    load_dir = Path(load_dir)

    # Skip if load_dir is nonexistent or empty
    if not load_dir.is_dir() or not any(load_dir.iterdir()):
        return None, ""

    sharded_load_dir = None
    sharded_prefix = ""
    # Read the tracker file and set the iteration if this is a MLM sharded checkpoint.
    # If no tracker file, assume it is a NeMo sharded checkpoint.
    tracker_filename = load_dir / 'latest_checkpointed_iteration.txt'
    if tracker_filename.is_file():
        with open(tracker_filename, 'r') as f:
            metastring = f.read().strip()
            try:
                iteration = int(metastring)
                sharded_load_dir = Path(load_dir) / 'iter_{:07d}'.format(iteration)
            except ValueError:
                sharded_load_dir = Path(load_dir) / metastring
    else:
        for nemo_dir_name, prefix in NEMO_WEIGHT_DIR_NAMES.items():
            nemo_weight_dir = Path(load_dir) / nemo_dir_name
            if nemo_weight_dir.is_dir():
                sharded_load_dir = nemo_weight_dir
                sharded_prefix = prefix
                break

    if sharded_load_dir is None:
        raise ValueError(f"{load_dir} is not a MLM or NeMo sharded checkpoint!")
    if not sharded_load_dir.exists():
        return None, ""

    return sharded_load_dir, sharded_prefix


def load_modelopt_state(model: nn.Module, load_dir: Optional[str] = None) -> None:
    """Loading modelopt_state without loading the model.

    If distributed checkpointing in use, we try to load from the sharded modelopt_state. This will not
    load the model state_dict. Otherwise, if the checkpoint is not sharded, we load the base checkpoint
    (which contains the model state as well) and extract the modelopt_state.

    Args:
        model: the model to load the modelopt_state into
        load_dir: optionally provide a different loading path
    """
    args = get_args()
    load_dir = load_dir or args.load

    if args.ckpt_format == "torch":
        # Non-sharded
        print_rank_0(f"Loading ModelOpt state from base checkpoint ({load_dir})")
        try:
            state_dict, _, _ = _load_base_checkpoint(args.load, rank0=False)
        except Exception:
            print_rank_0("Failed to load base checkpoint via megatron _load_base_checkpoint!")
            return
        if state_dict is None:
            print_rank_0("No checkpoint state_dict found. Skipping loading ModelOpt state.")
            return
        modelopt_state = state_dict.get("modelopt_state", None)
        if modelopt_state is not None:
            mto.restore_from_modelopt_state(model, modelopt_state)
    else:
        # Sharded
        sharded_load_dir, _ = get_sharded_load_dir(load_dir)
        if sharded_load_dir is None:
            print_rank_0("No sharded checkpoint found. Skipping loading modelopt_state.")
            return
        restore_sharded_modelopt_state([model], sharded_load_dir)


def load_modelopt_checkpoint(
    model,
    optimizer=None,
    opt_param_scheduler=None,
    strict: bool = True,
    additional_sharded_prefix: str = "",
    load_arg: str = "load",
) -> None:
    """Load a sharded (untar .nemo or megatron --use-dist-ckpt) or unsharded checkpoint.

    Essentially, the function is detecting whether the checkpoint is a .nemo sharded checkpoint.
    If so, we load the sharded state_dict with additional_sharded_prefix `model.`.
    This additional prefix is tha artifact of the lightning module wrapper. Once the sharded
    state_dict is loaded, we use a state_dict pre_hook to pop this additional prefix (`model.`)
    from all state_dict keys.

    If this is not a .nemo sharded checkpoint, then this function will simply call
    load_checkpoint. See megatron.checkpointing.load_checkpoint for explanation.

    Args:
        additional_sharded_prefix: append additional prefix to align the sharded checkpoint keys.
            When loading an .nemo sharded checkpoint, this is usually `model.`. Otherwise, this is
            typically an empty string.
    """

    def _remove_prefix_state_dict_pre_hook(
        state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """Pytorch state_dict pre_hook to remove prefix of the state_dict keys."""
        if additional_sharded_prefix is None:
            return
        key_rewrite_list = []
        for key, _ in state_dict.items():
            if key.startswith(additional_sharded_prefix):
                key_rewrite_list.append(key)
        for old_key in key_rewrite_list:
            new_key = old_key[len(additional_sharded_prefix) :]
            state_dict[new_key] = state_dict.pop(old_key)

    args = get_args()
    load_dir = getattr(args, load_arg)
    sharded_load_dir, additional_sharded_prefix = get_sharded_load_dir(load_dir)

    unwrapped_model = unwrap_model(model)

    if args.ckpt_format == "torch":
        state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
            load_dir, args, rank0=False
        )
        model_state_dict = state_dict["model"]
        unwrapped_model[0].load_state_dict(model_state_dict, strict=False)
        print_distributed_quant_summary(unwrapped_model[0])
    elif sharded_load_dir is not None and optimizer is None and opt_param_scheduler is None:
        sharded_state_dict_metadata = dist_checkpointing.load_content_metadata(sharded_load_dir)
        sharded_state_dict = unwrapped_model[0].sharded_state_dict(
            prefix=additional_sharded_prefix, metadata=sharded_state_dict_metadata
        )

        if additional_sharded_prefix:
            unwrapped_model[0]._register_load_state_dict_pre_hook(
                _remove_prefix_state_dict_pre_hook
            )
        model_state_dict = dist_checkpointing.load(
            sharded_state_dict, sharded_load_dir, strict=args.dist_ckpt_strictness
        )
        unwrapped_model[0].load_state_dict(model_state_dict, strict=False)
        print_distributed_quant_summary(unwrapped_model[0])
    else:
        _ = load_checkpoint(model, optimizer, opt_param_scheduler, strict=strict, load_arg=load_arg)