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"""
Utilities for checkpointing training-related states (i.e. model, optimizer, lr_scheduler, etc.)
We save both a HuggingFace model and a Fabric-specific checkpoint. The HuggingFace model is
saved at the step-specific checkpoint directory, while the Fabric-specific checkpoint is saved
in a subdirectory. This is done to facilitate easier versioning of the HuggingFace model files
(which are what gets uploaded to the Hub).
"""

import os
from dataclasses import asdict
from typing import Any, Dict, Tuple, Union

import yaml
from huggingface_hub import upload_file, upload_folder
from lightning.fabric import Fabric
from lightning.fabric.strategies import DeepSpeedStrategy
from lightning.fabric.utilities.seed import _collect_rng_states, _set_rng_states
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from transformers import PreTrainedTokenizerBase

from src.config import CheckpointingConfig
from src.training.utils.io import use_backoff


@use_backoff()
def load_checkpoint(
    checkpointing_config: CheckpointingConfig,
    checkpoint_step: Union[str, int],
    fabric: Fabric,
    model: nn.Module,
    optimizer: Optimizer,
    lr_scheduler: LRScheduler,
) -> Tuple[nn.Module, Optimizer, LRScheduler, int]:
    """Load model checkpoint and associated states from a given step.
    Args:
        checkpointing_config: Configuration object containing checkpoint settings
        checkpoint_step: The step at which to load the checkpoint
        fabric: Lightning Fabric instance for distributed training support
        model: The model instance to load weights into
        optimizer: The optimizer instance to load states into
        lr_scheduler: The learning rate scheduler to load states into
    Returns:
        Tuple containing the model, optimizer, lr_scheduler, and checkpoint step.
        Returns None if no checkpoint is found.
    """

    if isinstance(checkpoint_step, int):
        checkpoint_step = f"step_{checkpoint_step}"

    checkpoint_path = os.path.join(
        checkpointing_config.runs_dir,
        checkpointing_config.run_name,
        checkpointing_config.checkpoints_dir,
        checkpoint_step,
    )

    if not os.path.exists(checkpoint_path):
        return None

    # Load from specified fabric checkpoint subdirectory
    fabric_checkpoint_path = os.path.join(
        checkpoint_path, checkpointing_config.fabric_checkpoint_dir
    )

    checkpoint_state = {
        "_model": model,
        "_optimizer": optimizer,
        "_lr_scheduler": lr_scheduler,
    }

    if not isinstance(fabric.strategy, DeepSpeedStrategy):
        fabric_load_file = os.path.join(
            fabric_checkpoint_path, checkpointing_config.fabric_checkpoint_filename
        )
    else:
        # Deepspeed checkpoints create sub-directory with distributed checkpoint file
        fabric_load_file = fabric_checkpoint_path

    extra_state = fabric.load(os.path.join(fabric_load_file), state=checkpoint_state)

    # NOTE: extra_state will contain any additional states that were saved in the checkpoint
    checkpoint_step = extra_state["_checkpoint_step"]

    if "_rng_states" in extra_state:
        _rng_states = extra_state["_rng_states"]
        _set_rng_states(_rng_states)

    return model, optimizer, lr_scheduler, checkpoint_step


@use_backoff()
def save_checkpoint(
    configs: Dict[str, Any],
    checkpoint_step: int,
    fabric: Fabric,
    model: nn.Module,
    optimizer: Optimizer,
    lr_scheduler: LRScheduler,
    tokenizer: PreTrainedTokenizerBase,
    upload_logs: bool = False,
) -> None:
    """Save training checkpoint and associated states to disk and optionally to HuggingFace Hub.
    We save the following files:
    - HuggingFace model files (config.json, pytorch_model.bin)
    - Tokenizer files (vocab.json, merges.txt)
    - Fabric-specific files - fabric state of the model, optimizer, and lr_scheduler. If using
      DeepSpeed, the checkpoint is saved in a subdirectory, otherwise it is saved in a single file.
    Note that the HuggingFace model files are saved at the step-specific checkpoint directory, while the
    Fabric-specific files are saved in a subdirectory. This is done to facilitate easier
    versioning of the HuggingFace model files (which are what gets uploaded to the Hub).
    NOTE: Why do we save a HF model at all? We do this because it makes it easier to load the model
    in a separate script for evaluation and to play nicely with the HuggingFace Hub.
    Creates a versioned checkpoint directory with the following structure:
    {checkpointing_config.runs_dir}/
        └── {checkpointing_config.run_name}/
            └── training_config.yaml           # Training config
            └── {checkpointing_config.checkpoints_dir}/
                ├── step_{checkpoint_step}/
                │   ├── config.json                    # HuggingFace model config
                │   ├── model.safetensors              # HuggingFace model weights
                │   ├── pico_{model_type}.py           # HuggingFace custom model class
                │   ├── tokenizer.json                 # Tokenizer vocab
                │   ├── tokenizer_config.json          # Tokenizer config
                │   └── {checkpointing_config.fabric_checkpoint_dir}/  # Fabric-specific files
                │       └── checkpoint/                # Distributed model checkpoint files (if using DeepSpeed)
                │           OR
                │       └── checkpoint.pt              # Single checkpoint file (if using other strategies)
                └── latest -> step_{checkpoint_step}/
    Args:
        configs: A dictionary containing the initialized configuration objects.
        checkpoint_step: The current training checkpoint step (i.e. number of learning steps taken)
        fabric: Lightning Fabric instance for distributed training support
        model: The model instance to save
        optimizer: The optimizer instance to save
        lr_scheduler: The learning rate scheduler to save
        tokenizer: The tokenizer to save
        upload_logs: Whether to upload training logs to HF Hub (default: False)
    """

    checkpointing_config = configs["checkpointing"]

    # Get the directories from the training config
    runs_dir = checkpointing_config.runs_dir
    checkpoints_dir = checkpointing_config.checkpoints_dir
    fabric_checkpoint_dir = checkpointing_config.fabric_checkpoint_dir
    logs_dir = checkpointing_config.logs_dir

    run_path = os.path.join(runs_dir, checkpointing_config.run_name)
    root_checkpoint_path = os.path.join(run_path, checkpoints_dir)
    checkpoint_path = os.path.join(root_checkpoint_path, f"step_{checkpoint_step}")

    # Create directories
    os.makedirs(checkpoint_path, exist_ok=True)

    ########################################################
    #
    # Save HuggingFace files
    #
    ########################################################

    # NOTE: we convert the Pico model to a HuggingFace model before saving it. See `model.py`
    # for more details.
    if fabric.global_rank == 0:
        hf_model = model.convert_to_hf_model()
        hf_model.save_pretrained(checkpoint_path)
        tokenizer.save_pretrained(checkpoint_path)

    ########################################################
    #
    # Save Fabric-specific files
    #
    ########################################################

    # Create fabric-specific subdirectory
    fabric_checkpoint_path = os.path.join(checkpoint_path, fabric_checkpoint_dir)
    os.makedirs(fabric_checkpoint_path, exist_ok=True)

    # Save model states (use underscore to avoid conflicts with third-party libraries)
    checkpoint_state = {
        "_model": model,
        "_optimizer": optimizer,
        "_lr_scheduler": lr_scheduler,
        "_checkpoint_step": checkpoint_step,
    }

    if not isinstance(fabric.strategy, DeepSpeedStrategy):
        checkpoint_state["_rng_states"] = _collect_rng_states()
        fabric_save_file = os.path.join(
            fabric_checkpoint_path, checkpointing_config.fabric_checkpoint_filename
        )
    else:
        # Deepspeed checkpoints create sub-directory with distributed checkpoint file
        fabric_save_file = fabric_checkpoint_path

    fabric.save(fabric_save_file, checkpoint_state)

    if fabric.global_rank == 0:
        # Save config in fabric directory
        config_path = os.path.join(run_path, "training_config.yaml")
        if not os.path.exists(config_path):
            # Converting dataclasses to joined dicts and saving to file
            _training_config = {}
            for config_name, config in configs.items():
                _training_config[config_name] = asdict(config)
            with open(config_path, "w") as f:
                yaml.dump(_training_config, f)

        # Update latest symlink
        latest_symlink_path = os.path.join(root_checkpoint_path, "latest")
        if os.path.lexists(latest_symlink_path):
            os.remove(latest_symlink_path)
        os.symlink(
            f"step_{checkpoint_step}", latest_symlink_path, target_is_directory=True
        )

    ########################################################
    #
    # Push to HuggingFace Hub (if configured)
    #
    ########################################################

    if fabric.global_rank == 0:
        # Push only on rank zero thread

        if checkpointing_config.save_to_hf:
            repo_id = checkpointing_config.hf_checkpoint.repo_id

            # Upload the HF model
            hf_model.push_to_hub(
                repo_id=repo_id,
                commit_message=f"Saving HF Model -- Step {checkpoint_step}",
                revision=checkpointing_config.run_name,
                token=os.getenv("HF_TOKEN"),
            )

            if checkpoint_step == 0:
                # Uploading Tokenizer during first step since it never changes
                tokenizer.push_to_hub(
                    repo_id=repo_id,
                    commit_message=f"Saving Tokenizer -- Step {checkpoint_step}",
                    revision=checkpointing_config.run_name,
                    token=os.getenv("HF_TOKEN"),
                )

                # Upload training config, also only in first step
                upload_file(
                    path_or_fileobj=config_path,
                    path_in_repo="training_config.yaml",
                    repo_id=repo_id,
                    commit_message=f"Saving Training Config -- Step {checkpoint_step}",
                    revision=checkpointing_config.run_name,
                    token=os.getenv("HF_TOKEN"),
                )

            # Upload the fabric checkpoint directory
            upload_folder(
                folder_path=fabric_checkpoint_path,
                path_in_repo=fabric_checkpoint_dir,
                repo_id=repo_id,
                commit_message=f"Saving Fabric Checkpoint -- Step {checkpoint_step}",
                revision=checkpointing_config.run_name,
                token=os.getenv("HF_TOKEN"),
            )

            # Upload logs if requested
            if upload_logs:
                logs_path = os.path.join(run_path, logs_dir)
                upload_folder(
                    folder_path=logs_path,
                    path_in_repo=logs_dir,
                    repo_id=repo_id,
                    commit_message=f"Saving Logs -- Step {checkpoint_step}",
                    revision=checkpointing_config.run_name,
                    token=os.getenv("HF_TOKEN"),
                )

	"""
	Utilities for checkpointing training-related states (i.e. model, optimizer, lr_scheduler, etc.)

	We save both a HuggingFace model and a Fabric-specific checkpoint. The HuggingFace model is
	saved at the step-specific checkpoint directory, while the Fabric-specific checkpoint is saved
	in a subdirectory. This is done to facilitate easier versioning of the HuggingFace model files
	(which are what gets uploaded to the Hub).
	"""

	import os
	from dataclasses import asdict
	from typing import Any, Dict, Tuple, Union

	import yaml
	from huggingface_hub import upload_file, upload_folder
	from lightning.fabric import Fabric
	from lightning.fabric.strategies import DeepSpeedStrategy
	from lightning.fabric.utilities.seed import _collect_rng_states, _set_rng_states
	from torch import nn
	from torch.optim import Optimizer
	from torch.optim.lr_scheduler import LRScheduler
	from transformers import PreTrainedTokenizerBase

	from src.config import CheckpointingConfig
	from src.training.utils.io import use_backoff


	@use_backoff()
	def load_checkpoint(
	checkpointing_config: CheckpointingConfig,
	checkpoint_step: Union[str, int],
	fabric: Fabric,
	model: nn.Module,
	optimizer: Optimizer,
	lr_scheduler: LRScheduler,
	) -> Tuple[nn.Module, Optimizer, LRScheduler, int]:
	"""Load model checkpoint and associated states from a given step.

	Args:
	checkpointing_config: Configuration object containing checkpoint settings
	checkpoint_step: The step at which to load the checkpoint
	fabric: Lightning Fabric instance for distributed training support
	model: The model instance to load weights into
	optimizer: The optimizer instance to load states into
	lr_scheduler: The learning rate scheduler to load states into

	Returns:
	Tuple containing the model, optimizer, lr_scheduler, and checkpoint step.
	Returns None if no checkpoint is found.
	"""

	if isinstance(checkpoint_step, int):
	checkpoint_step = f"step_{checkpoint_step}"

	checkpoint_path = os.path.join(
	checkpointing_config.runs_dir,
	checkpointing_config.run_name,
	checkpointing_config.checkpoints_dir,
	checkpoint_step,
	)

	if not os.path.exists(checkpoint_path):
	return None

	# Load from specified fabric checkpoint subdirectory
	fabric_checkpoint_path = os.path.join(
	checkpoint_path, checkpointing_config.fabric_checkpoint_dir
	)

	checkpoint_state = {
	"_model": model,
	"_optimizer": optimizer,
	"_lr_scheduler": lr_scheduler,
	}

	if not isinstance(fabric.strategy, DeepSpeedStrategy):
	fabric_load_file = os.path.join(
	fabric_checkpoint_path, checkpointing_config.fabric_checkpoint_filename
	)
	else:
	# Deepspeed checkpoints create sub-directory with distributed checkpoint file
	fabric_load_file = fabric_checkpoint_path

	extra_state = fabric.load(os.path.join(fabric_load_file), state=checkpoint_state)

	# NOTE: extra_state will contain any additional states that were saved in the checkpoint
	checkpoint_step = extra_state["_checkpoint_step"]

	if "_rng_states" in extra_state:
	_rng_states = extra_state["_rng_states"]
	_set_rng_states(_rng_states)

	return model, optimizer, lr_scheduler, checkpoint_step


	@use_backoff()
	def save_checkpoint(
	configs: Dict[str, Any],
	checkpoint_step: int,
	fabric: Fabric,
	model: nn.Module,
	optimizer: Optimizer,
	lr_scheduler: LRScheduler,
	tokenizer: PreTrainedTokenizerBase,
	upload_logs: bool = False,
	) -> None:
	"""Save training checkpoint and associated states to disk and optionally to HuggingFace Hub.

	We save the following files:
	- HuggingFace model files (config.json, pytorch_model.bin)
	- Tokenizer files (vocab.json, merges.txt)
	- Fabric-specific files - fabric state of the model, optimizer, and lr_scheduler. If using
	DeepSpeed, the checkpoint is saved in a subdirectory, otherwise it is saved in a single file.

	Note that the HuggingFace model files are saved at the step-specific checkpoint directory, while the
	Fabric-specific files are saved in a subdirectory. This is done to facilitate easier
	versioning of the HuggingFace model files (which are what gets uploaded to the Hub).

	NOTE: Why do we save a HF model at all? We do this because it makes it easier to load the model
	in a separate script for evaluation and to play nicely with the HuggingFace Hub.

	Creates a versioned checkpoint directory with the following structure:

	{checkpointing_config.runs_dir}/
	└── {checkpointing_config.run_name}/
	└── training_config.yaml # Training config
	└── {checkpointing_config.checkpoints_dir}/
	├── step_{checkpoint_step}/
	│ ├── config.json # HuggingFace model config
	│ ├── model.safetensors # HuggingFace model weights
	│ ├── pico_{model_type}.py # HuggingFace custom model class
	│ ├── tokenizer.json # Tokenizer vocab
	│ ├── tokenizer_config.json # Tokenizer config
	│ └── {checkpointing_config.fabric_checkpoint_dir}/ # Fabric-specific files
	│ └── checkpoint/ # Distributed model checkpoint files (if using DeepSpeed)
	│ OR
	│ └── checkpoint.pt # Single checkpoint file (if using other strategies)
	└── latest -> step_{checkpoint_step}/

	Args:
	configs: A dictionary containing the initialized configuration objects.
	checkpoint_step: The current training checkpoint step (i.e. number of learning steps taken)
	fabric: Lightning Fabric instance for distributed training support
	model: The model instance to save
	optimizer: The optimizer instance to save
	lr_scheduler: The learning rate scheduler to save
	tokenizer: The tokenizer to save
	upload_logs: Whether to upload training logs to HF Hub (default: False)

	"""

	checkpointing_config = configs["checkpointing"]

	# Get the directories from the training config
	runs_dir = checkpointing_config.runs_dir
	checkpoints_dir = checkpointing_config.checkpoints_dir
	fabric_checkpoint_dir = checkpointing_config.fabric_checkpoint_dir
	logs_dir = checkpointing_config.logs_dir

	run_path = os.path.join(runs_dir, checkpointing_config.run_name)
	root_checkpoint_path = os.path.join(run_path, checkpoints_dir)
	checkpoint_path = os.path.join(root_checkpoint_path, f"step_{checkpoint_step}")

	# Create directories
	os.makedirs(checkpoint_path, exist_ok=True)

	########################################################
	#
	# Save HuggingFace files
	#
	########################################################

	# NOTE: we convert the Pico model to a HuggingFace model before saving it. See `model.py`
	# for more details.
	if fabric.global_rank == 0:
	hf_model = model.convert_to_hf_model()
	hf_model.save_pretrained(checkpoint_path)
	tokenizer.save_pretrained(checkpoint_path)

	########################################################
	#
	# Save Fabric-specific files
	#
	########################################################

	# Create fabric-specific subdirectory
	fabric_checkpoint_path = os.path.join(checkpoint_path, fabric_checkpoint_dir)
	os.makedirs(fabric_checkpoint_path, exist_ok=True)

	# Save model states (use underscore to avoid conflicts with third-party libraries)
	checkpoint_state = {
	"_model": model,
	"_optimizer": optimizer,
	"_lr_scheduler": lr_scheduler,
	"_checkpoint_step": checkpoint_step,
	}

	if not isinstance(fabric.strategy, DeepSpeedStrategy):
	checkpoint_state["_rng_states"] = _collect_rng_states()
	fabric_save_file = os.path.join(
	fabric_checkpoint_path, checkpointing_config.fabric_checkpoint_filename
	)
	else:
	# Deepspeed checkpoints create sub-directory with distributed checkpoint file
	fabric_save_file = fabric_checkpoint_path

	fabric.save(fabric_save_file, checkpoint_state)

	if fabric.global_rank == 0:
	# Save config in fabric directory
	config_path = os.path.join(run_path, "training_config.yaml")
	if not os.path.exists(config_path):
	# Converting dataclasses to joined dicts and saving to file
	_training_config = {}
	for config_name, config in configs.items():
	_training_config[config_name] = asdict(config)
	with open(config_path, "w") as f:
	yaml.dump(_training_config, f)

	# Update latest symlink
	latest_symlink_path = os.path.join(root_checkpoint_path, "latest")
	if os.path.lexists(latest_symlink_path):
	os.remove(latest_symlink_path)
	os.symlink(
	f"step_{checkpoint_step}", latest_symlink_path, target_is_directory=True
	)

	########################################################
	#
	# Push to HuggingFace Hub (if configured)
	#
	########################################################

	if fabric.global_rank == 0:
	# Push only on rank zero thread

	if checkpointing_config.save_to_hf:
	repo_id = checkpointing_config.hf_checkpoint.repo_id

	# Upload the HF model
	hf_model.push_to_hub(
	repo_id=repo_id,
	commit_message=f"Saving HF Model -- Step {checkpoint_step}",
	revision=checkpointing_config.run_name,
	token=os.getenv("HF_TOKEN"),
	)

	if checkpoint_step == 0:
	# Uploading Tokenizer during first step since it never changes
	tokenizer.push_to_hub(
	repo_id=repo_id,
	commit_message=f"Saving Tokenizer -- Step {checkpoint_step}",
	revision=checkpointing_config.run_name,
	token=os.getenv("HF_TOKEN"),
	)

	# Upload training config, also only in first step
	upload_file(
	path_or_fileobj=config_path,
	path_in_repo="training_config.yaml",
	repo_id=repo_id,
	commit_message=f"Saving Training Config -- Step {checkpoint_step}",
	revision=checkpointing_config.run_name,
	token=os.getenv("HF_TOKEN"),
	)

	# Upload the fabric checkpoint directory
	upload_folder(
	folder_path=fabric_checkpoint_path,
	path_in_repo=fabric_checkpoint_dir,
	repo_id=repo_id,
	commit_message=f"Saving Fabric Checkpoint -- Step {checkpoint_step}",
	revision=checkpointing_config.run_name,
	token=os.getenv("HF_TOKEN"),
	)

	# Upload logs if requested
	if upload_logs:
	logs_path = os.path.join(run_path, logs_dir)
	upload_folder(
	folder_path=logs_path,
	path_in_repo=logs_dir,
	repo_id=repo_id,
	commit_message=f"Saving Logs -- Step {checkpoint_step}",
	revision=checkpointing_config.run_name,
	token=os.getenv("HF_TOKEN"),
	)