import argparse
import math
import os
import re
import types
from pathlib import Path

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim
import numpy as np
import yaml

from .trainer.callbacks import WandBCallback
from .trainer.datasets import CacheDataset, load_dataset
from .trainer.sft_trainer import SFTTrainingArgs, TrainingCallback, evaluate_sft, train_sft
from .trainer.orpo_trainer import ORPOTrainingArgs, evaluate_orpo, train_orpo
from .trainer.dpo_trainer import DPOTrainingArgs, evaluate_dpo, train_dpo
from .trainer.grpo_trainer import GRPOTrainingArgs, evaluate_grpo, train_grpo

from .trainer.utils import (
    build_schedule,
    linear_to_lora_layers,
    load_adapters,
    print_trainable_parameters,
)
from mlx_lm.utils import load, save_config

yaml_loader = yaml.SafeLoader
yaml_loader.add_implicit_resolver(
    "tag:yaml.org,2002:float",
    re.compile(
        """^(?:
     [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
    |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
    |\\.[0-9_]+(?:[eE][-+][0-9]+)?
    |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
    |[-+]?\\.(?:inf|Inf|INF)
    |\\.(?:nan|NaN|NAN))$""",
        re.X,
    ),
    list("-+0123456789."),
)

CONFIG_DEFAULTS = {
    "model": "mlx_model",
    "train": False,
    "train_type": "lora",
    "optimizer": "adam",
    "optimizer_config": {
        "adam": {},
        "adamw": {},
    },
    "data": "data/",
    "seed": 0,
    "num_layers": 16,
    "batch_size": 4,
    "iters": 1000,
    "val_batches": 25,
    "learning_rate": 1e-5,
    "steps_per_report": 10,
    "steps_per_eval": 200,
    "resume_adapter_file": None,
    "adapter_path": "adapters",
    "save_every": 100,
    "test": False,
    "test_batches": 500,
    "max_seq_length": 2048,
    "config": None,
    "grad_checkpoint": False,
    "lr_schedule": None,
    "lora_parameters": {"rank": 8, "dropout": 0.0, "scale": 10.0},
    "mask_prompt": False,
    "wandb": None,

    # ORPO args
    "beta": 0.1,
    "reward_scaling": 1.0,

    # DPO args
    "beta": 0.1,
    "dpo_loss_type": "sigmoid",
    "delta": 50.0,
    "reference_model_path": None,
}


def build_parser():
    parser = argparse.ArgumentParser(description="LoRA or QLoRA finetuning.")
    parser.add_argument(
        "--model",
        type=str,
        help="The path to the local model directory or Hugging Face repo.",
    )

    # Training args
    parser.add_argument(
        "--train",
        action="store_true",
        help="Do training",
        default=None,
    )
    parser.add_argument(
        "--data",
        type=str,
        help=(
            "Directory with {train, valid, test}.jsonl files or the name "
            "of a Hugging Face dataset (e.g., 'mlx-community/wikisql')"
        ),
    )
    parser.add_argument(
        "--train-type",
        type=str,
        choices=["lora", "dora", "full"],
        help="Type of fine-tuning to perform: lora, dora, or full.",
    )
    parser.add_argument(
        "--train-mode",
        type=str,
        default="normal",
        choices=["normal", "dpo", "orpo", "grpo"],
        help="Training mode: normal, dpo, orpo, or grpo, default is normal",
    )
    parser.add_argument(
        "--optimizer",
        type=str,
        choices=["adam", "adamw"],
        default=None,
        help="Optimizer to use for training: adam or adamw",
    )
    parser.add_argument(
        "--mask-prompt",
        action="store_true",
        help="Mask the prompt in the loss when training",
        default=None,
    )
    parser.add_argument(
        "--num-layers",
        type=int,
        help="Number of layers to fine-tune. Default is 16, use -1 for all.",
    )
    parser.add_argument("--batch-size", type=int, help="Minibatch size.")
    parser.add_argument("--iters", type=int, help="Iterations to train for.")
    parser.add_argument(
        "--val-batches",
        type=int,
        help="Number of validation batches, -1 uses the entire validation set.",
    )
    parser.add_argument("--learning-rate", type=float, help="Adam learning rate.")
    parser.add_argument(
        "--steps-per-report",
        type=int,
        help="Number of training steps between loss reporting.",
    )
    parser.add_argument(
        "--steps-per-eval",
        type=int,
        help="Number of training steps between validations.",
    )
    parser.add_argument(
        "--resume-adapter-file",
        type=str,
        help="Load path to resume training from the given fine-tuned weights.",
    )
    parser.add_argument(
        "--adapter-path",
        type=str,
        help="Save/load path for the fine-tuned weights.",
    )
    parser.add_argument(
        "--save-every",
        type=int,
        help="Save the model every N iterations.",
    )
    parser.add_argument(
        "--test",
        action="store_true",
        help="Evaluate on the test set after training",
        default=None,
    )
    parser.add_argument(
        "--test-batches",
        type=int,
        help="Number of test set batches, -1 uses the entire test set.",
    )
    parser.add_argument(
        "--max-seq-length",
        type=int,
        help="Maximum sequence length.",
    )
    parser.add_argument(
        "-c",
        "--config",
        type=str,
        help="A YAML configuration file with the training options",
    )
    parser.add_argument(
        "--grad-checkpoint",
        action="store_true",
        help="Use gradient checkpointing to reduce memory use.",
        default=None,
    )
    parser.add_argument(
        "--wandb",
        type=str,
        default=None,
        help="WandB project name to report training metrics. Disabled if None.",
    )
    parser.add_argument("--seed", type=int, help="The PRNG seed")

    # ORPO args
    parser.add_argument(
        "--beta",
        type=float,
        help="Temperature parameter for ORPO training.",
        default=0.1,
    )
    parser.add_argument(
        "--reward-scaling",
        type=float,
        help="Reward scaling factor for ORPO training, not implemented.",
        default=1.0,
    )

    # DPO args
    parser.add_argument(
        "--beta",
        type=float,
        help="Temperature parameter for DPO training.",
        default=0.1,
    )
    parser.add_argument(
        "--dpo-loss-type",
        type=str,
        help="DPO loss type: 'sigmoid', 'hinge', 'ipo', or 'dpop'.",
        choices=["sigmoid", "hinge", "ipo", "dpop"],
        default="sigmoid",
    )
    parser.add_argument(
        "--delta", type=float, help="Delta parameter for DPOP loss type.", default=50.0
    )
    parser.add_argument(
        "--reference-model-path",
        type=str,
        help="Path to reference model weights. If None, uses the same model.",
        default=None,
    )
    return parser


def train_model(
    args,
    model: nn.Module,
    tokenizer,
    train_set,
    valid_set,
    training_callback: TrainingCallback = None,
):
    mx.random.seed(args.seed)
    model.freeze()
    if args.num_layers > len(model.layers):
        raise ValueError(
            f"Requested to train {args.num_layers} layers "
            f"but the model only has {len(model.layers)} layers."
        )

    if args.train_type == "full":
        for l in model.layers[-max(args.num_layers, 0) :]:
            l.unfreeze()
    elif args.train_type in ["lora", "dora"]:
        # Convert linear layers to lora/dora layers and unfreeze in the process
        linear_to_lora_layers(
            model,
            args.num_layers,
            args.lora_parameters,
            use_dora=(args.train_type == "dora"),
        )
    else:
        raise ValueError(f"Received unknown train-type {args.train_type}")

    # Resume from weights if provided
    if args.resume_adapter_file is not None:
        print(f"Loading fine-tuned weights from {args.resume_adapter_file}")
        model.load_weights(args.resume_adapter_file, strict=False)

    print_trainable_parameters(model)

    adapter_path = Path(args.adapter_path)
    adapter_path.mkdir(parents=True, exist_ok=True)

    adapter_file = adapter_path / "adapters.safetensors"
    save_config(vars(args), adapter_path / "adapter_config.json")

    # Initialize the selected optimizer
    lr = build_schedule(args.lr_schedule) if args.lr_schedule else args.learning_rate

    optimizer_name = args.optimizer.lower()
    optimizer_config = args.optimizer_config.get(optimizer_name, {})

    if optimizer_name == "adam":
        opt_class = optim.Adam
    elif optimizer_name == "adamw":
        opt_class = optim.AdamW
    else:
        raise ValueError(f"Unsupported optimizer: {optimizer_name}")

    opt = opt_class(learning_rate=lr, **optimizer_config)

    if args.training_mode == "orpo":
        orpo_training_args = ORPOTrainingArgs(
            batch_size=args.batch_size,
            iters=args.iters,
            val_batches=args.val_batches,
            steps_per_report=args.steps_per_report,
            steps_per_eval=args.steps_per_eval,
            steps_per_save=args.save_every,
            adapter_file=adapter_file,
            max_seq_length=args.max_seq_length,
            grad_checkpoint=args.grad_checkpoint,
            beta=args.beta,
            reward_scaling=args.reward_scaling,
        )

        train_orpo(
            model=model,
            optimizer=opt,
            train_dataset=CacheDataset(train_set),
            val_dataset=CacheDataset(valid_set),
            args=orpo_training_args,
            training_callback=training_callback,
        )
    elif args.training_mode == "dpo":
        dpo_training_args = DPOTrainingArgs(
            batch_size=args.batch_size,
            iters=args.iters,
            val_batches=args.val_batches,
            steps_per_report=args.steps_per_report,
            steps_per_eval=args.steps_per_eval,
            steps_per_save=args.save_every,
            adapter_file=adapter_file,
            max_seq_length=args.max_seq_length,
            grad_checkpoint=args.grad_checkpoint,
            beta=args.beta,
            loss_type=args.dpo_loss_type,
            delta=args.delta,
            reference_model_path=args.reference_model_path,
        )

        print("Loading pretrained reference model")
        if args.reference_model_path:
            reference_model, _ = load(args.reference_model_path)
        else:
            reference_model, _ = load(args.model)

        train_dpo(
            model=model,
            ref_model=reference_model.freeze(),
            optimizer=opt,
            train_dataset=CacheDataset(train_set),
            val_dataset=CacheDataset(valid_set),
            args=dpo_training_args,
            training_callback=training_callback,
        )

    elif args.training_mode == "grpo":
        grpo_training_args = GRPOTrainingArgs(
            batch_size=args.batch_size,
            iters=args.iters,
            val_batches=args.val_batches,
            steps_per_report=args.steps_per_report,
            steps_per_eval=args.steps_per_eval,
            steps_per_save=args.save_every,
            adapter_file=adapter_file,
            max_seq_length=args.max_seq_length,
            max_completion_length=args.max_completion_length,
            grad_checkpoint=args.grad_checkpoint,
            beta=args.beta,
            group_size=args.group_size,
            epsilon=args.epsilon,
            epsilon_high=args.epsilon_high,
            reference_model_path=args.reference_model_path,
            temperature=args.temperature,
            reward_weights=(
                [float(x) for x in args.reward_weights.strip("[]").split(",")]
                if args.reward_weights
                else None
            ),
        )

        train_grpo(
            model=model,
            ref_model=reference_model.freeze() if reference_model else None,
            tokenizer=tokenizer,
            optimizer=opt,
            train_dataset=train_set,
            val_dataset=valid_set,
            args=grpo_training_args,
            training_callback=training_callback,
        )

        print("Loading pretrained reference model")
        if args.reference_model_path:
            reference_model, _ = load(args.reference_model_path)
        elif args.beta == 0:
            reference_model = None
        else:
            reference_model, _ = load(args.model)

    elif args.training_mode == "normal":
        sft_training_args = SFTTrainingArgs(
            batch_size=args.batch_size,
            iters=args.iters,
            val_batches=args.val_batches,
            steps_per_report=args.steps_per_report,
            steps_per_eval=args.steps_per_eval,
            steps_per_save=args.save_every,
            adapter_file=adapter_file,
            max_seq_length=args.max_seq_length,
            grad_checkpoint=args.grad_checkpoint,
        )

        train_sft(
            model=model,
            args=sft_training_args,
            optimizer=opt,
            train_dataset=CacheDataset(train_set),
            val_dataset=CacheDataset(valid_set),
            training_callback=training_callback,
        )

    else:
        raise(f"The train mode {args.train_mode} does not exist.")


def evaluate_model(args, model: nn.Module, tokenizer, test_set):
    if args.training_mode == "orpo":
        test_loss, test_rewards, _, test_metrics = evaluate_orpo(
            model=model,
            dataset=test_set,
            batch_size=args.batch_size,
            num_batches=args.test_batches,
            max_seq_length=args.max_seq_length,
            beta=args.beta,
        )
        test_ppl = math.exp(test_loss)
        print(
            f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}, Rewards: {test_rewards[0]:.3f}, {test_rewards[1]:.3f}"
        )

        print("ORPO Test Metrics:")
        for metric_name, metric_value in test_metrics.items():
            print(f"  {metric_name}: {float(metric_value):.3f}")

    elif args.training_mode == "dpo":
        if args.reference_model_path:
            reference_model, _ = load(args.reference_model_path)
        else:
            reference_model = model

        test_loss, _, _, test_metrics = evaluate_dpo(
            model=model,
            ref_model=reference_model.freeze(),
            dataset=test_set,
            batch_size=args.batch_size,
            num_batches=args.test_batches,
            max_seq_length=args.max_seq_length,
            beta=args.beta,
            delta=args.delta,
            loss_type=args.dpo_loss_type,
        )

        test_ppl = math.exp(test_loss)

        print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}")
        print("DPO Test Metrics:")
        for metric_name, metric_value in test_metrics.items():
            print(f"  {metric_name}: {float(metric_value):.3f}")

    elif args.training_mode == "grpo":
        if args.reference_model_path:
            reference_model, _ = load(args.reference_model_path)
        else:
            reference_model = model

        test_loss, _, test_rewards = evaluate_grpo(
            model=model,
            ref_model=reference_model.freeze(),
            dataset=test_set,
            tokenizer=tokenizer,
            batch_size=args.batch_size,
            num_batches=args.test_batches,
            max_seq_length=args.max_seq_length,
            beta=args.beta,
            group_size=args.group_size,
            epsilon=args.epsilon,
            epsilon_high=args.epsilon_high,
            temperature=args.temperature,
            max_tokens=args.max_seq_length,
        )

        test_ppl = math.exp(test_loss)

        rewards_str = ", ".join([f"{k}: {v:.3f}" for k, v in test_rewards.items()])
        print(
            f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}, Rewards: {rewards_str}"
        )

    elif args.training_mode == "normal":
        test_loss = evaluate_sft(
            model=model,
            dataset=CacheDataset(test_set),
            batch_size=args.batch_size,
            num_batches=args.test_batches,
            max_seq_length=args.max_seq_length,
        )

        test_ppl = math.exp(test_loss)

        print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}.")


def run(args, training_callback: TrainingCallback = None):
    np.random.seed(args.seed)

    if args.wandb is not None:
        training_callback = WandBCallback(
            project_name=args.wandb,
            log_dir=args.adapter_path,
            config=vars(args),
            wrapped_callback=training_callback,
        )

    print("Loading pretrained model")
    model, tokenizer = load(args.model)

    print("Loading datasets")
    train_set, valid_set, test_set = load_dataset(args, tokenizer)

    if args.test and not args.train:
        if args.adapter_path != "":
            load_adapters(model, args.adapter_path)

    elif args.train:
        print("Training")
        train_model(args, model, tokenizer, train_set, valid_set, training_callback)
    else:
        raise ValueError("Must provide at least one of --train or --test")

    if args.test:
        print("Testing")
        evaluate_model(args, model, tokenizer, test_set)


def main():
    os.environ["TOKENIZERS_PARALLELISM"] = "true"
    parser = build_parser()
    args = parser.parse_args()
    config = args.config
    args = vars(args)
    if config:
        print("Loading configuration file", config)
        with open(config, "r") as file:
            config = yaml.load(file, yaml_loader)
        for k, v in config.items():
            if args.get(k, None) is None:
                args[k] = v

    for k, v in CONFIG_DEFAULTS.items():
        if args.get(k, None) is None:
            args[k] = v
    run(types.SimpleNamespace(**args))


if __name__ == "__main__":
    print(
        "Calling `python -m mlx_lm.lora...` directly is deprecated."
        " Use `mlx_lm.lora...` or `python -m mlx_lm lora ...` instead."
    )
    main()
