import argparse
import math
import os
import re
import types
import warnings
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 .tuner.callbacks import get_reporting_callbacks
from .tuner.datasets import CacheDataset, load_dataset
from .tuner.trainer import TrainingArgs, TrainingCallback, evaluate, train
from .tuner.utils import (
    build_schedule,
    linear_to_lora_layers,
    load_adapters,
    print_trainable_parameters,
)
from .utils import _parse_size, 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": "Qwen/Qwen3-0.6b",
    "train": False,
    "fine_tune_type": "lora",
    "optimizer": "adam",
    "optimizer_config": {
        "adam": {},
        "adamw": {},
        "muon": {},
        "sgd": {},
        "adafactor": {},
    },
    "data": "mlx-community/WikiSQL",
    "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,
    "grad_accumulation_steps": 1,
    "clear_cache_threshold": 0,
    "lr_schedule": None,
    "lora_parameters": {"rank": 8, "dropout": 0.0, "scale": 20.0},
    "mask_prompt": False,
    "report_to": None,
    "project_name": 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(
        "--fine-tune-type",
        type=str,
        choices=["lora", "dora", "full"],
        help="Type of fine-tuning to perform: lora, dora, or full.",
    )
    parser.add_argument(
        "--optimizer",
        type=str,
        choices=["adam", "adamw", "muon", "sgd", "adafactor"],
        default=None,
        help="Optimizer to use for training: adam, adamw, sgd, or adafactor.",
    )
    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(
        "--grad-accumulation-steps",
        type=int,
        help="Number of steps to accumulate before each optimizer update.",
    )
    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(
        "--clear-cache-threshold",
        type=_parse_size,
        default=0,
        help="Clear the allocator cache between steps if it grows too large.",
    )
    parser.add_argument(
        "--report-to",
        type=str,
        default=None,
        help="Services to report logs to ('wandb', 'swanlab', or 'wandb,swanlab').",
    )
    parser.add_argument(
        "--project-name",
        type=str,
        default=None,
        help="Project name for logging. Defaults to the name of the root directory.",
    )
    parser.add_argument("--seed", type=int, help="The PRNG seed")
    return parser


def train_model(
    args,
    model: nn.Module,
    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.fine_tune_type == "full":
        for l in model.layers[-max(args.num_layers, 0) :]:
            l.unfreeze()

        args.lora_parameters = None
    elif args.fine_tune_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.fine_tune_type == "dora"),
        )
    else:
        raise ValueError(f"Received unknown fine-tune-type {args.fine_tune_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")

    # init training args
    training_args = TrainingArgs(
        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,
        grad_accumulation_steps=args.grad_accumulation_steps,
    )

    # 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
    elif optimizer_name == "muon":
        opt_class = optim.Muon
    elif optimizer_name == "sgd":
        opt_class = optim.SGD
    elif optimizer_name == "adafactor":
        opt_class = optim.Adafactor
    else:
        raise ValueError(f"Unsupported optimizer: {optimizer_name}")

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

    # Train model
    train(
        model=model,
        args=training_args,
        optimizer=opt,
        train_dataset=CacheDataset(train_set),
        val_dataset=CacheDataset(valid_set),
        training_callback=training_callback,
    )


def evaluate_model(args, model: nn.Module, test_set):
    test_loss = evaluate(
        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)
    training_callback = get_reporting_callbacks(
        args.report_to,
        project_name=args.project_name,
        log_dir=args.adapter_path,
        config=vars(args),
    )

    print("Loading pretrained model")
    model, tokenizer = load(args.model, tokenizer_config={"trust_remote_code": True})

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

    if args.test and not args.train:
        # Allow testing without LoRA layers by providing empty path
        if args.adapter_path != "":
            load_adapters(model, args.adapter_path)

    elif args.train:
        print("Training")
        train_model(args, model, 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, 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)
        # Prefer parameters from command-line arguments
        for k, v in config.items():
            if args.get(k, None) is None:
                args[k] = v

    # Update defaults for unspecified parameters
    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()
