# Copyright © 2024 Apple Inc.

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

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

from .tuner.grpo_trainer import GRPOTrainingArgs, evaluate_grpo, train_grpo
from .tuner.trainer import TrainingArgs, TrainingCallback, evaluate, train
from .tokenizer_utils import TokenizerWrapper
from .tuner.datasets import load_dataset
from .tuner.utils import (
    build_schedule,
    linear_to_lora_layers,
    load_adapters,
    print_trainable_parameters,
)
from .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,
    "training_mode": "normal",
    "fine_tune_type": "lora",
    "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, "alpha": 16, "dropout": 0.0, "scale": 10.0},

    # GRPO args
    "reference_model_path": None,
    "group_size": 4,
    "beta": 0.1,
    "epsilon": 1e-4,
    "max_completion_length": 512,
    "use_chat_template": False,
    "use_prompt": False,
    "temperature": 1.0,
    "reward_weights": 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(
        "--mask-prompt",
        action="store_true",
        help="Mask the prompt in the loss when training",
        default=False,
    )

    parser.add_argument(
        "--training-mode",
        type=str,
        choices=["normal", "grpo"],
        help="Training mode: normal or GRPO",
    )
    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("--seed", type=int, help="The PRNG seed")

    # GRPO args
    parser.add_argument(
        "--group-size",
        type=int,
        help="Number of generations.",
        default=4,
    )
    parser.add_argument(
        "--max-completion-length",
        type=int,
        help="Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left.",
        default=512,
    )
    parser.add_argument(
        "--beta",
        type=float,
        help="KL penalty coefficient.",
        default=0.1,
    )
    parser.add_argument(
        "--epsilon",
        type=float,
        help="The Epsilon for numerical stability.",
        default=1e-4,
    )
    parser.add_argument(
        "--use-chat-template",
        action="store_true",
        help="If the model is a Chat model, use the Chat template.",
        default=None,
    )
    parser.add_argument(
        "--use-prompt",
        action="store_true",
        help="Rather to use the prompt from the R1 paper.",
        default=None,
    )
    parser.add_argument(
        "--temperature",
        type=float,
        help="Temperature for sampling. The higher the temperature, the more random the completions.",
        default=1.0,
    )
    parser.add_argument(
        "--reward-weights",
        type=str,
        help="Weights for each reward function. Must match the number of reward functions and be in this format [0.1, 0.2, 0.3, 0.4, 0.5]. If not given, all rewards are weighted equally with weight `1.0`.",
        default=None,
    )
    return parser

def train_model_grpo(model, tokenizer, args, opt, train_set, valid_set, adapter_file, training_callback):
    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,
        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
    )

    if args.reference_model_path:
        reference_model, _ = load(args.reference_model_path)
    else:
        reference_model, _ = load(args.model)

    train_grpo(
        model=model,
        ref_model=reference_model.freeze(),
        tokenizer=tokenizer,
        optimizer=opt,
        train_dataset=train_set,
        val_dataset=valid_set,
        args=training_args,
        training_callback=training_callback,
    )

def train_model(
    args,
    model: nn.Module,
    tokenizer: TokenizerWrapper,
    train_set,
    valid_set,
    training_callback: TrainingCallback = None,
):
    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()
    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")

    model.train()
    opt = optim.Adam(
        learning_rate=(
            build_schedule(args.lr_schedule) if args.lr_schedule else args.learning_rate
        )
    )

    # Train model
    if args.training_mode == "grpo":
        train_model_grpo(
            model,
            tokenizer,
            args,
            opt,
            train_set,
            valid_set,
            adapter_file,
            training_callback
        )
    else:
        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
        )

        train(
            model=model,
            tokenizer=tokenizer,
            args=training_args,
            optimizer=opt,
            train_dataset=train_set,
            val_dataset=valid_set,
            training_callback=training_callback,
        )


def evaluate_model(args, model: nn.Module, tokenizer: TokenizerWrapper, test_set):
    model.eval()

    if args.training_mode == "grpo":
        if args.reference_model_path:
            reference_model, _ = load(args.reference_model_path)
        else:
            reference_model, _ = load(args.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,
            temperature=args.temperature,
            max_tokens=args.max_seq_length
        )

        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}")
    else:
        test_loss = evaluate(
            model=model,
            dataset=test_set,
            tokenizer=tokenizer,
            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)

    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:
        # 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, 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)
        # 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__":
    main()