#!/usr/bin/env python3

"""
🦥 Starter Script for Fine-Tuning FastLanguageModel with Unsloth

This script is designed as a starting point for fine-tuning your models using unsloth.
It includes configurable options for model loading, PEFT parameters, training arguments, 
and model saving/pushing functionalities.

You will likely want to customize this script to suit your specific use case 
and requirements.

Here are a few suggestions for customization:
    - Modify the dataset loading and preprocessing steps to match your data.
    - Customize the model saving and pushing configurations.

Usage: (most of the options have valid default values this is an extended example for demonstration purposes)
    python unsloth-cli.py --model_name "unsloth/llama-3-8b" --max_seq_length 8192 --dtype None --load_in_4bit \
    --r 64 --lora_alpha 32 --lora_dropout 0.1 --bias "none" --use_gradient_checkpointing "unsloth" \
    --random_state 3407 --use_rslora --per_device_train_batch_size 4 --gradient_accumulation_steps 8 \
    --warmup_steps 5 --max_steps 400 --learning_rate 2e-6 --logging_steps 1 --optim "adamw_8bit" \
    --weight_decay 0.005 --lr_scheduler_type "linear" --seed 3407 --output_dir "outputs" \
    --report_to "tensorboard" --save_model --save_path "model" --quantization_method "f16" \
    --push_model --hub_path "hf/model" --hub_token "your_hf_token"

To see a full list of configurable options, use:
    python unsloth-cli.py --help

Happy fine-tuning!
"""

import argparse
import os


def run(args):
    from unsloth import FastLanguageModel
    from datasets import load_dataset
    from transformers.utils import strtobool
    from trl import SFTTrainer, SFTConfig
    from unsloth import is_bfloat16_supported
    from unsloth.models.loader_utils import prepare_device_map
    from unsloth.trainer import ASFTTrainer, ASFTStreamingConfig
    import logging
    from unsloth import RawTextDataLoader

    logging.getLogger("hf-to-gguf").setLevel(logging.WARNING)

    # Load model and tokenizer
    device_map, distributed = prepare_device_map()
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = args.model_name,
        max_seq_length = args.max_seq_length,
        dtype = args.dtype,
        load_in_4bit = args.load_in_4bit,
        device_map = device_map,
    )

    # Configure PEFT model
    model = FastLanguageModel.get_peft_model(
        model,
        r = args.r,
        target_modules = [
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],
        lora_alpha = args.lora_alpha,
        lora_dropout = args.lora_dropout,
        bias = args.bias,
        use_gradient_checkpointing = args.use_gradient_checkpointing,
        random_state = args.random_state,
        use_rslora = args.use_rslora,
        loftq_config = args.loftq_config,
    )

    alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

    ### Instruction:
    {}

    ### Input:
    {}

    ### Response:
    {}"""

    EOS_TOKEN = tokenizer.eos_token  # Must add EOS_TOKEN

    def formatting_prompts_func(examples):
        instructions = examples["instruction"]
        inputs = examples["input"]
        outputs = examples["output"]
        texts = []
        for instruction, input, output in zip(instructions, inputs, outputs):
            text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
            texts.append(text)
        return {"text": texts}

    def load_dataset_smart(args):
        from transformers.utils import strtobool

        if args.raw_text_file:
            # Use raw text loader
            loader = RawTextDataLoader(tokenizer, args.chunk_size, args.stride)
            dataset = loader.load_from_file(args.raw_text_file)
        elif args.dataset.endswith((".txt", ".md", ".json", ".jsonl")):
            # Auto-detect local raw text files
            loader = RawTextDataLoader(tokenizer)
            dataset = loader.load_from_file(args.dataset)
        else:
            # Check for modelscope usage
            use_modelscope = strtobool(
                os.environ.get("UNSLOTH_USE_MODELSCOPE", "False")
            )
            if use_modelscope:
                from modelscope import MsDataset

                dataset = MsDataset.load(args.dataset, split = "train")
            else:
                # Existing HuggingFace dataset logic
                dataset = load_dataset(args.dataset, split = "train")

            # Apply formatting for structured datasets
            dataset = dataset.map(formatting_prompts_func, batched = True)
        return dataset

    # Load dataset using smart loader
    dataset = load_dataset_smart(args)
    print("Data is formatted and ready!")

    # Configure training arguments
    training_args = SFTConfig(
        per_device_train_batch_size = args.per_device_train_batch_size,
        per_device_eval_batch_size = args.per_device_eval_batch_size,
        gradient_accumulation_steps = args.gradient_accumulation_steps,
        warmup_steps = args.warmup_steps,
        max_steps = args.max_steps,
        learning_rate = args.learning_rate,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = args.logging_steps,
        optim = args.optim,
        weight_decay = args.weight_decay,
        lr_scheduler_type = args.lr_scheduler_type,
        seed = args.seed,
        output_dir = args.output_dir,
        report_to = args.report_to,
        max_length = args.max_seq_length,
        dataset_num_proc = 2,
        ddp_find_unused_parameters = False if distributed else None,
        packing = args.packing,
    )

    # Initialize trainer - use ASFTTrainer if ASFT is enabled
    asft_enabled = getattr(args, "asft", False)
    if asft_enabled:
        # Build ASFT streaming config
        asft_streaming = ASFTStreamingConfig(
            mode = getattr(args, "asft_streaming", None),
            ref_microbatch_size = getattr(args, "ref_microbatch_size", None),
            seq_chunk_size = getattr(args, "seq_chunk_size", None),
        )
        trainer = ASFTTrainer(
            model = model,
            processing_class = tokenizer,
            train_dataset = dataset,
            args = training_args,
            asft_enabled = True,
            asft_mode = getattr(args, "asft_mode", "asft"),
            kl_weight = getattr(args, "kl_weight", 0.0),
            reference_policy = getattr(args, "reference_policy", "disable_adapter"),
            asft_streaming = asft_streaming,
        )
    else:
        trainer = SFTTrainer(
            model = model,
            processing_class = tokenizer,
            train_dataset = dataset,
            args = training_args,
        )

    trainer.train()

    # Save model
    if args.save_model:
        # if args.quantization_method is a list, we will save the model for each quantization method
        if args.save_gguf:
            quantization_methods = (
                args.quantization
                if isinstance(args.quantization, list)
                else [args.quantization]
            )
            if len(quantization_methods) > 1:
                for quantization_method in quantization_methods:
                    print(
                        f"Saving model with quantization method: {quantization_method}"
                    )
                    model.save_pretrained_gguf(
                        args.save_path,
                        tokenizer,
                        quantization_method = quantization_method,
                    )
                    if args.push_model:
                        model.push_to_hub_gguf(
                            hub_path = args.hub_path,
                            hub_token = args.hub_token,
                            quantization_method = quantization_method,
                        )
            else:
                quantization_method = quantization_methods[0]
                print(f"Saving model with quantization method: {quantization_method}")
                model.save_pretrained_gguf(
                    args.save_path,
                    tokenizer,
                    quantization_method = quantization_method,
                )
                if args.push_model:
                    model.push_to_hub_gguf(
                        hub_path = args.hub_path,
                        hub_token = args.hub_token,
                        quantization_method = quantization_method,
                    )
        else:
            model.save_pretrained_merged(args.save_path, tokenizer, args.save_method)
            if args.push_model:
                model.push_to_hub_merged(args.save_path, tokenizer, args.hub_token)
    else:
        print("Warning: The model is not saved!")


def build_parser():
    parser = argparse.ArgumentParser(
        description = "🦥 Fine-tune your llm faster using unsloth!"
    )

    model_group = parser.add_argument_group("🤖 Model Options")
    model_group.add_argument(
        "--model_name",
        type = str,
        default = "unsloth/llama-3-8b",
        help = "Model name to load",
    )
    model_group.add_argument(
        "--max_seq_length",
        type = int,
        default = 2048,
        help = "Maximum sequence length, default is 2048. We auto support RoPE Scaling internally!",
    )
    model_group.add_argument(
        "--dtype",
        type = str,
        default = None,
        help = "Data type for model (None for auto detection)",
    )
    model_group.add_argument(
        "--load_in_4bit",
        action = "store_true",
        help = "Use 4bit quantization to reduce memory usage",
    )
    model_group.add_argument(
        "--dataset",
        type = str,
        default = "yahma/alpaca-cleaned",
        help = "Huggingface dataset to use for training",
    )

    lora_group = parser.add_argument_group(
        "🧠 LoRA Options",
        "These options are used to configure the LoRA model.",
    )
    lora_group.add_argument(
        "--r",
        type = int,
        default = 16,
        help = "Rank for Lora model, default is 16.  (common values: 8, 16, 32, 64, 128)",
    )
    lora_group.add_argument(
        "--lora_alpha",
        type = int,
        default = 16,
        help = "LoRA alpha parameter, default is 16. (common values: 8, 16, 32, 64, 128)",
    )
    lora_group.add_argument(
        "--lora_dropout",
        type = float,
        default = 0.0,
        help = "LoRA dropout rate, default is 0.0 which is optimized.",
    )
    lora_group.add_argument(
        "--bias",
        type = str,
        default = "none",
        help = "Bias setting for LoRA",
    )
    lora_group.add_argument(
        "--use_gradient_checkpointing",
        type = str,
        default = "unsloth",
        help = "Use gradient checkpointing",
    )
    lora_group.add_argument(
        "--random_state",
        type = int,
        default = 3407,
        help = "Random state for reproducibility, default is 3407.",
    )
    lora_group.add_argument(
        "--use_rslora",
        action = "store_true",
        help = "Use rank stabilized LoRA",
    )
    lora_group.add_argument(
        "--loftq_config",
        type = str,
        default = None,
        help = "Configuration for LoftQ",
    )

    training_group = parser.add_argument_group("🎓 Training Options")
    training_group.add_argument(
        "--per_device_train_batch_size",
        type = int,
        default = 2,
        help = "Batch size per device during training, default is 2.",
    )
    training_group.add_argument(
        "--per_device_eval_batch_size",
        type = int,
        default = 4,
        help = "Batch size per device during evaluation, default is 4.",
    )
    training_group.add_argument(
        "--gradient_accumulation_steps",
        type = int,
        default = 4,
        help = "Number of gradient accumulation steps, default is 4.",
    )
    training_group.add_argument(
        "--warmup_steps",
        type = int,
        default = 5,
        help = "Number of warmup steps, default is 5.",
    )
    training_group.add_argument(
        "--max_steps",
        type = int,
        default = 400,
        help = "Maximum number of training steps.",
    )
    training_group.add_argument(
        "--learning_rate",
        type = float,
        default = 2e-4,
        help = "Learning rate, default is 2e-4.",
    )
    training_group.add_argument(
        "--optim",
        type = str,
        default = "adamw_8bit",
        help = "Optimizer type.",
    )
    training_group.add_argument(
        "--weight_decay",
        type = float,
        default = 0.01,
        help = "Weight decay, default is 0.01.",
    )
    training_group.add_argument(
        "--lr_scheduler_type",
        type = str,
        default = "linear",
        help = "Learning rate scheduler type, default is 'linear'.",
    )
    training_group.add_argument(
        "--seed",
        type = int,
        default = 3407,
        help = "Seed for reproducibility, default is 3407.",
    )
    training_group.add_argument(
        "--packing",
        action = "store_true",
        help = "Enable padding-free sample packing via TRL's bin packer.",
    )

    report_group = parser.add_argument_group("📊 Report Options")
    report_group.add_argument(
        "--report_to",
        type = str,
        default = "tensorboard",
        choices = [
            "azure_ml",
            "clearml",
            "codecarbon",
            "comet_ml",
            "dagshub",
            "dvclive",
            "flyte",
            "mlflow",
            "neptune",
            "tensorboard",
            "wandb",
            "all",
            "none",
        ],
        help = (
            "The list of integrations to report the results and logs to. Supported platforms are:\n\t\t "
            "'azure_ml', 'clearml', 'codecarbon', 'comet_ml', 'dagshub', 'dvclive', 'flyte', "
            "'mlflow', 'neptune', 'tensorboard', and 'wandb'. Use 'all' to report to all integrations "
            "installed, 'none' for no integrations."
        ),
    )
    report_group.add_argument(
        "--logging_steps",
        type = int,
        default = 1,
        help = "Logging steps, default is 1",
    )

    save_group = parser.add_argument_group("💾 Save Model Options")
    save_group.add_argument(
        "--output_dir",
        type = str,
        default = "outputs",
        help = "Output directory",
    )
    save_group.add_argument(
        "--save_model",
        action = "store_true",
        help = "Save the model after training",
    )
    save_group.add_argument(
        "--save_method",
        type = str,
        default = "merged_16bit",
        choices = ["merged_16bit", "merged_4bit", "forced_merged_4bit", "lora"],
        help = "Save method for the model, default is 'merged_16bit'",
    )
    save_group.add_argument(
        "--save_gguf",
        action = "store_true",
        help = "Convert the model to GGUF after training",
    )
    save_group.add_argument(
        "--save_path",
        type = str,
        default = "model",
        help = "Path to save the model",
    )
    save_group.add_argument(
        "--quantization",
        type = str,
        default = "q8_0",
        nargs = "+",
        help = (
            "Quantization method for saving the model. common values ('f16', 'q4_k_m', 'q8_0'), "
            "Check our wiki for all quantization methods https://github.com/unslothai/unsloth/wiki#saving-to-gguf"
        ),
    )

    push_group = parser.add_argument_group("🚀 Push Model Options")
    push_group.add_argument(
        "--push_model",
        action = "store_true",
        help = "Push the model to Hugging Face hub after training",
    )
    push_group.add_argument(
        "--push_gguf",
        action = "store_true",
        help = "Push the model as GGUF to Hugging Face hub after training",
    )
    push_group.add_argument(
        "--hub_path",
        type = str,
        default = "hf/model",
        help = "Path on Hugging Face hub to push the model",
    )
    push_group.add_argument(
        "--hub_token",
        type = str,
        help = "Token for pushing the model to Hugging Face hub",
    )

    parser.add_argument(
        "--raw_text_file", type = str, help = "Path to raw text file for training"
    )
    parser.add_argument(
        "--chunk_size", type = int, default = 2048, help = "Size of text chunks for training"
    )
    parser.add_argument(
        "--stride", type = int, default = 512, help = "Overlap between chunks"
    )

    # ASFT Options
    asft_group = parser.add_argument_group(
        "🎯 ASFT Options",
        "Anchored Supervised Fine-Tuning loss configuration (off by default)",
    )
    asft_group.add_argument(
        "--asft",
        action = "store_true",
        help = "Enable ASFT (Anchored Supervised Fine-Tuning) loss computation",
    )
    asft_group.add_argument(
        "--asft_mode",
        type = str,
        default = "asft",
        choices = ["sft", "dft", "sft+kl", "asft"],
        help = (
            "ASFT loss mode: 'sft' (standard CE), 'dft' (CE weighted by confidence), "
            "'sft+kl' (CE + KL from reference), 'asft' (DFT + KL). Default: 'asft'"
        ),
    )
    asft_group.add_argument(
        "--kl_weight",
        type = float,
        default = 0.0,
        help = "Weight for KL divergence term in sft+kl and asft modes. Default: 0.0",
    )
    asft_group.add_argument(
        "--reference_policy",
        type = str,
        default = "disable_adapter",
        choices = ["disable_adapter", "frozen_copy"],
        help = (
            "How to compute reference distribution: 'disable_adapter' (use model with LoRA disabled), "
            "'frozen_copy' (use frozen deepcopy). Default: 'disable_adapter'"
        ),
    )
    asft_group.add_argument(
        "--asft_streaming",
        nargs = "?",
        const = "auto",
        default = "off",
        choices = ["off", "auto", "batch", "seq", "hybrid"],
        help = (
            "Streaming mode for reference forward: 'off' (full forward), "
            "'auto' (seq_kv_cache with batch-micro fallback), "
            "'batch' (microbatch by batch), 'seq' (sequence chunking with KV cache), "
            "'hybrid' (batch micro + seq_kv_cache). "
            "Use flag without value for 'auto'."
        ),
    )
    asft_group.add_argument(
        "--ref_microbatch_size",
        type = int,
        default = None,
        help = "Microbatch size for batch_micro or seq_kv_cache strategy",
    )
    asft_group.add_argument(
        "--seq_chunk_size",
        type = int,
        default = None,
        help = "Sequence chunk size for seq_kv_cache strategy",
    )

    return parser


if __name__ == "__main__":
    parser = build_parser()
    args = parser.parse_args()
    run(args)