import gradio as gr
from alignment import (
    DataArguments,
    ModelArguments,
    apply_chat_template,
    get_datasets,
    get_tokenizer,
)


def reformat(dataset_name, train_split, test_split, model_name, upload_name, token):
    data_args = DataArguments(chat_template=None, dataset_mixer={dataset_name: 1.0}, dataset_splits=[train_split, test_split], max_train_samples=None, max_eval_samples=None, preprocessing_num_workers=12, truncation_side=None)
    model_args = ModelArguments(base_model_revision=None, model_name_or_path=model_name, model_revision='main', model_code_revision=None, torch_dtype='auto', trust_remote_code=True, use_flash_attention_2=True, use_peft=True, lora_r=64, lora_alpha=16, lora_dropout=0.1, lora_target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj'], lora_modules_to_save=None, load_in_8bit=False, load_in_4bit=True, bnb_4bit_quant_type='nf4', use_bnb_nested_quant=False)
    
    ###############
    # Load datasets
    ###############
    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
    output = f"Dataset successfully formatted and pushed! Dataset and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
    
    ################
    # Load tokenizer
    ################
    tokenizer = get_tokenizer(model_args, data_args)
    
    #####################
    # Apply chat template
    #####################
    raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
    train_dataset = raw_datasets["train"]
    eval_dataset = raw_datasets["test"]

    raw_datasets.push_to_hub(upload_name, token=token)
    return gr.Markdown(
        value=output
    )


with gr.Blocks() as demo:
    gr.Markdown("## Dataset Chat Template")
    gr.Markdown("Format Datasets like HuggingFaceH4/no_robots to be AutoTrain compatible.")
    token = gr.Textbox(
        label="Hugging Face Write Token",
        value="",
        lines=1,
        max_lines=1,
        interactive=True,
        type="password",
    )
    dataset_name = gr.Textbox(
        label="Dataset Name (e.g. HuggingFaceH4/no_robots)",
        value="HuggingFaceH4/no_robots",
        lines=1,
        max_lines=1,
        interactive=True,
    )
    train_split = gr.Textbox(
        label="Train Split Name (e.g. train_sft)",
        value="train_sft",
        lines=1,
        max_lines=1,
        interactive=True,
    )
    test_split = gr.Textbox(
        label="Test Split Name (e.g. test_sft)",
        value="test_sft",
        lines=1,
        max_lines=1,
        interactive=True,
    )
    model_name = gr.Textbox(
        label="Model Name (e.g. mistralai/Mistral-7B-v0.1)",
        value="mistralai/Mistral-7B-v0.1",
        lines=1,
        max_lines=1,
        interactive=True,
    )
    upload_name = gr.Textbox(
        label="New Dataset Name (e.g. rishiraj/no_robots)",
        value="",
        lines=1,
        max_lines=1,
        interactive=True,
    )
    submit = gr.Button(value="Apply Template & Push")
    op = gr.Markdown()
    submit.click(reformat, inputs=[dataset_name, train_split, test_split, model_name, upload_name, token], outputs=[op])


if __name__ == "__main__":
    demo.launch()