import gradio as gr
from typing import TYPE_CHECKING, Dict

from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR
from llmtuner.webui.components.data import create_preview_box
from llmtuner.webui.utils import can_preview, get_preview

if TYPE_CHECKING:
    from gradio.components import Component
    from llmtuner.webui.engine import Engine


def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
    input_elems = engine.manager.get_base_elems()
    elem_dict = dict()

    with gr.Row():
        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
        dataset = gr.Dropdown(multiselect=True, scale=4)
        data_preview_btn = gr.Button(interactive=False, scale=1)

    dataset_dir.change(list_dataset, [dataset_dir], [dataset], queue=False)
    dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False)

    input_elems.update({dataset_dir, dataset})
    elem_dict.update(dict(
        dataset_dir=dataset_dir, dataset=dataset, data_preview_btn=data_preview_btn
    ))

    preview_box, preview_count, preview_samples, close_btn = create_preview_box()

    data_preview_btn.click(
        get_preview,
        [dataset_dir, dataset],
        [preview_count, preview_samples, preview_box],
        queue=False
    )

    elem_dict.update(dict(
        preview_count=preview_count, preview_samples=preview_samples, close_btn=close_btn
    ))

    with gr.Row():
        cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
        max_samples = gr.Textbox(value="100000")
        batch_size = gr.Slider(value=8, minimum=1, maximum=512, step=1)
        predict = gr.Checkbox(value=True)

    input_elems.update({cutoff_len, max_samples, batch_size, predict})
    elem_dict.update(dict(
        cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict
    ))

    with gr.Row():
        max_new_tokens = gr.Slider(10, 2048, value=128, step=1)
        top_p = gr.Slider(0.01, 1, value=0.7, step=0.01)
        temperature = gr.Slider(0.01, 1.5, value=0.95, step=0.01)

    input_elems.update({max_new_tokens, top_p, temperature})
    elem_dict.update(dict(
        max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
    ))

    with gr.Row():
        cmd_preview_btn = gr.Button()
        start_btn = gr.Button()
        stop_btn = gr.Button()

    with gr.Row():
        resume_btn = gr.Checkbox(visible=False, interactive=False, value=False)
        process_bar = gr.Slider(visible=False, interactive=False)

    with gr.Box():
        output_box = gr.Markdown()

    output_elems = [output_box, process_bar]
    elem_dict.update(dict(
        cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn,
        resume_btn=resume_btn, process_bar=process_bar, output_box=output_box
    ))

    cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems)
    start_btn.click(engine.runner.run_eval, input_elems, output_elems)
    stop_btn.click(engine.runner.set_abort, queue=False)
    resume_btn.change(engine.runner.monitor, outputs=output_elems)

    return elem_dict