aurora

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import gradio as gr
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from typing import TYPE_CHECKING, Dict
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from llmtuner.data.template import templates
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from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
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from llmtuner.webui.common import get_model_path, get_template, list_checkpoint, save_config
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from llmtuner.webui.utils import can_quantize
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if TYPE_CHECKING:
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    from gradio.components import Component
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def create_top() -> Dict[str, "Component"]:
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    available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
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    with gr.Row():
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        lang = gr.Dropdown(choices=["en", "zh"], scale=1)
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        model_name = gr.Dropdown(choices=available_models, scale=3)
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        model_path = gr.Textbox(scale=3)
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    with gr.Row():
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        finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
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        checkpoints = gr.Dropdown(multiselect=True, scale=5)
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        refresh_btn = gr.Button(scale=1)
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    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
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        with gr.Row():
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            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none")
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            template = gr.Dropdown(choices=list(templates.keys()), value="default")
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            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
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            with gr.Column():
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                flash_attn = gr.Checkbox(value=False)
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                shift_attn = gr.Checkbox(value=False)
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    model_name.change(
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        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
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    ).then(
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        get_model_path, [model_name], [model_path], queue=False
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    ).then(
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        get_template, [model_name], [template], queue=False
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    ) # do not save config since the below line will save
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    model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)
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    finetuning_type.change(
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        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
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    ).then(
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        can_quantize, [finetuning_type], [quantization_bit], queue=False
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    )
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    refresh_btn.click(
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        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
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    )
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    return dict(
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        lang=lang,
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        model_name=model_name,
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        model_path=model_path,
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        finetuning_type=finetuning_type,
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        checkpoints=checkpoints,
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        refresh_btn=refresh_btn,
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        advanced_tab=advanced_tab,
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        quantization_bit=quantization_bit,
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        template=template,
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        rope_scaling=rope_scaling,
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        flash_attn=flash_attn,
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        shift_attn=shift_attn
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    )
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