from FateZero.test_fatezero import *

import copy
import gradio as gr


def merge_config_then_run(
        model_id,
        data_path,
        source_prompt,
        target_prompt,
        cross_replace_steps,
        self_replace_steps,
        enhance_words,
        enhance_words_value,
        num_steps,
        guidance_scale,
        user_input_video=None,
        
        # Temporal and spatial crop of the video
        start_sample_frame=0,
        n_sample_frame=8,
        stride=1,
        left_crop=0,
        right_crop=0,
        top_crop=0,
        bottom_crop=0,
    ):
    # , ] = inputs
    default_edit_config='FateZero/config/low_resource_teaser/jeep_watercolor_ddim_10_steps.yaml'
    Omegadict_default_edit_config = OmegaConf.load(default_edit_config)
    
    dataset_time_string = get_time_string()
    config_now = copy.deepcopy(Omegadict_default_edit_config)
    print(f"config_now['pretrained_model_path'] = model_id {model_id}")
    # config_now['pretrained_model_path'] = model_id
    config_now['train_dataset']['prompt'] = source_prompt
    config_now['train_dataset']['path'] = data_path
    # ImageSequenceDataset_dict = { }
    offset_dict = {
        "left": left_crop,
        "right": right_crop,
        "top": top_crop,
        "bottom": bottom_crop,
    }
    ImageSequenceDataset_dict = {
        "start_sample_frame" : start_sample_frame,
        "n_sample_frame" : n_sample_frame,
        "stride"       : stride,   
        "offset": offset_dict,
    }
    config_now['train_dataset'].update(ImageSequenceDataset_dict)
    if user_input_video and data_path is None:
        raise gr.Error('You need to upload a video or choose a provided video')
    if user_input_video is not None and user_input_video.name is not None:
        config_now['train_dataset']['path'] = user_input_video.name
    config_now['validation_sample_logger_config']['prompts'] = [target_prompt]
    

    # fatezero config
    p2p_config_now = copy.deepcopy(config_now['validation_sample_logger_config']['p2p_config'][0])
    p2p_config_now['cross_replace_steps']['default_'] = cross_replace_steps
    p2p_config_now['self_replace_steps'] = self_replace_steps
    p2p_config_now['eq_params']['words'] = enhance_words.split(" ")
    p2p_config_now['eq_params']['values'] = [enhance_words_value,]*len(p2p_config_now['eq_params']['words'])
    config_now['validation_sample_logger_config']['p2p_config'][0] = copy.deepcopy(p2p_config_now)


    # ddim config
    config_now['validation_sample_logger_config']['guidance_scale'] = guidance_scale
    config_now['validation_sample_logger_config']['num_inference_steps'] = num_steps
    

    logdir = default_edit_config.replace('config', 'result').replace('.yml', '').replace('.yaml', '')+f'_{dataset_time_string}'      
    config_now['logdir'] = logdir
    print(f'Saving at {logdir}')
    save_path = test(config=default_edit_config, **config_now)
    mp4_path = save_path.replace('_0.gif', '_0_0_0.mp4')
    return mp4_path


if __name__ == "__main__":
    run()