import argparse
import glob
import os.path

import gradio as gr

import pickle
import tqdm
import json

import MIDI
from midi_synthesizer import synthesis

in_space = os.getenv("SYSTEM") == "spaces"


def generate(model, prompt=None, max_len=512, temp=1.0, top_p=0.98, top_k=20,
             disable_patch_change=False, disable_control_change=False, disable_channels=None):
    if disable_channels is not None:
        disable_channels = [tokenizer.parameter_ids["channel"][c] for c in disable_channels]
    else:
        disable_channels = []
    max_token_seq = tokenizer.max_token_seq
    if prompt is None:
        input_tensor = np.full((1, max_token_seq), tokenizer.pad_id, dtype=np.int64)
        input_tensor[0, 0] = tokenizer.bos_id  # bos
    else:
        prompt = prompt[:, :max_token_seq]
        if prompt.shape[-1] < max_token_seq:
            prompt = np.pad(prompt, ((0, 0), (0, max_token_seq - prompt.shape[-1])),
                            mode="constant", constant_values=tokenizer.pad_id)
        input_tensor = prompt
    input_tensor = input_tensor[None, :, :]
    cur_len = input_tensor.shape[1]
    bar = tqdm.tqdm(desc="generating", total=max_len - cur_len, disable=in_space)
    with bar:
        while cur_len < max_len:
            end = False
            hidden = model[0].run(None, {'x': input_tensor})[0][:, -1]
            next_token_seq = np.empty((1, 0), dtype=np.int64)
            event_name = ""
            for i in range(max_token_seq):
                mask = np.zeros(tokenizer.vocab_size, dtype=np.int64)
                if i == 0:
                    mask_ids = list(tokenizer.event_ids.values()) + [tokenizer.eos_id]
                    if disable_patch_change:
                        mask_ids.remove(tokenizer.event_ids["patch_change"])
                    if disable_control_change:
                        mask_ids.remove(tokenizer.event_ids["control_change"])
                    mask[mask_ids] = 1
                else:
                    param_name = tokenizer.events[event_name][i - 1]
                    mask_ids = tokenizer.parameter_ids[param_name]
                    if param_name == "channel":
                        mask_ids = [i for i in mask_ids if i not in disable_channels]
                    mask[mask_ids] = 1
                logits = model[1].run(None, {'x': next_token_seq, "hidden": hidden})[0][:, -1:]
                scores = softmax(logits / temp, -1) * mask
                sample = sample_top_p_k(scores, top_p, top_k)
                if i == 0:
                    next_token_seq = sample
                    eid = sample.item()
                    if eid == tokenizer.eos_id:
                        end = True
                        break
                    event_name = tokenizer.id_events[eid]
                else:
                    next_token_seq = np.concatenate([next_token_seq, sample], axis=1)
                    if len(tokenizer.events[event_name]) == i:
                        break
            if next_token_seq.shape[1] < max_token_seq:
                next_token_seq = np.pad(next_token_seq, ((0, 0), (0, max_token_seq - next_token_seq.shape[-1])),
                                        mode="constant", constant_values=tokenizer.pad_id)
            next_token_seq = next_token_seq[None, :, :]
            input_tensor = np.concatenate([input_tensor, next_token_seq], axis=1)
            cur_len += 1
            bar.update(1)
            yield next_token_seq.reshape(-1)
            if end:
                break


def create_msg(name, data):
    return {"name": name, "data": data}


def run(search_prompt):
    mid_seq = []
    gen_events = int(gen_events)
    max_len = gen_events

    disable_patch_change = False
    disable_channels = None
    if tab == 0:
        mid_seq = []
       
    elif mid is not None:
        mid_seq = MIDI.midi2score(mid)

    init_msgs = [create_msg("visualizer_clear", None)]
    for tokens in mid_seq:
        init_msgs.append(create_msg("visualizer_append", tokens))
    yield mid_seq, None, None, init_msgs

    for i in range(len(mid_seq)):
        yield mid_seq, None, None, [create_msg("visualizer_append", mid_seq[i]), create_msg("progress", [i + 1, mid_seq[i+1]])]

    with open(f"output.mid", 'wb') as f:
        f.write(MIDI.score2midi(mid_seq))
    audio = synthesis(MIDI.score2opus(mid_seq), soundfont_path)
    yield mid_seq, "output.mid", (44100, audio), [create_msg("visualizer_end", None)]


def cancel_run(mid_seq):
    if mid_seq is None:
        return None, None

    with open(f"output.mid", 'wb') as f:
        f.write(MIDI.score2midi(mid_seq))
    audio = synthesis(MIDI.score2opus(mid_seq), soundfont_path)
    return "output.mid", (44100, audio), [create_msg("visualizer_end", None)]


def load_javascript(dir="javascript"):
    scripts_list = glob.glob(f"{dir}/*.js")
    javascript = ""
    for path in scripts_list:
        with open(path, "r", encoding="utf8") as jsfile:
            javascript += f"\n<!-- {path} --><script>{jsfile.read()}</script>"
    template_response_ori = gr.routes.templates.TemplateResponse

    def template_response(*args, **kwargs):
        res = template_response_ori(*args, **kwargs)
        res.body = res.body.replace(
            b'</head>', f'{javascript}</head>'.encode("utf8"))
        res.init_headers()
        return res

    gr.routes.templates.TemplateResponse = template_response


class JSMsgReceiver(gr.HTML):
    def __init__(self, **kwargs):
        super().__init__(elem_id="msg_receiver", visible=False, **kwargs)
    
    def postprocess(self, y):
        if y:
            y = f"<p>{json.dumps(y)}</p>"
        return super().postprocess(y)
    
    def get_block_name(self) -> str:
        return "html"

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
    parser.add_argument("--port", type=int, default=7860, help="gradio server port")
    parser.add_argument("--max-gen", type=int, default=1024, help="max")
    
    opt = parser.parse_args()
    
    soundfont_path = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
    meta_data_path = "meta-data/LAMD_META_10000.pickle"
    
    models_info = {"generic pretrain model": ["skytnt/midi-model", ""],
                   "j-pop finetune model": ["skytnt/midi-model-ft", "jpop/"],
                   "touhou finetune model": ["skytnt/midi-model-ft", "touhou/"]}


    print('Loading meta-data...')
    with open(meta_data_path, 'rb') as f:
        meta_data = pickle.load(f)
    print('Done!')
    
    
    load_javascript()
    app = gr.Blocks()
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>MIDI Search</h1>")
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.MIDI-Search&style=flat)\n\n"
                    "MIDI Search and Explore\n\n"
                    "Demo for [MIDI Search](https://github.com/asigalov61)\n\n"
                    "[Open In Colab]"
                    "(https://colab.research.google.com/github/asigalov61/MIDI-Search/blob/main/demo.ipynb)"
                    " for faster running and longer generation"
                    )
        
        js_msg = JSMsgReceiver()
        
        with gr.Tabs():
            with gr.TabItem("instrument prompt") as tab1:
                
                search_prompt = gr.Textbox(label="search prompt")
                
            with gr.TabItem("midi prompt") as tab2:
                input_midi = gr.File(label="input midi", file_types=[".midi", ".mid"], type="binary")

        with gr.Accordion("options", open=False):
 
            input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True)
            
        search_btn = gr.Button("search", variant="primary")
        stop_btn = gr.Button("stop and output")
        output_midi_seq = gr.Textbox()
        output_midi_visualizer = gr.HTML(elem_id="midi_visualizer_container")
        output_audio = gr.Audio(label="output audio", format="mp3", elem_id="midi_audio")
        output_midi = gr.File(label="output midi", file_types=[".mid"])
        run_event = search_btn.click(run, [search_prompt],
                                  [output_midi_seq, output_midi, output_audio, js_msg])
        stop_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio, js_msg], cancels=run_event, queue=False)
    app.queue(1).launch(server_port=opt.port, share=opt.share, inbrowser=True)