import argparse
import glob
import os
import os.path
from sys import exit
import shutil
import gradio as gr
import numpy as np
import onnxruntime as rt
import PIL
import PIL.ImageColor
import tqdm
from huggingface_hub import hf_hub_download

import MIDI
from midi_synthesizer import synthesis
from midi_tokenizer import MIDITokenizer

def softmax(x, axis):
    x_max = np.amax(x, axis=axis, keepdims=True)
    exp_x_shifted = np.exp(x - x_max)
    return exp_x_shifted / np.sum(exp_x_shifted, axis=axis, keepdims=True)


def sample_top_p_k(probs, p, k):
    probs_idx = np.argsort(-probs, axis=-1)
    probs_sort = np.take_along_axis(probs, probs_idx, -1)
    probs_sum = np.cumsum(probs_sort, axis=-1)
    mask = probs_sum - probs_sort > p
    probs_sort[mask] = 0.0
    mask = np.zeros(probs_sort.shape[-1])
    mask[:k] = 1
    probs_sort = probs_sort * mask
    probs_sort /= np.sum(probs_sort, axis=-1, keepdims=True)
    shape = probs_sort.shape
    probs_sort_flat = probs_sort.reshape(-1, shape[-1])
    probs_idx_flat = probs_idx.reshape(-1, shape[-1])
    next_token = np.stack([np.random.choice(idxs, p=pvals) for pvals, idxs in zip(probs_sort_flat, probs_idx_flat)])
    next_token = next_token.reshape(*shape[:-1])
    return next_token


def generate(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)
    with bar:
        while cur_len < max_len:
            end = False
            hidden = model_base.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_token.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 run(tab, instruments, drum_kit, mid, midi_events, gen_events, temp, top_p, top_k, allow_cc):
    mid_seq = []
    max_len = int(gen_events)
    img_len = 1024
    img = np.full((128 * 2, img_len, 3), 255, dtype=np.uint8)
    state = {"t1": 0, "t": 0, "cur_pos": 0}
    colors = ['navy', 'blue', 'deepskyblue', 'teal', 'green', 'lightgreen', 'lime', 'orange',
              'brown', 'grey', 'red', 'pink', 'aqua', 'orchid', 'bisque', 'coral']
    colors = [PIL.ImageColor.getrgb(color) for color in colors]

    def draw_event(tokens):
        if tokens[0] in tokenizer.id_events:
            name = tokenizer.id_events[tokens[0]]
            if len(tokens) <= len(tokenizer.events[name]):
                return
            params = tokens[1:]
            params = [params[i] - tokenizer.parameter_ids[p][0] for i, p in enumerate(tokenizer.events[name])]
            if not all([0 <= params[i] < tokenizer.event_parameters[p] for i, p in enumerate(tokenizer.events[name])]):
                return
            event = [name] + params
            state["t1"] += event[1]
            t = state["t1"] * 16 + event[2]
            state["t"] = t
            if name == "note":
                tr, d, c, p = event[3:7]
                shift = t + d - (state["cur_pos"] + img_len)
                if shift > 0:
                    img[:, :-shift] = img[:, shift:]
                    img[:, -shift:] = 255
                    state["cur_pos"] += shift
                t = t - state["cur_pos"]
                img[p * 2:(p + 1) * 2, t: t + d] = colors[c]

    def get_img():
        t = state["t"] - state["cur_pos"]
        img_new = img.copy()
        img_new[:, t: t + 2] = 0
        return PIL.Image.fromarray(np.flip(img_new, 0))

    disable_patch_change = False
    disable_channels = None
    if tab == 0:
        i = 0
        mid = [[tokenizer.bos_id] + [tokenizer.pad_id] * (tokenizer.max_token_seq - 1)]
        patches = {}
        for instr in instruments:
            patches[i] = patch2number[instr]
            i = (i + 1) if i != 9 else 10
        if drum_kit != "None":
            patches[9] = drum_kits2number[drum_kit]
        for i, (c, p) in enumerate(patches.items()):
            mid.append(tokenizer.event2tokens(["patch_change", 0, 0, i, c, p]))
        mid_seq = mid
        mid = np.asarray(mid, dtype=np.int64)
        if len(instruments) > 0:
            disable_patch_change = True
            disable_channels = [i for i in range(16) if i not in patches]
    elif mid is not None:
        mid = tokenizer.tokenize(MIDI.midi2score(mid))
        mid = np.asarray(mid, dtype=np.int64)
        mid = mid[:int(midi_events)]
        max_len += len(mid)
        for token_seq in mid:
            mid_seq.append(token_seq)
            draw_event(token_seq)
    generator = generate(mid, max_len=max_len, temp=temp, top_p=top_p, top_k=top_k,
                         disable_patch_change=disable_patch_change, disable_control_change=not allow_cc,
                         disable_channels=disable_channels)
    for token_seq in generator:
        mid_seq.append(token_seq)
        draw_event(token_seq)
        yield mid_seq, get_img(), None, None
    mid = tokenizer.detokenize(mid_seq)
    with open(f"output.mid", 'wb') as f:
        f.write(MIDI.score2midi(mid))
    audio = synthesis(MIDI.score2opus(mid), soundfont_path)
    yield mid_seq, get_img(), "output.mid", (44100, audio)


def cancel_run(mid_seq):
    if mid_seq is None:
        return None, None
    mid = tokenizer.detokenize(mid_seq)
    with open(f"output.mid", 'wb') as f:
        f.write(MIDI.score2midi(mid))
    audio = synthesis(MIDI.score2opus(mid), soundfont_path)
    return "output.mid", (44100, audio)


number2drum_kits = {-1: "None", 0: "Standard", 8: "Room", 16: "Power", 24: "Electric", 25: "TR-808", 32: "Jazz",
                    40: "Blush", 48: "Orchestra"}
patch2number = {v: k for k, v in MIDI.Number2patch.items()}
drum_kits2number = {v: k for k, v in number2drum_kits.items()}

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=256, help="max")
    opt = parser.parse_args()
    soundfont_path = hf_hub_download(repo_id="skytnt/midi-model", filename="soundfont.sf2")
    model_base_path = hf_hub_download(repo_id="skytnt/midi-model", filename="onnx/model_base.onnx")
    model_token_path = hf_hub_download(repo_id="skytnt/midi-model", filename="onnx/model_token.onnx")

    tokenizer = MIDITokenizer()
    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
    model_base = rt.InferenceSession(model_base_path, providers=providers)
    model_token = rt.InferenceSession(model_token_path, providers=providers)

    app = gr.Blocks()
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Midi Composer</h1>")
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=skytnt.midi-composer&style=flat)\n\n"
                    "Midi event transformer for music generation\n\n"
                    "Demo for [SkyTNT/midi-model](https://github.com/SkyTNT/midi-model)\n\n"
                    "[Open In Colab]"
                    "(https://colab.research.google.com/github/SkyTNT/midi-model/blob/main/demo.ipynb)"
                    " for faster running and longer generation"
                    )

        tab_select = gr.Variable(value=0)
        with gr.Tabs():
            with gr.TabItem("instrument prompt") as tab1:
                input_instruments = gr.Dropdown(label="instruments (auto if empty)", choices=list(patch2number.keys()),
                                                multiselect=True, max_choices=15, type="value")
                input_drum_kit = gr.Dropdown(label="drum kit", choices=list(drum_kits2number.keys()), type="value",
                                             value="None")
                example1 = gr.Examples([
                    [[], "None"],
                    [["Acoustic Grand"], "None"],
                    [["Acoustic Grand", "Violin", "Viola", "Cello", "Contrabass", "Timpani"], "Orchestra"],
                    [["Acoustic Guitar(nylon)", "Acoustic Guitar(steel)", "Electric Guitar(jazz)",
                      "Electric Guitar(clean)", "Electric Guitar(muted)", "Overdriven Guitar", "Distortion Guitar",
                      "Electric Bass(finger)"], "Standard"],
                    [["Acoustic Grand", "String Ensemble 1", "Trombone", "Tuba", "Muted Trumpet", "French Horn", "Oboe",
                      "English Horn", "Bassoon", "Clarinet"], "Orchestra"]

                ], [input_instruments, input_drum_kit])
            with gr.TabItem("midi prompt") as tab2:
                input_midi = gr.File(label="input midi", file_types=[".midi", ".mid"], type="binary")
                input_midi_events = gr.Slider(label="use first n midi events as prompt", minimum=1, maximum=512,
                                              step=1,
                                              value=128)
                example2 = gr.Examples([[file, 128] for file in glob.glob("example/*.mid")],
                                       [input_midi, input_midi_events])

        tab1.select(lambda: 0, None, tab_select, queue=False)
        tab2.select(lambda: 1, None, tab_select, queue=False)
        input_gen_events = gr.Slider(label="generate n midi events", minimum=1, maximum=opt.max_gen,
                                     step=1, value=opt.max_gen)
        input_temp = gr.Slider(label="temperature", minimum=0.1, maximum=1.2, step=0.01, value=1)
        input_top_p = gr.Slider(label="top p", minimum=0.1, maximum=1, step=0.01, value=0.98)
        input_top_k = gr.Slider(label="top k", minimum=1, maximum=50, step=1, value=20)
        input_allow_cc = gr.Checkbox(label="allow midi cc event", value=True)
        run_btn = gr.Button("generate", variant="primary")
        stop_btn = gr.Button("stop")
        output_midi_seq = gr.Variable()
        output_midi_img = gr.Image(label="output image")
        output_midi = gr.File(label="output midi", file_types=[".mid"])
        output_audio = gr.Audio(label="output audio", format="wav")
        run_event = run_btn.click(run, [tab_select, input_instruments, input_drum_kit, input_midi, input_midi_events,
                                        input_gen_events, input_temp, input_top_p, input_top_k,
                                        input_allow_cc],
                                  [output_midi_seq, output_midi_img, output_midi, output_audio])
        stop_btn.click(cancel_run, output_midi_seq, [output_midi, output_audio], cancels=run_event, queue=False)
    app.queue(2).launch(server_port=opt.port, share=opt.share, inbrowser=True)