import gradio as gr
import torch
import random
from unidecode import unidecode
from transformers import GPT2LMHeadModel
from samplings import top_p_sampling, temperature_sampling

device = torch.device("cpu")

description = """
<div>
<a style="display:inline-block" href='https://github.com/suno-ai/bark'><img src='https://img.shields.io/github/stars/suno-ai/bark?style=social' /></a>
<a style='display:inline-block' href='https://discord.gg/J2B2vsjKuE'><img src='https://dcbadge.vercel.app/api/server/J2B2vsjKuE?compact=true&style=flat' /></a>
<a style="display:inline-block; margin-left: 1em" href="https://huggingface.co/spaces/suno/bark?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space%20to%20skip%20the%20queue-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
</div>
Bark is a universal text-to-audio model created by [Suno](www.suno.ai), with code publicly available [here](https://github.com/suno-ai/bark). \
Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. \
This demo should be used for research purposes only. Commercial use is strictly prohibited. \
The model output is not censored and the authors do not endorse the opinions in the generated content. \
Use at your own risk.
"""

article = """
## 🌎 Foreign Language
Bark supports various languages out-of-the-box and automatically determines language from input text. \
When prompted with code-switched text, Bark will even attempt to employ the native accent for the respective languages in the same voice.
Try the prompt:
```
Buenos días Miguel. Tu colega piensa que tu alemán es extremadamente malo. But I suppose your english isn't terrible.
```
## 🤭 Non-Speech Sounds
Below is a list of some known non-speech sounds, but we are finding more every day. \
Please let us know if you find patterns that work particularly well on Discord!
* [laughter]
* [laughs]
* [sighs]
* [music]
* [gasps]
* [clears throat]
* — or ... for hesitations
* ♪ for song lyrics
* capitalization for emphasis of a word
* MAN/WOMAN: for bias towards speaker
Try the prompt:
```
" [clears throat] Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as... ♪ singing ♪."
```
## 🎶 Music
Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. \
Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics.
Try the prompt:
```
♪ In the jungle, the mighty jungle, the lion barks tonight ♪
```
## 🧬 Voice Cloning
Bark has the capability to fully clone voices - including tone, pitch, emotion and prosody. \
The model also attempts to preserve music, ambient noise, etc. from input audio. \
However, to mitigate misuse of this technology, we limit the audio history prompts to a limited set of Suno-provided, fully synthetic options to choose from.
## 👥 Speaker Prompts
You can provide certain speaker prompts such as NARRATOR, MAN, WOMAN, etc. \
Please note that these are not always respected, especially if a conflicting audio history prompt is given.
Try the prompt:
```
WOMAN: I would like an oatmilk latte please.
MAN: Wow, that's expensive!
```
## Details
Bark model by [Suno](https://suno.ai/), including official [code](https://github.com/suno-ai/bark) and model weights. \
Gradio demo supported by 🤗 Hugging Face. Bark is licensed under a non-commercial license: CC-BY 4.0 NC, see details on [GitHub](https://github.com/suno-ai/bark).
"""

# examples = [
#     "Jazz standard in Minor key with a swing feel.",
#     "Jazz standard in Major key with a fast tempo.",
#     "Jazz standard in Blues form with a soulfoul melody.",
#     "a painting of a starry night with the moon in the sky",
#     "a green field with a blue sky and clouds",
#     "a beach with a castle on top of it"
# ]

class ABCTokenizer():
    def __init__(self):
        self.pad_token_id = 0
        self.bos_token_id = 2
        self.eos_token_id = 3
        self.merged_tokens = []
        
        for i in range(8):
            self.merged_tokens.append('[SECS_'+str(i+1)+']')
        for i in range(32):
            self.merged_tokens.append('[BARS_'+str(i+1)+']')
        for i in range(11):
            self.merged_tokens.append('[SIM_'+str(i)+']')

    def __len__(self):
        return 128+len(self.merged_tokens)
    
    def encode(self, text):
        encodings = {}
        encodings['input_ids'] = torch.tensor(self.txt2ids(text, self.merged_tokens))
        encodings['attention_mask'] = torch.tensor([1]*len(encodings['input_ids']))
        return encodings

    def decode(self, ids, skip_special_tokens=False):
        txt = ""
        for i in ids:
            if i>=128:
                if not skip_special_tokens:
                    txt += self.merged_tokens[i-128]
            elif i!=self.bos_token_id and i!=self.eos_token_id:
                txt += chr(i)
        return txt

    def txt2ids(self, text, merged_tokens):
        ids = ["\""+str(ord(c))+"\"" for c in text]
        txt_ids = ' '.join(ids)
        for t_idx, token in enumerate(merged_tokens):
            token_ids = ["\""+str(ord(c))+"\"" for c in token]
            token_txt_ids = ' '.join(token_ids)
            txt_ids = txt_ids.replace(token_txt_ids, "\""+str(t_idx+128)+"\"")
        
        txt_ids = txt_ids.split(' ')
        txt_ids = [int(i[1:-1]) for i in txt_ids]
        return [self.bos_token_id]+txt_ids+[self.eos_token_id]

def generate_abc(control_codes, prefix, num_tunes, max_length, top_p, temperature, seed):

    try:
        seed = int(seed)
    except:
        seed = None

    prefix = unidecode(control_codes + prefix)
    tokenizer = ABCTokenizer()
    model = GPT2LMHeadModel.from_pretrained('sander-wood/tunesformer').to(device)

    if prefix:
        ids = tokenizer.encode(prefix)['input_ids'][:-1]
    else:
        ids = torch.tensor([tokenizer.bos_token_id])

    random.seed(seed)
    tunes = ""

    for c_idx in range(num_tunes):
        print("\nX:"+str(c_idx+1)+"\n", end="")
        print(tokenizer.decode(ids[1:], skip_special_tokens=True), end="")
        input_ids = ids.unsqueeze(0)
        for t_idx in range(max_length):
            if seed!=None:
                n_seed = random.randint(0, 1000000)
                random.seed(n_seed)
            else:
                n_seed = None

            outputs = model(input_ids=input_ids.to(device))
            probs = outputs.logits[0][-1]
            probs = torch.nn.Softmax(dim=-1)(probs).cpu().detach().numpy()
            sampled_id = temperature_sampling(probs=top_p_sampling(probs, 
                                                                top_p=top_p, 
                                                                seed=n_seed,
                                                                return_probs=True),
                                            seed=n_seed,
                                            temperature=temperature)
            input_ids = torch.cat((input_ids, torch.tensor([[sampled_id]])), 1)
            if sampled_id!=tokenizer.eos_token_id:
                print(tokenizer.decode([sampled_id], skip_special_tokens=True), end="")
                continue
            else:
                tune = "X:"+str(c_idx+1)+"\n"+tokenizer.decode(input_ids.squeeze(), skip_special_tokens=True)
                tunes += tune+"\n\n"
                print("\n")
                break
    
    return tunes

input_control_codes = gr.inputs.Textbox(lines=5, label="Control Codes", default="[SECS_2][BARS_9][SIM_3][BARS_9]")
input_prefix = gr.inputs.Textbox(lines=5, label="Prefix", default="L:1/8\nQ:1/4=114\nM:3/4\nK:D\nde | \"D\"")
input_num_tunes = gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Number of Tunes")
input_max_length = gr.inputs.Slider(minimum=10, maximum=1000, step=10, default=500, label="Max Length")
input_top_p = gr.inputs.Slider(minimum=0.0, maximum=1.0, step=0.05, default=0.9, label="Top P")
input_temperature = gr.inputs.Slider(minimum=0.0, maximum=2.0, step=0.1, default=1.0, label="Temperature")
input_seed = gr.inputs.Textbox(lines=1, label="Seed (int)", default="None")
output_abc = gr.outputs.Textbox(label="Generated Tunes")

gr.Interface(generate_abc,
                [input_control_codes, input_prefix, input_num_tunes, input_max_length, input_top_p, input_temperature, input_seed],
                output_abc,
            title="TunesFormer: Forming Tunes with Control Codes",
            description=description,
            article=article).launch()