import gradio as gr
import gc, copy, re
from huggingface_hub import hf_hub_download
from rwkv.model import RWKV
from rwkv.utils import PIPELINE, PIPELINE_ARGS

ctx_limit = 2048
title = "RWKV-5-World-0.4B-v2-20231113-ctx4096.pth"

model_path = hf_hub_download(repo_id="BlinkDL/rwkv-5-world", filename=f"{title}")
model = RWKV(model=model_path, strategy='cpu bf16')
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

def generate_prompt(instruction, input=None):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
    if input and len(input) > 0:
        return f"""Instruction: {instruction}

Input: {input}

Response:"""
    else:
        return f"""User: hi

Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.

User: {instruction}

Assistant:"""

examples = [
    ["東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。", "", 300, 1.2, 0.5, 0.5, 0.5],
    ["Écrivez un programme Python pour miner 1 Bitcoin, avec des commentaires.", "", 300, 1.2, 0.5, 0.5, 0.5],
    ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.5, 0.5],
    ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.5, 0.5],
    ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.5, 0.5],
    ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.5, 0.5],
    ["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.5, 0.5],
]

def evaluate(
    instruction,
    input=None,
    token_count=200,
    temperature=1.0,
    top_p=0.5,
    presencePenalty = 0.5,
    countPenalty = 0.5,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here

    instruction = re.sub(r'\n{2,}', '\n', instruction).strip().replace('\r\n','\n')
    input = re.sub(r'\n{2,}', '\n', input).strip().replace('\r\n','\n')
    ctx = generate_prompt(instruction, input)
    print(ctx + "\n")
    
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        for xxx in occurrence:
            occurrence[xxx] *= 0.996
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1
        if '\n\n' in out_str:
            break

    del out
    del state
    gc.collect()
    yield out_str.strip()

def user(message, chatbot):
    chatbot = chatbot or []
    return "", chatbot + [[message, None]]

def alternative(chatbot, history):
    if not chatbot or not history:
        return chatbot, history
    
    chatbot[-1][1] = None
    history[0] = copy.deepcopy(history[1])

    return chatbot, history


with gr.Blocks(title=title) as demo:
    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>🌍World - {title}</h1>\n</div>")
    with gr.Tab("Instruct mode"):
        gr.Markdown(f"100% RNN RWKV-LM **trained on 100+ world languages**. Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, ** keep you prompt short and clear **.</b>.") # <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(lines=2, label="Instruction", value='東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。')
                input = gr.Textbox(lines=2, label="Input", placeholder="")
                token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300)
                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.5)
                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.5)
            with gr.Column():
                with gr.Row():
                    submit = gr.Button("Submit", variant="primary")
                    clear = gr.Button("Clear", variant="secondary")
                output = gr.Textbox(label="Output", lines=5)
        data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
        submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
        clear.click(lambda: None, [], [output])
        data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])

demo.queue(max_size=10)
demo.launch(share=False)