import gradio as gr
import os, gc, copy, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 2000
title = "RWKV-5-World-1.5B-v2-OnlyForTest_56%_trained-20231013-ctx4096"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="BlinkDL/temp", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

def generate_prompt(instruction, input=""):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    if input:
        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:"""

def evaluate(
    ctx,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    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
    ctx = ctx.strip()
    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

    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
    del out
    del state
    gc.collect()
    torch.cuda.empty_cache()
    yield out_str.strip()

examples = [
    [generate_prompt("Tell me about ravens."), 333, 1, 0.5, 0.4, 0.4],
    [generate_prompt("Écrivez un programme Python pour miner 1 Bitcoin."), 333, 1, 0.5, 0.4, 0.4],
    [generate_prompt("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。"), 333, 1, 0.5, 0.4, 0.4],
    [generate_prompt("Write a story using the following information", "A man named Alex chops a tree down"), 333, 1, 0.5, 0.4, 0.4],
    ["Here is a list of adjectives that describe a person as brave:", 333, 1, 0.5, 0.4, 0.4],
    ["Here is my proposal to kill all mosquitos.", 333, 1, 0.5, 0.4, 0.4],
    [generate_prompt("写一篇关于水利工程的流体力学模型的论文，需要详细全面。"), 333, 1, 0.5, 0.4, 0.4],
    [generate_prompt("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."), 333, 1, 0.5, 0.4, 0.4],
]

##########################################################################

with gr.Blocks(title=title) as demo:
    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>RWKV-5 World v2 - {title}</h1>\n</div>")
    with gr.Tab("Raw Generation"):
        gr.Markdown(f"This is [RWKV-5 World v2](https://huggingface.co/BlinkDL/rwkv-5-world) with 1.5B params - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.")
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(lines=2, label="Prompt", value=generate_prompt("Tell me about ravens."))
                token_count = gr.Slider(10, 333, label="Max Tokens", step=10, value=333)
                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
                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=0.4)
                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
            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=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
        submit.click(evaluate, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
        clear.click(lambda: None, [], [output])
        data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])

demo.queue(concurrency_count=1, max_size=10)
demo.launch(share=False)