import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
# from optimum.bettertransformer import BetterTransformer
from tokenization_yi import YiTokenizer
import torch
import os
import bitsandbytes
import gradio as gr
import sentencepiece


# os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:126'
MAX_MAX_NEW_TOKENS = 160000
DEFAULT_MAX_NEW_TOKENS = 20000
MAX_INPUT_TOKEN_LENGTH = 160000
DESCRIPTION = """
# Welcome to Tonic'sYI-6B-200K
You can use this Space to test out the current model [01-ai/Yi-6B-200K](https://huggingface.co/01-ai/Yi-6B-200K)
You can also use YI-200 by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic1Tonics-Yi-6B-200K/?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-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></h3> 
Join us : TeamTonic is always making cool demos! Join our active builder's community on Discord: [Discord](https://discord.gg/nXx5wbX9) On Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On Github: [Polytonic](https://github.com/tonic-ai) & contribute to [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)
"""


# Set up the model and tokenizer
model_name = "01-ai/Yi-6B-200K"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_name, device_map="cuda", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    load_in_4bit=True,
    trust_remote_code=True
)
model.to(device)

def run(prompt, max_new_tokens, temperature, top_p, top_k):
    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
    response_ids = model.generate(
        input_ids,
        max_length=max_new_tokens + input_ids.shape[1],
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True
    )
    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    return response

def generate(prompt, max_new_tokens, temperature, top_p, top_k):
    response = run(prompt, max_new_tokens, temperature, top_p, top_k)
    return response

# Gradio Interface
with gr.Blocks(theme='ParityError/Anime') as demo:
    gr.Markdown(DESCRIPTION)
    
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(
                label='Enter your prompt',
                placeholder='Type something...',
                lines=5
            )
            submit_button = gr.Button('Generate')

    with gr.Accordion(label='Advanced options', open=False):
        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=2.0, step=0.1, value=1.2)
        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=900)

    output = gr.Textbox(label='Generated Text', lines=10, readonly=True)

    submit_button.click(
        fn=generate,
        inputs=[prompt, max_new_tokens, temperature, top_p, top_k],
        outputs=output
    )

demo.queue(max_size=5).launch(show_api=True)