from transformers import AutoModelForCausalLM, AutoTokenizer
from tokenization_yi import YiTokenizer
import torch

# Load the model and tokenizer
model_name = "01-ai/Yi-34B-200K"
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
tokenizer_dir = "Tonic1/YiTonic"
vocab_file = os.path.join(tokenizer_dir, "tokenizer.model")
tokenizer_json = os.path.join(tokenizer_dir, "tokenizer.json")
tokenizer_config = os.path.join(tokenizer_dir, "tokenizer_config.json")
tokenizer = YiTokenizer(vocab_file=vocab_file)

def run(message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.3, top_p=0.9, top_k=50):
    prompt = get_prompt(message, chat_history, system_prompt)

    # Encode the prompt to tensor
    input_ids = tokenizer.encode(prompt, return_tensors='pt')

    # Generate a response using the model with adjusted parameters
    response_ids = model.generate(
        input_ids,
        max_length=max_new_tokens + input_ids.shape[1],
        temperature=temperature,  # Controls randomness. Lower values make text more deterministic.
        top_p=top_p,              # Nucleus sampling: higher values allow more diversity.
        top_k=top_k,              # Top-k sampling: limits the number of top tokens considered.
        pad_token_id=tokenizer.eos_token_id
    )

    # Decode the response
    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    return response

def get_prompt(message, chat_history, system_prompt):
    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]

    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
    message = message.strip() if do_strip else message
    texts.append(f"{message} [/INST]")
    return ''.join(texts)

DEFAULT_SYSTEM_PROMPT = """
    You are Yi. You are an AI assistant, you are moderately-polite and give only true information.
    You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. 
    If you think there might not be a correct answer, you say so. Since you are autoregressive, 
    each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, 
    assumptions, and step-by-step thinking BEFORE you try to answer a question.
"""
MAX_MAX_NEW_TOKENS = 200000
DEFAULT_MAX_NEW_TOKENS = 100000
MAX_INPUT_TOKEN_LENGTH = 100000

DESCRIPTION = "# [Yi-6B](https://huggingface.co/01-ai/Yi-6B)"

def clear_and_save_textbox(message): return '', message

def display_input(message, history=[]):
    history.append((message, ''))
    return history

def delete_prev_fn(history=[]):
    try:
        message, _ = history.pop()
    except IndexError:
        message = ''
    return history, message or ''

def generate(message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k):
    if max_new_tokens > MAX_MAX_NEW_TOKENS:
        raise ValueError

    history = history_with_input[:-1]
    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
    try:
        first_response = next(generator)
        yield history + [(message, first_response)]
    except StopIteration:
        yield history + [(message, '')]
    for response in generator:
        yield history + [(message, response)]

def process_example(message):
    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
    for x in generator:
        pass
    return '', x

def check_input_token_length(message, chat_history, system_prompt):
    input_token_length = len(message) + len(chat_history)
    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.")

with gr.Blocks(theme='ParityError/Anime') as demo:
    gr.Markdown(DESCRIPTION)


    
    with gr.Group():
        chatbot = gr.Chatbot(label='Yi-6B')
        with gr.Row():
            textbox = gr.Textbox(
                container=False,
                show_label=False,
                placeholder='Hi, Yi',
                scale=10
            )
            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)

    with gr.Row():
        retry_button = gr.Button('Retry', variant='secondary')
        undo_button = gr.Button('Undo', variant='secondary')
        clear_button = gr.Button('Clear', variant='secondary')

    saved_input = gr.State()

    with gr.Accordion(label='Advanced options', open=False):
        system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=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=4.0, step=0.1, value=0.1)
        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=10)

    textbox.submit(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot, system_prompt],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    button_event_preprocess = submit_button.click(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot, system_prompt],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    retry_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    undo_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=lambda x: x,
        inputs=[saved_input],
        outputs=textbox,
        api_name=False,
        queue=False,
    )

    clear_button.click(
        fn=lambda: ([], ''),
        outputs=[chatbot, saved_input],
        queue=False,
        api_name=False,
    )

demo.queue(max_size=32).launch(show_api=False)