from typing import Iterator

import gradio as gr
import torch

from model import get_input_token_length, run

DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant with a deep knowledge of code and software design. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\
"""
MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4000

DESCRIPTION = """
# Code Llama 13B Chat

This Space demonstrates model [CodeLlama-13b-Instruct](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) by Meta, a Code Llama model with 13B parameters fine-tuned for chat instructions and specialized on code tasks. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).

🔎 For more details about the Code Llama family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/codellama) or [the paper](https://huggingface.co/papers/2308.12950).

🏃🏻 Check out our [Playground](https://huggingface.co/spaces/codellama/codellama-playground) for a super-fast code completion demo that leverages a streaming [inference endpoint](https://huggingface.co/inference-endpoints).

💪 For a chat demo of the largest Code Llama model (34B parameters), you can [select Code Llama in Hugging Chat!](https://huggingface.co/chat/)

"""

LICENSE = """
<p/>

---
As a derivate work of Code Llama by Meta,
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/codellama-2-13b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/codellama-2-13b-chat/blob/main/USE_POLICY.md).
"""

if not torch.cuda.is_available():
    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'


def clear_and_save_textbox(message: str) -> tuple[str, str]:
    return '', message


def display_input(message: str,
                  history: list[tuple[str, str]]) -> list[tuple[str, str]]:
    history.append((message, ''))
    return history


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


def generate(
    message: str,
    history_with_input: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int,
) -> Iterator[list[tuple[str, str]]]:
    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: str) -> tuple[str, list[tuple[str, str]]]:
    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
    for x in generator:
        pass
    return '', x


def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
    input_token_length = get_input_token_length(message, chat_history, system_prompt)
    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(css='style.css') as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value='Duplicate Space for private use',
                       elem_id='duplicate-button')

    with gr.Group():
        chatbot = gr.Chatbot(label='Chatbot')
        with gr.Row():
            textbox = gr.Textbox(
                container=False,
                show_label=False,
                placeholder='Type a message...',
                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=6)
        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,
        )

    gr.Examples(
        examples=[
            'What is the Fibonacci sequence?',
            'Can you explain briefly what Python is good for?',
            'How can I display a grid of images in SwiftUI?',
        ],
        inputs=textbox,
        outputs=[textbox, chatbot],
        fn=process_example,
        cache_examples=True,
    )

    gr.Markdown(LICENSE)

    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=20).launch()