import json
import os
import re
import shutil
import requests
import warnings
import gradio as gr
from huggingface_hub import Repository
from text_generation import Client
from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css

HF_TOKEN = os.environ.get("HF_TOKEN", None)
DIALOGUES_DATASET = "ArmelR/gradio_playground_dialogues"

API_URL_G = "https://api-inference.huggingface.co/models/ArmelR/starcoder-gradio-v0"
API_URL_S = "https://api-inference.huggingface.co/models/HuggingFaceH4/starcoderbase-finetuned-oasst1"
API_URL_B = "starchat-beta": "https://api-inference.huggingface.co/models/HuggingFaceH4/starchat-beta"

model2endpoint = {
    "starChat-alpha": API_URL_S,
    "starCoder-gradio": API_URL_G,
    "starChat-beta": API_URL_B
}
model_names = list(model2endpoint.keys())

with open("./HHH_prompt_short.txt", "r") as f:
    HHH_PROMPT = f.read() + "\n\n"

with open("./TA_prompt_v0.txt", "r") as f:
    TA_PROMPT = f.read()

NO_PROMPT = ""

FIM_PREFIX = "<fim_prefix>"
FIM_MIDDLE = "<fim_middle>"
FIM_SUFFIX = "<fim_suffix>"

FIM_INDICATOR = "<FILL_HERE>"


FORMATS = """
# Chat mode
Chat mode prepends the custom [TA prompt](https://huggingface.co/spaces/bigcode/chat-playground/blob/main/TA_prompt_v0.txt) or the [HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt) from Anthropic to the request which conditions the model to serve as an assistant.

⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode) are provided for demonstration purposes; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](hf.co/bigcode)

"""


theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[
        gr.themes.GoogleFont("Open Sans"),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
)

client_g = Client(
    API_URL_G, headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

client_s = Client(
    API_URL_S, headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

def randomize_seed_generator():
    seed = random.randint(0, 1000000)
    return seed


def save_inputs_and_outputs(now, inputs, outputs, generate_kwargs, model):
    buffer = StringIO()
    timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")
    file_name = f"prompts_{timestamp}.jsonl"
    data = {"model": model, "inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}
    pd.DataFrame([data]).to_json(buffer, orient="records", lines=True)

    # Push to Hub
    upload_file(
        path_in_repo=f"{now.date()}/{now.hour}/{file_name}",
        path_or_fileobj=buffer.getvalue().encode(),
        repo_id=DIALOGUES_DATASET,
        token=HF_TOKEN,
        repo_type="dataset",
    )

    # Clean and rerun
    buffer.close()

def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep):
    past = []
    for data in chatbot:
        user_data, model_data = data

        if not user_data.startswith(user_name):
            user_data = user_name + user_data
        if not model_data.startswith(sep + assistant_name):
            model_data = sep + assistant_name + model_data

        past.append(user_data + model_data.rstrip() + sep)

    if not inputs.startswith(user_name):
        inputs = user_name + inputs

    total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()

    return total_inputs
    
def wrap_html_code(text):
    pattern = r"<.*?>"
    matches = re.findall(pattern, text)
    if len(matches) > 0:
        return f"```{text}```"
    else:
        return text

def has_no_history(chatbot, history):
    return not chatbot and not history

def get_inference_prompt(messages, model_name):
    if model_name == "starChat-beta" :
        prompt = "<|system|>\n<|endoftext|>\n"
        for message in messages :
            if message["role"] == "user" :
                prompt += f"<|user|>\n{message['content']}<|endoftext|>\n<|assistant|>"
            else : #message["role"] == "assistant"
                prompt += f"{message['content']}<|endoftext|>\n"
    elif model_name == "starChat-alpha" :
        prompt = "<|system|>\n<|end|>\n"
        for message in messages :
            if message["role"] == "user" :
                prompt += f"<|user|>\n{message['content']}<|end|>\n<|assistant|>"
            else : #message["role"] == "assistant"
                prompt += f"{message['content']}<|end|>\n"
    else : # starCoder-gradio
        prompt = ""
        for message in messages :
            if message["role"] == "user" :
                prompt += f"Question: {message['content']}\n\nAnswer:"
            else : #message["role"] == "assistant"
                prompt += f"{message['content']}\n\n"
    return prompt
    
def generate(
    RETRY_FLAG,
    model_name,
    system_message,
    user_message,
    chatbot,
    history,
    temperature,
    top_k,
    top_p,
    max_new_tokens,
    repetition_penalty,
    do_save=True,
):
    client = Client(
        model2endpoint[model_name],
        headers={"Authorization": f"Bearer {API_TOKEN}"},
        timeout=60,
    )
    # Don't return meaningless message when the input is empty
    if not user_message:
        print("Empty input")

    if not RETRY_FLAG:
        history.append(user_message)
        seed = 42
    else:
        seed = randomize_seed_generator()

    past_messages = []
    for data in chatbot:
        user_data, model_data = data

        past_messages.extend(
            [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}]
        )

    if len(past_messages) < 1:
        prompt = get_inference_prompt(messages=[{"role": "user", "content": user_message}], model_name=model_name)
    else:
        prompt = dialogue_template.get_inference_prompt(messages=past_messages + [{"role": "user", "content": user_message}], model_name=model_name)

    generate_kwargs = {
        "temperature": temperature,
        "top_k": top_k,
        "top_p": top_p,
        "max_new_tokens": max_new_tokens,
    }

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        truncate=4096,
        seed=seed,
        stop_sequences=["<|end|>", "Question:"],
    )

    stream = client.generate_stream(
        prompt,
        **generate_kwargs,
    )

    output = ""
    for idx, response in enumerate(stream):
        if response.token.special:
            continue
        output += response.token.text
        if idx == 0:
            history.append(" " + output)
        else:
            history[-1] = output

        chat = [
            (wrap_html_code(history[i].strip()), wrap_html_code(history[i + 1].strip()))
            for i in range(0, len(history) - 1, 2)
        ]

        # chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)]

        yield chat, history, user_message, ""

    if HF_TOKEN and do_save:
        try:
            now = datetime.datetime.now()
            current_time = now.strftime("%Y-%m-%d %H:%M:%S")
            print(f"[{current_time}] Pushing prompt and completion to the Hub")
            save_inputs_and_outputs(now, prompt, output, generate_kwargs, model_name)
        except Exception as e:
            print(e)

    return chat, history, user_message, ""

examples = [
    "How can I write a Python function to generate the nth Fibonacci number?",
    "How do I get the current date using shell commands? Explain how it works.",
    "What's the meaning of life?",
    "Write a function in Javascript to reverse words in a given string.",
    "Give the following data {'Name':['Tom', 'Brad', 'Kyle', 'Jerry'], 'Age':[20, 21, 19, 18], 'Height' : [6.1, 5.9, 6.0, 6.1]}. Can you plot one graph with two subplots as columns. The first is a bar graph showing the height of each person. The second is a bargraph showing the age of each person? Draw the graph in seaborn talk mode.",
    "Create a regex to extract dates from logs",
    "How to decode JSON into a typescript object",
    "Write a list into a jsonlines file and save locally",
]

def clear_chat():
    return [], []

def delete_last_turn(chat, history):
    if chat and history:
        chat.pop(-1)
        history.pop(-1)
        history.pop(-1)
    return chat, history

def process_example(args):
    for [x, y] in generate(args):
        pass
    return [x, y]

# Regenerate response
def retry_last_answer(
    selected_model,
    system_message,
    user_message,
    chat,
    history,
    temperature,
    top_k,
    top_p,
    max_new_tokens,
    repetition_penalty,
    do_save,
):
    if chat and history:
        # Removing the previous conversation from chat
        chat.pop(-1)
        # Removing bot response from the history
        history.pop(-1)
        # Setting up a flag to capture a retry
        RETRY_FLAG = True
        # Getting last message from user
        user_message = history[-1]

    yield from generate(
        RETRY_FLAG,
        selected_model,
        system_message,
        user_message,
        chat,
        history,
        temperature,
        top_k,
        top_p,
        max_new_tokens,
        repetition_penalty,
        do_save,
    )

title = """<h1 align="center">⭐ Gradio Playground 💬</h1>"""
custom_css = """
#banner-image {
    display: block;
    margin-left: auto;
    margin-right: auto;
}
#chat-message {
    font-size: 14px;
    min-height: 300px;
}
"""

with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
    gr.HTML(title)

    with gr.Row():
        with gr.Column():
            gr.Image("thumbnail.png", elem_id="banner-image", show_label=False)
        with gr.Column():
            gr.Markdown(
                """
            💻 This demo showcases a series of **[StarChat](https://huggingface.co/models?search=huggingfaceh4/starchat)** language models, which are fine-tuned versions of the StarCoder family to act as helpful coding assistants.  The base model has 16B parameters and was pretrained on one trillion tokens sourced from 80+ programming languages, GitHub issues, Git commits, and Jupyter notebooks (all permissively licensed).
            📝 For more details, check out our [blog post](https://huggingface.co/blog/starchat-alpha).
            ⚠️ **Intended Use**: this app and its [supporting models](https://huggingface.co/models?search=huggingfaceh4/starchat) are provided as educational tools to explain large language model fine-tuning; not to serve as replacement for human expertise.
            ⚠️ **Known Failure Modes**: the alpha and beta version of **StarChat** have not been aligned to human preferences with techniques like RLHF, so they can produce problematic outputs (especially when prompted to do so). Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.  For example, it may produce code that does not compile or that produces incorrect results.  It may also produce code that is vulnerable to security exploits.  We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. For more details on the model's limitations in terms of factuality and biases, see the [model card](https://huggingface.co/HuggingFaceH4/starchat-alpha#bias-risks-and-limitations).
            ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the models. Do **NOT** share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below.
    """
            )

    with gr.Row():
        do_save = gr.Checkbox(
            value=True,
            label="Store data",
            info="You agree to the storage of your prompt and generated text for research and development purposes:",
        )

    with gr.Row():
        selected_model = gr.Radio(choices=model_names, value=model_names[1], label="Select a model")

    with gr.Accordion(label="System Prompt", open=False, elem_id="parameters-accordion"):
        system_message = gr.Textbox(
            elem_id="system-message",
            placeholder="Below is a conversation between a human user and a helpful AI coding assistant.",
            show_label=False,
        )
    with gr.Row():
        with gr.Box():
            output = gr.Markdown()
            chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")

    with gr.Row():
        with gr.Column(scale=3):
            user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input")
            with gr.Row():
                send_button = gr.Button("Send", elem_id="send-btn", visible=True)

                regenerate_button = gr.Button("Regenerate", elem_id="retry-btn", visible=True)

                delete_turn_button = gr.Button("Delete last turn", elem_id="delete-btn", visible=True)

                clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)

            with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.2,
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                top_k = gr.Slider(
                    label="Top-k",
                    value=50,
                    minimum=0.0,
                    maximum=100,
                    step=1,
                    interactive=True,
                    info="Sample from a shortlist of top-k tokens",
                )
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)",
                    value=0.95,
                    minimum=0.0,
                    maximum=1,
                    step=0.05,
                    interactive=True,
                    info="Higher values sample more low-probability tokens",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=512,
                    minimum=0,
                    maximum=1024,
                    step=4,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                repetition_penalty = gr.Slider(
                    label="Repetition Penalty",
                    value=1.2,
                    minimum=0.0,
                    maximum=10,
                    step=0.1,
                    interactive=True,
                    info="The parameter for repetition penalty. 1.0 means no penalty.",
                )
            # with gr.Group(elem_id="share-btn-container"):
            #     community_icon = gr.HTML(community_icon_html, visible=True)
            #     loading_icon = gr.HTML(loading_icon_html, visible=True)
            # share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
            with gr.Row():
                gr.Examples(
                    examples=examples,
                    inputs=[user_message],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )

    history = gr.State([])
    RETRY_FLAG = gr.Checkbox(value=False, visible=False)

    # To clear out "message" input textbox and use this to regenerate message
    last_user_message = gr.State("")

    user_message.submit(
        generate,
        inputs=[
            RETRY_FLAG,
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    send_button.click(
        generate,
        inputs=[
            RETRY_FLAG,
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    regenerate_button.click(
        retry_last_answer,
        inputs=[
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    delete_turn_button.click(delete_last_turn, [chatbot, history], [chatbot, history])
    clear_chat_button.click(clear_chat, outputs=[chatbot, history])
    selected_model.change(clear_chat, outputs=[chatbot, history])
    # share_button.click(None, [], [], _js=share_js)

demo.queue(concurrency_count=16).launch(debug=True)