import argparse
import os
import json
import re
from sentence_transformers import SentenceTransformer, CrossEncoder
import hnswlib
from typing import Iterator

import gradio as gr
import pandas as pd
import torch

from easyllm.clients import huggingface

from agent import get_input_token_length

huggingface.prompt_builder = "llama2"
huggingface.api_key = os.environ["HUGGINGFACE_TOKEN"]
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4000
EMBED_DIM = 1024
K = 10
EF = 100
SEARCH_INDEX = "search_index.bin"
DOCUMENT_DATASET = "chunked_data.parquet"
COSINE_THRESHOLD = 0.7

torch_device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on device:", torch_device)
print("CPU threads:", torch.get_num_threads())

biencoder = SentenceTransformer("intfloat/e5-large-v2", device=torch_device)
cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2", max_length=512, device=torch_device)


def create_qa_prompt(query, relevant_chunks):
    stuffed_context = " ".join(relevant_chunks)
    return f"""\
Use the following pieces of context given in to answer the question at the end. \
If you don't know the answer, just say that you don't know, don't try to make up an answer. \
Keep the answer short and succinct.
        
Context: {stuffed_context}
Question: {query}
Helpful Answer: \
"""


def create_condense_question_prompt(question, chat_history):
    return f"""\
Given the following conversation and a follow up question, \
rephrase the follow up question to be a standalone question in its original language. \
Output the json object with single field `question` and value being the rephrased standalone question. 
Only output json object and nothing else.

Chat History:
{chat_history}
Follow Up Input: {question}
"""


# https://www.philschmid.de/llama-2#how-to-prompt-llama-2-chat
def get_completion(
    prompt,
    system_prompt=None,
    model="meta-llama/Llama-2-70b-chat-hf",
    max_new_tokens=1024,
    temperature=0.2,
    top_p=0.95,
    top_k=50,
    stream=False,
    debug=False,
):
    if temperature < 1e-2:
        temperature = 1e-2
    messages = []
    if system_prompt is not None:
        messages.append({"role": "system", "content": system_prompt})
    messages.append({"role": "user", "content": prompt})
    response = huggingface.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature,  # this is the degree of randomness of the model's output
        max_tokens=max_new_tokens,  # this is the number of new tokens being generated
        top_p=top_p,
        top_k=top_k,
        stream=stream,
        debug=debug,
    )
    return response["choices"][0]["message"]["content"] if not stream else response


# load the index for the PEFT docs
def load_hnsw_index(index_file):
    # Load the HNSW index from the specified file
    index = hnswlib.Index(space="ip", dim=EMBED_DIM)
    index.load_index(index_file)
    return index


def create_query_embedding(query):
    # Encode the query to get its embedding
    embedding = biencoder.encode([query], normalize_embeddings=True)[0]
    return embedding


def find_nearest_neighbors(query_embedding):
    search_index.set_ef(EF)
    # Find the k-nearest neighbors for the query embedding
    labels, distances = search_index.knn_query(query_embedding, k=K)
    labels = [label for label, distance in zip(labels[0], distances[0]) if (1 - distance) >= COSINE_THRESHOLD]
    relevant_chunks = data_df.iloc[labels]["chunk_content"].tolist()
    return relevant_chunks


def rerank_chunks_with_cross_encoder(query, chunks):
    # Create a list of tuples, each containing a query-chunk pair
    pairs = [(query, chunk) for chunk in chunks]

    # Get scores for each query-chunk pair using the cross encoder
    scores = cross_encoder.predict(pairs)

    # Sort the chunks based on their scores in descending order
    sorted_chunks = [chunk for _, chunk in sorted(zip(scores, chunks), reverse=True)]

    return sorted_chunks


def generate_condensed_query(query, history):
    chat_history = ""
    for turn in history:
        chat_history += f"Human: {turn[0]}\n"
        chat_history += f"Assistant: {turn[1]}\n"

    condense_question_prompt = create_condense_question_prompt(query, chat_history)
    condensed_question = json.loads(get_completion(condense_question_prompt, max_new_tokens=64, temperature=0))
    return condensed_question["question"]


DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. 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.

If 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 = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4000

DESCRIPTION = """
# PEFT Docs QA Chatbot 🤗
"""

LICENSE = """
<p/>

---
As a derivate work of [Llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat) by Meta,
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-70b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-70b-chat/blob/main/USE_POLICY.md).
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running 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 wrap_html_code(text):
    pattern = r"<.*?>"
    matches = re.findall(pattern, text)
    if len(matches) > 0:
        return f"```{text}```"
    else:
        return text


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]
    if len(history) > 0:
        condensed_query = generate_condensed_query(message, history)
        print(f"{condensed_query=}")
    else:
        condensed_query = message
    query_embedding = create_query_embedding(condensed_query)
    relevant_chunks = find_nearest_neighbors(query_embedding)
    reranked_relevant_chunks = rerank_chunks_with_cross_encoder(condensed_query, relevant_chunks)
    qa_prompt = create_qa_prompt(condensed_query, reranked_relevant_chunks)
    print(f"{qa_prompt=}")
    generator = get_completion(
        qa_prompt,
        system_prompt=system_prompt,
        stream=True,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
    )

    output = ""
    for idx, response in enumerate(generator):
        token = response["choices"][0]["delta"].get("content", "") or ""
        output += token
        if idx == 0:
            history.append((message, output))
        else:
            history[-1] = (message, output)

        history = [
            (wrap_html_code(history[i][0].strip()), wrap_html_code(history[i][1].strip()))
            for i in range(0, len(history))
        ]
        yield history

    return history


def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 0.2, 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."
        )


search_index = load_hnsw_index(SEARCH_INDEX)
data_df = pd.read_parquet(DOCUMENT_DATASET).reset_index()
with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)

    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=0.2,
        )
        top_p = gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.95,
        )
        top_k = gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        )

    gr.Examples(
        examples=[
            "What is 🤗 PEFT?",
            "How do I create a LoraConfig?",
            "What are the different tuners supported?",
            "How do I use LoRA with custom models?",
            "What are the different real-world applications that I can use PEFT for?",
        ],
        inputs=textbox,
        outputs=[textbox, chatbot],
        # fn=process_example,
        cache_examples=False,
    )

    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(debug=True, share=True)


# if __name__ == "__main__":
#     parser = argparse.ArgumentParser(description="Script to create and use an HNSW index for similarity search.")
#     parser.add_argument("--input_file", help="Input file containing text chunks in a Parquet format")
#     parser.add_argument("--index_file", help="HNSW index file with .bin extension")
#     args = parser.parse_args()

#     data_df = pd.read_parquet(args.input_file).reset_index()
#     search_index = load_hnsw_index(args.index_file)
#     main()