#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 Statistics and Machine Learning Research Group at HKUST. All rights reserved.
"""A simple shell chatbot implemented with lmflow APIs.
"""
import logging
import json
import sys
import warnings
import gradio as gr
from dataclasses import dataclass, field
from transformers import HfArgumentParser
from typing import Optional
import torch
from lmflow.datasets.dataset import Dataset
from lmflow.pipeline.auto_pipeline import AutoPipeline
from lmflow.models.auto_model import AutoModel
from lmflow.args import ModelArguments, DatasetArguments, AutoArguments

MAX_BOXES = 20

logging.disable(logging.ERROR)
warnings.filterwarnings("ignore")

title = """
<h1 align="center">LMFlow-CHAT</h1>
<link rel="stylesheet" href="/path/to/styles/default.min.css">
<script src="/path/to/highlight.min.js"></script>
<script>hljs.highlightAll();</script>

<img src="https://optimalscale.github.io/LMFlow/_static/logo.png" alt="LMFlow" style="width: 30%; min-width: 60px; display: block; margin: auto; background-color: transparent;">

<p>LMFlow is in extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.</p>

<p>We have thoroughly tested this toolkit and are pleased to make it available under <a class="reference external" href="https://github.com/OptimalScale/LMFlow">Github</a>.</p>
"""
css = """
#user {                                                                         
    float: right;
    position:relative;
    right:5px;
    width:auto;
    min-height:32px;
    max-width: 60%
    line-height: 32px;
    padding: 2px 8px;
    font-size: 14px;
    background:	#9DC284;
    border-radius:5px; 
    margin:10px 0px;
}
                                             
#chatbot {                                                                      
    float: left;
    position:relative;
    right:5px;
    width:auto;
    min-height:32px;
    max-width: 60%
    line-height: 32px;
    padding: 2px 8px;
    font-size: 14px;
    background:#7BA7D7;
    border-radius:5px; 
    margin:10px 0px;
}
"""


@dataclass
class ChatbotArguments:
    prompt_structure: Optional[str] = field(
        default="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: {input_text}###Assistant:",
        metadata={
            "help": "prompt structure given user's input text"
        },
    )
    end_string: Optional[str] = field(
        default="#",
        metadata={
            "help": "end string mark of the chatbot's output"
        },
    )
    max_new_tokens: Optional[int] = field(
        default=200,
        metadata={
            "help": "maximum number of generated tokens"
        },
    )
    temperature: Optional[float] = field(
        default=0.7,
        metadata={
            "help": "higher this value, more random the model output"
        },
    )


def main():
    pipeline_name = "inferencer"
    PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name)

    parser = HfArgumentParser((
        ModelArguments,
        PipelineArguments,
        ChatbotArguments,
    ))
    model_args, pipeline_args, chatbot_args = (
        parser.parse_args_into_dataclasses()
    )
    model_args.model_name_or_path = "pinkmanlove/llama-7b-hf"
    model_args.lora_model_path = "./robin-7b"
    pipeline_args.deepspeed = "configs/ds_config_chatbot.json"
    model_args.torch_dtype = "float16"

    with open (pipeline_args.deepspeed, "r") as f:
        ds_config = json.load(f)

    model = AutoModel.get_model(
        model_args,
        tune_strategy='none',
        ds_config=ds_config,
        device=pipeline_args.device,
    )

    # We don't need input data, we will read interactively from stdin
    data_args = DatasetArguments(dataset_path=None)
    dataset = Dataset(data_args)

    inferencer = AutoPipeline.get_pipeline(
        pipeline_name=pipeline_name,
        model_args=model_args,
        data_args=data_args,
        pipeline_args=pipeline_args,
    )

    # Chats
    model_name = model_args.model_name_or_path
    if model_args.lora_model_path is not None:
        model_name += f" + {model_args.lora_model_path}"


    # context = (
    #     "You are a helpful assistant who follows the given instructions"
    #     " unconditionally."
    # )


    end_string = chatbot_args.end_string
    prompt_structure = chatbot_args.prompt_structure


    token_per_step = 4


    def chat_stream( context, query: str, history= None, **kwargs):
        if history is None:
            history = []

        print_index = 0
        context += prompt_structure.format(input_text=query)
        context = context[-model.get_max_length():] 
        input_dataset = dataset.from_dict({
            "type": "text_only",
            "instances": [ { "text": context } ]
        })
        for response, flag_break in inferencer.stream_inference(context=context, model=model, max_new_tokens=chatbot_args.max_new_tokens, 
                                        token_per_step=token_per_step, temperature=chatbot_args.temperature,
                                        end_string=end_string, input_dataset=input_dataset):
            delta = response[print_index:]
            seq = response
            print_index = len(response)
            
            yield delta, history + [(query, seq)]
            if flag_break:
                context += response + "\n"
                break




    def predict(input, history=None): 
        try:
            global context
            context = ""
        except SyntaxError:
            pass

        if history is None:
            history = []
        for response, history in chat_stream(context, input, history):
            updates = []
            for query, response in history:
                updates.append(gr.update(visible=True, value="" + query))
                updates.append(gr.update(visible=True, value="" + response))
            if len(updates) < MAX_BOXES:
                updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
            yield [history] + updates





    with gr.Blocks(css=css) as demo:
        gr.HTML(title)
        state = gr.State([])
        text_boxes = []
        for i in range(MAX_BOXES):
            if i % 2 == 0:
                text_boxes.append(gr.Markdown(visible=False, label="Q:", elem_id="user"))
            else:
                text_boxes.append(gr.Markdown(visible=False, label="A:", elem_id="chatbot"))

        txt = gr.Textbox(
            show_label=False,
            placeholder="Enter text and press send.",
        )
        button = gr.Button("Send")

        button.click(predict, [txt, state], [state] + text_boxes)
        demo.queue().launch()




if __name__ == "__main__":
    main()