import os
import json
import subprocess
from threading import Thread

import torch
import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer

from ui import css, PLACEHOLDER

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

MODEL_ID = os.environ.get("MODEL_ID")
CHAT_TEMPLATE = os.environ.get("CHAT_TEMPLATE")
MODEL_NAME = MODEL_ID.split("/")[-1]
CONTEXT_LENGTH = int(os.environ.get("CONTEXT_LENGTH"))
COLOR = os.environ.get("COLOR")
EMOJI = os.environ.get("EMOJI")
DESCRIPTION = os.environ.get("DESCRIPTION")


@spaces.GPU()
def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
    # Format history with a given chat template
    if CHAT_TEMPLATE == "ChatML":
        stop_tokens = ["<|endoftext|>", "<|im_end|>"]
        instruction = '<|im_start|>system\n' + system_prompt + '\n<|im_end|>\n'
        for human, assistant in history:
            instruction += '<|im_start|>user\n' + human + '\n<|im_end|>\n<|im_start|>assistant\n' + assistant
        instruction += '\n<|im_start|>user\n' + message + '\n<|im_end|>\n<|im_start|>assistant\n'
    elif CHAT_TEMPLATE == "Mistral Instruct":
        stop_tokens = ["</s>", "[INST]", "[INST] ", "<s>", "[/INST]", "[/INST] "]
        instruction = '<s>[INST] ' + system_prompt
        for human, assistant in history:
            instruction += human + ' [/INST] ' + assistant + '</s>[INST]'
        instruction += ' ' + message + ' [/INST]'
    else:
        raise Exception("Incorrect chat template, select 'ChatML' or 'Mistral Instruct'")
    print(instruction)
    
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    enc = tokenizer([instruction], return_tensors="pt", padding=True, truncation=True)
    input_ids, attention_mask = enc.input_ids, enc.attention_mask

    if input_ids.shape[1] > CONTEXT_LENGTH:
        input_ids = input_ids[:, -CONTEXT_LENGTH:]

    generate_kwargs = dict(
        {"input_ids": input_ids.to(device), "attention_mask": attention_mask.to(device)},
        streamer=streamer,
        do_sample=True,
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        top_p=top_p
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for new_token in streamer:
        outputs.append(new_token)
        if new_token in stop_tokens:
            break
        yield "".join(outputs)


# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    # bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    quantization_config=quantization_config,
    attn_implementation="flash_attention_2",
)

# Create Gradio interface
gr.ChatInterface(
    predict,
    title=EMOJI + " " + MODEL_NAME,
    description=DESCRIPTION,
    examples=[
        ["Can you solve the equation 2x + 3 = 11 for x?"],
        ["Write an epic poem about Ancient Rome."],
        ["Who was the first person to walk on the Moon?"],
        ["Use a list comprehension to create a list of squares for numbers from 1 to 10."],
        ["Recommend some popular science fiction books."],
        ["Can you write a short story about a time-traveling detective?"]
    ],
    additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
    additional_inputs=[
        gr.Textbox("Perform the task to the best of your ability.", label="System prompt"),
        gr.Slider(0, 1, 0.8, label="Temperature"),
        gr.Slider(128, 4096, 1024, label="Max new tokens"),
        gr.Slider(1, 80, 40, label="Top K sampling"),
        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
        gr.Slider(0, 1, 0.95, label="Top P sampling"),
    ],
    theme=gr.themes.Soft(primary_hue=COLOR).set(
        background_fill_primary="#020417",
        background_fill_secondary_dark="#020417",
        body_background_fill_dark="#020417",
        block_background_fill_dark="#020417",
        block_border_width="1px",
        block_title_background_fill_dark="#15172c",
        input_background_fill_dark="#15172c",
        button_secondary_background_fill_dark="#15172c",
        border_color_accent_dark="#15172c",
        border_color_primary_dark="#15172c",
        color_accent_soft_dark="#10132c",
        code_background_fill_dark="#15172c",
    ),
    css=css,
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    chatbot=gr.Chatbot(
        scale=1,
        placeholder=PLACEHOLDER,
        show_copy_button=True
    )
).queue().launch()