import os
from threading import Thread
from typing import Iterator, List, Dict, Any

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

MAX_MAX_NEW_TOKENS = 1024
DEFAULT_MAX_NEW_TOKENS = 256
MAX_INPUT_TOKEN_LENGTH = 512

DESCRIPTION = """\
# Buzz-3B-Small
This Space demonstrates Buzz-3b-small-v0.6.3.
"""

LICENSE = """
<p/>
---
Chat with Buzz-small!
only 3b,  this demo runs on the fp8 weights of the model in pytorch format, its brains are probably significantly damaged, converting to cpp soon, dont worry!
"""

device = 0 if torch.cuda.is_available() else -1

model_id = "H-D-T/Buzz-3b-small-v0.6.3"
chatbot = pipeline(model=model_id, device=device, task="conversational",model_kwargs={"load_in_8bit": True})

tokenizer = AutoTokenizer.from_pretrained(model_id)
bos_token = "<|begin_of_text|>"
eos_token = "<|eot_id|>"
start_header_id = "<|start_header_id|>"
end_header_id = "<|end_header_id|>"

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.pad_token_id = tokenizer.eos_token_id
    model.config.pad_token_id = tokenizer.eos_token_id

def format_conversation(chat_history: List[Dict[str, str]], add_generation_prompt=False) -> str:
    """
    Formats the chat history according to the model's chat template.
    """
    formatted_history = []
    for i, message in enumerate(chat_history):
        role, content = message["role"], message["content"]
        formatted_message = f"{start_header_id}{role}{end_header_id}\n\n{content.strip()}{eos_token}"
        if i == 0:
            formatted_message = bos_token + formatted_message
        formatted_history.append(formatted_message)
    
    if add_generation_prompt:
        formatted_history.append(f"{start_header_id}assistant{end_header_id}\n\n")
    else:
        formatted_history.append(eos_token)
    
    return "".join(formatted_history)

@spaces.GPU
def generate(
    message: str,
    chat_history: List[Dict[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.4,
) -> Iterator[str]:
    
    chat_history.append({"role": "user", "content": message})
    chat_context = format_conversation(chat_history, add_generation_prompt=True)
    input_ids = tokenizer([chat_context], return_tensors="pt").input_ids
    
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        pad_token_id=tokenizer.eos_token_id,
        repetition_penalty=repetition_penalty,
        no_repeat_ngram_size=5,
        early_stopping=False,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.4,
        ),
    ],
    stop_btn=None,
    examples=[
        ["A recipe for a chocolate cake:"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["Question: What is the capital of France?\nAnswer:"],
        ["Question: I am very tired, what should I do?\nAnswer:"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    gr.Markdown(LICENSE)

if __name__ == "__main__":
    demo.queue(max_size=20).launch()