import itertools

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"device: {device}")

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b-instruction-sft", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b-instruction-sft", device_map="auto", torch_dtype=torch.float16)
model = model.to(device)


@torch.no_grad()
def inference_func(prompt, max_new_tokens=128, temperature=0.7):
  token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
  output_ids = model.generate(
      token_ids.to(model.device),
      do_sample=True,
      max_new_tokens=max_new_tokens,
      temperature=temperature,
      pad_token_id=tokenizer.pad_token_id,
      bos_token_id=tokenizer.bos_token_id,
      eos_token_id=tokenizer.eos_token_id
  )
  output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True)
  output = output.replace("<NL>", "\n")
  return output


def make_prompt(message, chat_history, max_context_size: int = 10):
  contexts = chat_history + [[message, ""]]
  contexts = list(itertools.chain.from_iterable(contexts))
  if max_context_size > 0:
    context_size = max_context_size - 1
  else:
    context_size = 100000
  contexts = contexts[-context_size:]
  prompt = []
  for idx, context in enumerate(reversed(contexts)):
    if idx % 2 == 0:
      prompt = [f"システム: {context}"] + prompt
    else:
      prompt = [f"ユーザー: {context}"] + prompt    
  prompt = "<NL>".join(prompt)
  return prompt

def interact_func(message, chat_history, max_context_size, max_new_tokens, temperature):
  prompt = make_prompt(message, chat_history, max_context_size)
  print(f"prompt: {prompt}")
  generated = inference_func(prompt, max_new_tokens, temperature)
  print(f"generated: {generated}")
  chat_history.append((message, generated))
  return "", chat_history


with gr.Blocks() as demo:
  with gr.Accordion("Configs", open=False):
      # max_context_size = the number of turns * 2
      max_context_size = gr.Number(value=10, label="max_context_size", precision=0)
      max_new_tokens = gr.Number(value=128, label="max_new_tokens", precision=0)
      temperature = gr.Slider(0.0, 2.0, value=0.7, step=0.1, label="temperature")
  chatbot = gr.Chatbot()
  msg = gr.Textbox()
  clear = gr.Button("Clear")
  msg.submit(interact_func, [msg, chatbot, max_context_size, max_new_tokens, temperature], [msg, chatbot])
  clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch(debug=True)