import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import colorful as cf

cf.use_true_colors()
cf.use_style('monokai')
class CadetTinyAgent:
    def __init__(self):
        print(cf.bold | cf.purple("Waking up Cadet-Tiny..."))
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained("t5-small", model_max_length=512)
        self.model = AutoModelForSeq2SeqLM.from_pretrained("ToddGoldfarb/Cadet-Tiny", low_cpu_mem_usage=True).to(self.device)
        self.conversation_history = ""

    def observe(self, observation):
        self.conversation_history = self.conversation_history + observation
        # The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.
        if len(self.conversation_history) > 400:
            self.conversation_history = self.conversation_history[112:]

    def set_input(self, situation_narrative="", role_instruction=""):
        input_text = "dialogue: "

        if situation_narrative != "":
            input_text = input_text + situation_narrative

        if role_instruction != "":
            input_text = input_text + " <SEP> " + role_instruction

        input_text = input_text + " <TURN> " + self.conversation_history

        # Uncomment the line below to see what is fed to the model.
        # print(input_text)

        return input_text

    def generate(self, situation_narrative, role_instruction, user_response):
        user_response = user_response + " <TURN> "
        self.observe(user_response)

        input_text = self.set_input(situation_narrative, role_instruction)

        inputs = self.tokenizer([input_text], return_tensors="pt").to(self.device)
        
        # I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.
        outputs = self.model.generate(inputs["input_ids"], max_new_tokens=512, temperature=0.75, top_p=.95,
                                      do_sample=True)
        cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
        added_turn = cadet_response + " <TURN> "
        self.observe(added_turn)

        return cadet_response

    def reset_history(self):
        self.conversation_history = []

    def run(self):
        def get_valid_input(prompt, default):
            while True:
                user_input = input(prompt)
                if user_input in ["Y", "N", "y", "n"]:
                    return user_input
                if user_input == "":
                    return default

        while True:
            continue_chat = ""

            # MODIFY THESE STRINGS TO YOUR LIKING :)
            situation_narrative = "Imagine you are Cadet-Tiny talking to ???."
            role_instruction = "You are Cadet-Tiny, and you are talking to ???."

            self.chat(situation_narrative, role_instruction)
            continue_chat = get_valid_input(cf.purple("Start a new conversation with new setup? [Y/N]:"), "Y")
            if continue_chat in ["N", "n"]:
                break

        print(cf.blue("CT: See you!"))

    def chat(self, situation_narrative, role_instruction):
        print(cf.green(
            "Cadet-Tiny is running! Input [RESET] to reset the conversation history and [END] to end the conversation."))
        while True:
            user_input = input("You: ")
            if user_input == "[RESET]":
                self.reset_history()
                print(cf.green("[Conversation history cleared. Chat with Cadet-Tiny!]"))
                continue
            if user_input == "[END]":
                break
            response = self.generate(situation_narrative, role_instruction, user_input)
            print(cf.blue("CT: " + response))


def main():
    print(cf.bold | cf.blue("LOADING MODEL"))

    CadetTiny = CadetTinyAgent()
    CadetTiny.run()


if __name__ == '__main__':
    main()

@article{kim2022soda,
    title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
    author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
    journal={ArXiv},
    year={2022},
    volume={abs/2212.10465}
}