import torch
import logging
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

# MODEL_NAME = "IlyaGusev/gigasaiga_lora"
# MODEL_NAME = "evilfreelancer/ruGPT-3.5-13B-lora"
# MODEL_NAME = "evilfreelancer/saiga_mistral_7b_128k_lora"
# MODEL_NAME = "./yarn_mistral_7b_128k"
MODEL_NAME = "./yarn_mistral_7b_128k_yakovlev"

DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
# DEFAULT_SYSTEM_PROMPT = """
# Ты — Saiga 2, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.
# """
DEFAULT_SYSTEM_PROMPT = """
Ты - Иван Яковлевич Яковлев. Ты был просветителем, православным миссионером, российский педагогом, организатором народных школ, инспектором (руководителем) чувашских школ Казанского учебного округа (1875-1903) и созданной им Симбирской чувашской учительской школы (1868-1919). Я создал современный чувашский алфавит (1870-е годы) и учебники чувашского и русского языков для чувашей, был писателем, переводчиком, фольклористом. Действительный статский советник (1904).
"""


class Conversation:
    def __init__(
            self,
            message_template=DEFAULT_MESSAGE_TEMPLATE,
            system_prompt=DEFAULT_SYSTEM_PROMPT,
            start_token_id=2,
            # Bot token may be a list or single int
            bot_token_id=10093,  # yarn_mistral_7b_128k
            # bot_token_id=46787,  # rugpt35_13b
            # int (amount of questions and answers) or None (unlimited)
            history_limit=None,
    ):
        self.logger = logging.getLogger('Conversation')
        self.message_template = message_template
        self.start_token_id = start_token_id
        self.bot_token_id = bot_token_id
        self.history_limit = history_limit
        self.messages = [{
            "role": "system",
            "content": system_prompt
        }]

    def get_start_token_id(self):
        return self.start_token_id

    def get_bot_token_id(self):
        return self.bot_token_id

    def add_message(self, role, message):
        self.messages.append({
            "role": role,
            "content": message
        })
        self.trim_history()

    def add_user_message(self, message):
        self.add_message("user", message)

    def add_bot_message(self, message):
        self.add_message("assistant", message)

    def trim_history(self):
        if self.history_limit is not None and len(self.messages) > self.history_limit + 1:
            overflow = len(self.messages) - (self.history_limit + 1)
            self.messages = [self.messages[0]] + self.messages[overflow + 1:]  # remove old messages except system

    def get_prompt(self, tokenizer):
        final_text = ""
        # print(self.messages)
        for message in self.messages:
            message_text = self.message_template.format(**message)
            final_text += message_text

        # Bot token id may be an array
        if isinstance(self.bot_token_id, (list, tuple)):
            final_text += tokenizer.decode([self.start_token_id] + self.bot_token_id)
        else:
            final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])

        return final_text.strip()


def generate(model, tokenizer, prompt, generation_config):
    data = tokenizer(prompt, return_tensors="pt")
    data = {k: v.to(model.device) for k, v in data.items()}
    output_ids = model.generate(
        **data,
        max_length=10240,
        generation_config=generation_config
    )[0]
    output_ids = output_ids[len(data["input_ids"][0]):]
    output = tokenizer.decode(output_ids, skip_special_tokens=True)
    return output.strip()


config = PeftConfig.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
    use_flash_attention_2=True,
)
model = PeftModel.from_pretrained(
    model,
    MODEL_NAME,
    torch_dtype=torch.float16
)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
print(generation_config)

conversation = Conversation()
while True:
    user_message = input("User: ")

    # Reset chat command
    if user_message.strip() == "/reset":
        conversation = Conversation()
        print("History reset completed!")
        continue

    # Skip empty messages from user
    if user_message.strip() == "":
        continue

    conversation.add_user_message(user_message)
    prompt = conversation.get_prompt(tokenizer)
    output = generate(
        model=model,
        tokenizer=tokenizer,
        prompt=prompt,
        generation_config=generation_config
    )
    conversation.add_bot_message(output)
    print("Bot:", output)
    print()
    print("==============================")
    print()