import json
from pathlib import Path

from intel_npu_acceleration_library import NPUModelForCausalLM, int4
from intel_npu_acceleration_library.compiler import CompilerConfig
from transformers import AutoTokenizer

from repository.repository_abc import Repository, Model


class IntelNpuRepository(Repository):
    def __init__(self, model_info: Model, system_msg: str = None, log_to_file: Path = None):
        self.model_info: Model = model_info
        self.message_history: list[dict[str, str]] = []
        self.set_message_for_role(self.model_info.roles.system_role, system_msg)
        self.model = None
        self.tokenizer = None
        self.terminators = None
        self.log_to_file = log_to_file

    def get_model_info(self) -> Model:
        return self.model_info

    def get_message_history(self) -> list[dict[str, str]]:
        return self.message_history

    def init(self):
        compiler_conf = CompilerConfig(dtype=int4)
        self.model = NPUModelForCausalLM.from_pretrained(self.model_info.name, use_cache=True, config=compiler_conf,
                                                          export=True, temperature=0).eval()
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_info.name)
        self.terminators = [self.tokenizer.eos_token_id, self.tokenizer.convert_tokens_to_ids("<|eot_id|>")]

    def send_prompt(self, prompt: str, add_to_history: bool = True) -> dict[str, str]:
        pass
        print("prompt to be sent: " + prompt)
        user_prompt = {"role": self.model_info.roles.user_role, "content": prompt}
        if self.log_to_file:
            with open(self.log_to_file, "a+") as log_file:
                log_file.write(json.dumps(user_prompt, indent=2))
                log_file.write("\n")
        self.get_message_history().append(user_prompt)
        input_ids = (self.tokenizer.apply_chat_template(self.get_message_history(), add_generation_prompt=True,
                                                        return_tensors="pt")
                     .to(self.model.device))
        outputs = self.model.generate(input_ids, eos_token_id=self.terminators, do_sample=True, max_new_tokens=2000, cache_position=None)
        generated_token_array = outputs[0][len(input_ids[0]):]
        generated_tokens = "".join(self.tokenizer.batch_decode(generated_token_array, skip_special_tokens=True))
        answer = {"role": self.get_model_info().roles.ai_role, "content": generated_tokens}
        if self.log_to_file:
            with open(self.log_to_file, "a+") as log_file:
                log_file.write(json.dumps(answer, indent=2))
                log_file.write("\n")
        if add_to_history:
            self.message_history.append(answer)
        else:
            self.message_history.pop()
        return answer