import logging
from threading import Thread
from time import perf_counter
import time

from PIL import Image

try:
    from transformers import (
        AutoModel,
        AutoProcessor,
        TextIteratorStreamer,
    )
    import torch

    from keye_vl_utils import process_vision_info

except ModuleNotFoundError as e:
    logging.error(f"Exception: {e}")
    logging.error(
        "In order to use Keye VLM, you need to `pip install achatbot[llm_transformers_manual_vision_keye]`"
    )
    raise Exception(f"Missing module: {e}")


from achatbot.common.utils.helper import get_device, print_model_params
from achatbot.common.random import set_all_random_seed
from achatbot.common.chat_history import ChatHistory
from achatbot.common.session import Session
from achatbot.types.speech.language import TO_LLM_LANGUAGE
from achatbot.types.llm.transformers import TransformersLMArgs
from .base import TransformersBaseLLM


class TransformersManualVisionKeye(TransformersBaseLLM):
    TAG = "llm_transformers_manual_vision_keye"

    def __init__(self, **args) -> None:
        self.args = TransformersLMArgs(**args)
        gpu_prop = torch.cuda.get_device_properties("cuda")

        if self.args.lm_device_map:
            self._model = AutoModel.from_pretrained(
                self.args.lm_model_name_or_path,
                torch_dtype=torch.bfloat16,
                #!NOTE: https://github.com/huggingface/transformers/issues/20896
                # device_map for multi cpu/gpu with accelerate
                device_map=self.args.lm_device_map,
                attn_implementation="flash_attention_2" if gpu_prop.major >= 8 else None,
                trust_remote_code=True,
            ).eval()
        else:
            self._model = (
                AutoModel.from_pretrained(
                    self.args.lm_model_name_or_path,
                    torch_dtype=torch.bfloat16,
                    attn_implementation="flash_attention_2" if gpu_prop.major >= 8 else None,
                    trust_remote_code=True,
                )
                .eval()
                .to(self.args.lm_device)
            )

        logging.info(f"TransformersLMArgs: {self.args}")
        print_model_params(self._model, self.TAG)
        self._tokenizer = AutoProcessor.from_pretrained(
            self.args.lm_model_name_or_path,
            use_fast=True,
            trust_remote_code=True,
        )

        self._chat_history = ChatHistory(self.args.chat_history_size)
        if self.args.init_chat_role and self.args.init_chat_prompt:
            self._chat_history.init(
                {
                    "role": self.args.init_chat_role,
                    "content": [{"type": "text", "text": self.args.init_chat_prompt}],
                }
            )

        self.warmup()

    def warmup(self):
        if self.args.warmup_steps <= 0:
            return
        dummy_input_text = self.args.warmup_prompt
        dummy_pil_image = Image.new("RGB", (100, 100), color="white")
        dummy_msgs = [
            {
                "role": self.args.user_role,
                "content": [
                    {"type": "text", "text": dummy_input_text},
                    {"type": "image", "image": dummy_pil_image},
                ],
            }
        ]

        inputs = self._tokenizer.apply_chat_template(
            dummy_msgs,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
        ).to(
            self._model.device,
            dtype=torch.bfloat16,
        )

        streamer = TextIteratorStreamer(self._tokenizer, skip_prompt=True, skip_special_tokens=True)

        warmup_gen_kwargs = dict(
            **inputs,
            streamer=streamer,
            do_sample=True if self.args.lm_gen_temperature > 0 else False,
            temperature=self.args.lm_gen_temperature,
            top_k=self.args.lm_gen_top_k,
            top_p=self.args.lm_gen_top_p,
            repetition_penalty=self.args.lm_gen_repetition_penalty,
            min_new_tokens=1,
            max_new_tokens=128,
        )

        self._warmup(
            target=self._model.generate,
            kwargs=warmup_gen_kwargs,
            streamer=streamer,
        )

    @torch.inference_mode()
    def generate(self, session: Session, **kwargs):
        seed = kwargs.get("seed", self.args.lm_gen_seed)
        set_all_random_seed(seed)

        prompt = session.ctx.state["prompt"]
        assert len(prompt) > 0
        message = {"role": self.args.user_role, "content": prompt}
        self._chat_history.append(message)
        chat_history = self._chat_history.to_list()
        content = chat_history[-1]["content"]
        for item in content:
            if item.get("type") == "text":
                if self.args.lm_gen_thinking is True:
                    item["text"] += "/think"
                if self.args.lm_gen_thinking is False:
                    item["text"] += "/no_think"
        # logging.info(f"chat_history:{chat_history}")

        inputs = self._tokenizer.apply_chat_template(
            chat_history,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
        ).to(
            self._model.device,
            dtype=torch.bfloat16,
        )

        streamer = TextIteratorStreamer(self._tokenizer, skip_prompt=True, skip_special_tokens=True)

        generation_kwargs = dict(
            **inputs,
            streamer=streamer,
            do_sample=True
            if kwargs.get("temperature", self.args.lm_gen_temperature) > 0
            else False,
            temperature=kwargs.get("temperature", self.args.lm_gen_temperature),
            top_k=kwargs.get("top_k", self.args.lm_gen_top_k),
            top_p=kwargs.get("top_p", self.args.lm_gen_top_p),
            repetition_penalty=kwargs.get(
                "repetition_penalty", self.args.lm_gen_repetition_penalty
            ),
            min_new_tokens=kwargs.get("min_new_tokens", self.args.lm_gen_min_new_tokens),
            max_new_tokens=kwargs.get("max_new_tokens", self.args.lm_gen_max_new_tokens),
            use_cache=True,
        )
        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
        thread.start()

        generated_text = ""
        start = perf_counter()
        times = []
        is_output_think = self.args.lm_gen_think_output
        is_analysis = False
        is_thinking = False
        is_answer = True
        analysis_text = ""
        think_text = ""
        for new_text in streamer:
            times.append(perf_counter() - start)
            if "<analysis>" in new_text:
                is_analysis = True
                analysis_text = ""
                analysis_text += new_text
                continue
            if "</analysis>" in new_text:
                is_analysis = False
                analysis_text += new_text
                logging.info(f"{analysis_text=}")
                new_text = new_text.replace("</analysis>", "")
                analysis_text = ""
            if is_analysis is True:
                analysis_text += new_text
                continue

            if "<think>" in new_text:
                yield "思考中，请稍等。"
                is_thinking = True
                think_text = ""
                think_text += new_text
                continue
            if "</think>" in new_text:
                is_thinking = False
                think_text += new_text
                logging.info(f"{think_text=}")
                think_text = ""
                new_text = new_text.replace("</think>", "")
            if is_thinking is True:
                think_text += new_text
                if is_output_think is True:
                    generated_text += new_text
                    yield new_text
                else:
                    yield None
                continue

            if "<answer>" in new_text:
                is_answer = True
                new_text = new_text.replace("<answer>", "")
            if "</answer>" in new_text:
                is_answer = False
                continue

            if is_answer is True:
                generated_text += new_text
                yield new_text
            start = perf_counter()
        yield "."  # end the sentence for downstream process sentence, e.g.: tts
        logging.info(f"{generated_text=} TTFT: {times[0]:.4f}s total time: {sum(times):.4f}s")
        torch.cuda.empty_cache()
        self._chat_history.append(
            {"role": "assistant", "content": [{"type": "text", "text": generated_text}]}
        )
