from dataclasses import dataclass
from typing import Dict
from typing import Iterable, Optional
import time
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from torch import nn
import whisper
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


from funasr.register import tables


@tables.register("model_classes", "Qwen/Qwen-Audio")
@tables.register("model_classes", "Qwen-Audio")
@tables.register("model_classes", "Qwen/QwenAudio")
@tables.register("model_classes", "QwenAudio")
@tables.register("model_classes", "QwenAudioWarp")
class QwenAudioWarp(nn.Module):
    """
    Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
    https://arxiv.org/abs/2311.07919
    Modified from https://github.com/QwenLM/Qwen-Audio
    """

    def __init__(self, *args, **kwargs):
        super().__init__()
        from transformers import AutoModelForCausalLM, AutoTokenizer
        from transformers.generation import GenerationConfig

        model_or_path = kwargs.get("model_path", "QwenAudio")
        model = AutoModelForCausalLM.from_pretrained(
            model_or_path, device_map="cpu", trust_remote_code=True
        )
        tokenizer = AutoTokenizer.from_pretrained(model_or_path, trust_remote_code=True)

        self.model = model
        self.tokenizer = tokenizer

    def forward(
        self,
    ):
        pass

    def inference(
        self,
        data_in,
        data_lengths=None,
        key: list = None,
        tokenizer=None,
        frontend=None,
        **kwargs,
    ):
        if kwargs.get("batch_size", 1) > 1:
            raise NotImplementedError("batch decoding is not implemented")

        meta_data = {}
        # meta_data["batch_data_time"] = -1
        prompt = kwargs.get(
            "prompt", "<|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
        )
        query = f"<audio>{data_in[0]}</audio>{prompt}"
        audio_info = self.tokenizer.process_audio(query)
        inputs = self.tokenizer(query, return_tensors="pt", audio_info=audio_info)
        inputs = inputs.to(self.model.device)
        pred = self.model.generate(**inputs, audio_info=audio_info)
        response = self.tokenizer.decode(
            pred.cpu()[0], skip_special_tokens=False, audio_info=audio_info
        )

        results = []
        result_i = {"key": key[0], "text": response}

        results.append(result_i)

        return results, meta_data


@tables.register("model_classes", "Qwen/Qwen-Audio-Chat")
@tables.register("model_classes", "Qwen/QwenAudioChat")
@tables.register("model_classes", "Qwen-Audio-Chat")
@tables.register("model_classes", "QwenAudioChat")
@tables.register("model_classes", "QwenAudioChatWarp")
class QwenAudioChatWarp(nn.Module):
    def __init__(self, *args, **kwargs):
        """
        Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
        https://arxiv.org/abs/2311.07919
        Modified from https://github.com/QwenLM/Qwen-Audio
        """
        super().__init__()
        from transformers import AutoModelForCausalLM, AutoTokenizer
        from transformers.generation import GenerationConfig

        model_or_path = kwargs.get("model_path", "QwenAudio")
        bf16 = kwargs.get("bf16", False)
        fp16 = kwargs.get("fp16", False)
        model = AutoModelForCausalLM.from_pretrained(
            model_or_path, device_map="cpu", bf16=bf16, fp16=fp16, trust_remote_code=True
        )
        tokenizer = AutoTokenizer.from_pretrained(model_or_path, trust_remote_code=True)

        self.model = model
        self.tokenizer = tokenizer

    def forward(
        self,
    ):
        pass

    def inference(
        self,
        data_in,
        data_lengths=None,
        key: list = None,
        tokenizer=None,
        frontend=None,
        **kwargs,
    ):
        if kwargs.get("batch_size", 1) > 1:
            raise NotImplementedError("batch decoding is not implemented")

        meta_data = {}

        prompt = kwargs.get("prompt", "what does the person say?")
        cache = kwargs.get("cache", {})
        history = cache.get("history", None)
        if data_in[0] is not None:
            # 1st dialogue turn
            query = self.tokenizer.from_list_format(
                [
                    {"audio": data_in[0]},  # Either a local path or an url
                    {"text": prompt},
                ]
            )
        else:
            query = prompt
        response, history = self.model.chat(self.tokenizer, query=query, history=history)
        cache["history"] = history
        # print(response)
        # The person says: "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel".

        results = []
        result_i = {"key": key[0], "text": response}

        results.append(result_i)

        return results, meta_data