import base64
import os
import uuid
import requests
import ormsgpack
from pathlib import Path
from pydantic import BaseModel, Field, conint, model_validator
from typing_extensions import Annotated
from datetime import datetime
from typing import Literal
from core.providers.tts.base import TTSProviderBase
from config.logger import setup_logging

TAG = __name__
logger = setup_logging()


class ServeReferenceAudio(BaseModel):
    audio: bytes
    text: str

    @model_validator(mode="before")
    def decode_audio(cls, values):
        audio = values.get("audio")
        if (
            isinstance(audio, str) and len(audio) > 255
        ):  # Check if audio is a string (Base64)
            try:
                values["audio"] = base64.b64decode(audio)
            except Exception as e:
                # If the audio is not a valid base64 string, we will just ignore it and let the server handle it
                pass
        return values

    def __repr__(self) -> str:
        return f"ServeReferenceAudio(text={self.text!r}, audio_size={len(self.audio)})"

class ServeTTSRequest(BaseModel):
    text: str
    chunk_length: Annotated[int, conint(ge=100, le=300, strict=True)] = 200
    # Audio format
    format: Literal["wav", "pcm", "mp3"] = "wav"
    # References audios for in-context learning
    references: list[ServeReferenceAudio] = []
    # Reference id
    # For example, if you want use https://fish.audio/m/7f92f8afb8ec43bf81429cc1c9199cb1/
    # Just pass 7f92f8afb8ec43bf81429cc1c9199cb1
    reference_id: str | None = None
    seed: int | None = None
    use_memory_cache: Literal["on", "off"] = "off"
    # Normalize text for en & zh, this increase stability for numbers
    normalize: bool = True
    # not usually used below
    streaming: bool = False
    max_new_tokens: int = 1024
    top_p: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7
    repetition_penalty: Annotated[float, Field(ge=0.9, le=2.0, strict=True)] = 1.2
    temperature: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7

    class Config:
        # Allow arbitrary types for pytorch related types
        arbitrary_types_allowed = True


def audio_to_bytes(file_path):
    if not file_path or not Path(file_path).exists():
        return None
    with open(file_path, "rb") as wav_file:
        wav = wav_file.read()
    return wav

def read_ref_text(ref_text):
    path = Path(ref_text)
    if path.exists() and path.is_file():
        with path.open("r", encoding="utf-8") as file:
            return file.read()
    return ref_text

class TTSProvider(TTSProviderBase):

    def __init__(self, config, delete_audio_file):
        super().__init__(config, delete_audio_file)

        self.reference_id = config.get("reference_id")
        self.reference_audio = config.get("reference_audio",[]) 
        self.reference_text = config.get("reference_text",[])
        self.format =  config.get("format","wav")
        self.channels = config.get("channels",1)
        self.rate = config.get("rate",44100)
        self.api_key = config.get("api_key","YOUR_API_KEY")
        if "你" in self.api_key:
            logger.bind(tag=TAG).error("你还没配置FishSpeech TTS的密钥，请在配置文件中配置密钥，否则无法正常工作")
            return
        self.normalize = config.get("normalize",True)
        self.max_new_tokens = config.get("max_new_tokens",1024)
        self.chunk_length = config.get("chunk_length",200)
        self.top_p = config.get("top_p",0.7)
        self.repetition_penalty = config.get("repetition_penalty",1.2)
        self.temperature = config.get("temperature",0.7)
        self.streaming = config.get("streaming",False)
        self.use_memory_cache = config.get("use_memory_cache","on")
        self.seed = config.get("seed")
        self.api_url =   config.get("api_url","http://127.0.0.1:8080/v1/tts")

    def generate_filename(self, extension=".wav"):
        return os.path.join(self.output_file, f"tts-{datetime.now().date()}@{uuid.uuid4().hex}{extension}")

    async def text_to_speak(self, text, output_file):
        # Prepare reference data
        byte_audios = [audio_to_bytes(ref_audio) for ref_audio in self.reference_audio]
        ref_texts = [read_ref_text(ref_text) for ref_text in self.reference_text]

        data = {
            "text": text,
            "references": [
                ServeReferenceAudio(
                    audio=audio if audio else b"", text=text
                )
                for text, audio in zip(ref_texts, byte_audios)
            ],
            "reference_id": self.reference_id,
            "normalize": self.normalize,
            "format": self.format,
            "max_new_tokens": self.max_new_tokens,
            "chunk_length": self.chunk_length,
            "top_p": self.top_p,
            "repetition_penalty": self.repetition_penalty,
            "temperature": self.temperature,
            "streaming": self.streaming,
            "use_memory_cache": self.use_memory_cache,
            "seed": self.seed,
        }

        pydantic_data = ServeTTSRequest(**data)

        response = requests.post(
            self.api_url,
            data=ormsgpack.packb(pydantic_data, option=ormsgpack.OPT_SERIALIZE_PYDANTIC),
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/msgpack",
            },
        )

        if response.status_code == 200:
            audio_content = response.content

            with open(output_file, "wb") as audio_file:
                audio_file.write(audio_content)
                


        else:
            print(f"Request failed with status code {response.status_code}")
            print(response.json())