# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import io
import time

import librosa
import numpy as np
import paddle
import soundfile as sf
from scipy.io import wavfile

from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import change_speed
from paddlespeech.server.utils.errors import ErrorCode
from paddlespeech.server.utils.exception import ServerBaseException
from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.config import get_config
from paddlespeech.server.utils.util import denorm
from paddlespeech.server.utils.util import get_chunks


import math

__all__ = ['TTSEngine']


class TTSServerExecutor(TTSExecutor):
    def __init__(self):
        super().__init__()
        pass

    @paddle.no_grad()
    def infer(self,
              text: str,
              lang: str='zh',
              am: str='fastspeech2_csmsc',
              spk_id: int=0,
              am_block: int=42,
              am_pad: int=12,
              voc_block: int=14,
              voc_pad: int=14,):
        """
        Model inference and result stored in self.output.
        """
        am_name = am[:am.rindex('_')]
        am_dataset = am[am.rindex('_') + 1:]
        get_tone_ids = False
        merge_sentences = False
        frontend_st = time.time()
        if am_name == 'speedyspeech':
            get_tone_ids = True
        if lang == 'zh':
            input_ids = self.frontend.get_input_ids(
                text,
                merge_sentences=merge_sentences,
                get_tone_ids=get_tone_ids)
            phone_ids = input_ids["phone_ids"]
            if get_tone_ids:
                tone_ids = input_ids["tone_ids"]
        elif lang == 'en':
            input_ids = self.frontend.get_input_ids(
                text, merge_sentences=merge_sentences)
            phone_ids = input_ids["phone_ids"]
        else:
            print("lang should in {'zh', 'en'}!")
        self.frontend_time = time.time() - frontend_st

        for i in range(len(phone_ids)):
            am_st = time.time()
            part_phone_ids = phone_ids[i]
            # am
            if am_name == 'speedyspeech':
                part_tone_ids = tone_ids[i]
                mel = self.am_inference(part_phone_ids, part_tone_ids)
            # fastspeech2
            else:
                # multi speaker
                if am_dataset in {"aishell3", "vctk"}:
                    mel = self.am_inference(
                        part_phone_ids, spk_id=paddle.to_tensor(spk_id))
                else:
                    mel = self.am_inference(part_phone_ids)
            am_et = time.time()
            
            # voc streaming
            voc_upsample = self.voc_config.n_shift
            mel_chunks = get_chunks(mel, voc_block, voc_pad, "voc")
            chunk_num = len(mel_chunks)
            voc_st = time.time()
            for i, mel_chunk in enumerate(mel_chunks):
                sub_wav = self.voc_inference(mel_chunk)
                front_pad = min(i*voc_block, voc_pad)

                if i == 0:
                    sub_wav = sub_wav[: voc_block * voc_upsample]
                elif i == chunk_num - 1:
                    sub_wav = sub_wav[front_pad * voc_upsample : ]
                else:
                    sub_wav = sub_wav[front_pad * voc_upsample: (front_pad + voc_block) * voc_upsample]
            
                yield sub_wav

class TTSEngine(BaseEngine):
    """TTS server engine

    Args:
        metaclass: Defaults to Singleton.
    """

    def __init__(self, name=None):
        """Initialize TTS server engine
        """
        super(TTSEngine, self).__init__()

    def init(self, config: dict) -> bool:
        self.executor = TTSServerExecutor()

        try:
            self.config = config
            if self.config.device:
                self.device = self.config.device
            else:
                self.device = paddle.get_device()
            paddle.set_device(self.device)
        except Exception as e:
            logger.error(
                "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
            )
            logger.error("Initialize TTS server engine Failed on device: %s." %
                         (self.device))
            return False

        try:
            self.executor._init_from_path(
                am=self.config.am,
                am_config=self.config.am_config,
                am_ckpt=self.config.am_ckpt,
                am_stat=self.config.am_stat,
                phones_dict=self.config.phones_dict,
                tones_dict=self.config.tones_dict,
                speaker_dict=self.config.speaker_dict,
                voc=self.config.voc,
                voc_config=self.config.voc_config,
                voc_ckpt=self.config.voc_ckpt,
                voc_stat=self.config.voc_stat,
                lang=self.config.lang)
        except Exception as e:
            logger.error("Failed to get model related files.")
            logger.error("Initialize TTS server engine Failed on device: %s." %
                         (self.device))
            return False

        self.am_block = self.config.am_block
        self.am_pad = self.config.am_pad
        self.voc_block = self.config.voc_block
        self.voc_pad = self.config.voc_pad

        logger.info("Initialize TTS server engine successfully on device: %s." %
                    (self.device))
        return True

    def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
        # Convert byte to text
        if text_bese64:
            text_bytes = base64.b64decode(text_bese64)   # base64 to bytes
        text = text_bytes.decode('utf-8')    # bytes to text

        return text

    def postprocess(self,
                    wav,
                    original_fs: int,
                    target_fs: int=0,
                    volume: float=1.0,
                    speed: float=1.0,
                    audio_path: str=None):
        """Post-processing operations, including speech, volume, sample rate, save audio file

        Args:
            wav (numpy(float)): Synthesized audio sample points
            original_fs (int): original audio sample rate
            target_fs (int): target audio sample rate
            volume (float): target volume
            speed (float): target speed

        Raises:
            ServerBaseException: Throws an exception if the change speed unsuccessfully.

        Returns:
            target_fs: target sample rate for synthesized audio.
            wav_base64: The base64 format of the synthesized audio.
        """

        # transform sample_rate
        if target_fs == 0 or target_fs > original_fs:
            target_fs = original_fs
            wav_tar_fs = wav
            logger.info(
                "The sample rate of synthesized audio is the same as model, which is {}Hz".
                format(original_fs))
        else:
            wav_tar_fs = librosa.resample(
                np.squeeze(wav), original_fs, target_fs)
            logger.info(
                "The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
                format(original_fs, target_fs))
        # transform volume
        wav_vol = wav_tar_fs * volume
        logger.info("Transform the volume of the audio successfully.")

        # transform speed
        try:  # windows not support soxbindings
            wav_speed = change_speed(wav_vol, speed, target_fs)
            logger.info("Transform the speed of the audio successfully.")
        except ServerBaseException:
            raise ServerBaseException(
                ErrorCode.SERVER_INTERNAL_ERR,
                "Failed to transform speed. Can not install soxbindings on your system. \
                 You need to set speed value 1.0.")
        except BaseException:
            logger.error("Failed to transform speed.")

        # wav to base64
        buf = io.BytesIO()
        wavfile.write(buf, target_fs, wav_speed)
        base64_bytes = base64.b64encode(buf.read())
        wav_base64 = base64_bytes.decode('utf-8')
        logger.info("Audio to string successfully.")

        # save audio
        if audio_path is not None:
            if audio_path.endswith(".wav"):
                sf.write(audio_path, wav_speed, target_fs)
            elif audio_path.endswith(".pcm"):
                wav_norm = wav_speed * (32767 / max(0.001,
                                                    np.max(np.abs(wav_speed))))
                with open(audio_path, "wb") as f:
                    f.write(wav_norm.astype(np.int16))
            logger.info("Save audio to {} successfully.".format(audio_path))
        else:
            logger.info("There is no need to save audio.")

        return target_fs, wav_base64

    def run(self,
            sentence: str,
            spk_id: int=0,
            speed: float=1.0,
            volume: float=1.0,
            sample_rate: int=0,
            save_path: str=None):
        """ run include inference and postprocess.

        Args:
            sentence (str): text to be synthesized
            spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0.
            speed (float, optional): speed. Defaults to 1.0.
            volume (float, optional): volume. Defaults to 1.0.
            sample_rate (int, optional): target sample rate for synthesized audio, 
            0 means the same as the model sampling rate. Defaults to 0.
            save_path (str, optional): The save path of the synthesized audio. 
            None means do not save audio. Defaults to None.

        Raises:
            ServerBaseException: Throws an exception if tts inference unsuccessfully.
            ServerBaseException: Throws an exception if postprocess unsuccessfully.

        Returns:
            lang: model language 
            target_sample_rate: target sample rate for synthesized audio.
            wav_base64: The base64 format of the synthesized audio.
        """

        lang = self.config.lang
        wav_list = []

        for wav in self.executor.infer(text=sentence, lang=lang, am=self.config.am, spk_id=spk_id, am_block=self.am_block, am_pad=self.am_pad, voc_block=self.voc_block, voc_pad=self.voc_pad):
            # wav type: <class 'numpy.ndarray'>  float32, convert to pcm (base64)
            wav = float2pcm(wav) # float32 to int16
            wav_bytes = wav.tobytes()  # to bytes
            wav_base64 = base64.b64encode(wav_bytes).decode('utf8')  # to base64
            wav_list.append(wav)
            
            yield wav_base64
        
        wav_all = np.concatenate(wav_list, axis=0)
        logger.info("The durations of audio is: {} s".format(len(wav_all)/self.executor.am_config.fs))



 
