import time
from typing import List, Union

import numpy as np
import pysbd
import torch

from TTS.config import load_config
from TTS.tts.models import setup_model as setup_tts_model

# pylint: disable=unused-wildcard-import
# pylint: disable=wildcard-import
from TTS.tts.utils.synthesis import synthesis, transfer_voice, trim_silence
from TTS.utils.audio import AudioProcessor
from TTS.vocoder.models import setup_model as setup_vocoder_model
from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input


class Synthesizer(object):
    def __init__(
        self,
        tts_checkpoint: str,
        tts_config_path: str,
        tts_speakers_file: str = "",
        tts_emotions_file: str = "",
        tts_languages_file: str = "",
        vocoder_checkpoint: str = "",
        vocoder_config: str = "",
        encoder_checkpoint: str = "",
        encoder_config: str = "",
        use_cuda: bool = False,
    ) -> None:
        """General 🐸 TTS interface for inference. It takes a tts and a vocoder
        model and synthesize speech from the provided text.

        The text is divided into a list of sentences using `pysbd` and synthesize
        speech on each sentence separately.

        If you have certain special characters in your text, you need to handle
        them before providing the text to Synthesizer.

        TODO: set the segmenter based on the source language

        Args:
            tts_checkpoint (str): path to the tts model file.
            tts_config_path (str): path to the tts config file.
            vocoder_checkpoint (str, optional): path to the vocoder model file. Defaults to None.
            vocoder_config (str, optional): path to the vocoder config file. Defaults to None.
            encoder_checkpoint (str, optional): path to the speaker encoder model file. Defaults to `""`,
            encoder_config (str, optional): path to the speaker encoder config file. Defaults to `""`,
            use_cuda (bool, optional): enable/disable cuda. Defaults to False.
        """
        self.tts_checkpoint = tts_checkpoint
        self.tts_config_path = tts_config_path
        self.tts_speakers_file = tts_speakers_file
        self.tts_emotions_file = tts_emotions_file
        self.tts_languages_file = tts_languages_file
        self.vocoder_checkpoint = vocoder_checkpoint
        self.vocoder_config = vocoder_config
        self.encoder_checkpoint = encoder_checkpoint
        self.encoder_config = encoder_config
        self.use_cuda = use_cuda

        self.tts_model = None
        self.vocoder_model = None
        self.speaker_manager = None
        self.num_speakers = 0
        self.tts_speakers = {}
        self.language_manager = None
        self.num_languages = 0
        self.tts_languages = {}
        self.d_vector_dim = 0
        self.seg = self._get_segmenter("en")
        self.use_cuda = use_cuda

        if self.use_cuda:
            assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
        self._load_tts(tts_checkpoint, tts_config_path, use_cuda)
        self.output_sample_rate = self.tts_config.audio["sample_rate"]
        if vocoder_checkpoint:
            self._load_vocoder(vocoder_checkpoint, vocoder_config, use_cuda)
            self.output_sample_rate = self.vocoder_config.audio["sample_rate"]
        else:
            print(" > Using Griffin-Lim as no vocoder model defined")

    @staticmethod
    def _get_segmenter(lang: str):
        """get the sentence segmenter for the given language.

        Args:
            lang (str): target language code.

        Returns:
            [type]: [description]
        """
        return pysbd.Segmenter(language=lang, clean=True)

    def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None:
        """Load the TTS model.

        1. Load the model config.
        2. Init the AudioProcessor.
        3. Init the model from the config.
        4. Move the model to the GPU if CUDA is enabled.
        5. Init the speaker manager for the model.

        Args:
            tts_checkpoint (str): path to the model checkpoint.
            tts_config_path (str): path to the model config file.
            use_cuda (bool): enable/disable CUDA use.
        """
        # pylint: disable=global-statement
        self.tts_config = load_config(tts_config_path)
        self.use_phonemes = self.tts_config.use_phonemes
        self.tts_model = setup_tts_model(config=self.tts_config)

        if not self.encoder_checkpoint:
            self._set_speaker_encoder_paths_from_tts_config()

        self.tts_model.load_checkpoint(self.tts_config, tts_checkpoint, eval=True)
        if use_cuda:
            self.tts_model.cuda()

        if (
            self.encoder_checkpoint
            and hasattr(self.tts_model, "speaker_manager")
            and self.tts_model.speaker_manager is not None
        ):
            self.tts_model.speaker_manager.init_encoder(self.encoder_checkpoint, self.encoder_config)

        if (
            self.tts_emotions_file
            and hasattr(self.tts_model, "emotion_manager")
            and self.tts_model.emotion_manager is not None
        ):
            if getattr(self.tts_config, "use_external_emotions_embeddings", False) or (
                getattr(self.tts_config, "model_args", None)
                and getattr(self.tts_config.model_args, "use_external_emotions_embeddings", False)
            ):
                self.tts_model.emotion_manager.load_embeddings_from_file(self.tts_emotions_file)
            else:
                self.tts_model.emotion_manager.load_ids_from_file(self.tts_emotions_file)

        if (
            self.tts_speakers_file
            and hasattr(self.tts_model, "speaker_manager")
            and self.tts_model.speaker_manager is not None
        ):
            if getattr(self.tts_config, "use_d_vector_file", False) or (
                getattr(self.tts_config, "model_args", None)
                and getattr(self.tts_config.model_args, "use_d_vector_file", False)
            ):
                self.tts_model.speaker_manager.load_embeddings_from_file(self.tts_speakers_file)
            else:
                self.tts_model.speaker_manager.load_ids_from_file(self.tts_speakers_file)

    def _set_speaker_encoder_paths_from_tts_config(self):
        """Set the encoder paths from the tts model config for models with speaker encoders."""
        if hasattr(self.tts_config, "model_args") and hasattr(self.tts_config.model_args, "encoder_config_path"):
            self.encoder_checkpoint = self.tts_config.model_args.encoder_model_path
            self.encoder_config = self.tts_config.model_args.encoder_config_path

    def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None:
        """Load the vocoder model.

        1. Load the vocoder config.
        2. Init the AudioProcessor for the vocoder.
        3. Init the vocoder model from the config.
        4. Move the model to the GPU if CUDA is enabled.

        Args:
            model_file (str): path to the model checkpoint.
            model_config (str): path to the model config file.
            use_cuda (bool): enable/disable CUDA use.
        """
        self.vocoder_config = load_config(model_config)
        self.vocoder_ap = AudioProcessor(verbose=False, **self.vocoder_config.audio)
        self.vocoder_model = setup_vocoder_model(self.vocoder_config)
        self.vocoder_model.load_checkpoint(self.vocoder_config, model_file, eval=True)
        if use_cuda:
            self.vocoder_model.cuda()

    def split_into_sentences(self, text) -> List[str]:
        """Split give text into sentences.

        Args:
            text (str): input text in string format.

        Returns:
            List[str]: list of sentences.
        """
        return self.seg.segment(text)

    def save_wav(self, wav: List[int], path: str) -> None:
        """Save the waveform as a file.

        Args:
            wav (List[int]): waveform as a list of values.
            path (str): output path to save the waveform.
        """
        wav = np.array(wav)
        self.tts_model.ap.save_wav(wav, path, self.output_sample_rate)

    def tts(
        self,
        text: str = "",
        speaker_name: str = "",
        language_name: str = "",
        speaker_wav: Union[str, List[str]] = None,
        style_wav=None,
        reference_wav=None,
        reference_speaker_name=None,
        emotion_name=None,
    ) -> List[int]:
        """🐸 TTS magic. Run all the models and generate speech.

        Args:
            text (str): input text.
            speaker_name (str, optional): spekaer id for multi-speaker models. Defaults to "".
            language_name (str, optional): language id for multi-language models. Defaults to "".
            speaker_wav (Union[str, List[str]], optional): path to the speaker wav. Defaults to None.
            style_wav ([type], optional): style waveform for GST. Defaults to None.
            reference_wav ([type], optional): reference waveform for voice conversion. Defaults to None.
            reference_speaker_name ([type], optional): spekaer id of reference waveform. Defaults to None.
        Returns:
            List[int]: [description]
        """
        start_time = time.time()
        wavs = []

        if not text and not reference_wav:
            raise ValueError(
                "You need to define either `text` (for sythesis) or a `reference_wav` (for voice conversion) to use the Coqui TTS API."
            )

        if text:
            sens = self.split_into_sentences(text)
            print(" > Text splitted to sentences.")
            print(sens)

        # handle multi-speaker
        speaker_embedding = None
        speaker_id = None
        if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "ids"):
            if speaker_name and isinstance(speaker_name, str):
                if self.tts_config.use_d_vector_file:
                    # get the average speaker embedding from the saved d_vectors.
                    speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
                        speaker_name, num_samples=None, randomize=False
                    )
                    speaker_embedding = np.array(speaker_embedding)[None, :]  # [1 x embedding_dim]
                else:
                    # get speaker idx from the speaker name
                    speaker_id = self.tts_model.speaker_manager.ids[speaker_name]

            elif not speaker_name and not speaker_wav:
                raise ValueError(
                    " [!] Look like you use a multi-speaker model. "
                    "You need to define either a `speaker_name` or a `speaker_wav` to use a multi-speaker model."
                )
            else:
                speaker_embedding = None
        else:
            if speaker_name:
                raise ValueError(
                    f" [!] Missing speakers.json file path for selecting speaker {speaker_name}."
                    "Define path for speaker.json if it is a multi-speaker model or remove defined speaker idx. "
                )

        # handle multi-lingual
        language_id = None
        if self.tts_languages_file or (
            hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None
        ):
            if language_name and isinstance(language_name, str):
                language_id = self.tts_model.language_manager.ids[language_name]

            elif not language_name:
                raise ValueError(
                    " [!] Look like you use a multi-lingual model. "
                    "You need to define either a `language_name` or a `style_wav` to use a multi-lingual model."
                )

            else:
                raise ValueError(
                    f" [!] Missing language_ids.json file path for selecting language {language_name}."
                    "Define path for language_ids.json if it is a multi-lingual model or remove defined language idx. "
                )

        # handle emotion
        emotion_embedding, emotion_id = None, None
        if self.tts_emotions_file or (
            getattr(self.tts_model, "emotion_manager", None) and getattr(self.tts_model.emotion_manager, "ids", None)
        ):
            if emotion_name and isinstance(emotion_name, str):
                if getattr(self.tts_config, "use_external_emotions_embeddings", False) or (
                    getattr(self.tts_config, "model_args", None)
                    and getattr(self.tts_config.model_args, "use_external_emotions_embeddings", False)
                ):
                    # get the average speaker embedding from the saved embeddings.
                    emotion_embedding = self.tts_model.emotion_manager.get_mean_embedding(
                        emotion_name, num_samples=None, randomize=False
                    )
                    emotion_embedding = np.array(emotion_embedding)[None, :]  # [1 x embedding_dim]
                else:
                    # get speaker idx from the speaker name
                    emotion_id = self.tts_model.emotion_manager.ids[emotion_name]
            elif not emotion_name:
                raise ValueError(
                    " [!] Look like you use an emotion model. "
                    "You need to define either a `emotion_name`  to use an emotion model."
                )
        else:
            if emotion_name:
                raise ValueError(
                    f" [!] Missing emotion.json file path for selecting the emotion {emotion_name}."
                    "Define path for emotion.json if it is an emotion model or remove defined emotion idx. "
                )

        # compute a new d_vector from the given clip.
        if speaker_wav is not None:
            speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav)

        use_gl = self.vocoder_model is None

        if not reference_wav:
            for sen in sens:
                # synthesize voice
                outputs = synthesis(
                    model=self.tts_model,
                    text=sen,
                    CONFIG=self.tts_config,
                    use_cuda=self.use_cuda,
                    speaker_id=speaker_id,
                    language_id=language_id,
                    style_wav=style_wav,
                    use_griffin_lim=use_gl,
                    d_vector=speaker_embedding,
                    emotion_embedding=emotion_embedding,
                    emotion_id=emotion_id,
                )
                waveform = outputs["wav"]
                mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy()
                if not use_gl:
                    # denormalize tts output based on tts audio config
                    mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
                    device_type = "cuda" if self.use_cuda else "cpu"
                    # renormalize spectrogram based on vocoder config
                    vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                    # compute scale factor for possible sample rate mismatch
                    scale_factor = [
                        1,
                        self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate,
                    ]
                    if scale_factor[1] != 1:
                        print(" > interpolating tts model output.")
                        vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
                    else:
                        vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                    # run vocoder model
                    # [1, T, C]
                    waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
                if self.use_cuda and not use_gl:
                    waveform = waveform.cpu()
                if not use_gl:
                    waveform = waveform.numpy()
                waveform = waveform.squeeze()

                # trim silence
                if self.tts_config.audio["do_trim_silence"] is True:
                    waveform = trim_silence(waveform, self.tts_model.ap)

                wavs += list(waveform)
                wavs += [0] * 10000
        else:
            # get the speaker embedding or speaker id for the reference wav file
            reference_speaker_embedding = None
            reference_speaker_id = None
            if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "speaker_ids"):
                if reference_speaker_name and isinstance(reference_speaker_name, str):
                    if self.tts_config.use_d_vector_file:
                        # get the speaker embedding from the saved d_vectors.
                        reference_speaker_embedding = self.tts_model.speaker_manager.get_embeddings_by_name(
                            reference_speaker_name
                        )[0]
                        reference_speaker_embedding = np.array(reference_speaker_embedding)[
                            None, :
                        ]  # [1 x embedding_dim]
                    else:
                        # get speaker idx from the speaker name
                        reference_speaker_id = self.tts_model.speaker_manager.ids[reference_speaker_name]
                else:
                    reference_speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(
                        reference_wav
                    )

            outputs = transfer_voice(
                model=self.tts_model,
                CONFIG=self.tts_config,
                use_cuda=self.use_cuda,
                reference_wav=reference_wav,
                speaker_id=speaker_id,
                d_vector=speaker_embedding,
                use_griffin_lim=use_gl,
                reference_speaker_id=reference_speaker_id,
                reference_d_vector=reference_speaker_embedding,
            )
            waveform = outputs
            if not use_gl:
                mel_postnet_spec = outputs[0].detach().cpu().numpy()
                # denormalize tts output based on tts audio config
                mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
                device_type = "cuda" if self.use_cuda else "cpu"
                # renormalize spectrogram based on vocoder config
                vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                # compute scale factor for possible sample rate mismatch
                scale_factor = [
                    1,
                    self.vocoder_config["audio"]["sample_rate"] / self.tts_model.ap.sample_rate,
                ]
                if scale_factor[1] != 1:
                    print(" > interpolating tts model output.")
                    vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
                else:
                    vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                # run vocoder model
                # [1, T, C]
                waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
            if self.use_cuda:
                waveform = waveform.cpu()
            if not use_gl:
                waveform = waveform.numpy()
            wavs = waveform.squeeze()

        # compute stats
        process_time = time.time() - start_time
        audio_time = len(wavs) / self.tts_config.audio["sample_rate"]
        print(f" > Processing time: {process_time}")
        print(f" > Real-time factor: {process_time / audio_time}")
        return wavs
