# 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 io
from collections import namedtuple
from typing import Optional

import kaldiio
import numpy as np
from yacs.config import CfgNode

from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
from deepspeech.frontend.normalizer import FeatureNormalizer
from deepspeech.frontend.speech import SpeechSegment
from deepspeech.frontend.utility import IGNORE_ID
from deepspeech.io.utility import pad_sequence
from deepspeech.utils.log import Log

__all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"]

logger = Log(__name__).getlog()

# namedtupe need global for pickle.
TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])


class SpeechCollator():
    @classmethod
    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
        default = CfgNode(
            dict(
                augmentation_config="",
                random_seed=0,
                mean_std_filepath="",
                unit_type="char",
                vocab_filepath="",
                spm_model_prefix="",
                specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
                feat_dim=0,  # 'mfcc', 'fbank'
                delta_delta=False,  # 'mfcc', 'fbank'
                stride_ms=10.0,  # ms
                window_ms=20.0,  # ms
                n_fft=None,  # fft points
                max_freq=None,  # None for samplerate/2
                target_sample_rate=16000,  # target sample rate
                use_dB_normalization=True,
                target_dB=-20,
                dither=1.0,  # feature dither
                keep_transcription_text=False))

        if config is not None:
            config.merge_from_other_cfg(default)
        return default

    @classmethod
    def from_config(cls, config):
        """Build a SpeechCollator object from a config.

        Args:
            config (yacs.config.CfgNode): configs object.

        Returns:
            SpeechCollator: collator object.
        """
        assert 'augmentation_config' in config.collator
        assert 'keep_transcription_text' in config.collator
        assert 'mean_std_filepath' in config.collator
        assert 'vocab_filepath' in config.collator
        assert 'specgram_type' in config.collator
        assert 'n_fft' in config.collator
        assert config.collator

        if isinstance(config.collator.augmentation_config, (str, bytes)):
            if config.collator.augmentation_config:
                aug_file = io.open(
                    config.collator.augmentation_config,
                    mode='r',
                    encoding='utf8')
            else:
                aug_file = io.StringIO(initial_value='{}', newline='')
        else:
            aug_file = config.collator.augmentation_config
            assert isinstance(aug_file, io.StringIO)

        speech_collator = cls(
            aug_file=aug_file,
            random_seed=0,
            mean_std_filepath=config.collator.mean_std_filepath,
            unit_type=config.collator.unit_type,
            vocab_filepath=config.collator.vocab_filepath,
            spm_model_prefix=config.collator.spm_model_prefix,
            specgram_type=config.collator.specgram_type,
            feat_dim=config.collator.feat_dim,
            delta_delta=config.collator.delta_delta,
            stride_ms=config.collator.stride_ms,
            window_ms=config.collator.window_ms,
            n_fft=config.collator.n_fft,
            max_freq=config.collator.max_freq,
            target_sample_rate=config.collator.target_sample_rate,
            use_dB_normalization=config.collator.use_dB_normalization,
            target_dB=config.collator.target_dB,
            dither=config.collator.dither,
            keep_transcription_text=config.collator.keep_transcription_text)
        return speech_collator

    def __init__(
            self,
            aug_file,
            mean_std_filepath,
            vocab_filepath,
            spm_model_prefix,
            random_seed=0,
            unit_type="char",
            specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
            feat_dim=0,  # 'mfcc', 'fbank'
            delta_delta=False,  # 'mfcc', 'fbank'
            stride_ms=10.0,  # ms
            window_ms=20.0,  # ms
            n_fft=None,  # fft points
            max_freq=None,  # None for samplerate/2
            target_sample_rate=16000,  # target sample rate
            use_dB_normalization=True,
            target_dB=-20,
            dither=1.0,
            keep_transcription_text=True):
        """SpeechCollator Collator

        Args:
            unit_type(str): token unit type, e.g. char, word, spm
            vocab_filepath (str): vocab file path.
            mean_std_filepath (str): mean and std file path, which suffix is *.npy
            spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
            augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
            stride_ms (float, optional): stride size in ms. Defaults to 10.0.
            window_ms (float, optional): window size in ms. Defaults to 20.0.
            n_fft (int, optional): fft points for rfft. Defaults to None.
            max_freq (int, optional): max cut freq. Defaults to None.
            target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
            specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
            feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
            delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
            use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
            target_dB (int, optional): target dB. Defaults to -20.
            random_seed (int, optional): for random generator. Defaults to 0.
            keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
            if ``keep_transcription_text`` is False, text is token ids else is raw string.

        Do augmentations
        Padding audio features with zeros to make them have the same shape (or
        a user-defined shape) within one batch.
        """
        self._keep_transcription_text = keep_transcription_text

        self._local_data = TarLocalData(tar2info={}, tar2object={})
        self._augmentation_pipeline = AugmentationPipeline(
            augmentation_config=aug_file.read(), random_seed=random_seed)

        self._normalizer = FeatureNormalizer(
            mean_std_filepath) if mean_std_filepath else None

        self._stride_ms = stride_ms
        self._target_sample_rate = target_sample_rate

        self._speech_featurizer = SpeechFeaturizer(
            unit_type=unit_type,
            vocab_filepath=vocab_filepath,
            spm_model_prefix=spm_model_prefix,
            specgram_type=specgram_type,
            feat_dim=feat_dim,
            delta_delta=delta_delta,
            stride_ms=stride_ms,
            window_ms=window_ms,
            n_fft=n_fft,
            max_freq=max_freq,
            target_sample_rate=target_sample_rate,
            use_dB_normalization=use_dB_normalization,
            target_dB=target_dB,
            dither=dither)

    def _parse_tar(self, file):
        """Parse a tar file to get a tarfile object
        and a map containing tarinfoes
        """
        result = {}
        f = tarfile.open(file)
        for tarinfo in f.getmembers():
            result[tarinfo.name] = tarinfo
        return f, result

    def _subfile_from_tar(self, file):
        """Get subfile object from tar.

        It will return a subfile object from tar file
        and cached tar file info for next reading request.
        """
        tarpath, filename = file.split(':', 1)[1].split('#', 1)
        if 'tar2info' not in self._local_data.__dict__:
            self._local_data.tar2info = {}
        if 'tar2object' not in self._local_data.__dict__:
            self._local_data.tar2object = {}
        if tarpath not in self._local_data.tar2info:
            object, infoes = self._parse_tar(tarpath)
            self._local_data.tar2info[tarpath] = infoes
            self._local_data.tar2object[tarpath] = object
        return self._local_data.tar2object[tarpath].extractfile(
            self._local_data.tar2info[tarpath][filename])

    @property
    def manifest(self):
        return self._manifest

    @property
    def vocab_size(self):
        return self._speech_featurizer.vocab_size

    @property
    def vocab_list(self):
        return self._speech_featurizer.vocab_list

    @property
    def vocab_dict(self):
        return self._speech_featurizer.vocab_dict

    @property
    def text_feature(self):
        return self._speech_featurizer.text_feature

    @property
    def feature_size(self):
        return self._speech_featurizer.feature_size

    @property
    def stride_ms(self):
        return self._speech_featurizer.stride_ms

    def process_utterance(self, audio_file, translation):
        """Load, augment, featurize and normalize for speech data.

        :param audio_file: Filepath or file object of audio file.
        :type audio_file: str | file
        :param translation: translation text.
        :type translation: str
        :return: Tuple of audio feature tensor and data of translation part,
                 where translation part could be token ids or text.
        :rtype: tuple of (2darray, list)
        """
        if isinstance(audio_file, str) and audio_file.startswith('tar:'):
            speech_segment = SpeechSegment.from_file(
                self._subfile_from_tar(audio_file), translation)
        else:
            speech_segment = SpeechSegment.from_file(audio_file, translation)

        # audio augment
        self._augmentation_pipeline.transform_audio(speech_segment)

        specgram, translation_part = self._speech_featurizer.featurize(
            speech_segment, self._keep_transcription_text)
        if self._normalizer:
            specgram = self._normalizer.apply(specgram)

        # specgram augment
        specgram = self._augmentation_pipeline.transform_feature(specgram)
        return specgram, translation_part

    def __call__(self, batch):
        """batch examples

        Args:
            batch ([List]): batch is (audio, text)
                audio (np.ndarray) shape (T, D)
                text (List[int] or str): shape (U,)

        Returns:
            tuple(audio, text, audio_lens, text_lens): batched data.
                audio : (B, Tmax, D)
                audio_lens: (B)
                text : (B, Umax)
                text_lens: (B)
        """
        audios = []
        audio_lens = []
        texts = []
        text_lens = []
        utts = []
        for utt, audio, text in batch:
            audio, text = self.process_utterance(audio, text)
            #utt
            utts.append(utt)
            # audio
            audios.append(audio)  # [T, D]
            audio_lens.append(audio.shape[0])
            # text
            # for training, text is token ids
            # else text is string, convert to unicode ord
            tokens = []
            if self._keep_transcription_text:
                assert isinstance(text, str), (type(text), text)
                tokens = [ord(t) for t in text]
            else:
                tokens = text  # token ids
            tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
                tokens, dtype=np.int64)
            texts.append(tokens)
            text_lens.append(tokens.shape[0])

        padded_audios = pad_sequence(
            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
        audio_lens = np.array(audio_lens).astype(np.int64)
        padded_texts = pad_sequence(
            texts, padding_value=IGNORE_ID).astype(np.int64)
        text_lens = np.array(text_lens).astype(np.int64)
        return utts, padded_audios, audio_lens, padded_texts, text_lens


class TripletSpeechCollator(SpeechCollator):
    def process_utterance(self, audio_file, translation, transcript):
        """Load, augment, featurize and normalize for speech data.

        :param audio_file: Filepath or file object of audio file.
        :type audio_file: str | file
        :param translation: translation text.
        :type translation: str
        :return: Tuple of audio feature tensor and data of translation part,
                    where translation part could be token ids or text.
        :rtype: tuple of (2darray, list)
        """
        if isinstance(audio_file, str) and audio_file.startswith('tar:'):
            speech_segment = SpeechSegment.from_file(
                self._subfile_from_tar(audio_file), translation)
        else:
            speech_segment = SpeechSegment.from_file(audio_file, translation)

        # audio augment
        self._augmentation_pipeline.transform_audio(speech_segment)

        specgram, translation_part = self._speech_featurizer.featurize(
            speech_segment, self._keep_transcription_text)
        transcript_part = self._speech_featurizer._text_featurizer.featurize(
            transcript)
        if self._normalizer:
            specgram = self._normalizer.apply(specgram)

        # specgram augment
        specgram = self._augmentation_pipeline.transform_feature(specgram)
        return specgram, translation_part, transcript_part

    def __call__(self, batch):
        """batch examples

        Args:
            batch ([List]): batch is (audio, text)
                audio (np.ndarray) shape (T, D)
                text (List[int] or str): shape (U,)

        Returns:
            tuple(audio, text, audio_lens, text_lens): batched data.
                audio : (B, Tmax, D)
                audio_lens: (B)
                text : (B, Umax)
                text_lens: (B)
        """
        audios = []
        audio_lens = []
        translation_text = []
        translation_text_lens = []
        transcription_text = []
        transcription_text_lens = []

        utts = []
        for utt, audio, translation, transcription in batch:
            audio, translation, transcription = self.process_utterance(
                audio, translation, transcription)
            #utt
            utts.append(utt)
            # audio
            audios.append(audio)  # [T, D]
            audio_lens.append(audio.shape[0])
            # text
            # for training, text is token ids
            # else text is string, convert to unicode ord
            tokens = [[], []]
            for idx, text in enumerate([translation, transcription]):
                if self._keep_transcription_text:
                    assert isinstance(text, str), (type(text), text)
                    tokens[idx] = [ord(t) for t in text]
                else:
                    tokens[idx] = text  # token ids
                tokens[idx] = tokens[idx] if isinstance(
                    tokens[idx], np.ndarray) else np.array(
                        tokens[idx], dtype=np.int64)
            translation_text.append(tokens[0])
            translation_text_lens.append(tokens[0].shape[0])
            transcription_text.append(tokens[1])
            transcription_text_lens.append(tokens[1].shape[0])

        padded_audios = pad_sequence(
            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
        audio_lens = np.array(audio_lens).astype(np.int64)
        padded_translation = pad_sequence(
            translation_text, padding_value=IGNORE_ID).astype(np.int64)
        translation_lens = np.array(translation_text_lens).astype(np.int64)
        padded_transcription = pad_sequence(
            transcription_text, padding_value=IGNORE_ID).astype(np.int64)
        transcription_lens = np.array(transcription_text_lens).astype(np.int64)
        return utts, padded_audios, audio_lens, (
            padded_translation, padded_transcription), (translation_lens,
                                                        transcription_lens)


class KaldiPrePorocessedCollator(SpeechCollator):
    @classmethod
    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
        default = CfgNode(
            dict(
                augmentation_config="",
                random_seed=0,
                unit_type="char",
                vocab_filepath="",
                spm_model_prefix="",
                feat_dim=0,
                stride_ms=10.0,
                keep_transcription_text=False))

        if config is not None:
            config.merge_from_other_cfg(default)
        return default

    @classmethod
    def from_config(cls, config):
        """Build a SpeechCollator object from a config.

        Args:
            config (yacs.config.CfgNode): configs object.

        Returns:
            SpeechCollator: collator object.
        """
        assert 'augmentation_config' in config.collator
        assert 'keep_transcription_text' in config.collator
        assert 'vocab_filepath' in config.collator
        assert config.collator

        if isinstance(config.collator.augmentation_config, (str, bytes)):
            if config.collator.augmentation_config:
                aug_file = io.open(
                    config.collator.augmentation_config,
                    mode='r',
                    encoding='utf8')
            else:
                aug_file = io.StringIO(initial_value='{}', newline='')
        else:
            aug_file = config.collator.augmentation_config
            assert isinstance(aug_file, io.StringIO)

        speech_collator = cls(
            aug_file=aug_file,
            random_seed=0,
            unit_type=config.collator.unit_type,
            vocab_filepath=config.collator.vocab_filepath,
            spm_model_prefix=config.collator.spm_model_prefix,
            feat_dim=config.collator.feat_dim,
            stride_ms=config.collator.stride_ms,
            keep_transcription_text=config.collator.keep_transcription_text)
        return speech_collator

    def __init__(self,
                 aug_file,
                 vocab_filepath,
                 spm_model_prefix,
                 random_seed=0,
                 unit_type="char",
                 feat_dim=0,
                 stride_ms=10.0,
                 keep_transcription_text=True):
        """SpeechCollator Collator

        Args:
            unit_type(str): token unit type, e.g. char, word, spm
            vocab_filepath (str): vocab file path.
            spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
            augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
            random_seed (int, optional): for random generator. Defaults to 0.
            keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
            if ``keep_transcription_text`` is False, text is token ids else is raw string.

        Do augmentations
        Padding audio features with zeros to make them have the same shape (or
        a user-defined shape) within one batch.
        """
        self._keep_transcription_text = keep_transcription_text
        self._feat_dim = feat_dim
        self._stride_ms = stride_ms

        self._local_data = TarLocalData(tar2info={}, tar2object={})
        self._augmentation_pipeline = AugmentationPipeline(
            augmentation_config=aug_file.read(), random_seed=random_seed)

        self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
                                               spm_model_prefix)

    def process_utterance(self, audio_file, translation):
        """Load, augment, featurize and normalize for speech data.

        :param audio_file: Filepath or file object of kaldi processed feature.
        :type audio_file: str | file
        :param translation: Translation text.
        :type translation: str
        :return: Tuple of audio feature tensor and data of translation part,
                 where translation part could be token ids or text.
        :rtype: tuple of (2darray, list)
        """
        specgram = kaldiio.load_mat(audio_file)
        assert specgram.shape[
            1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
                self._feat_dim, specgram.shape[1])

        # specgram augment
        specgram = self._augmentation_pipeline.transform_feature(specgram)

        if self._keep_transcription_text:
            return specgram, translation
        else:
            text_ids = self._text_featurizer.featurize(translation)
            return specgram, text_ids


class TripletKaldiPrePorocessedCollator(KaldiPrePorocessedCollator):
    def process_utterance(self, audio_file, translation, transcript):
        """Load, augment, featurize and normalize for speech data.

        :param audio_file: Filepath or file object of kali processed feature.
        :type audio_file: str | file
        :param translation: Translation text.
        :type translation: str
        :param transcript: Transcription text.
        :type transcript: str
        :return: Tuple of audio feature tensor and data of translation and transcription parts,
                 where translation and transcription parts could be token ids or text.
        :rtype: tuple of (2darray, (list, list))
        """
        specgram = kaldiio.load_mat(audio_file)
        assert specgram.shape[
            1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
                self._feat_dim, specgram.shape[1])

        # specgram augment
        specgram = self._augmentation_pipeline.transform_feature(specgram)

        if self._keep_transcription_text:
            return specgram, translation, transcript
        else:
            translation_text_ids = self._text_featurizer.featurize(translation)
            transcript_text_ids = self._text_featurizer.featurize(transcript)
            return specgram, translation_text_ids, transcript_text_ids

    def __call__(self, batch):
        """batch examples

        Args:
            batch ([List]): batch is (audio, text)
                audio (np.ndarray) shape (T, D)
                translation (List[int] or str): shape (U,)
                transcription (List[int] or str): shape (V,)

        Returns:
            tuple(audio, text, audio_lens, text_lens): batched data.
                audio : (B, Tmax, D)
                audio_lens: (B)
                translation_text : (B, Umax)
                translation_text_lens: (B)
                transcription_text : (B, Vmax)
                transcription_text_lens: (B)
        """
        audios = []
        audio_lens = []
        translation_text = []
        translation_text_lens = []
        transcription_text = []
        transcription_text_lens = []

        utts = []
        for utt, audio, translation, transcription in batch:
            audio, translation, transcription = self.process_utterance(
                audio, translation, transcription)
            #utt
            utts.append(utt)
            # audio
            audios.append(audio)  # [T, D]
            audio_lens.append(audio.shape[0])
            # text
            # for training, text is token ids
            # else text is string, convert to unicode ord
            tokens = [[], []]
            for idx, text in enumerate([translation, transcription]):
                if self._keep_transcription_text:
                    assert isinstance(text, str), (type(text), text)
                    tokens[idx] = [ord(t) for t in text]
                else:
                    tokens[idx] = text  # token ids
                tokens[idx] = tokens[idx] if isinstance(
                    tokens[idx], np.ndarray) else np.array(
                        tokens[idx], dtype=np.int64)
            translation_text.append(tokens[0])
            translation_text_lens.append(tokens[0].shape[0])
            transcription_text.append(tokens[1])
            transcription_text_lens.append(tokens[1].shape[0])

        padded_audios = pad_sequence(
            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
        audio_lens = np.array(audio_lens).astype(np.int64)
        padded_translation = pad_sequence(
            translation_text, padding_value=IGNORE_ID).astype(np.int64)
        translation_lens = np.array(translation_text_lens).astype(np.int64)
        padded_transcription = pad_sequence(
            transcription_text, padding_value=IGNORE_ID).astype(np.int64)
        transcription_lens = np.array(transcription_text_lens).astype(np.int64)
        return utts, padded_audios, audio_lens, (
            padded_translation, padded_transcription), (translation_lens,
                                                        transcription_lens)
