# coding=utf-8
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"""Feature extractor class for SpeechT5."""

import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


logger = logging.get_logger(__name__)


class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
    r"""
    Constructs a SpeechT5 feature extractor.

    This class can pre-process a raw speech signal by (optionally) normalizing to zero-mean unit-variance, for use by
    the SpeechT5 speech encoder prenet.

    This class can also extract log-mel filter bank features from raw speech, for use by the SpeechT5 speech decoder
    prenet.

    This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
    most of the main methods. Users should refer to this superclass for more information regarding those methods.

    Args:
        feature_size (`int`, *optional*, defaults to 1):
            The feature dimension of the extracted features.
        sampling_rate (`int`, *optional*, defaults to 16000):
            The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
        padding_value (`float`, *optional*, defaults to 0.0):
            The value that is used to fill the padding values.
        do_normalize (`bool`, *optional*, defaults to `False`):
            Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
            improve the performance for some models.
        num_mel_bins (`int`, *optional*, defaults to 80):
            The number of mel-frequency bins in the extracted spectrogram features.
        hop_length (`int`, *optional*, defaults to 16):
            Number of ms between windows. Otherwise referred to as "shift" in many papers.
        win_length (`int`, *optional*, defaults to 64):
            Number of ms per window.
        win_function (`str`, *optional*, defaults to `"hann_window"`):
            Name for the window function used for windowing, must be accessible via `torch.{win_function}`
        frame_signal_scale (`float`, *optional*, defaults to 1.0):
            Constant multiplied in creating the frames before applying DFT. This argument is deprecated.
        fmin (`float`, *optional*, defaults to 80):
            Minimum mel frequency in Hz.
        fmax (`float`, *optional*, defaults to 7600):
            Maximum mel frequency in Hz.
        mel_floor (`float`, *optional*, defaults to 1e-10):
            Minimum value of mel frequency banks.
        reduction_factor (`int`, *optional*, defaults to 2):
            Spectrogram length reduction factor. This argument is deprecated.
        return_attention_mask (`bool`, *optional*, defaults to `True`):
            Whether or not [`~SpeechT5FeatureExtractor.__call__`] should return `attention_mask`.
    """

    model_input_names = ["input_values", "attention_mask"]

    def __init__(
        self,
        feature_size: int = 1,
        sampling_rate: int = 16000,
        padding_value: float = 0.0,
        do_normalize: bool = False,
        num_mel_bins: int = 80,
        hop_length: int = 16,
        win_length: int = 64,
        win_function: str = "hann_window",
        frame_signal_scale: float = 1.0,
        fmin: float = 80,
        fmax: float = 7600,
        mel_floor: float = 1e-10,
        reduction_factor: int = 2,
        return_attention_mask: bool = True,
        **kwargs,
    ):
        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
        self.do_normalize = do_normalize
        self.return_attention_mask = return_attention_mask

        self.num_mel_bins = num_mel_bins
        self.hop_length = hop_length
        self.win_length = win_length
        self.win_function = win_function
        self.frame_signal_scale = frame_signal_scale
        self.fmin = fmin
        self.fmax = fmax
        self.mel_floor = mel_floor
        self.reduction_factor = reduction_factor

        self.sample_size = win_length * sampling_rate // 1000
        self.sample_stride = hop_length * sampling_rate // 1000
        self.n_fft = optimal_fft_length(self.sample_size)
        self.n_freqs = (self.n_fft // 2) + 1

        window = getattr(torch, self.win_function)(window_length=self.sample_size, periodic=True)
        self.window = window.numpy().astype(np.float64)

        self.mel_filters = mel_filter_bank(
            num_frequency_bins=self.n_freqs,
            num_mel_filters=self.num_mel_bins,
            min_frequency=self.fmin,
            max_frequency=self.fmax,
            sampling_rate=self.sampling_rate,
            norm="slaney",
            mel_scale="slaney",
        )

        if frame_signal_scale != 1.0:
            warnings.warn(
                "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers",
                FutureWarning,
            )
        if reduction_factor != 2.0:
            warnings.warn(
                "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers",
                FutureWarning,
            )

    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def zero_mean_unit_var_norm(
        input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0
    ) -> List[np.ndarray]:
        """
        Every array in the list is normalized to have zero mean and unit variance
        """
        if attention_mask is not None:
            attention_mask = np.array(attention_mask, np.int32)
            normed_input_values = []

            for vector, length in zip(input_values, attention_mask.sum(-1)):
                normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                if length < normed_slice.shape[0]:
                    normed_slice[length:] = padding_value

                normed_input_values.append(normed_slice)
        else:
            normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

        return normed_input_values

    def _extract_mel_features(
        self,
        one_waveform: np.ndarray,
    ) -> np.ndarray:
        """
        Extracts log-mel filterbank features for one waveform array (unbatched).
        """
        log_mel_spec = spectrogram(
            one_waveform,
            window=self.window,
            frame_length=self.sample_size,
            hop_length=self.sample_stride,
            fft_length=self.n_fft,
            mel_filters=self.mel_filters,
            mel_floor=self.mel_floor,
            log_mel="log10",
        )
        return log_mel_spec.T

    def __call__(
        self,
        audio: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None,
        audio_target: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        truncation: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        sampling_rate: Optional[int] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Main method to featurize and prepare for the model one or several sequence(s).

        Pass in a value for `audio` to extract waveform features. Pass in a value for `audio_target` to extract log-mel
        spectrogram features.

        Args:
            audio (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`, *optional*):
                The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float
                values, a list of numpy arrays or a list of list of float values. This outputs waveform features. Must
                be mono channel audio, not stereo, i.e. single float per timestep.
            audio_target (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`, *optional*):
                The sequence or batch of sequences to be processed as targets. Each sequence can be a numpy array, a
                list of float values, a list of numpy arrays or a list of list of float values. This outputs log-mel
                spectrogram features.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (`bool`):
                Activates truncation to cut input sequences longer than *max_length* to *max_length*.
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            return_attention_mask (`bool`, *optional*):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific feature_extractor's default.

                [What are attention masks?](../glossary#attention-mask)

            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
            sampling_rate (`int`, *optional*):
                The sampling rate at which the `audio` or `audio_target` input was sampled. It is strongly recommended
                to pass `sampling_rate` at the forward call to prevent silent errors.
        """
        if audio is None and audio_target is None:
            raise ValueError("You must provide either `audio` or `audio_target` values.")

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                    f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
                    f" {self.sampling_rate} and not {sampling_rate}."
                )
        else:
            logger.warning(
                "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                "Failing to do so can result in silent errors that might be hard to debug."
            )

        if audio is not None:
            inputs = self._process_audio(
                audio,
                False,
                padding,
                max_length,
                truncation,
                pad_to_multiple_of,
                return_attention_mask,
                return_tensors,
                **kwargs,
            )
        else:
            inputs = None

        if audio_target is not None:
            inputs_target = self._process_audio(
                audio_target,
                True,
                padding,
                max_length,
                truncation,
                pad_to_multiple_of,
                return_attention_mask,
                return_tensors,
                **kwargs,
            )

            if inputs is None:
                return inputs_target
            else:
                inputs["labels"] = inputs_target["input_values"]
                decoder_attention_mask = inputs_target.get("attention_mask")
                if decoder_attention_mask is not None:
                    inputs["decoder_attention_mask"] = decoder_attention_mask

        return inputs

    def _process_audio(
        self,
        speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        is_target: bool = False,
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        truncation: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchFeature:
        is_batched_numpy = isinstance(speech, np.ndarray) and len(speech.shape) > 1
        if is_batched_numpy and len(speech.shape) > 2:
            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
        is_batched = is_batched_numpy or (
            isinstance(speech, (list, tuple)) and (isinstance(speech[0], (np.ndarray, tuple, list)))
        )

        if is_batched:
            speech = [np.asarray(speech, dtype=np.float32) for speech in speech]
        elif not is_batched and not isinstance(speech, np.ndarray):
            speech = np.asarray(speech, dtype=np.float32)
        elif isinstance(speech, np.ndarray) and speech.dtype is np.dtype(np.float64):
            speech = speech.astype(np.float32)

        # always return batch
        if not is_batched:
            speech = [speech]

        # needed to make pad() work on spectrogram inputs
        feature_size_hack = self.feature_size

        # convert into correct format for padding
        if is_target:
            features = [self._extract_mel_features(waveform) for waveform in speech]
            encoded_inputs = BatchFeature({"input_values": features})
            self.feature_size = self.num_mel_bins
        else:
            encoded_inputs = BatchFeature({"input_values": speech})

        padded_inputs = self.pad(
            encoded_inputs,
            padding=padding,
            max_length=max_length,
            truncation=truncation,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            **kwargs,
        )

        self.feature_size = feature_size_hack

        # convert input values to correct format
        input_values = padded_inputs["input_values"]
        if not isinstance(input_values[0], np.ndarray):
            padded_inputs["input_values"] = [np.asarray(array, dtype=np.float32) for array in input_values]
        elif (
            not isinstance(input_values, np.ndarray)
            and isinstance(input_values[0], np.ndarray)
            and input_values[0].dtype is np.dtype(np.float64)
        ):
            padded_inputs["input_values"] = [array.astype(np.float32) for array in input_values]
        elif isinstance(input_values, np.ndarray) and input_values.dtype is np.dtype(np.float64):
            padded_inputs["input_values"] = input_values.astype(np.float32)

        # convert attention_mask to correct format
        attention_mask = padded_inputs.get("attention_mask")
        if attention_mask is not None:
            padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if not is_target and self.do_normalize:
            attention_mask = (
                attention_mask
                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            padded_inputs["input_values"] = self.zero_mean_unit_var_norm(
                padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value
            )

        if return_tensors is not None:
            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)

        return padded_inputs

    def to_dict(self) -> Dict[str, Any]:
        output = super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        names = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
        for name in names:
            if name in output:
                del output[name]

        return output
