# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Feature extractor class for M-CTC-T
"""

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...file_utils import PaddingStrategy, TensorType
from ...utils import logging


logger = logging.get_logger(__name__)


class MCTCTFeatureExtractor(SequenceFeatureExtractor):
    r"""
    Constructs a M-CTC-T feature extractor.

    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. This
    code has been adapted from Flashlight's C++ code. For more information about the implementation, one can refer to
    this [notebook](https://colab.research.google.com/drive/1GLtINkkhzms-IsdcGy_-tVCkv0qNF-Gt#scrollTo=pMCRGMmUC_an)
    that takes the user step-by-step in the implementation.

    Args:
        feature_size (`int`, defaults to 80):
            The feature dimension of the extracted features. This is the number of mel_frequency
        sampling_rate (`int`, defaults to 16000):
            The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
        padding_value (`float`, defaults to 0.0):
            The value that is used to fill the padding values.
        hop_length (`int`, defaults to 10):
            Number of audio samples between windows. Otherwise referred to as "shift" in many papers.
        win_length (`int`, defaults to 25):
            Number of ms per window
        win_function (`str`, defaults to `"hamming_window"`):
            Name for the window function used for windowing, must be accessible via `torch.{win_function}`
        frame_signal_scale (`float`, defaults to 32768.0):
            Constant multiplied in creating the frames before applying DFT.
        preemphasis_coeff (`float`, defaults to 0.97):
            Constant multiplied in applying Pre-emphasis before DFT.
        mel_floor (`float` defaults to 1.0):
            Minimum value of mel frequency banks.
        normalize_means (`bool`, *optional*, defaults to `True`):
            Whether or not to zero-mean normalize the extracted features.
        normalize_vars (`bool`, *optional*, defaults to `True`):
            Whether or not to unit-variance normalize the extracted features.
    """

    model_input_names = ["input_features", "attention_mask"]

    def __init__(
        self,
        feature_size=80,
        sampling_rate=16000,
        padding_value=0.0,
        hop_length=10,
        win_length=25,
        win_function="hamming_window",
        frame_signal_scale=32768.0,
        preemphasis_coeff=0.97,
        mel_floor=1.0,
        normalize_means=True,
        normalize_vars=True,
        return_attention_mask=False,
        **kwargs
    ):
        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)

        self.feature_size = feature_size
        self.sampling_rate = sampling_rate
        self.padding_value = padding_value
        self.hop_length = hop_length
        self.win_length = win_length
        self.frame_signal_scale = frame_signal_scale
        self.preemphasis_coeff = preemphasis_coeff
        self.mel_floor = mel_floor
        self.normalize_means = normalize_means
        self.normalize_vars = normalize_vars
        self.win_function = win_function
        self.return_attention_mask = return_attention_mask

        self.sample_size = win_length * sampling_rate // 1000
        self.sample_stride = hop_length * sampling_rate // 1000

        self.n_fft = 2 ** int(np.ceil(np.log2(self.sample_size)))
        self.n_freqs = (self.n_fft // 2) + 1

    @staticmethod
    def _num_frames_calc(in_size, frame_size, frame_stride):
        return int(1 + np.floor((in_size - frame_size) * 1 / frame_stride))

    @staticmethod
    def _frame_signal(one_waveform, n_frames, frame_signal_scale, window_length, sample_stride):
        scale = frame_signal_scale
        frames = np.zeros(n_frames * window_length)
        for frame_idx in range(n_frames):
            start = frame_idx * window_length
            end = (frame_idx + 1) * window_length
            wave_start = frame_idx * sample_stride
            wave_end = frame_idx * sample_stride + window_length
            frames[start:end] = scale * one_waveform[wave_start:wave_end]

        return frames

    @staticmethod
    def _apply_preemphasis_inplace(frames, window_length, preemphasis_coeff):
        if frames.size % window_length != 0:
            raise ValueError(
                f"`frames` is supposed to have length divisble by `window_length`, but is {frames.size} with"
                f" window_length={window_length}."
            )

        n_frames = frames.size // window_length
        for frame_idx in range(n_frames, 0, -1):
            start = (frame_idx - 1) * window_length
            end = frame_idx * window_length - 1
            frames[start + 1 : end + 1] -= preemphasis_coeff * frames[start:end]
            frames[start] *= 1 - preemphasis_coeff

    @staticmethod
    def _windowing(frames, window_length, window):
        if frames.size % window_length != 0:
            raise ValueError(
                f"`frames` is supposed to have length divisble by `window_length`, but is {frames.size} with"
                f" window_length={window_length}."
            )

        shaped = frames.reshape(-1, window_length)
        shaped = window * shaped
        return shaped

    @staticmethod
    def _dft(frames, K, n_frames, n_samples, n_fft):
        dft = np.zeros([n_frames, K])

        for frame in range(n_frames):
            begin = frame * n_samples

            inwards_buffer = frames[begin : begin + n_samples]
            inwards_buffer = np.pad(inwards_buffer, (0, n_fft - n_samples), "constant")
            out = np.fft.rfft(inwards_buffer)

            dft[frame] = np.abs(out[:K])

        return dft

    def _extract_mfsc_features(self, one_waveform: np.array) -> np.ndarray:
        """
        Extracts MFSC Features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC code.
        """
        if self.win_function == "hamming_window":
            window = torch.hamming_window(window_length=self.sample_size, periodic=False, alpha=0.54, beta=0.46)
        else:
            window = getattr(torch, self.win_function)()

        window = window.numpy()

        fbanks = torchaudio.functional.melscale_fbanks(
            n_freqs=self.n_freqs,
            f_min=0.0,  # change this to zeros
            f_max=self.sampling_rate / 2.0,
            n_mels=self.feature_size,
            sample_rate=self.sampling_rate,
        )

        fbanks = fbanks.numpy()

        n_frames = self._num_frames_calc(one_waveform.size, self.sample_size, self.sample_stride)

        frames = self._frame_signal(
            one_waveform, n_frames, self.frame_signal_scale, self.sample_size, self.sample_stride
        )

        self._apply_preemphasis_inplace(frames, self.sample_size, self.preemphasis_coeff)

        frames = self._windowing(frames, self.sample_size, window)

        dft_out = self._dft(frames.flatten(), self.n_freqs, n_frames, self.sample_size, self.n_fft)

        # msfc_features = STFT * mel frequency banks.
        msfc_features = np.einsum("...tf,fm->...tm", dft_out, fbanks)

        # clamp feature values then log scale, as implemented in flashlight
        msfc_features = np.maximum(msfc_features, self.mel_floor)
        msfc_features = np.log(msfc_features)

        return msfc_features

    def _normalize_one(self, x, input_length, padding_value):
        # make sure we normalize float32 arrays
        if self.normalize_means:
            mean = x[:input_length].mean(axis=0)
            x = np.subtract(x, mean)
        if self.normalize_vars:
            std = x[:input_length].std(axis=0)
            x = np.divide(x, std)

        if input_length < x.shape[0]:
            x[input_length:] = padding_value

        # make sure array is in float32
        x = x.astype(np.float32)

        return x

    def normalize(
        self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
    ) -> List[np.ndarray]:
        lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [self._normalize_one(x, n, self.padding_value) for x, n in zip(input_features, lengths)]

    def __call__(
        self,
        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        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). sequences. It returns the
        log-mel spectrogram of the input audio, as implemented in the original Flashlight MFSC feature extraction code.

        Args:
            raw_speech (`torch.Tensor`, `np.ndarray`, `List[float]`, `List[torch.Tensor]`, `List[np.ndarray]`, `List[List[float]]`):
                The sequence or batch of sequences to be padded. Each sequence can be a tensor, a numpy array, a list
                of float values, a list of tensors, a list of numpy arrays or a list of list of float values.
            padding (`bool`, `str` or [`~file_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 [`~file_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 `raw_speech` input was sampled. It is strongly recommended to pass
                `sampling_rate` at the forward call to prevent silent errors.
            padding_value (`float`, defaults to 0.0):
        """

        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 `raw_speech` 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."
            )

        is_batched = bool(
            isinstance(raw_speech, (list, tuple))
            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
        )

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

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

        # extract fbank features
        features = [self._extract_mfsc_features(one_waveform) for one_waveform in raw_speech]

        # convert into correct format for padding
        encoded_inputs = BatchFeature({"input_features": features})

        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=True,
            **kwargs,
        )
        # make sure list is in array format
        input_features = padded_inputs.get("input_features")
        if isinstance(input_features[0], list):
            padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]

        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]

        if self.normalize_means or self.normalize_vars:
            attention_mask = (
                np.array(attention_mask, dtype=np.int32)
                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
                and padding
                else None
            )
            padded_inputs["input_features"] = self.normalize(
                padded_inputs["input_features"], attention_mask=attention_mask
            )

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

        return padded_inputs
