# Copyright 2017 The TensorFlow 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.
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# ==============================================================================

"""Compute input examples for VGGish from audio waveform."""

# Modification: Return torch tensors rather than numpy arrays
import torch

import numpy as np
import resampy

import .mel_features
import .vggish_params

import torchaudio


def waveform_to_examples(data, sample_rate, return_tensor=True):
    """Converts audio waveform into an array of examples for VGGish.

  Args:
    data: np.array of either one dimension (mono) or two dimensions
      (multi-channel, with the outer dimension representing channels).
      Each sample is generally expected to lie in the range [-1.0, +1.0],
      although this is not required.
    sample_rate: Sample rate of data.
    return_tensor: Return data as a Pytorch tensor ready for VGGish

  Returns:
    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
    a sequence of examples, each of which contains a patch of log mel
    spectrogram, covering num_frames frames of audio and num_bands mel frequency
    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.

  """
    # Convert to mono.
    if len(data.shape) > 1:
        data = np.mean(data, axis=1)
    # Resample to the rate assumed by VGGish.
    if sample_rate != vggish_params.SAMPLE_RATE:
        data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)

    # Compute log mel spectrogram features.
    log_mel = mel_features.log_mel_spectrogram(
        data,
        audio_sample_rate=vggish_params.SAMPLE_RATE,
        log_offset=vggish_params.LOG_OFFSET,
        window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
        hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
        num_mel_bins=vggish_params.NUM_MEL_BINS,
        lower_edge_hertz=vggish_params.MEL_MIN_HZ,
        upper_edge_hertz=vggish_params.MEL_MAX_HZ)

    # Frame features into examples.
    features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
    example_window_length = int(round(
        vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
    example_hop_length = int(round(
        vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
    log_mel_examples = mel_features.frame(
        log_mel,
        window_length=example_window_length,
        hop_length=example_hop_length)

    if return_tensor:
        log_mel_examples = torch.tensor(
            log_mel_examples, requires_grad=True)[:, None, :, :].float()

    return log_mel_examples


def wavfile_to_examples(wav_file, return_tensor=True):
    """Convenience wrapper around waveform_to_examples() for a common WAV format.

  Args:
    wav_file: String path to a file, or a file-like object. The file
    is assumed to contain WAV audio data with signed 16-bit PCM samples.
    torch: Return data as a Pytorch tensor ready for VGGish

  Returns:
    See waveform_to_examples.
  """
    data, sr = torchaudio.load(wav_file)
    wav_data = data.detach().numpy().transpose()
    return waveform_to_examples(wav_data, sr, return_tensor)