# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Compute input examples for VGGish from audio waveform."""

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

import numpy as np
import resampy

from . import mel_features
from . import vggish_params

import soundfile as sf


def waveform_to_examples(data, sample_rate, numFrames, fps, 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)
    window_length_seconds = vggish_params.STFT_WINDOW_LENGTH_SECONDS * 25. / fps
    hop_length_seconds = vggish_params.STFT_HOP_LENGTH_SECONDS * 25. / fps

    # 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=window_length_seconds,
                                               hop_length_secs=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)

    maxAudio = int(numFrames * 4)
    if log_mel.shape[0] < maxAudio:
        shortage = maxAudio - log_mel.shape[0]
        log_mel = np.pad(log_mel, ((0, shortage), (0, 0)), 'wrap')
    log_mel = log_mel[:int(round(numFrames * 4)), :]

    # 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
    return log_mel


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.
  """
    wav_data, sr = sf.read(wav_file, dtype='int16')
    assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
    samples = wav_data / 32768.0    # Convert to [-1.0, +1.0]
    return waveform_to_examples(samples, sr, return_tensor)