import tensorflow as tf

def augment_freq_time_mask(mel_spectrogram,
                           frequency_masking_para=30,
                           time_masking_para=10,
                           frequency_mask_num=3,
                           time_mask_num=3):
    freq_max = tf.shape(mel_spectrogram)[1]
    time_max = tf.shape(mel_spectrogram)[2]
    # Frequency masking
    for _ in range(frequency_mask_num):
        f = tf.random.uniform(shape=(), minval=0, maxval=frequency_masking_para, dtype=tf.dtypes.int32)
        f0 = tf.random.uniform(shape=(), minval=0, maxval=freq_max - f, dtype=tf.dtypes.int32)
        value_ones_freq_prev = tf.ones(shape=[1, f0, time_max])
        value_zeros_freq = tf.zeros(shape=[1, f, time_max])
        value_ones_freq_next = tf.ones(shape=[1, freq_max-(f0+f), time_max])
        freq_mask = tf.concat([value_ones_freq_prev, value_zeros_freq, value_ones_freq_next], axis=1)
        #mel_spectrogram[:, f0:f0 + f, :] = 0 #can't assign to tensor
        #mel_spectrogram[:, f0:f0 + f, :] = value_zeros_freq #can't assign to tensor
        mel_spectrogram = mel_spectrogram*freq_mask

    # Time masking
    for _ in range(time_mask_num):
        t = tf.random.uniform(shape=(), minval=0, maxval=time_masking_para, dtype=tf.dtypes.int32)
        t0 = tf.random.uniform(shape=(), minval=0, maxval=time_max - t, dtype=tf.dtypes.int32)
        value_zeros_time_prev = tf.ones(shape=[1, freq_max, t0])
        value_zeros_time = tf.zeros(shape=[1, freq_max, t])
        value_zeros_time_next = tf.ones(shape=[1, freq_max, time_max-(t0+t)])
        time_mask = tf.concat([value_zeros_time_prev, value_zeros_time, value_zeros_time_next], axis=2)
        #mel_spectrogram[:, :, t0:t0 + t] = 0 #can't assign to tensor
        #mel_spectrogram[:, :, t0:t0 + t] = value_zeros_time #can't assign to tensor
        mel_spectrogram = mel_spectrogram*time_mask

    return mel_spectrogram

def augment_pitch_and_tempo(spectrogram,
                            max_tempo=1.2,
                            max_pitch=1.1,
                            min_pitch=0.95):
    original_shape = tf.shape(spectrogram)
    choosen_pitch = tf.random.uniform(shape=(), minval=min_pitch, maxval=max_pitch)
    choosen_tempo = tf.random.uniform(shape=(), minval=1, maxval=max_tempo)
    new_height = tf.cast(tf.cast(original_shape[1], tf.float32)*choosen_pitch, tf.int32)
    new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_tempo), tf.int32)
    spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width])
    spectrogram_aug = tf.image.crop_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0, target_height=tf.minimum(original_shape[1], new_height), target_width=tf.shape(spectrogram_aug)[2])
    spectrogram_aug = tf.cond(choosen_pitch < 1,
                              lambda: tf.image.pad_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0,
                                                                   target_height=original_shape[1], target_width=tf.shape(spectrogram_aug)[2]),
                              lambda: spectrogram_aug)
    return spectrogram_aug[:, :, :, 0]


def augment_speed_up(spectrogram,
                     speed_std=0.1):
    original_shape = tf.shape(spectrogram)
    choosen_speed = tf.math.abs(tf.random.normal(shape=(), stddev=speed_std)) # abs makes sure the augmention will only speed up
    choosen_speed = 1 + choosen_speed
    new_height = tf.cast(tf.cast(original_shape[1], tf.float32), tf.int32)
    new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_speed), tf.int32)
    spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width])
    return spectrogram_aug[:, :, :, 0]

def augment_dropout(spectrogram,
                    keep_prob=0.95):
    return tf.nn.dropout(spectrogram, rate=1-keep_prob)