import numpy as np
import librosa
import os, copy
from scipy import signal
import paddle.fluid.layers as layers


def get_positional_table(d_pos_vec, n_position=1024):
    position_enc = np.array([
        [pos / np.power(10000, 2*i/d_pos_vec) for i in range(d_pos_vec)]
        if pos != 0 else np.zeros(d_pos_vec) for pos in range(n_position)])

    position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2]) # dim 2i
    position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2]) # dim 2i+1
    return position_enc

def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
    ''' Sinusoid position encoding table '''

    def cal_angle(position, hid_idx):
        return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)

    def get_posi_angle_vec(position):
        return [cal_angle(position, hid_j) for hid_j in range(d_hid)]

    sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])

    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

    if padding_idx is not None:
        # zero vector for padding dimension
        sinusoid_table[padding_idx] = 0.

    return sinusoid_table

def get_non_pad_mask(seq):
    return layers.unsqueeze((seq != 0).astype(np.float32),[-1])

def get_attn_key_pad_mask(seq_k, seq_q):
    ''' For masking out the padding part of key sequence. '''

    # Expand to fit the shape of key query attention matrix.
    len_q = seq_q.shape[1]
    padding_mask = (seq_k != 0).astype(np.float32)
    padding_mask = layers.expand(layers.unsqueeze(padding_mask,[1]), [1, len_q, 1]) 
    return padding_mask

def get_triu_tensor(seq_k, seq_q):
    ''' For make a triu tensor '''
    len_k = seq_k.shape[1]
    len_q = seq_q.shape[1]
    batch_size = seq_k.shape[0]
    triu_tensor = np.triu(np.ones([len_k, len_q]), 1)
    triu_tensor = np.repeat(np.expand_dims(triu_tensor, axis=0) ,batch_size, axis=0)
    
    return triu_tensor

def guided_attention(N, T, g=0.2):
    '''Guided attention. Refer to page 3 on the paper.'''
    W = np.zeros((N, T), dtype=np.float32)
    for n_pos in range(W.shape[0]):
        for t_pos in range(W.shape[1]):
            W[n_pos, t_pos] = 1 - np.exp(-(t_pos / float(T) - n_pos / float(N)) ** 2 / (2 * g * g))
    return W


def cross_entropy(input, label, position_weight=1.0, epsilon=1e-30):
    output = -1 * label * layers.log(input + epsilon) - (1-label) * layers.log(1 - input + epsilon)
    output = output * (label * (position_weight - 1) + 1)

    return layers.reduce_sum(output, dim=[0, 1])