import copy
import math
import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F

import modules.commons as commons
import modules.modules as modules
from modules.modules import LayerNorm


class FFT(nn.Layer):
  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
               proximal_bias=False, proximal_init=True, **kwargs):
    super().__init__()
    self.hidden_channels = hidden_channels
    self.filter_channels = filter_channels
    self.n_heads = n_heads
    self.n_layers = n_layers
    self.kernel_size = kernel_size
    self.p_dropout = p_dropout
    self.proximal_bias = proximal_bias
    self.proximal_init = proximal_init

    self.drop = nn.Dropout(p_dropout)
    self.self_attn_layers = nn.LayerList()
    self.norm_layers_0 = nn.LayerList()
    self.ffn_layers = nn.LayerList()
    self.norm_layers_1 = nn.LayerList()
    for i in range(self.n_layers):
      self.self_attn_layers.append(
        MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
                           proximal_init=proximal_init))
      self.norm_layers_0.append(LayerNorm(hidden_channels))
      self.ffn_layers.append(
        FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
      self.norm_layers_1.append(LayerNorm(hidden_channels))

  def forward(self, x, x_mask):
    """
    x: decoder input
    h: encoder output
    """
    self_attn_mask = commons.subsequent_mask(x_mask.shape[2]).astype(dtype=x.dtype)
    x = x * x_mask
    for i in range(self.n_layers):
      y = self.self_attn_layers[i](x, x, self_attn_mask)
      y = self.drop(y)
      x = self.norm_layers_0[i](x + y)

      y = self.ffn_layers[i](x, x_mask)
      y = self.drop(y)
      x = self.norm_layers_1[i](x + y)
    x = x * x_mask
    return x


class Encoder(nn.Layer):
  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
    super().__init__()
    self.hidden_channels = hidden_channels
    self.filter_channels = filter_channels
    self.n_heads = n_heads
    self.n_layers = n_layers
    self.kernel_size = kernel_size
    self.p_dropout = p_dropout
    self.window_size = window_size

    self.drop = nn.Dropout(p_dropout)
    self.attn_layers = nn.LayerList()
    self.norm_layers_1 = nn.LayerList()
    self.ffn_layers = nn.LayerList()
    self.norm_layers_2 = nn.LayerList()
    for i in range(self.n_layers):
      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
      self.norm_layers_1.append(LayerNorm(hidden_channels))
      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
      self.norm_layers_2.append(LayerNorm(hidden_channels))

  def forward(self, x, x_mask):
    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
    x = x * x_mask
    for i in range(self.n_layers):
      y = self.attn_layers[i](x, x, attn_mask)
      y = self.drop(y)
      x = self.norm_layers_1[i](x + y)
      y = self.ffn_layers[i](x, x_mask)
      y = self.drop(y)
      x = self.norm_layers_2[i](x + y)
    x = x * x_mask
    return x


class Decoder(nn.Layer):
  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
    super().__init__()
    self.hidden_channels = hidden_channels
    self.filter_channels = filter_channels
    self.n_heads = n_heads
    self.n_layers = n_layers
    self.kernel_size = kernel_size
    self.p_dropout = p_dropout
    self.proximal_bias = proximal_bias
    self.proximal_init = proximal_init

    self.drop = nn.Dropout(p_dropout)
    self.self_attn_layers = nn.LayerList()
    self.norm_layers_0 = nn.LayerList()
    self.encdec_attn_layers = nn.LayerList()
    self.norm_layers_1 = nn.LayerList()
    self.ffn_layers = nn.LayerList()
    self.norm_layers_2 = nn.LayerList()
    for i in range(self.n_layers):
      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
      self.norm_layers_0.append(LayerNorm(hidden_channels))
      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
      self.norm_layers_1.append(LayerNorm(hidden_channels))
      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
      self.norm_layers_2.append(LayerNorm(hidden_channels))

  def forward(self, x, x_mask, h, h_mask):
    """
    x: decoder input
    h: encoder output
    """
    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).astype(dtype=x.dtype)
    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
    x = x * x_mask
    for i in range(self.n_layers):
      y = self.self_attn_layers[i](x, x, self_attn_mask)
      y = self.drop(y)
      x = self.norm_layers_0[i](x + y)

      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
      y = self.drop(y)
      x = self.norm_layers_1[i](x + y)
      
      y = self.ffn_layers[i](x, x_mask)
      y = self.drop(y)
      x = self.norm_layers_2[i](x + y)
    x = x * x_mask
    return x


class MultiHeadAttention(nn.Layer):
  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
    super().__init__()
    assert channels % n_heads == 0

    self.channels = channels
    self.out_channels = out_channels
    self.n_heads = n_heads
    self.p_dropout = p_dropout
    self.window_size = window_size
    self.heads_share = heads_share
    self.block_length = block_length
    self.proximal_bias = proximal_bias
    self.proximal_init = proximal_init
    self.attn = None

    self.k_channels = channels // n_heads

    self.conv_q = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
    self.conv_k = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
    self.conv_v = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
    self.conv_o = nn.Conv1D(channels, out_channels, 1)
    self.drop = nn.Dropout(p_dropout)

    if window_size is not None:
      n_heads_rel = 1 if heads_share else n_heads
      rel_stddev = self.k_channels**-0.5

      rand = paddle.randn((n_heads_rel, window_size * 2 + 1, self.k_channels)) * rel_stddev

      self.emb_rel_k = paddle.create_parameter(rand.shape,'float32',None)
      self.emb_rel_v = paddle.create_parameter(rand.shape,'float32',None)

    #nn.init.xavier_uniform_(self.conv_q.weight)
    #nn.init.xavier_uniform_(self.conv_k.weight)
    #nn.init.xavier_uniform_(self.conv_v.weight)
    if proximal_init:
      with paddle.no_grad():
        self.conv_k.weight = (self.conv_q.weight)
        self.conv_k.bias = (self.conv_q.bias)
      
  def forward(self, x, c, attn_mask=None):
    #print(x)
    #print(self.conv_q.weight)
    q = self.conv_q(x)
    k = self.conv_k(c)
    v = self.conv_v(c)

    x, self.attn = self.attention(q, k, v, mask=attn_mask)

    x = self.conv_o(x)
    return x

  @staticmethod
  def _masked_fill(x, mask, value:float):
    y = paddle.full(x.shape, value, x.dtype)
    return paddle.where(mask, y, x)

  def attention(self, query, key, value, mask=None):
    # reshape [b, d, t] -> [b, n_h, t, d_k]
    b, d, t_s, t_t = (*key.shape, query.shape[2])
    query = query.reshape((b, self.n_heads, self.k_channels, t_t)).transpose([0,1,3,2])
    key = key.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])
    value = value.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])

    scores = paddle.matmul(query / math.sqrt(self.k_channels), key.transpose([0,1,3,2])) # 0 1 2 3     -4 -3 -2 -1
    if self.window_size is not None:
      assert t_s == t_t, "Relative attention is only available for self-attention."

      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
      scores_local = self._relative_position_to_absolute_position(rel_logits)
      scores = scores + scores_local
    if self.proximal_bias:
      assert t_s == t_t, "Proximal bias is only available for self-attention."
      scores = scores + self._attention_bias_proximal(t_s).astype(dtype=scores.dtype)
    if mask is not None:
      scores = self._masked_fill(scores, mask == 0, -1e4)
      if self.block_length is not None:
        assert t_s == t_t, "Local attention is only available for self-attention."
        block_mask = paddle.tril(paddle.triu(paddle.ones_like(scores), -self.block_length),self.block_length)
        scores = self._masked_fill(scores, block_mask == 0, -1e4)
    p_attn = F.softmax(scores, axis=-1) # [b, n_h, t_t, t_s]
    p_attn = self.drop(p_attn)
    output = paddle.matmul(p_attn, value)
    if self.window_size is not None:
      relative_weights = self._absolute_position_to_relative_position(p_attn)
      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
    output = output.transpose([0,1,3,2]).reshape((b, d, t_t)) # [b, n_h, t_t, d_k] -> [b, d, t_t]
    return output, p_attn

  def _matmul_with_relative_values(self, x, y):
    """
    x: [b, h, l, m]
    y: [h or 1, m, d]
    ret: [b, h, l, d]
    """
    ret = paddle.matmul(x, y.unsqueeze(0))
    return ret

  def _matmul_with_relative_keys(self, x, y):
    """
    x: [b, h, l, d]
    y: [h or 1, m, d]
    ret: [b, h, l, m]
    """
    ret = paddle.matmul(x, y.unsqueeze(0).transpose([0,1,3,2]))
    return ret

  def _get_relative_embeddings(self, relative_embeddings, length):
    max_relative_position = 2 * self.window_size + 1
    # Pad first before slice to avoid using cond ops.
    pad_length = max(length - (self.window_size + 1), 0)
    slice_start_position = max((self.window_size + 1) - length, 0)
    slice_end_position = slice_start_position + 2 * length - 1
    if pad_length > 0:
      padding = commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])

      padded_relative_embeddings = F.pad(
          x = relative_embeddings.unsqueeze(0),
          pad = padding[0:4]).squeeze(0)

    else:
      padded_relative_embeddings = relative_embeddings
    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
    return used_relative_embeddings

  def _relative_position_to_absolute_position(self, x):
    """
    x: [b, h, l, 2*l-1]
    ret: [b, h, l, l]
    """
    batch, heads, length, _ = x.shape
    # Concat columns of pad to shift from relative to absolute indexing.
    pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])
    pad_shape = commons.fix_pad_shape(pad_shape, x)
    x = F.pad(x, pad_shape)
    # Concat extra elements so to add up to shape (len+1, 2*len-1).
    x_flat = x.reshape([batch, heads, length * 2 * length])
    pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])
    pad_shape = commons.fix_pad_shape(pad_shape,x_flat)
    x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
    # Reshape and slice out the padded elements.
    x_final = x_flat.reshape([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
    return x_final

  def _absolute_position_to_relative_position(self, x):
    """
    x: [b, h, l, l]
    ret: [b, h, l, 2*l-1]
    """
    batch, heads, length, _ = x.shape
    # padd along column
    pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])
    pad_shape = commons.fix_pad_shape(pad_shape, x)
    x = F.pad(x, pad_shape)
    x_flat = x.reshape([batch, heads, length**2 + length*(length -1)])
    # add 0's in the beginning that will skew the elements after reshape
    pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])
    pad_shape = commons.fix_pad_shape(pad_shape, x_flat)
    x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
    x_final = x_flat.reshape([batch, heads, length, 2*length])[:,:,:,1:]
    return x_final

  def _attention_bias_proximal(self, length):
    """Bias for self-attention to encourage attention to close positions.
    Args:
      length: an integer scalar.
    Returns:
      a Tensor with shape [1, 1, length, length]
    """
    r = paddle.arange(length, dtype=np.float32)
    diff = paddle.unsqueeze(r, 0) - paddle.unsqueeze(r, 1)
    return paddle.unsqueeze(paddle.unsqueeze(-paddle.log1p(paddle.abs(diff)), 0), 0)


class FFN(nn.Layer):
  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
    super().__init__()
    self.in_channels = in_channels
    self.out_channels = out_channels
    self.filter_channels = filter_channels
    self.kernel_size = kernel_size
    self.p_dropout = p_dropout
    self.activation = activation
    self.causal = causal

    if causal:
      self.padding = self._causal_padding
    else:
      self.padding = self._same_padding

    self.conv_1 = nn.Conv1D(in_channels, filter_channels, kernel_size)
    self.conv_2 = nn.Conv1D(filter_channels, out_channels, kernel_size)
    self.drop = nn.Dropout(p_dropout)

  def forward(self, x, x_mask):
    x = x * x_mask
    x = self.padding(x)
    x = self.conv_1(x)
    if self.activation == "gelu":
      x = x * F.sigmoid(1.702 * x)
    else:
      x = F.relu(x)
    x = self.drop(x)
    x = x * x_mask
    x = self.padding(x)
    x = self.conv_2(x)
    return x * x_mask
  
  def _causal_padding(self, x):
    if self.kernel_size == 1:
      return x
    pad_l = self.kernel_size - 1
    pad_r = 0
    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
    pad_shape:list = commons.convert_pad_shape(padding)
    pad_shape = commons.fix_pad_shape(pad_shape, x)
    x = F.pad(x, pad_shape,data_format='NCL')
    return x

  def _same_padding(self, x):
    if self.kernel_size == 1:
      return x
    pad_l = (self.kernel_size - 1) // 2
    pad_r = self.kernel_size // 2
    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
    pad_shape = commons.convert_pad_shape(padding)
    pad_shape = commons.fix_pad_shape(pad_shape, x)
    x = F.pad(x, pad_shape, data_format='NCL')
    return x