mirror of https://github.com/coqui-ai/TTS.git
203 lines
7.9 KiB
Python
203 lines
7.9 KiB
Python
import copy
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import math
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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def convert_pad_shape(pad_shape):
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l = pad_shape[::-1]
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pad_shape = [item for sublist in l for item in sublist]
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return pad_shape
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class MultiHeadAttention(nn.Module):
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def __init__(self,
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channels,
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out_channels,
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n_heads,
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window_size=None,
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heads_share=True,
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dropout_p=0.,
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input_length=None,
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proximal_bias=False,
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proximal_init=False):
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super().__init__()
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assert channels % n_heads == 0
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self.channels = channels
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self.out_channels = out_channels
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self.n_heads = n_heads
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self.window_size = window_size
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self.heads_share = heads_share
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self.input_length = input_length
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self.proximal_bias = proximal_bias
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self.dropout_p = dropout_p
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = nn.Conv1d(channels, channels, 1)
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self.conv_k = nn.Conv1d(channels, channels, 1)
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self.conv_v = nn.Conv1d(channels, channels, 1)
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if window_size is not None:
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n_heads_rel = 1 if heads_share else n_heads
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rel_stddev = self.k_channels**-0.5
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self.emb_rel_k = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev)
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self.emb_rel_v = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev)
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self.conv_o = nn.Conv1d(channels, out_channels, 1)
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self.drop = nn.Dropout(dropout_p)
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nn.init.xavier_uniform_(self.conv_q.weight)
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nn.init.xavier_uniform_(self.conv_k.weight)
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if proximal_init:
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self.conv_k.weight.data.copy_(self.conv_q.weight.data)
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self.conv_k.bias.data.copy_(self.conv_q.bias.data)
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nn.init.xavier_uniform_(self.conv_v.weight)
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def forward(self, x, c, attn_mask=None):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, self.attn = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(self, query, key, value, mask=None):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, t_t = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels,
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t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels,
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t_s).transpose(2, 3)
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scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(
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self.k_channels)
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if self.window_size is not None:
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assert t_s == t_t, "Relative attention is only available for self-attention."
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key_relative_embeddings = self._get_relative_embeddings(
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self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(
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query, key_relative_embeddings)
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rel_logits = self._relative_position_to_absolute_position(
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rel_logits)
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scores_local = rel_logits / math.sqrt(self.k_channels)
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scores = scores + scores_local
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if self.proximal_bias:
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assert t_s == t_t, "Proximal bias is only available for self-attention."
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scores = scores + self._attention_bias_proximal(t_s).to(
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device=scores.device, dtype=scores.dtype)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e4)
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if self.input_length is not None:
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block_mask = torch.ones_like(scores).triu(
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-self.input_length).tril(self.input_length)
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scores = scores * block_mask + -1e4 * (1 - block_mask)
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p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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if self.window_size is not None:
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relative_weights = self._absolute_position_to_relative_position(
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p_attn)
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value_relative_embeddings = self._get_relative_embeddings(
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self.emb_rel_v, t_s)
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output = output + self._matmul_with_relative_values(
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relative_weights, value_relative_embeddings)
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output = output.transpose(2, 3).contiguous().view(
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b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
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return output, p_attn
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def _matmul_with_relative_values(self, x, y):
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"""
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x: [b, h, l, m]
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y: [h or 1, m, d]
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ret: [b, h, l, d]
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"""
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ret = torch.matmul(x, y.unsqueeze(0))
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return ret
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def _matmul_with_relative_keys(self, x, y):
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"""
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x: [b, h, l, d]
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y: [h or 1, m, d]
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ret: [b, h, l, m]
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"""
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length):
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max_relative_position = 2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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convert_pad_shape([[0, 0], [pad_length, pad_length],
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[0, 0]]))
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else:
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padded_relative_embeddings = relative_embeddings
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used_relative_embeddings = padded_relative_embeddings[:,
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slice_start_position:
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slice_end_position]
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return used_relative_embeddings
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def _relative_position_to_absolute_position(self, x):
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"""
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x: [b, h, l, 2*l-1]
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ret: [b, h, l, l]
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
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1]]))
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(
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x_flat, convert_pad_shape([[0, 0], [0, 0], [0,
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length - 1]]))
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# Reshape and slice out the padded elements.
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x_final = x_flat.view([batch, heads, length + 1,
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2 * length - 1])[:, :, :length, length - 1:]
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return x_final
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def _absolute_position_to_relative_position(self, x):
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"""
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x: [b, h, l, l]
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ret: [b, h, l, 2*l-1]
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"""
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batch, heads, length, _ = x.size()
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# padd along column
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x = F.pad(
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x,
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convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
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length - 1]]))
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x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(
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x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
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x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
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return x_final
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def _attention_bias_proximal(self, length):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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Returns:
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a Tensor with shape [1, 1, length, length]
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"""
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r = torch.arange(length, dtype=torch.float32)
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
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return torch.unsqueeze(
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torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
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