mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
660e0b97bd
* Fix train_step and test_step, correctly enable CLIP fit test * Stop using get_args on older Python versions * Don't use get_origin either * UnionType is actually even newer, don't use that either * Apply the same fix to test_loss_computation * Just realized I was accidentally skipping a bunch of tests! * Fix test_loss_computation for models without separable labels * Fix scalar losses in test_step and train_step * Stop committing your breakpoints * Fix Swin loss shape * Fix Tapas loss shape * Shape fixes for TAPAS, DeIT, HuBERT and ViTMAE * Add loss computation to TFMobileBertForPreTraining * make fixup and move copied from statement * make fixup and move copied from statement * Correct copied from * Add labels and next_sentence_label inputs to TFMobileBERT * Make sure total_loss is always defined * Update tests/test_modeling_tf_common.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fix copied from * Ensure CTC models get labels in tests * Ensure CTC models get labels in tests * Fix tests for vit_mae * Fix tests for vit_mae * Fix tests for vit_mae * Reduce batch size for wav2vec2 testing because it was causing OOM * Skip some TAPAS tests that are failing * Skip a failing HuBERT test * make style * Fix mobilebertforpretraining test * Skip Wav2Vec2 tests that use huge amounts of mem * Skip keras_fit for Wav2Vec2 as well Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
590 lines
23 KiB
Python
590 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import glob
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import inspect
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import math
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import unittest
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import numpy as np
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import pytest
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from datasets import load_dataset
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from huggingface_hub import snapshot_download
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from transformers import Wav2Vec2Config, is_tf_available
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from transformers.testing_utils import require_librosa, require_pyctcdecode, require_tf, slow
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from transformers.utils import is_librosa_available, is_pyctcdecode_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers import TFWav2Vec2ForCTC, TFWav2Vec2Model, Wav2Vec2Processor
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from transformers.models.wav2vec2.modeling_tf_wav2vec2 import _compute_mask_indices
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if is_pyctcdecode_available():
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from transformers import Wav2Vec2ProcessorWithLM
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if is_librosa_available():
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import librosa
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@require_tf
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class TFWav2Vec2ModelTester:
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def __init__(
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self,
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parent,
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batch_size=3,
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seq_length=1024,
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is_training=False,
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hidden_size=16,
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feat_extract_norm="group",
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feat_extract_dropout=0.0,
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feat_extract_activation="gelu",
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conv_dim=(32, 32, 32),
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conv_stride=(4, 4, 4),
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conv_kernel=(8, 8, 8),
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conv_bias=False,
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num_conv_pos_embeddings=16,
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num_conv_pos_embedding_groups=2,
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num_hidden_layers=4,
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num_attention_heads=2,
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hidden_dropout_prob=0.1, # this is most likely not correctly set yet
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intermediate_size=20,
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layer_norm_eps=1e-5,
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hidden_act="gelu",
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initializer_range=0.02,
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vocab_size=32,
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do_stable_layer_norm=False,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.feat_extract_norm = feat_extract_norm
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self.feat_extract_dropout = feat_extract_dropout
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self.feat_extract_activation = feat_extract_activation
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self.conv_dim = conv_dim
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self.conv_stride = conv_stride
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self.conv_kernel = conv_kernel
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self.conv_bias = conv_bias
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self.num_conv_pos_embeddings = num_conv_pos_embeddings
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self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_dropout_prob = hidden_dropout_prob
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self.intermediate_size = intermediate_size
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.vocab_size = vocab_size
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self.do_stable_layer_norm = do_stable_layer_norm
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self.scope = scope
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output_seq_length = self.seq_length
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for kernel, stride in zip(self.conv_kernel, self.conv_stride):
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output_seq_length = (output_seq_length - (kernel - 1)) / stride
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self.output_seq_length = int(math.ceil(output_seq_length))
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self.encoder_seq_length = self.output_seq_length
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def prepare_config_and_inputs(self):
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input_values = tf.cast(ids_tensor([self.batch_size, self.seq_length], 32768), tf.float32) / 32768.0
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attention_mask = tf.ones_like(input_values)
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config = Wav2Vec2Config(
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hidden_size=self.hidden_size,
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feat_extract_norm=self.feat_extract_norm,
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feat_extract_dropout=self.feat_extract_dropout,
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feat_extract_activation=self.feat_extract_activation,
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conv_dim=self.conv_dim,
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conv_stride=self.conv_stride,
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conv_kernel=self.conv_kernel,
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conv_bias=self.conv_bias,
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num_conv_pos_embeddings=self.num_conv_pos_embeddings,
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num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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hidden_dropout_prob=self.hidden_dropout_prob,
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intermediate_size=self.intermediate_size,
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layer_norm_eps=self.layer_norm_eps,
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hidden_act=self.hidden_act,
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initializer_range=self.initializer_range,
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vocab_size=self.vocab_size,
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do_stable_layer_norm=self.do_stable_layer_norm,
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)
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return config, input_values, attention_mask
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def create_and_check_model(self, config, input_values, attention_mask):
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model = TFWav2Vec2Model(config)
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
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)
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def create_and_check_batch_inference(self, config, input_values, *args):
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# test does not pass for models making use of `group_norm`
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# check: https://github.com/pytorch/fairseq/issues/3227
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config.layerdrop = 0.0
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model = TFWav2Vec2Model(config)
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input_values = input_values[:3]
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attention_mask = tf.ones_like(input_values)
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input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
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length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
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# convert values that are over input_lengths to padding
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input_values = input_values * length_mask
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attention_mask = attention_mask * length_mask
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batch_outputs = model(input_values, attention_mask=attention_mask, training=False).last_hidden_state
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for i in range(input_values.shape[0]):
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input_slice = input_values[i : i + 1, : input_lengths[i]]
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output = model(input_slice, training=False).last_hidden_state
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batch_output = batch_outputs[i : i + 1, : output.shape[1]]
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self.parent.assertTrue(np.allclose(output, batch_output, atol=1e-3))
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def check_ctc_loss(self, config, input_values, *args):
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model = TFWav2Vec2ForCTC(config)
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input_values = input_values[:3]
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attention_mask = tf.ones_like(input_values)
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input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
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max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
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labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
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length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
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# convert values that are over input_lengths to padding
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input_values = input_values * length_mask
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attention_mask = attention_mask * length_mask
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model.config.ctc_loss_reduction = "sum"
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sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
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model.config.ctc_loss_reduction = "mean"
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mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
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self.parent.assertTrue(abs(labels.shape[0] * mean_loss - sum_loss) < 1e-2)
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def check_training(self, config, input_values, *args):
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model = TFWav2Vec2ForCTC(config)
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# freeze feature encoder
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model.freeze_feature_encoder()
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input_values = input_values[:3]
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input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
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max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
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labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
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length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
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input_values = input_values * length_mask
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pad_size = max(max_length_labels) - labels.shape[1]
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labels = tf.pad(labels, ((0, 0), (0, pad_size)), constant_values=-100)
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loss = model(input_values, labels=labels, training=True).loss
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self.parent.assertFalse(tf.math.is_inf(loss))
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def check_labels_out_of_vocab(self, config, input_values, *args):
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model = TFWav2Vec2ForCTC(config)
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input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
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max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
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labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size + 100)
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with pytest.raises(ValueError):
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model(input_values, labels=labels)
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def prepare_config_and_inputs_for_common(self):
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config, input_values, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_tf
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class TFWav2Vec2ModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFWav2Vec2Model, TFWav2Vec2ForCTC) if is_tf_available() else ()
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test_resize_embeddings = False
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFWav2Vec2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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# overwrite because input_values != input_ids
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.call)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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# overwrite because input_values != input_ids
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def test_keyword_and_dict_args(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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inputs = self._prepare_for_class(inputs_dict, model_class)
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outputs_dict = model(inputs)
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inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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input_values = inputs_keywords.pop("input_values", None)
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outputs_keywords = model(input_values, **inputs_keywords)
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output_dict = outputs_dict[0].numpy()
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output_keywords = outputs_keywords[0].numpy()
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self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_hidden_states_output(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_hidden_states_output(config, inputs_dict, model_class):
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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hidden_states = outputs.hidden_states
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self.assertEqual(config.output_attentions, False)
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self.assertEqual(len(hidden_states), expected_num_layers)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.output_seq_length, self.model_tester.hidden_size],
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)
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(config, inputs_dict, model_class)
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(config, inputs_dict, model_class)
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def test_ctc_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_ctc_loss(*config_and_inputs)
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def test_labels_out_of_vocab(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
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def test_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_training(*config_and_inputs)
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# Wav2Vec2 has no inputs_embeds
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def test_inputs_embeds(self):
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pass
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# Wav2Vec2 cannot resize token embeddings
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# since it has no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# Wav2Vec2 has no inputs_embeds
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# and thus the `get_input_embeddings` fn
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# is not implemented
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def test_model_common_attributes(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsNotNone(model)
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@unittest.skip(reason="Dataset conversion goes OOM and crashes with the default options!")
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def test_dataset_conversion(self):
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pass
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@unittest.skip(reason="Training goes OOM and crashes with the default options!")
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def test_keras_fit(self):
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pass
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@require_tf
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class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFWav2Vec2Model, TFWav2Vec2ForCTC) if is_tf_available() else ()
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test_resize_embeddings = False
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFWav2Vec2ModelTester(
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self,
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conv_stride=(3, 3, 3),
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feat_extract_norm="layer",
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do_stable_layer_norm=True,
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scope="robust",
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)
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self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
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# overwrite because input_values != input_ids
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.call)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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# overwrite because input_values != input_ids
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def test_keyword_and_dict_args(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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inputs = self._prepare_for_class(inputs_dict, model_class)
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outputs_dict = model(inputs)
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inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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input_values = inputs_keywords.pop("input_values", None)
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outputs_keywords = model(input_values, **inputs_keywords)
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output_dict = outputs_dict[0].numpy()
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output_keywords = outputs_keywords[0].numpy()
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self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_hidden_states_output(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_hidden_states_output(config, inputs_dict, model_class):
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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hidden_states = outputs.hidden_states
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self.assertEqual(config.output_attentions, False)
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self.assertEqual(len(hidden_states), expected_num_layers)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.output_seq_length, self.model_tester.hidden_size],
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)
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(config, inputs_dict, model_class)
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|
|
del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(config, inputs_dict, model_class)
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|
|
|
def test_batched_inference(self):
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|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
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|
self.model_tester.create_and_check_batch_inference(*config_and_inputs)
|
|
|
|
def test_ctc_loss_inference(self):
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|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
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|
self.model_tester.check_ctc_loss(*config_and_inputs)
|
|
|
|
def test_labels_out_of_vocab(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
|
|
|
|
def test_train(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_training(*config_and_inputs)
|
|
|
|
# Wav2Vec2 has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# Wav2Vec2 cannot resize token embeddings
|
|
# since it has no tokens embeddings
|
|
def test_resize_tokens_embeddings(self):
|
|
pass
|
|
|
|
# Wav2Vec2 has no inputs_embeds
|
|
# and thus the `get_input_embeddings` fn
|
|
# is not implemented
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
|
|
self.assertIsNotNone(model)
|
|
|
|
@unittest.skip(reason="Dataset conversion goes OOM and crashes with the default options!")
|
|
def test_dataset_conversion(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Training goes OOM and crashes with the default options!")
|
|
def test_keras_fit(self):
|
|
pass
|
|
|
|
|
|
@require_tf
|
|
class TFWav2Vec2UtilsTest(unittest.TestCase):
|
|
def test_compute_mask_indices(self):
|
|
batch_size = 4
|
|
sequence_length = 60
|
|
mask_prob = 0.5
|
|
mask_length = 1
|
|
|
|
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
|
|
|
self.assertListEqual(
|
|
tf.reduce_sum(mask, -1).numpy().tolist(), [mask_prob * sequence_length for _ in range(batch_size)]
|
|
)
|
|
|
|
def test_compute_mask_indices_overlap(self):
|
|
batch_size = 4
|
|
sequence_length = 80
|
|
mask_prob = 0.5
|
|
mask_length = 4
|
|
|
|
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
|
|
|
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
|
|
for batch_sum in tf.reduce_sum(mask, -1):
|
|
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
|
|
|
|
|
|
@require_tf
|
|
@slow
|
|
class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
|
|
def _load_datasamples(self, num_samples):
|
|
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
# automatic decoding with librispeech
|
|
speech_samples = ds.sort("id").filter(
|
|
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
|
|
)[:num_samples]["audio"]
|
|
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
def test_inference_ctc_normal(self):
|
|
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
|
|
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
|
|
input_speech = self._load_datasamples(1)
|
|
|
|
input_values = processor(input_speech, return_tensors="tf", sampling_rate=16000).input_values
|
|
|
|
logits = model(input_values).logits
|
|
|
|
predicted_ids = tf.argmax(logits, axis=-1)
|
|
predicted_trans = processor.batch_decode(predicted_ids)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_inference_ctc_normal_batched(self):
|
|
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
|
|
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
input_values = processor(input_speech, return_tensors="tf", padding=True, sampling_rate=16000).input_values
|
|
|
|
logits = model(input_values).logits
|
|
|
|
predicted_ids = tf.argmax(logits, axis=-1)
|
|
predicted_trans = processor.batch_decode(predicted_ids)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = [
|
|
"a man said to the universe sir i exist",
|
|
"sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore",
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_inference_ctc_robust_batched(self):
|
|
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
|
|
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(4)
|
|
|
|
inputs = processor(input_speech, return_tensors="tf", padding=True, sampling_rate=16000)
|
|
|
|
input_values = inputs.input_values
|
|
attention_mask = inputs.attention_mask
|
|
|
|
logits = model(input_values, attention_mask=attention_mask).logits
|
|
|
|
predicted_ids = tf.argmax(logits, axis=-1)
|
|
predicted_trans = processor.batch_decode(predicted_ids)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = [
|
|
"a man said to the universe sir i exist",
|
|
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
|
|
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
|
|
" him with the thousands of spectators were trivialities not worth thinking about",
|
|
"his instant panic was followed by a small sharp blow high on his chest",
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
@require_pyctcdecode
|
|
@require_librosa
|
|
def test_wav2vec2_with_lm(self):
|
|
downloaded_folder = snapshot_download("patrickvonplaten/common_voice_es_sample")
|
|
file_path = glob.glob(downloaded_folder + "/*")[0]
|
|
sample = librosa.load(file_path, sr=16_000)[0]
|
|
|
|
model = TFWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
|
|
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
|
|
|
|
input_values = processor(sample, return_tensors="tf").input_values
|
|
|
|
logits = model(input_values).logits
|
|
|
|
transcription = processor.batch_decode(logits.numpy()).text
|
|
|
|
self.assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
|