348 lines
14 KiB
Python
348 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace 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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"""Tests for the Wav2Vec2 tokenizer."""
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import inspect
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import json
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import os
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import random
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.wav2vec2 import Wav2Vec2Config, Wav2Vec2Tokenizer
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from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
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from transformers.testing_utils import slow
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global_rng = random.Random()
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def floats_list(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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if rng is None:
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rng = global_rng
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values = []
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for batch_idx in range(shape[0]):
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values.append([])
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for _ in range(shape[1]):
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values[-1].append(rng.random() * scale)
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return values
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class Wav2Vec2TokenizerTest(unittest.TestCase):
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tokenizer_class = Wav2Vec2Tokenizer
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def setUp(self):
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super().setUp()
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vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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self.tmpdirname = tempfile.mkdtemp()
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return Wav2Vec2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def test_tokenizer_decode(self):
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# TODO(PVP) - change to facebook
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
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[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
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]
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tokens = tokenizer.decode(sample_ids[0])
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batch_tokens = tokenizer.batch_decode(sample_ids)
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self.assertEqual(tokens, batch_tokens[0])
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self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
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def test_tokenizer_decode_special(self):
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# TODO(PVP) - change to facebook
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
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[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
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]
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sample_ids_2 = [
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[11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
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[
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24,
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22,
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5,
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tokenizer.pad_token_id,
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tokenizer.pad_token_id,
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tokenizer.pad_token_id,
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tokenizer.word_delimiter_token_id,
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24,
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22,
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5,
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77,
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tokenizer.word_delimiter_token_id,
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],
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]
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batch_tokens = tokenizer.batch_decode(sample_ids)
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batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
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self.assertEqual(batch_tokens, batch_tokens_2)
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self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
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def test_tokenizer_decode_added_tokens(self):
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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tokenizer.add_tokens(["!", "?"])
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tokenizer.add_special_tokens({"cls_token": "$$$"})
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sample_ids = [
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[
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11,
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5,
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15,
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tokenizer.pad_token_id,
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15,
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8,
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98,
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32,
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32,
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33,
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tokenizer.word_delimiter_token_id,
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32,
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32,
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33,
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34,
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34,
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],
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[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34],
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]
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batch_tokens = tokenizer.batch_decode(sample_ids)
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self.assertEqual(batch_tokens, ["HELLO<unk>!?!?$$$", "BYE BYE<unk>$$$"])
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def test_call(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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tokenizer = self.get_tokenizer()
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# create three inputs of length 800, 1000, and 1200
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
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# Test not batched input
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encoded_sequences_1 = tokenizer(speech_inputs[0], return_tensors="np").input_values
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encoded_sequences_2 = tokenizer(np_speech_inputs[0], return_tensors="np").input_values
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self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
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# Test batched
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encoded_sequences_1 = tokenizer(speech_inputs, return_tensors="np").input_values
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encoded_sequences_2 = tokenizer(np_speech_inputs, return_tensors="np").input_values
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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def test_padding(self, max_length=50):
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def _input_values_have_equal_length(input_values):
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length = len(input_values[0])
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for input_values_slice in input_values[1:]:
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if len(input_values_slice) != length:
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return False
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return True
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def _input_values_are_equal(input_values_1, input_values_2):
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if len(input_values_1) != len(input_values_2):
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return False
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for input_values_slice_1, input_values_slice_2 in zip(input_values_1, input_values_2):
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if not np.allclose(np.asarray(input_values_slice_1), np.asarray(input_values_slice_2), atol=1e-3):
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return False
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return True
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tokenizer = self.get_tokenizer()
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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input_values_1 = tokenizer(speech_inputs).input_values
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input_values_2 = tokenizer(speech_inputs, padding="longest").input_values
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input_values_3 = tokenizer(speech_inputs, padding="longest", max_length=1600).input_values
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self.assertFalse(_input_values_have_equal_length(input_values_1))
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self.assertTrue(_input_values_have_equal_length(input_values_2))
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self.assertTrue(_input_values_have_equal_length(input_values_3))
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self.assertTrue(_input_values_are_equal(input_values_2, input_values_3))
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self.assertTrue(len(input_values_1[0]) == 800)
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self.assertTrue(len(input_values_2[0]) == 1200)
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_2[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_2[1])[1000:])) < 1e-3)
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input_values_4 = tokenizer(speech_inputs, padding="max_length").input_values
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input_values_5 = tokenizer(speech_inputs, padding="max_length", max_length=1600).input_values
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self.assertTrue(_input_values_are_equal(input_values_1, input_values_4))
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self.assertTrue(input_values_5.shape, (3, 1600))
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_5[0])[800:1200])) < 1e-3)
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input_values_6 = tokenizer(speech_inputs, pad_to_multiple_of=500).input_values
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input_values_7 = tokenizer(speech_inputs, padding="longest", pad_to_multiple_of=500).input_values
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input_values_8 = tokenizer(
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speech_inputs, padding="max_length", pad_to_multiple_of=500, max_length=2400
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).input_values
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self.assertTrue(_input_values_are_equal(input_values_1, input_values_6))
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self.assertTrue(input_values_7.shape, (3, 1500))
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self.assertTrue(input_values_8.shape, (3, 2500))
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_7[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_7[1])[1000:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_7[2])[1200:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[1])[1000:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[2])[1200:])) < 1e-3)
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def test_save_pretrained(self):
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pretrained_name = list(self.tokenizer_class.pretrained_vocab_files_map["vocab_file"].keys())[0]
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tokenizer = self.tokenizer_class.from_pretrained(pretrained_name)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_files = tokenizer.save_pretrained(tmpdirname2)
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self.assertSequenceEqual(
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sorted(tuple(VOCAB_FILES_NAMES.values()) + ("special_tokens_map.json", "added_tokens.json")),
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sorted(tuple(x.split("/")[-1] for x in tokenizer_files)),
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)
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# Checks everything loads correctly in the same way
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tokenizer_p = self.tokenizer_class.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer.special_tokens_map:
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self.assertTrue(key in tokenizer_p.special_tokens_map)
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shutil.rmtree(tmpdirname2)
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def test_get_vocab(self):
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tokenizer = self.get_tokenizer()
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vocab_dict = tokenizer.get_vocab()
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self.assertIsInstance(vocab_dict, dict)
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self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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tokenizer.add_tokens(["asdfasdfasdfasdf"])
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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def test_save_and_load_tokenizer(self):
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tokenizer = self.get_tokenizer()
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# Isolate this from the other tests because we save additional tokens/etc
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tmpdirname = tempfile.mkdtemp()
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sample_ids = [0, 1, 4, 8, 9, 0, 12]
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before_tokens = tokenizer.decode(sample_ids)
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before_vocab = tokenizer.get_vocab()
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tokenizer.save_pretrained(tmpdirname)
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after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
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after_tokens = after_tokenizer.decode(sample_ids)
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after_vocab = after_tokenizer.get_vocab()
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self.assertEqual(before_tokens, after_tokens)
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self.assertDictEqual(before_vocab, after_vocab)
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shutil.rmtree(tmpdirname)
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tokenizer = self.get_tokenizer()
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# Isolate this from the other tests because we save additional tokens/etc
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tmpdirname = tempfile.mkdtemp()
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before_len = len(tokenizer)
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sample_ids = [0, 1, 4, 8, 9, 0, 12, before_len, before_len + 1, before_len + 2]
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tokenizer.add_tokens(["?", "!"])
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additional_special_tokens = tokenizer.additional_special_tokens
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additional_special_tokens.append("&")
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tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
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before_tokens = tokenizer.decode(sample_ids)
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before_vocab = tokenizer.get_vocab()
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tokenizer.save_pretrained(tmpdirname)
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after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
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after_tokens = after_tokenizer.decode(sample_ids)
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after_vocab = after_tokenizer.get_vocab()
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self.assertEqual(before_tokens, after_tokens)
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self.assertDictEqual(before_vocab, after_vocab)
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self.assertTrue(len(tokenizer), before_len + 3)
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self.assertTrue(len(tokenizer), len(after_tokenizer))
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shutil.rmtree(tmpdirname)
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def test_tokenizer_slow_store_full_signature(self):
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signature = inspect.signature(self.tokenizer_class.__init__)
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tokenizer = self.get_tokenizer()
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for parameter_name, parameter in signature.parameters.items():
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if parameter.default != inspect.Parameter.empty:
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self.assertIn(parameter_name, tokenizer.init_kwargs)
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def test_zero_mean_unit_variance_normalization(self):
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tokenizer = self.get_tokenizer(do_normalize=True)
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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processed = tokenizer(speech_inputs, padding="longest")
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input_values = processed.input_values
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def _check_zero_mean_unit_variance(input_vector):
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self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
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self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
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_check_zero_mean_unit_variance(input_values[0, :800])
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_check_zero_mean_unit_variance(input_values[1, :1000])
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_check_zero_mean_unit_variance(input_values[2])
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def test_return_attention_mask(self):
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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# default case -> no attention_mask is returned
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tokenizer = self.get_tokenizer()
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processed = tokenizer(speech_inputs)
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self.assertNotIn("attention_mask", processed)
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# wav2vec2-lv60 -> return attention_mask
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tokenizer = self.get_tokenizer(return_attention_mask=True)
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processed = tokenizer(speech_inputs, padding="longest")
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self.assertIn("attention_mask", processed)
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self.assertListEqual(list(processed.attention_mask.shape), list(processed.input_values.shape))
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self.assertListEqual(processed.attention_mask.sum(-1).tolist(), [800, 1000, 1200])
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@slow
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def test_pretrained_checkpoints_are_set_correctly(self):
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# this test makes sure that models that are using
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# group norm don't have their tokenizer return the
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# attention_mask
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for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
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config = Wav2Vec2Config.from_pretrained(model_id)
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tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_id)
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# only "layer" feature extraction norm should make use of
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# attention_mask
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self.assertEqual(tokenizer.return_attention_mask, config.feat_extract_norm == "layer")
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