372 lines
12 KiB
Python
372 lines
12 KiB
Python
# Copyright 2020 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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import unittest
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from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
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from transformers.utils import cached_property
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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@require_sentencepiece
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@require_tokenizers
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class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "google/reformer-crime-and-punishment"
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tokenizer_class = ReformerTokenizer
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rust_tokenizer_class = ReformerTokenizerFast
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test_rust_tokenizer = True
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test_seq2seq = False
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "<s>"
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token_id = 1
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<unk>")
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self.assertEqual(vocab_keys[1], "<s>")
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self.assertEqual(vocab_keys[-1], "j")
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self.assertEqual(len(vocab_keys), 1_000)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 1_000)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_padding(self, max_length=15):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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p = ("This is a simple input", "This is a pair")
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p2 = [
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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]
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# Simple input tests
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self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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s2,
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max_length=max_length,
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padding="max_length",
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)
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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p2,
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max_length=max_length,
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padding="max_length",
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)
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# tokenizer has no padding token
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def test_padding_different_model_input_name(self):
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pass
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def test_full_tokenizer(self):
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tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[285, 46, 10, 170, 382],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids,
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[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
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)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@cached_property
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def big_tokenizer(self):
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return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
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@slow
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def test_tokenization_base_easy_symbols(self):
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symbols = "Hello World!"
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original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
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self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
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@slow
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def test_tokenization_base_hard_symbols(self):
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symbols = (
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'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
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" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
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)
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original_tokenizer_encodings = [
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108,
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265,
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24,
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111,
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4,
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258,
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156,
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35,
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28,
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275,
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3,
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259,
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297,
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260,
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84,
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4,
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35,
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110,
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44,
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8,
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259,
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91,
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268,
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21,
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11,
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209,
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274,
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109,
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266,
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277,
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117,
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86,
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93,
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315,
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258,
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278,
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258,
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277,
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258,
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0,
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258,
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288,
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258,
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319,
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258,
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0,
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258,
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0,
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258,
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0,
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258,
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0,
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258,
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287,
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258,
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315,
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258,
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289,
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258,
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278,
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99,
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269,
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266,
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262,
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8,
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259,
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241,
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4,
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217,
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230,
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268,
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266,
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55,
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168,
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106,
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75,
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193,
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266,
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223,
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27,
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49,
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26,
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282,
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25,
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264,
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299,
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19,
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26,
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0,
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258,
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277,
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117,
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86,
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93,
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176,
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183,
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270,
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11,
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262,
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42,
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61,
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265,
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]
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self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
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@require_torch
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@slow
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def test_torch_encode_plus_sent_to_model(self):
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import torch
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from transformers import ReformerConfig, ReformerModel
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# Build sequence
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first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
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sequence = " ".join(first_ten_tokens)
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encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
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batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
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config = ReformerConfig()
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# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
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config.axial_pos_shape = encoded_sequence["input_ids"].shape
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model = ReformerModel(config)
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# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
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assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
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with torch.no_grad():
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model(**encoded_sequence)
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model(**batch_encoded_sequence)
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@slow
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def test_tokenizer_integration(self):
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expected_encoding = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip
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# This tokenizer does not know some characters like ")".
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# That is the reason why we use very simple texts here.
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# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
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sequences = [
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"This is a very simple sentence.",
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"The quick brown fox jumps over the lazy dog.",
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]
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="google/reformer-crime-and-punishment",
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revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a",
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padding=False,
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sequences=sequences,
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)
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