142 lines
4.9 KiB
Python
142 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors, Allegro.pl and The HuggingFace Inc. team.
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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 json
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import os
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import unittest
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from transformers import HerbertTokenizer, HerbertTokenizerFast
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from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
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from transformers.testing_utils import get_tests_dir, require_sacremoses, require_tokenizers, slow
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from ...test_tokenization_common import TokenizerTesterMixin
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@require_sacremoses
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@require_tokenizers
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class HerbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "allegro/herbert-base-cased"
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tokenizer_class = HerbertTokenizer
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rust_tokenizer_class = HerbertTokenizerFast
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test_rust_tokenizer = True
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def setUp(self):
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super().setUp()
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# Use a simpler test file without japanese/chinese characters
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with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt", encoding="utf-8") as f_data:
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self._data = f_data.read().replace("\n\n", "\n").strip()
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vocab = [
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"<s>",
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"</s>",
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"w</w>",
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"r</w>",
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"t</w>",
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"lo",
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"low",
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"er</w>",
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"low</w>",
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"lowest</w>",
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"newer</w>",
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"wider</w>",
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",</w>",
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"<unk>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w") as fp:
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fp.write(json.dumps(vocab_tokens))
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with open(self.merges_file, "w") as fp:
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fp.write("\n".join(merges))
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.tokenizer_class(vocab_file=self.vocab_file, merges_file=self.merges_file)
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text = "lower"
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bpe_tokens = ["low", "er</w>"]
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tokens = tokenizer.tokenize(text)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + ["<unk>"]
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input_bpe_tokens = [16, 17, 23]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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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 = "lower,newer"
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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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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("allegro/herbert-base-cased")
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text = tokenizer.encode("konstruowanie sekwencji", add_special_tokens=False)
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text_2 = tokenizer.encode("konstruowanie wielu sekwencji", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_sentence == [0] + text + [2]
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assert encoded_pair == [0] + text + [2] + text_2 + [2]
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@unittest.skip(
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"Test passes if run individually but not with the full tests (internal state of the tokenizer is modified). Will fix later"
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)
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def test_training_new_tokenizer_with_special_tokens_change(self):
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pass
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@unittest.skip(
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"Test passes if run individually but not with the full tests (internal state of the tokenizer is modified). Will fix later"
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)
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def test_training_new_tokenizer(self):
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pass
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