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| # coding=utf-8 | |
| # Copyright 2021 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import json | |
| import os | |
| import unittest | |
| from transformers import CLIPTokenizer, CLIPTokenizerFast | |
| from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES | |
| from transformers.testing_utils import require_ftfy, require_tokenizers | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| class CLIPTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = CLIPTokenizer | |
| rust_tokenizer_class = CLIPTokenizerFast | |
| test_rust_tokenizer = True | |
| from_pretrained_kwargs = {} | |
| test_seq2seq = False | |
| def setUp(self): | |
| super().setUp() | |
| # fmt: off | |
| vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"] | |
| # fmt: on | |
| vocab_tokens = dict(zip(vocab, range(len(vocab)))) | |
| merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>"] | |
| self.special_tokens_map = {"unk_token": "<unk>"} | |
| self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) | |
| self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) | |
| with open(self.vocab_file, "w", encoding="utf-8") as fp: | |
| fp.write(json.dumps(vocab_tokens) + "\n") | |
| with open(self.merges_file, "w", encoding="utf-8") as fp: | |
| fp.write("\n".join(merges)) | |
| def get_tokenizer(self, **kwargs): | |
| kwargs.update(self.special_tokens_map) | |
| return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_rust_tokenizer(self, **kwargs): | |
| kwargs.update(self.special_tokens_map) | |
| return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_input_output_texts(self, tokenizer): | |
| input_text = "lower newer" | |
| output_text = "lower newer" | |
| return input_text, output_text | |
| def test_full_tokenizer(self): | |
| tokenizer = CLIPTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) | |
| text = "lower newer" | |
| bpe_tokens = ["lo", "w", "er</w>", "n", "e", "w", "er</w>"] | |
| tokens = tokenizer.tokenize(text) | |
| self.assertListEqual(tokens, bpe_tokens) | |
| input_tokens = tokens + [tokenizer.unk_token] | |
| input_bpe_tokens = [10, 2, 16, 9, 3, 2, 16, 20] | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) | |
| def test_check_encoding_slow_fast(self): | |
| for tokenizer, pretrained_name, kwargs in self.tokenizers_list: | |
| with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): | |
| tokenizer_s = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) | |
| text = "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat" | |
| text_tokenized_s = tokenizer_s.tokenize(text) | |
| text_tokenized_r = tokenizer_r.tokenize(text) | |
| self.assertListEqual(text_tokenized_s, text_tokenized_r) | |
| # Test that the tokenization is identical on an example containing a character (Latin Small Letter A | |
| # with Tilde) encoded in 2 different ways | |
| text = "xa\u0303y" + " " + "x\xe3y" | |
| text_tokenized_s = tokenizer_s.tokenize(text) | |
| text_tokenized_r = tokenizer_r.tokenize(text) | |
| self.assertListEqual(text_tokenized_s, text_tokenized_r) | |
| # Test that the tokenization is identical on unicode of space type | |
| spaces_unicodes = [ | |
| "\u0009", # (horizontal tab, '\t') | |
| "\u000B", # (vertical tab) | |
| "\u000C", # (form feed) | |
| "\u0020", # (space, ' ') | |
| "\u200E", # (left-to-right mark):w | |
| "\u200F", # (right-to-left mark) | |
| ] | |
| for unicode_seq in spaces_unicodes: | |
| text_tokenized_s = tokenizer_s.tokenize(unicode_seq) | |
| text_tokenized_r = tokenizer_r.tokenize(unicode_seq) | |
| self.assertListEqual(text_tokenized_s, text_tokenized_r) | |
| # Test that the tokenization is identical on unicode of line break type | |
| line_break_unicodes = [ | |
| "\u000A", # (line feed, '\n') | |
| "\r\n", # (carriage return and line feed, '\r\n') | |
| "\u000D", # (carriage return, '\r') | |
| "\r", # (carriage return, '\r') | |
| "\u000D", # (carriage return, '\r') | |
| "\u2028", # (line separator) | |
| "\u2029", # (paragraph separator) | |
| # "\u0085", # (next line) | |
| ] | |
| # The tokenization is not identical for the character "\u0085" (next line). The slow version transforms | |
| # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a | |
| # space (and thus into an empty list). | |
| for unicode_seq in line_break_unicodes: | |
| text_tokenized_s = tokenizer_s.tokenize(unicode_seq) | |
| text_tokenized_r = tokenizer_r.tokenize(unicode_seq) | |
| self.assertListEqual(text_tokenized_s, text_tokenized_r) | |
| def test_offsets_mapping_with_different_add_prefix_space_argument(self): | |
| # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` | |
| for tokenizer, pretrained_name, kwargs in self.tokenizers_list: | |
| with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): | |
| text_of_1_token = "hello" # `hello` is a token in the vocabulary of `pretrained_name` | |
| text = f"{text_of_1_token} {text_of_1_token}" | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained( | |
| pretrained_name, | |
| use_fast=True, | |
| ) | |
| encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) | |
| self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token))) | |
| self.assertEqual( | |
| encoding.offset_mapping[1], | |
| (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)), | |
| ) | |
| text = f" {text}" | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained( | |
| pretrained_name, | |
| use_fast=True, | |
| ) | |
| encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) | |
| self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token))) | |
| self.assertEqual( | |
| encoding.offset_mapping[1], | |
| (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), | |
| ) | |
| def test_log_warning(self): | |
| # Test related to the breaking change introduced in transformers v4.17.0 | |
| # We need to check that an error in raised when the user try to load a previous version of the tokenizer. | |
| with self.assertRaises(ValueError) as context: | |
| self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer") | |
| self.assertTrue( | |
| context.exception.args[0].startswith( | |
| "The `backend_tokenizer` provided does not match the expected format." | |
| ) | |
| ) | |
| def test_tokenization_python_rust_equals(self): | |
| super().test_tokenization_python_rust_equals() | |
| # overwrite common test | |
| def test_added_tokens_do_lower_case(self): | |
| # CLIP always lower cases letters | |
| pass | |