Spaces:
Paused
Paused
File size: 8,557 Bytes
ee6e328 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
# 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
@require_tokenizers
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)
@require_ftfy
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 to-$''d."
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 using ftfy 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."
)
)
@require_ftfy
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
|