# coding=utf-8 # Copyright 2020 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 itertools import json import os import unittest from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class RobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = RobertaTokenizer rust_tokenizer_class = RobertaTokenizerFast test_rust_tokenizer = True from_pretrained_kwargs = {"cls_token": ""} def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": ""} 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 self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return self.rust_tokenizer_class.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 = self.tokenizer_class(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text) # , add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def roberta_dict_integration_testing(self): tokenizer = self.get_tokenizer() self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [0, 31414, 232, 328, 2]) self.assertListEqual( tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False), [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2], ) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("roberta-base") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_text_from_decode = tokenizer.encode( "sequence builders", add_special_tokens=True, add_prefix_space=False ) encoded_pair_from_decode = tokenizer.encode( "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False ) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode def test_space_encoding(self): tokenizer = self.get_tokenizer() sequence = "Encode this sequence." space_encoding = tokenizer.byte_encoder[" ".encode("utf-8")[0]] # Testing encoder arguments encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=False) first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0] self.assertNotEqual(first_char, space_encoding) encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0] self.assertEqual(first_char, space_encoding) tokenizer.add_special_tokens({"bos_token": ""}) encoded = tokenizer.encode(sequence, add_special_tokens=True) first_char = tokenizer.convert_ids_to_tokens(encoded[1])[0] self.assertNotEqual(first_char, space_encoding) # Testing spaces after special tokens mask = "" tokenizer.add_special_tokens( {"mask_token": AddedToken(mask, lstrip=True, rstrip=False)} ) # mask token has a left space mask_ind = tokenizer.convert_tokens_to_ids(mask) sequence = "Encode sequence" sequence_nospace = "Encode sequence" encoded = tokenizer.encode(sequence) mask_loc = encoded.index(mask_ind) first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0] self.assertEqual(first_char, space_encoding) encoded = tokenizer.encode(sequence_nospace) mask_loc = encoded.index(mask_ind) first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0] self.assertNotEqual(first_char, space_encoding) def test_pretokenized_inputs(self): pass def test_embeded_special_tokens(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) sentence = "A, AllenNLP sentence." tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) # token_type_ids should put 0 everywhere self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) # attention_mask should put 1 everywhere, so sum over length should be 1 self.assertEqual( sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]), sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]), ) tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]) tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) # Rust correctly handles the space before the mask while python doesnt self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) self.assertSequenceEqual( tokens_p_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] ) self.assertSequenceEqual( tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] ) def test_change_add_prefix_space_and_trim_offsets_args(self): for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2): tokenizer_r = self.rust_tokenizer_class.from_pretrained( self.tmpdirname, use_fast=True, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets ) pre_tokenizer_state = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()) post_processor_state = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()) self.assertEqual(pre_tokenizer_state["add_prefix_space"], add_prefix_space) self.assertEqual(post_processor_state["add_prefix_space"], add_prefix_space) self.assertEqual(post_processor_state["trim_offsets"], trim_offsets) def test_offsets_mapping_with_different_add_prefix_space_and_trim_space_arguments(self): # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and # `trim_offsets` 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, add_prefix_space=True, trim_offsets=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)), ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=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)), ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False ) 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), len(text_of_1_token) + 1 + len(text_of_1_token)), ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False ) 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), 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, add_prefix_space=True, trim_offsets=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)), # ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=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)), ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False ) encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token))) self.assertEqual( encoding.offset_mapping[1], (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), ) tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False ) encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token))) self.assertEqual( encoding.offset_mapping[1], (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), )