1235 lines
62 KiB
Python
1235 lines
62 KiB
Python
# coding=utf-8
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# Copyright 2020 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 inspect
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import os
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import shutil
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import tempfile
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import unittest
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from typing import List
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import numpy as np
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import pandas as pd
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from transformers import AddedToken
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from transformers.models.tapas.tokenization_tapas import (
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VOCAB_FILES_NAMES,
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BasicTokenizer,
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TapasTokenizer,
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WordpieceTokenizer,
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_is_control,
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_is_punctuation,
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_is_whitespace,
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)
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from transformers.testing_utils import (
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is_pt_tf_cross_test,
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require_pandas,
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require_scatter,
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require_tokenizers,
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require_torch,
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slow,
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)
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from .test_tokenization_common import TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings
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@require_tokenizers
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@require_pandas
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class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = TapasTokenizer
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test_rust_tokenizer = False
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space_between_special_tokens = True
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from_pretrained_filter = filter_non_english
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test_seq2seq = False
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def get_table(
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self,
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tokenizer: TapasTokenizer,
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length=5,
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):
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toks = [tokenizer.decode([i], clean_up_tokenization_spaces=False) for i in range(len(tokenizer))]
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if length == 0:
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data = {}
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else:
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data = {toks[0]: [toks[tok] for tok in range(1, length)]}
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table = pd.DataFrame.from_dict(data)
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return table
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def get_table_and_query(
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self,
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tokenizer: TapasTokenizer,
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length=5,
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):
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toks = [tokenizer.decode([i], clean_up_tokenization_spaces=False) for i in range(len(tokenizer))]
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table = self.get_table(tokenizer, length=length - 3)
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query = " ".join(toks[:3])
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return table, query
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def get_clean_sequence(
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self,
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tokenizer: TapasTokenizer,
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with_prefix_space=False,
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max_length=20,
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min_length=5,
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empty_table: bool = False,
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add_special_tokens: bool = True,
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return_table_and_query: bool = False,
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):
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toks = [tokenizer.decode([i], clean_up_tokenization_spaces=False) for i in range(len(tokenizer))]
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if empty_table:
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table = pd.DataFrame.from_dict({})
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query = " ".join(toks[:min_length])
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else:
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data = {toks[0]: [toks[tok] for tok in range(1, min_length - 3)]}
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table = pd.DataFrame.from_dict(data)
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query = " ".join(toks[:3])
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output_ids = tokenizer.encode(table, query, add_special_tokens=add_special_tokens)
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output_txt = tokenizer.decode(output_ids)
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assert len(output_ids) >= min_length, "Update the code to generate the sequences so that they are larger"
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assert len(output_ids) <= max_length, "Update the code to generate the sequences so that they are smaller"
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if return_table_and_query:
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return output_txt, output_ids, table, query
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return output_txt, output_ids
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def setUp(self):
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super().setUp()
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"[PAD]",
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"[MASK]",
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"want",
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"##want",
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"##ed",
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"wa",
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"un",
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"runn",
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"##ing",
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",",
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"low",
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"lowest",
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]
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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def get_input_output_texts(self, tokenizer):
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input_text = "UNwant\u00E9d,running"
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output_text = "unwanted, running"
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return input_text, output_text
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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 = "UNwant\u00E9d,running"
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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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# With lower casing
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tokenizer = self.get_tokenizer(do_lower_case=True)
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rust_tokenizer = self.get_rust_tokenizer(do_lower_case=True)
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sequence = "UNwant\u00E9d,running"
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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_chinese(self):
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tokenizer = BasicTokenizer()
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self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"])
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def test_basic_tokenizer_lower(self):
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tokenizer = BasicTokenizer(do_lower_case=True)
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self.assertListEqual(
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tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"]
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)
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self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
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def test_basic_tokenizer_lower_strip_accents_false(self):
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tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False)
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self.assertListEqual(
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tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"]
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)
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self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"])
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def test_basic_tokenizer_lower_strip_accents_true(self):
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tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=True)
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self.assertListEqual(
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tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
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)
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self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
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def test_basic_tokenizer_lower_strip_accents_default(self):
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tokenizer = BasicTokenizer(do_lower_case=True)
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self.assertListEqual(
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tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
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)
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self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
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def test_basic_tokenizer_no_lower(self):
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tokenizer = BasicTokenizer(do_lower_case=False)
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self.assertListEqual(
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tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
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)
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def test_basic_tokenizer_no_lower_strip_accents_false(self):
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tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False)
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self.assertListEqual(
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tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"]
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)
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def test_basic_tokenizer_no_lower_strip_accents_true(self):
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tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True)
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self.assertListEqual(
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tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"]
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)
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def test_basic_tokenizer_respects_never_split_tokens(self):
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tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"])
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self.assertListEqual(
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tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]
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)
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def test_wordpiece_tokenizer(self):
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
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vocab = {}
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for (i, token) in enumerate(vocab_tokens):
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vocab[token] = i
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tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
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self.assertListEqual(tokenizer.tokenize(""), [])
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self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"])
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self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
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def test_is_whitespace(self):
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self.assertTrue(_is_whitespace(" "))
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self.assertTrue(_is_whitespace("\t"))
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self.assertTrue(_is_whitespace("\r"))
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self.assertTrue(_is_whitespace("\n"))
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self.assertTrue(_is_whitespace("\u00A0"))
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self.assertFalse(_is_whitespace("A"))
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self.assertFalse(_is_whitespace("-"))
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def test_is_control(self):
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self.assertTrue(_is_control("\u0005"))
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self.assertFalse(_is_control("A"))
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self.assertFalse(_is_control(" "))
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self.assertFalse(_is_control("\t"))
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self.assertFalse(_is_control("\r"))
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def test_is_punctuation(self):
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self.assertTrue(_is_punctuation("-"))
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self.assertTrue(_is_punctuation("$"))
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self.assertTrue(_is_punctuation("`"))
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self.assertTrue(_is_punctuation("."))
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self.assertFalse(_is_punctuation("A"))
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self.assertFalse(_is_punctuation(" "))
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def test_clean_text(self):
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tokenizer = self.get_tokenizer()
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# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
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self.assertListEqual(
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[tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], ["[EMPTY]"], ["[UNK]"]]
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)
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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("google/tapas-base-finetuned-wtq")
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empty_table = self.get_table(tokenizer, length=0)
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table = self.get_table(tokenizer, length=10)
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text = tokenizer.encode(table, add_special_tokens=False)
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text_2 = tokenizer.encode(empty_table, "multi-sequence build", add_special_tokens=False)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_pair == [101] + text + [102] + text_2
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def test_offsets_with_special_characters(self):
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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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sentence = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
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tokens = tokenizer_r.encode_plus(
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sentence,
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return_attention_mask=False,
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return_token_type_ids=False,
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return_offsets_mapping=True,
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add_special_tokens=True,
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)
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do_lower_case = tokenizer_r.do_lower_case if hasattr(tokenizer_r, "do_lower_case") else False
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expected_results = (
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[
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((0, 0), tokenizer_r.cls_token),
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((0, 1), "A"),
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((1, 2), ","),
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((3, 5), "na"),
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((5, 6), "##ï"),
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((6, 8), "##ve"),
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((9, 15), tokenizer_r.mask_token),
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((16, 21), "Allen"),
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((21, 23), "##NL"),
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((23, 24), "##P"),
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((25, 33), "sentence"),
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((33, 34), "."),
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((0, 0), tokenizer_r.sep_token),
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]
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if not do_lower_case
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else [
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((0, 0), tokenizer_r.cls_token),
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((0, 1), "a"),
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((1, 2), ","),
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((3, 8), "naive"),
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((9, 15), tokenizer_r.mask_token),
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((16, 21), "allen"),
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((21, 23), "##nl"),
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((23, 24), "##p"),
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((25, 33), "sentence"),
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((33, 34), "."),
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((0, 0), tokenizer_r.sep_token),
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]
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)
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self.assertEqual(
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[e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"])
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)
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self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
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def test_add_special_tokens(self):
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tokenizers: List[TapasTokenizer] = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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input_table = self.get_table(tokenizer, length=0)
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special_token = "[SPECIAL_TOKEN]"
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tokenizer.add_special_tokens({"cls_token": special_token})
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encoded_special_token = tokenizer.encode(input_table, special_token, add_special_tokens=False)
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self.assertEqual(len(encoded_special_token), 1)
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decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
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self.assertTrue(special_token not in decoded)
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def test_add_tokens_tokenizer(self):
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tokenizers: List[TapasTokenizer] = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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table = self.get_table(tokenizer, length=0)
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode(table, "aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(
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table,
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">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
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add_special_tokens=False,
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)
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokens[-3])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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@require_tokenizers
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|
def test_encode_decode_with_spaces(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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table = self.get_table(tokenizer, length=0)
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new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
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tokenizer.add_tokens(new_toks)
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input = "[ABC][DEF][ABC][DEF]"
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if self.space_between_special_tokens:
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output = "[ABC] [DEF] [ABC] [DEF]"
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else:
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output = input
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encoded = tokenizer.encode(table, input, add_special_tokens=False)
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decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
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self.assertIn(decoded, [output, output.lower()])
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def test_encode_plus_with_padding(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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table = self.get_table(tokenizer, length=0)
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|
sequence = "Sequence"
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequence)
|
|
|
|
padding_size = 10
|
|
padding_idx = tokenizer.pad_token_id
|
|
token_type_padding_idx = tokenizer.pad_token_type_id
|
|
|
|
encoded_sequence = tokenizer.encode_plus(table, sequence, return_special_tokens_mask=True)
|
|
input_ids = encoded_sequence["input_ids"]
|
|
special_tokens_mask = encoded_sequence["special_tokens_mask"]
|
|
sequence_length = len(input_ids)
|
|
|
|
# Test 'longest' and 'no_padding' don't do anything
|
|
tokenizer.padding_side = "right"
|
|
|
|
not_padded_sequence = tokenizer.encode_plus(
|
|
table,
|
|
sequence,
|
|
padding=False,
|
|
return_special_tokens_mask=True,
|
|
)
|
|
not_padded_input_ids = not_padded_sequence["input_ids"]
|
|
|
|
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
|
|
not_padded_sequence_length = len(not_padded_input_ids)
|
|
|
|
assert sequence_length == not_padded_sequence_length
|
|
assert input_ids == not_padded_input_ids
|
|
assert special_tokens_mask == not_padded_special_tokens_mask
|
|
|
|
not_padded_sequence = tokenizer.encode_plus(
|
|
table,
|
|
sequence,
|
|
padding=False,
|
|
return_special_tokens_mask=True,
|
|
)
|
|
not_padded_input_ids = not_padded_sequence["input_ids"]
|
|
|
|
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
|
|
not_padded_sequence_length = len(not_padded_input_ids)
|
|
|
|
assert sequence_length == not_padded_sequence_length
|
|
assert input_ids == not_padded_input_ids
|
|
assert special_tokens_mask == not_padded_special_tokens_mask
|
|
|
|
# Test right padding
|
|
tokenizer.padding_side = "right"
|
|
|
|
right_padded_sequence = tokenizer.encode_plus(
|
|
table,
|
|
sequence,
|
|
max_length=sequence_length + padding_size,
|
|
padding="max_length",
|
|
return_special_tokens_mask=True,
|
|
)
|
|
right_padded_input_ids = right_padded_sequence["input_ids"]
|
|
|
|
right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
|
|
right_padded_sequence_length = len(right_padded_input_ids)
|
|
|
|
assert sequence_length + padding_size == right_padded_sequence_length
|
|
assert input_ids + [padding_idx] * padding_size == right_padded_input_ids
|
|
assert special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask
|
|
|
|
# Test left padding
|
|
tokenizer.padding_side = "left"
|
|
left_padded_sequence = tokenizer.encode_plus(
|
|
table,
|
|
sequence,
|
|
max_length=sequence_length + padding_size,
|
|
padding="max_length",
|
|
return_special_tokens_mask=True,
|
|
)
|
|
left_padded_input_ids = left_padded_sequence["input_ids"]
|
|
left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
|
|
left_padded_sequence_length = len(left_padded_input_ids)
|
|
|
|
assert sequence_length + padding_size == left_padded_sequence_length
|
|
assert [padding_idx] * padding_size + input_ids == left_padded_input_ids
|
|
assert [1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask
|
|
|
|
if "token_type_ids" in tokenizer.model_input_names:
|
|
token_type_ids = encoded_sequence["token_type_ids"]
|
|
left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
|
|
right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
|
|
|
|
assert (
|
|
token_type_ids + [[token_type_padding_idx] * 7] * padding_size == right_padded_token_type_ids
|
|
)
|
|
assert [[token_type_padding_idx] * 7] * padding_size + token_type_ids == left_padded_token_type_ids
|
|
|
|
if "attention_mask" in tokenizer.model_input_names:
|
|
attention_mask = encoded_sequence["attention_mask"]
|
|
right_padded_attention_mask = right_padded_sequence["attention_mask"]
|
|
left_padded_attention_mask = left_padded_sequence["attention_mask"]
|
|
|
|
assert attention_mask + [0] * padding_size == right_padded_attention_mask
|
|
assert [0] * padding_size + attention_mask == left_padded_attention_mask
|
|
|
|
def test_internal_consistency(self):
|
|
tokenizers = self.get_tokenizers()
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
input_text, output_text = self.get_input_output_texts(tokenizer)
|
|
|
|
tokens = tokenizer.tokenize(input_text)
|
|
ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
ids_2 = tokenizer.encode(table, input_text, add_special_tokens=False)
|
|
self.assertListEqual(ids, ids_2)
|
|
|
|
tokens_2 = tokenizer.convert_ids_to_tokens(ids)
|
|
self.assertNotEqual(len(tokens_2), 0)
|
|
text_2 = tokenizer.decode(ids)
|
|
self.assertIsInstance(text_2, str)
|
|
|
|
self.assertEqual(text_2, output_text)
|
|
|
|
def test_mask_output(self):
|
|
tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table, query = self.get_table_and_query(tokenizer)
|
|
|
|
if (
|
|
tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
|
|
and "token_type_ids" in tokenizer.model_input_names
|
|
):
|
|
information = tokenizer.encode_plus(table, query, add_special_tokens=True)
|
|
sequences, mask = information["input_ids"], information["token_type_ids"]
|
|
self.assertEqual(len(sequences), len(mask))
|
|
|
|
@unittest.skip("TAPAS tokenizer only handles two sequences.")
|
|
def test_maximum_encoding_length_pair_input(self):
|
|
pass
|
|
|
|
@unittest.skip("TAPAS tokenizer only handles two sequences.")
|
|
def test_maximum_encoding_length_single_input(self):
|
|
pass
|
|
|
|
def test_number_of_added_tokens(self):
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
|
|
table, query = self.get_table_and_query(tokenizer)
|
|
|
|
sequences = tokenizer.encode(table, query, add_special_tokens=False)
|
|
attached_sequences = tokenizer.encode(table, query, add_special_tokens=True)
|
|
|
|
# Method is implemented (e.g. not GPT-2)
|
|
if len(attached_sequences) != 2:
|
|
self.assertEqual(
|
|
tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences)
|
|
)
|
|
|
|
def test_padding_to_max_length(self):
|
|
"""We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated"""
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer)
|
|
sequence = "Sequence"
|
|
padding_size = 10
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequence)
|
|
|
|
padding_idx = tokenizer.pad_token_id
|
|
|
|
# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
|
|
tokenizer.padding_side = "right"
|
|
encoded_sequence = tokenizer.encode(table, sequence)
|
|
sequence_length = len(encoded_sequence)
|
|
# FIXME: the next line should be padding(max_length) to avoid warning
|
|
padded_sequence = tokenizer.encode(
|
|
table, sequence, max_length=sequence_length + padding_size, padding=True
|
|
)
|
|
padded_sequence_length = len(padded_sequence)
|
|
assert sequence_length + padding_size == padded_sequence_length
|
|
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
|
|
|
|
# Check that nothing is done when a maximum length is not specified
|
|
encoded_sequence = tokenizer.encode(table, sequence)
|
|
sequence_length = len(encoded_sequence)
|
|
|
|
tokenizer.padding_side = "right"
|
|
padded_sequence_right = tokenizer.encode(table, sequence, pad_to_max_length=True)
|
|
padded_sequence_right_length = len(padded_sequence_right)
|
|
assert sequence_length == padded_sequence_right_length
|
|
assert encoded_sequence == padded_sequence_right
|
|
|
|
def test_call(self):
|
|
# Tests that all call wrap to encode_plus and batch_encode_plus
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
sequences = [
|
|
"Testing batch encode plus",
|
|
"Testing batch encode plus with different sequence lengths",
|
|
"Testing batch encode plus with different sequence lengths correctly pads",
|
|
]
|
|
|
|
# Test not batched
|
|
table = self.get_table(tokenizer, length=0)
|
|
encoded_sequences_1 = tokenizer.encode_plus(table, sequences[0])
|
|
encoded_sequences_2 = tokenizer(table, sequences[0])
|
|
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
|
|
|
|
# Test not batched pairs
|
|
table = self.get_table(tokenizer, length=10)
|
|
encoded_sequences_1 = tokenizer.encode_plus(table, sequences[1])
|
|
encoded_sequences_2 = tokenizer(table, sequences[1])
|
|
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
|
|
|
|
# Test batched
|
|
table = self.get_table(tokenizer, length=0)
|
|
encoded_sequences_1 = tokenizer.batch_encode_plus(table, sequences)
|
|
encoded_sequences_2 = tokenizer(table, sequences)
|
|
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
|
|
|
|
def test_batch_encode_plus_batch_sequence_length(self):
|
|
# Tests that all encoded values have the correct size
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
sequences = [
|
|
"Testing batch encode plus",
|
|
"Testing batch encode plus with different sequence lengths",
|
|
"Testing batch encode plus with different sequence lengths correctly pads",
|
|
]
|
|
|
|
encoded_sequences = [tokenizer.encode_plus(table, sequence) for sequence in sequences]
|
|
encoded_sequences_batch = tokenizer.batch_encode_plus(table, sequences, padding=False)
|
|
self.assertListEqual(
|
|
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
|
|
)
|
|
|
|
maximum_length = len(
|
|
max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
|
|
)
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequences)
|
|
|
|
encoded_sequences_padded = [
|
|
tokenizer.encode_plus(table, sequence, max_length=maximum_length, padding="max_length")
|
|
for sequence in sequences
|
|
]
|
|
|
|
encoded_sequences_batch_padded = tokenizer.batch_encode_plus(table, sequences, padding=True)
|
|
self.assertListEqual(
|
|
encoded_sequences_padded,
|
|
self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
|
|
)
|
|
|
|
# check 'longest' is unsensitive to a max length
|
|
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(table, sequences, padding=True)
|
|
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
|
|
table, sequences, max_length=maximum_length + 10, padding="longest"
|
|
)
|
|
for key in encoded_sequences_batch_padded_1.keys():
|
|
self.assertListEqual(
|
|
encoded_sequences_batch_padded_1[key],
|
|
encoded_sequences_batch_padded_2[key],
|
|
)
|
|
|
|
# check 'no_padding' is unsensitive to a max length
|
|
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(table, sequences, padding=False)
|
|
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
|
|
table, sequences, max_length=maximum_length + 10, padding=False
|
|
)
|
|
for key in encoded_sequences_batch_padded_1.keys():
|
|
self.assertListEqual(
|
|
encoded_sequences_batch_padded_1[key],
|
|
encoded_sequences_batch_padded_2[key],
|
|
)
|
|
|
|
@unittest.skip("batch_encode_plus does not handle overflowing tokens.")
|
|
def test_batch_encode_plus_overflowing_tokens(self):
|
|
pass
|
|
|
|
def test_batch_encode_plus_padding(self):
|
|
# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
|
|
|
|
# Right padding tests
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
sequences = [
|
|
"Testing batch encode plus",
|
|
"Testing batch encode plus with different sequence lengths",
|
|
"Testing batch encode plus with different sequence lengths correctly pads",
|
|
]
|
|
|
|
max_length = 100
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequences)
|
|
|
|
encoded_sequences = [
|
|
tokenizer.encode_plus(table, sequence, max_length=max_length, padding="max_length")
|
|
for sequence in sequences
|
|
]
|
|
encoded_sequences_batch = tokenizer.batch_encode_plus(
|
|
table, sequences, max_length=max_length, padding="max_length"
|
|
)
|
|
self.assertListEqual(
|
|
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
|
|
)
|
|
|
|
# Left padding tests
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
tokenizer.padding_side = "left"
|
|
sequences = [
|
|
"Testing batch encode plus",
|
|
"Testing batch encode plus with different sequence lengths",
|
|
"Testing batch encode plus with different sequence lengths correctly pads",
|
|
]
|
|
|
|
max_length = 100
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequences)
|
|
|
|
encoded_sequences = [
|
|
tokenizer.encode_plus(table, sequence, max_length=max_length, padding="max_length")
|
|
for sequence in sequences
|
|
]
|
|
encoded_sequences_batch = tokenizer.batch_encode_plus(
|
|
table, sequences, max_length=max_length, padding="max_length"
|
|
)
|
|
self.assertListEqual(
|
|
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
|
|
)
|
|
|
|
def test_padding_to_multiple_of(self):
|
|
tokenizers = self.get_tokenizers()
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
if tokenizer.pad_token is None:
|
|
self.skipTest("No padding token.")
|
|
else:
|
|
empty_tokens = tokenizer(table, padding=True, pad_to_multiple_of=8)
|
|
normal_tokens = tokenizer(table, "This is a sample input", padding=True, pad_to_multiple_of=8)
|
|
for key, value in empty_tokens.items():
|
|
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
|
|
for key, value in normal_tokens.items():
|
|
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
|
|
|
|
normal_tokens = tokenizer(table, "This", pad_to_multiple_of=8)
|
|
for key, value in normal_tokens.items():
|
|
self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
|
|
|
|
# Should also work with truncation
|
|
normal_tokens = tokenizer(table, "This", padding=True, truncation=True, pad_to_multiple_of=8)
|
|
for key, value in normal_tokens.items():
|
|
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
|
|
|
|
@unittest.skip("TAPAS cannot handle `prepare_for_model` without passing by `encode_plus` or `batch_encode_plus`")
|
|
def test_prepare_for_model(self):
|
|
pass
|
|
|
|
def test_tokenizer_slow_store_full_signature(self):
|
|
signature = inspect.signature(self.tokenizer_class.__init__)
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
for parameter_name, parameter in signature.parameters.items():
|
|
if parameter.default != inspect.Parameter.empty:
|
|
self.assertIn(parameter_name, tokenizer.init_kwargs)
|
|
|
|
def test_special_tokens_mask_input_pairs(self):
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
sequence_0 = "Encode this."
|
|
empty_table = self.get_table(tokenizer, length=0)
|
|
table = self.get_table(tokenizer, length=10)
|
|
encoded_sequence = tokenizer.encode(empty_table, sequence_0, add_special_tokens=False)
|
|
encoded_sequence += tokenizer.encode(table, "", add_special_tokens=False)
|
|
encoded_sequence_dict = tokenizer.encode_plus(
|
|
table,
|
|
sequence_0,
|
|
add_special_tokens=True,
|
|
return_special_tokens_mask=True,
|
|
# add_prefix_space=False,
|
|
)
|
|
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
|
|
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
|
|
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
|
|
|
|
filtered_sequence = [
|
|
(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
|
|
]
|
|
filtered_sequence = [x for x in filtered_sequence if x is not None]
|
|
self.assertEqual(encoded_sequence, filtered_sequence)
|
|
|
|
def test_special_tokens_mask(self):
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
sequence_0 = "Encode this."
|
|
# Testing single inputs
|
|
encoded_sequence = tokenizer.encode(table, sequence_0, add_special_tokens=False)
|
|
encoded_sequence_dict = tokenizer.encode_plus(
|
|
table, sequence_0, add_special_tokens=True, return_special_tokens_mask=True
|
|
)
|
|
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
|
|
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
|
|
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
|
|
|
|
filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
|
|
self.assertEqual(encoded_sequence, filtered_sequence)
|
|
|
|
def test_save_and_load_tokenizer(self):
|
|
# safety check on max_len default value so we are sure the test works
|
|
tokenizers = self.get_tokenizers()
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
self.assertNotEqual(tokenizer.model_max_length, 42)
|
|
|
|
# Now let's start the test
|
|
tokenizers = self.get_tokenizers()
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
# Isolate this from the other tests because we save additional tokens/etc
|
|
table = self.get_table(tokenizer, length=0)
|
|
tmpdirname = tempfile.mkdtemp()
|
|
|
|
sample_text = " He is very happy, UNwant\u00E9d,running"
|
|
before_tokens = tokenizer.encode(table, sample_text, add_special_tokens=False)
|
|
before_vocab = tokenizer.get_vocab()
|
|
tokenizer.save_pretrained(tmpdirname)
|
|
|
|
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
|
|
after_tokens = after_tokenizer.encode(table, sample_text, add_special_tokens=False)
|
|
after_vocab = after_tokenizer.get_vocab()
|
|
self.assertListEqual(before_tokens, after_tokens)
|
|
self.assertDictEqual(before_vocab, after_vocab)
|
|
|
|
shutil.rmtree(tmpdirname)
|
|
|
|
def test_right_and_left_padding(self):
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
table = self.get_table(tokenizer, length=0)
|
|
sequence = "Sequence"
|
|
padding_size = 10
|
|
|
|
# check correct behaviour if no pad_token_id exists and add it eventually
|
|
self._check_no_pad_token_padding(tokenizer, sequence)
|
|
|
|
padding_idx = tokenizer.pad_token_id
|
|
|
|
# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
|
|
tokenizer.padding_side = "right"
|
|
encoded_sequence = tokenizer.encode(table, sequence)
|
|
sequence_length = len(encoded_sequence)
|
|
padded_sequence = tokenizer.encode(
|
|
table, sequence, max_length=sequence_length + padding_size, padding="max_length"
|
|
)
|
|
padded_sequence_length = len(padded_sequence)
|
|
assert sequence_length + padding_size == padded_sequence_length
|
|
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
|
|
|
|
# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
|
|
tokenizer.padding_side = "left"
|
|
encoded_sequence = tokenizer.encode(table, sequence)
|
|
sequence_length = len(encoded_sequence)
|
|
padded_sequence = tokenizer.encode(
|
|
table, sequence, max_length=sequence_length + padding_size, padding="max_length"
|
|
)
|
|
padded_sequence_length = len(padded_sequence)
|
|
assert sequence_length + padding_size == padded_sequence_length
|
|
assert [padding_idx] * padding_size + encoded_sequence == padded_sequence
|
|
|
|
# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
|
|
encoded_sequence = tokenizer.encode(table, sequence)
|
|
sequence_length = len(encoded_sequence)
|
|
|
|
tokenizer.padding_side = "right"
|
|
padded_sequence_right = tokenizer.encode(table, sequence, padding=True)
|
|
padded_sequence_right_length = len(padded_sequence_right)
|
|
assert sequence_length == padded_sequence_right_length
|
|
assert encoded_sequence == padded_sequence_right
|
|
|
|
tokenizer.padding_side = "left"
|
|
padded_sequence_left = tokenizer.encode(table, sequence, padding="longest")
|
|
padded_sequence_left_length = len(padded_sequence_left)
|
|
assert sequence_length == padded_sequence_left_length
|
|
assert encoded_sequence == padded_sequence_left
|
|
|
|
tokenizer.padding_side = "right"
|
|
padded_sequence_right = tokenizer.encode(table, sequence)
|
|
padded_sequence_right_length = len(padded_sequence_right)
|
|
assert sequence_length == padded_sequence_right_length
|
|
assert encoded_sequence == padded_sequence_right
|
|
|
|
tokenizer.padding_side = "left"
|
|
padded_sequence_left = tokenizer.encode(table, sequence, padding=False)
|
|
padded_sequence_left_length = len(padded_sequence_left)
|
|
assert sequence_length == padded_sequence_left_length
|
|
assert encoded_sequence == padded_sequence_left
|
|
|
|
def test_token_type_ids(self):
|
|
tokenizers = self.get_tokenizers()
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
empty_table = self.get_table(tokenizer, length=0)
|
|
seq_0 = "Test this method."
|
|
|
|
# We want to have sequence 0 and sequence 1 are tagged
|
|
# respectively with 0 and 1 token_ids
|
|
# (regardless of whether the model use token type ids)
|
|
# We use this assumption in the QA pipeline among other place
|
|
output = tokenizer(empty_table, seq_0, return_token_type_ids=True)
|
|
|
|
# Assert that the token type IDs have the same length as the input IDs
|
|
self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))
|
|
|
|
# Assert that each token type ID has 7 values
|
|
self.assertTrue(all(len(token_type_ids) == 7 for token_type_ids in output["token_type_ids"]))
|
|
|
|
# Do the same test as modeling common.
|
|
self.assertIn(0, output["token_type_ids"][0])
|
|
|
|
@require_torch
|
|
@slow
|
|
@require_scatter
|
|
def test_torch_encode_plus_sent_to_model(self):
|
|
import torch
|
|
|
|
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
|
|
|
|
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
|
|
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
|
|
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
|
|
return
|
|
|
|
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
|
|
config = config_class()
|
|
|
|
if config.is_encoder_decoder or config.pad_token_id is None:
|
|
return
|
|
|
|
model = model_class(config)
|
|
|
|
# Make sure the model contains at least the full vocabulary size in its embedding matrix
|
|
is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight")
|
|
assert (
|
|
(model.get_input_embeddings().weight.shape[0] >= len(tokenizer))
|
|
if is_using_common_embeddings
|
|
else True
|
|
)
|
|
|
|
# Build sequence
|
|
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
|
|
sequence = " ".join(first_ten_tokens)
|
|
table = self.get_table(tokenizer, length=0)
|
|
encoded_sequence = tokenizer.encode_plus(table, sequence, return_tensors="pt")
|
|
batch_encoded_sequence = tokenizer.batch_encode_plus(table, [sequence, sequence], return_tensors="pt")
|
|
# This should not fail
|
|
|
|
with torch.no_grad(): # saves some time
|
|
model(**encoded_sequence)
|
|
model(**batch_encoded_sequence)
|
|
|
|
@unittest.skip("TAPAS doesn't handle pre-tokenized inputs.")
|
|
def test_pretokenized_inputs(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_tapas_truncation_integration_test(self):
|
|
data = {
|
|
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
|
|
"Age": ["56", "45", "59"],
|
|
"Number of movies": ["87", "53", "69"],
|
|
"Date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
|
|
}
|
|
queries = [
|
|
"When was Brad Pitt born?",
|
|
"Which actor appeared in the least number of movies?",
|
|
"What is the average number of movies?",
|
|
]
|
|
table = pd.DataFrame.from_dict(data)
|
|
|
|
tokenizer = TapasTokenizer.from_pretrained("lysandre/tapas-temporary-repo", model_max_length=512)
|
|
|
|
for i in range(12):
|
|
# The table cannot even encode the headers, so raise an error
|
|
with self.assertRaises(ValueError):
|
|
tokenizer.encode(table=table, query=queries[0], max_length=i, truncation="drop_rows_to_fit")
|
|
|
|
for i in range(12, 512):
|
|
new_encoded_inputs = tokenizer.encode(
|
|
table=table, query=queries[0], max_length=i, truncation="drop_rows_to_fit"
|
|
)
|
|
|
|
# Ensure that the input IDs are less than the max length defined.
|
|
self.assertLessEqual(len(new_encoded_inputs), i)
|
|
|
|
tokenizer.model_max_length = 20
|
|
new_encoded_inputs = tokenizer.encode(table=table, query=queries[0], truncation=True)
|
|
dropped_encoded_inputs = tokenizer.encode(table=table, query=queries[0], truncation="drop_rows_to_fit")
|
|
|
|
# Ensure that the input IDs are still truncated when no max_length is specified
|
|
self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs)
|
|
self.assertLessEqual(len(new_encoded_inputs), 20)
|
|
|
|
@slow
|
|
def test_min_max_question_length(self):
|
|
data = {
|
|
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
|
|
"Age": ["56", "45", "59"],
|
|
"Number of movies": ["87", "53", "69"],
|
|
"Date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
|
|
}
|
|
queries = "When was Brad Pitt born?"
|
|
table = pd.DataFrame.from_dict(data)
|
|
|
|
# test max_question_length
|
|
tokenizer = TapasTokenizer.from_pretrained("lysandre/tapas-temporary-repo", max_question_length=2)
|
|
|
|
encoding = tokenizer(table=table, queries=queries)
|
|
|
|
# query should not be tokenized as it's longer than the specified max_question_length
|
|
expected_results = [101, 102]
|
|
|
|
self.assertListEqual(encoding.input_ids[:2], expected_results)
|
|
|
|
# test min_question_length
|
|
tokenizer = TapasTokenizer.from_pretrained("lysandre/tapas-temporary-repo", min_question_length=30)
|
|
|
|
encoding = tokenizer(table=table, queries=queries)
|
|
|
|
# query should not be tokenized as it's shorter than the specified min_question_length
|
|
expected_results = [101, 102]
|
|
|
|
self.assertListEqual(encoding.input_ids[:2], expected_results)
|
|
|
|
@is_pt_tf_cross_test
|
|
def test_batch_encode_plus_tensors(self):
|
|
tokenizers = self.get_tokenizers(do_lower_case=False)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
sequences = [
|
|
"Testing batch encode plus",
|
|
"Testing batch encode plus with different sequence lengths",
|
|
"Testing batch encode plus with different sequence lengths correctly pads",
|
|
]
|
|
|
|
table = self.get_table(tokenizer, length=0)
|
|
|
|
# A Tensor cannot be build by sequences which are not the same size
|
|
self.assertRaises(ValueError, tokenizer.batch_encode_plus, table, sequences, return_tensors="pt")
|
|
self.assertRaises(ValueError, tokenizer.batch_encode_plus, table, sequences, return_tensors="tf")
|
|
|
|
if tokenizer.pad_token_id is None:
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer.batch_encode_plus,
|
|
table,
|
|
sequences,
|
|
padding=True,
|
|
return_tensors="pt",
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer.batch_encode_plus,
|
|
table,
|
|
sequences,
|
|
padding="longest",
|
|
return_tensors="tf",
|
|
)
|
|
else:
|
|
pytorch_tensor = tokenizer.batch_encode_plus(table, sequences, padding=True, return_tensors="pt")
|
|
tensorflow_tensor = tokenizer.batch_encode_plus(
|
|
table, sequences, padding="longest", return_tensors="tf"
|
|
)
|
|
encoded_sequences = tokenizer.batch_encode_plus(table, sequences, padding=True)
|
|
|
|
for key in encoded_sequences.keys():
|
|
pytorch_value = pytorch_tensor[key].tolist()
|
|
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
|
|
encoded_value = encoded_sequences[key]
|
|
|
|
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
|
|
|
|
@slow
|
|
def test_tapas_integration_test(self):
|
|
data = {
|
|
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
|
|
"Age": ["56", "45", "59"],
|
|
"Number of movies": ["87", "53", "69"],
|
|
"Date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
|
|
}
|
|
queries = [
|
|
"When was Brad Pitt born?",
|
|
"Which actor appeared in the least number of movies?",
|
|
"What is the average number of movies?",
|
|
]
|
|
table = pd.DataFrame.from_dict(data)
|
|
|
|
tokenizer = TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq", model_max_length=512)
|
|
|
|
# fmt: off
|
|
expected_results = {'input_ids':[101,2043,2001,8226,15091,2141,1029,102,5889,2287,2193,1997,5691,3058,1997,4182,8226,15091,5179,6584,2324,2285,3699,14720,4487,6178,9488,3429,5187,2340,2281,3326,2577,18856,7828,3240,5354,6353,1020,2089,3777],'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],'token_type_ids':[[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[1,1,0,0,0,0,0],[1,2,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,1,1,0,0,0,0],[1,1,1,0,0,0,0],[1,2,1,0,2,2,0],[1,3,1,0,3,1,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,2,2,0,1,3,0],[1,3,2,0,1,3,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,2,3,0,3,1,0],[1,3,3,0,2,2,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0]]} # noqa: E231
|
|
# fmt: on
|
|
|
|
new_encoded_inputs = tokenizer.encode_plus(table=table, query=queries[0])
|
|
|
|
self.assertDictEqual(dict(new_encoded_inputs), expected_results)
|
|
|
|
@slow
|
|
def test_full_tokenizer(self):
|
|
data = [
|
|
["Pos", "No", "Driver", "Team", "Laps", "Time/Retired", "Grid", "Points"],
|
|
["1", "32", "Patrick Carpentier", "Team Player's", "87", "1:48:11.023", "1", "22"],
|
|
["2", "1", "Bruno Junqueira", "Newman/Haas Racing", "87", "+0.8 secs", "2", "17"],
|
|
["3", "3", "Paul Tracy", "Team Player's", "87", "+28.6 secs", "3", "14"],
|
|
["4", "9", "Michel Jourdain, Jr.", "Team Rahal", "87", "+40.8 secs", "13", "12"],
|
|
["5", "34", "Mario Haberfeld", "Mi-Jack Conquest Racing", "87", "+42.1 secs", "6", "10"],
|
|
["6", "20", "Oriol Servia", "Patrick Racing", "87", "+1:00.2", "10", "8"],
|
|
["7", "51", "Adrian Fernandez", "Fernandez Racing", "87", "+1:01.4", "5", "6"],
|
|
["8", "12", "Jimmy Vasser", "American Spirit Team Johansson", "87", "+1:01.8", "8", "5"],
|
|
["9", "7", "Tiago Monteiro", "Fittipaldi-Dingman Racing", "86", "+ 1 Lap", "15", "4"],
|
|
["10", "55", "Mario Dominguez", "Herdez Competition", "86", "+ 1 Lap", "11", "3"],
|
|
["11", "27", "Bryan Herta", "PK Racing", "86", "+ 1 Lap", "12", "2"],
|
|
["12", "31", "Ryan Hunter-Reay", "American Spirit Team Johansson", "86", "+ 1 Lap", "17", "1"],
|
|
["13", "19", "Joel Camathias", "Dale Coyne Racing", "85", "+ 2 Laps", "18", "0"],
|
|
["14", "33", "Alex Tagliani", "Rocketsports Racing", "85", "+ 2 Laps", "14", "0"],
|
|
["15", "4", "Roberto Moreno", "Herdez Competition", "85", "+ 2 Laps", "9", "0"],
|
|
["16", "11", "Geoff Boss", "Dale Coyne Racing", "83", "Mechanical", "19", "0"],
|
|
["17", "2", "Sebastien Bourdais", "Newman/Haas Racing", "77", "Mechanical", "4", "0"],
|
|
["18", "15", "Darren Manning", "Walker Racing", "12", "Mechanical", "7", "0"],
|
|
["19", "5", "Rodolfo Lavin", "Walker Racing", "10", "Mechanical", "16", "0"],
|
|
]
|
|
query = "what were the drivers names?"
|
|
table = pd.DataFrame.from_records(data[1:], columns=data[0])
|
|
|
|
tokenizer = TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq", model_max_length=512)
|
|
model_inputs = tokenizer(table, query, padding="max_length")
|
|
|
|
input_ids = model_inputs["input_ids"]
|
|
token_type_ids = np.array(model_inputs["token_type_ids"])
|
|
segment_ids = token_type_ids[:, 0]
|
|
column_ids = token_type_ids[:, 1]
|
|
row_ids = token_type_ids[:, 2]
|
|
|
|
# fmt: off
|
|
expected_results = {'input_ids':[101,2054,2020,1996,6853,3415,1029,102,13433,2015,2053,4062,2136,10876,2051,1013,3394,8370,2685,1015,3590,4754,29267,4765,3771,2136,2447,1005,1055,6584,1015,1024,4466,1024,2340,1012,6185,2509,1015,2570,1016,1015,10391,12022,4226,7895,10625,1013,22996,3868,6584,1009,1014,1012,1022,10819,2015,1016,2459,1017,1017,2703,10555,2136,2447,1005,1055,6584,1009,2654,1012,1020,10819,2015,1017,2403,1018,1023,8709,8183,3126,21351,2078,1010,3781,1012,2136,10958,8865,6584,1009,2871,1012,1022,10819,2015,2410,2260,1019,4090,7986,5292,5677,8151,2771,1011,2990,9187,3868,6584,1009,4413,1012,1015,10819,2015,1020,2184,1020,2322,2030,20282,14262,9035,4754,3868,6584,1009,1015,1024,4002,1012,1016,2184,1022,1021,4868,7918,12023,12023,3868,6584,1009,1015,1024,5890,1012,1018,1019,1020,1022,2260,5261,12436,18116,2137,4382,2136,26447,6584,1009,1015,1024,5890,1012,1022,1022,1019,1023,1021,27339,3995,10125,9711,4906,25101,24657,1011,22033,2386,3868,6564,1009,1015,5001,2321,1018,2184,4583,7986,14383,2075,29488,14906,9351,2971,6564,1009,1015,5001,2340,1017,2340,2676,8527,2014,2696,1052,2243,3868,6564,1009,1015,5001,2260,1016,2260,2861,4575,4477,1011,2128,4710,2137,4382,2136,26447,6564,1009,1015,5001,2459,1015,2410,2539,8963,11503,25457,3022,8512,2522,9654,3868,5594,1009,1016,10876,2324,1014,2403,3943,4074,6415,15204,2072,12496,25378,3868,5594,1009,1016,10876,2403,1014,2321,1018,10704,17921,14906,9351,2971,5594,1009,1016,10876,1023,1014,2385,2340,14915,5795,8512,2522,9654,3868,6640,6228,2539,1014,2459,1016,28328,8945,3126,21351,2015,10625,1013,22996,3868,6255,6228,1018,1014,2324,2321,12270,11956,5232,3868,2260,6228,1021,1014,2539,1019,8473,28027,2080,2474,6371,5232,3868,2184,6228,2385,1014,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'column_ids':[0,0,0,0,0,0,0,0,1,1,2,3,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,3,3,3,3,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,3,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,4,4,4,4,5,6,7,8,1,2,3,3,3,3,3,4,4,4,4,5,6,7,8,1,2,3,3,4,4,5,6,7,8,1,2,3,3,3,3,3,4,4,5,6,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'row_ids':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,15,15,15,15,15,15,15,15,15,15,15,15,15,16,16,16,16,16,16,16,16,16,16,16,16,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'segment_ids':[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]} # noqa: E231
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# fmt: on
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|
|
self.assertListEqual(input_ids, expected_results["input_ids"])
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|
self.assertListEqual(segment_ids.tolist(), expected_results["segment_ids"])
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|
self.assertListEqual(column_ids.tolist(), expected_results["column_ids"])
|
|
self.assertListEqual(row_ids.tolist(), expected_results["row_ids"])
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|
|
|
@unittest.skip("Skip this test while all models are still to be uploaded.")
|
|
def test_pretrained_model_lists(self):
|
|
pass
|
|
|
|
@unittest.skip("Doesn't support another framework than PyTorch")
|
|
def test_np_encode_plus_sent_to_model(self):
|
|
pass
|