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Removed isort as a dependency
449 lines
20 KiB
Python
449 lines
20 KiB
Python
# Copyright 2018 HuggingFace Inc..
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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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"""
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ruff: isort: skip_file
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"""
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import os
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import pickle
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import tempfile
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import unittest
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from typing import Callable, Optional
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import numpy as np
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from transformers import (
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BatchEncoding,
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BertTokenizer,
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BertTokenizerFast,
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LlamaTokenizerFast,
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PreTrainedTokenizer,
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PreTrainedTokenizerFast,
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TensorType,
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TokenSpan,
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is_tokenizers_available,
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)
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
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from transformers.testing_utils import (
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CaptureStderr,
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require_flax,
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require_sentencepiece,
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require_tf,
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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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if is_tokenizers_available():
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import tokenizers
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from tokenizers import Tokenizer
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from tokenizers.models import WordPiece
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class TokenizerUtilsTest(unittest.TestCase):
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def check_tokenizer_from_pretrained(self, tokenizer_class):
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s3_models = list(tokenizer_class.max_model_input_sizes.keys())
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for model_name in s3_models[:1]:
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tokenizer = tokenizer_class.from_pretrained(model_name)
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self.assertIsNotNone(tokenizer)
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self.assertIsInstance(tokenizer, tokenizer_class)
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self.assertIsInstance(tokenizer, PreTrainedTokenizer)
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for special_tok in tokenizer.all_special_tokens:
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self.assertIsInstance(special_tok, str)
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special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
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self.assertIsInstance(special_tok_id, int)
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def assert_dump_and_restore(self, be_original: BatchEncoding, equal_op: Optional[Callable] = None):
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batch_encoding_str = pickle.dumps(be_original)
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self.assertIsNotNone(batch_encoding_str)
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be_restored = pickle.loads(batch_encoding_str)
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# Ensure is_fast is correctly restored
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self.assertEqual(be_restored.is_fast, be_original.is_fast)
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# Ensure encodings are potentially correctly restored
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if be_original.is_fast:
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self.assertIsNotNone(be_restored.encodings)
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else:
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self.assertIsNone(be_restored.encodings)
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# Ensure the keys are the same
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for original_v, restored_v in zip(be_original.values(), be_restored.values()):
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if equal_op:
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self.assertTrue(equal_op(restored_v, original_v))
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else:
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self.assertEqual(restored_v, original_v)
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@slow
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def test_pretrained_tokenizers(self):
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self.check_tokenizer_from_pretrained(GPT2Tokenizer)
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def test_tensor_type_from_str(self):
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self.assertEqual(TensorType("tf"), TensorType.TENSORFLOW)
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self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
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self.assertEqual(TensorType("np"), TensorType.NUMPY)
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@require_tokenizers
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def test_batch_encoding_pickle(self):
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import numpy as np
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tokenizer_p = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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tokenizer_r = BertTokenizerFast.from_pretrained("google-bert/bert-base-cased")
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# Python no tensor
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with self.subTest("BatchEncoding (Python, return_tensors=None)"):
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self.assert_dump_and_restore(tokenizer_p("Small example to encode"))
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with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"):
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self.assert_dump_and_restore(
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tokenizer_p("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
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)
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with self.subTest("BatchEncoding (Rust, return_tensors=None)"):
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self.assert_dump_and_restore(tokenizer_r("Small example to encode"))
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with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"):
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self.assert_dump_and_restore(
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tokenizer_r("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
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)
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@require_tf
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@require_tokenizers
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def test_batch_encoding_pickle_tf(self):
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import tensorflow as tf
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def tf_array_equals(t1, t2):
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return tf.reduce_all(tf.equal(t1, t2))
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tokenizer_p = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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tokenizer_r = BertTokenizerFast.from_pretrained("google-bert/bert-base-cased")
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with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"):
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self.assert_dump_and_restore(
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tokenizer_p("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
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)
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with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"):
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self.assert_dump_and_restore(
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tokenizer_r("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
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)
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@require_torch
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@require_tokenizers
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def test_batch_encoding_pickle_pt(self):
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import torch
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tokenizer_p = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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tokenizer_r = BertTokenizerFast.from_pretrained("google-bert/bert-base-cased")
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with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"):
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self.assert_dump_and_restore(
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tokenizer_p("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
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)
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with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"):
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self.assert_dump_and_restore(
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tokenizer_r("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
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)
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@require_tokenizers
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def test_batch_encoding_is_fast(self):
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tokenizer_p = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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tokenizer_r = BertTokenizerFast.from_pretrained("google-bert/bert-base-cased")
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with self.subTest("Python Tokenizer"):
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self.assertFalse(tokenizer_p("Small example to_encode").is_fast)
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with self.subTest("Rust Tokenizer"):
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self.assertTrue(tokenizer_r("Small example to_encode").is_fast)
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@require_tokenizers
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def test_batch_encoding_word_to_tokens(self):
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tokenizer_r = BertTokenizerFast.from_pretrained("google-bert/bert-base-cased")
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encoded = tokenizer_r(["Test", "\xad", "test"], is_split_into_words=True)
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self.assertEqual(encoded.word_to_tokens(0), TokenSpan(start=1, end=2))
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self.assertEqual(encoded.word_to_tokens(1), None)
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self.assertEqual(encoded.word_to_tokens(2), TokenSpan(start=2, end=3))
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def test_batch_encoding_with_labels(self):
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batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
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tensor_batch = batch.convert_to_tensors(tensor_type="np")
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self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
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self.assertEqual(tensor_batch["labels"].shape, (2,))
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# test converting the converted
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with CaptureStderr() as cs:
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tensor_batch = batch.convert_to_tensors(tensor_type="np")
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self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
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batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
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tensor_batch = batch.convert_to_tensors(tensor_type="np", prepend_batch_axis=True)
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self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
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self.assertEqual(tensor_batch["labels"].shape, (1,))
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@require_torch
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def test_batch_encoding_with_labels_pt(self):
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batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
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tensor_batch = batch.convert_to_tensors(tensor_type="pt")
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self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
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self.assertEqual(tensor_batch["labels"].shape, (2,))
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# test converting the converted
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with CaptureStderr() as cs:
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tensor_batch = batch.convert_to_tensors(tensor_type="pt")
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self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
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batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
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tensor_batch = batch.convert_to_tensors(tensor_type="pt", prepend_batch_axis=True)
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self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
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self.assertEqual(tensor_batch["labels"].shape, (1,))
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@require_tf
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def test_batch_encoding_with_labels_tf(self):
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batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
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tensor_batch = batch.convert_to_tensors(tensor_type="tf")
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self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
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self.assertEqual(tensor_batch["labels"].shape, (2,))
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# test converting the converted
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with CaptureStderr() as cs:
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tensor_batch = batch.convert_to_tensors(tensor_type="tf")
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self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
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batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
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tensor_batch = batch.convert_to_tensors(tensor_type="tf", prepend_batch_axis=True)
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self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
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self.assertEqual(tensor_batch["labels"].shape, (1,))
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@require_flax
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def test_batch_encoding_with_labels_jax(self):
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batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
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tensor_batch = batch.convert_to_tensors(tensor_type="jax")
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self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
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self.assertEqual(tensor_batch["labels"].shape, (2,))
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# test converting the converted
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with CaptureStderr() as cs:
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tensor_batch = batch.convert_to_tensors(tensor_type="jax")
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self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
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batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
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tensor_batch = batch.convert_to_tensors(tensor_type="jax", prepend_batch_axis=True)
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self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
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self.assertEqual(tensor_batch["labels"].shape, (1,))
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def test_padding_accepts_tensors(self):
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features = [{"input_ids": np.array([0, 1, 2])}, {"input_ids": np.array([0, 1, 2, 3])}]
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tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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batch = tokenizer.pad(features, padding=True)
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self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
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self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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batch = tokenizer.pad(features, padding=True, return_tensors="np")
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self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
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self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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@require_tokenizers
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def test_decoding_single_token(self):
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for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
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with self.subTest(f"{tokenizer_class}"):
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tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
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token_id = 2300
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decoded_flat = tokenizer.decode(token_id)
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decoded_list = tokenizer.decode([token_id])
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self.assertEqual(decoded_flat, "Force")
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self.assertEqual(decoded_list, "Force")
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token_id = 0
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decoded_flat = tokenizer.decode(token_id)
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decoded_list = tokenizer.decode([token_id])
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self.assertEqual(decoded_flat, "[PAD]")
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self.assertEqual(decoded_list, "[PAD]")
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last_item_id = tokenizer.vocab_size - 1
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decoded_flat = tokenizer.decode(last_item_id)
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decoded_list = tokenizer.decode([last_item_id])
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self.assertEqual(decoded_flat, "##:")
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self.assertEqual(decoded_list, "##:")
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def test_extra_special_tokens_multimodal(self):
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special_tokens_list = [
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"bos_token",
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"eos_token",
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"unk_token",
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"sep_token",
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"pad_token",
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"cls_token",
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"mask_token",
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"additional_special_tokens",
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]
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llama_tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b")
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llama_tokenizer.extra_special_tokens = {
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"boi_token": "<image_start>",
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"eoi_token": "<image_end>",
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"image_token": "<image>",
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}
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self.assertListEqual(llama_tokenizer.SPECIAL_TOKENS_ATTRIBUTES, special_tokens_list)
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with tempfile.TemporaryDirectory() as tmpdirname:
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llama_tokenizer.save_pretrained(tmpdirname)
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# load back and check we have extra special tokens set
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loaded_tokenizer = LlamaTokenizerFast.from_pretrained(tmpdirname)
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multimodal_special_tokens_list = special_tokens_list + ["boi_token", "eoi_token", "image_token"]
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self.assertListEqual(loaded_tokenizer.SPECIAL_TOKENS_ATTRIBUTES, multimodal_special_tokens_list)
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# We set an image_token_id before, so we can get an "image_token" as str that matches the id
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self.assertTrue(loaded_tokenizer.image_token == "<image>")
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self.assertTrue(loaded_tokenizer.image_token_id == loaded_tokenizer.convert_tokens_to_ids("<image>"))
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# save one more time and make sure the image token can get loaded back
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with tempfile.TemporaryDirectory() as tmpdirname:
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loaded_tokenizer.save_pretrained(tmpdirname)
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loaded_tokenizer_with_extra_tokens = LlamaTokenizerFast.from_pretrained(tmpdirname)
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self.assertTrue(loaded_tokenizer_with_extra_tokens.image_token == "<image>")
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# test that we can also indicate extra tokens during load time
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extra_special_tokens = {
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"boi_token": "<image_start>",
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"eoi_token": "<image_end>",
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"image_token": "<image>",
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}
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tokenizer = LlamaTokenizerFast.from_pretrained(
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"huggyllama/llama-7b", extra_special_tokens=extra_special_tokens
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)
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self.assertTrue(tokenizer.image_token == "<image>")
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self.assertTrue(tokenizer.image_token_id == loaded_tokenizer.convert_tokens_to_ids("<image>"))
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@require_tokenizers
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def test_decoding_skip_special_tokens(self):
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for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
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with self.subTest(f"{tokenizer_class}"):
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tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
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tokenizer.add_tokens(["ஐ"], special_tokens=True)
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# test special token with other tokens, skip the special tokens
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sentence = "This is a beautiful flower ஐ"
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ids = tokenizer(sentence)["input_ids"]
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decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
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self.assertEqual(decoded_sent, "This is a beautiful flower")
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# test special token with other tokens, do not skip the special tokens
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ids = tokenizer(sentence)["input_ids"]
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decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
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self.assertEqual(decoded_sent, "[CLS] This is a beautiful flower ஐ [SEP]")
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# test special token stand alone, skip the special tokens
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sentence = "ஐ"
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ids = tokenizer(sentence)["input_ids"]
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decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
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self.assertEqual(decoded_sent, "")
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# test special token stand alone, do not skip the special tokens
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ids = tokenizer(sentence)["input_ids"]
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decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
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self.assertEqual(decoded_sent, "[CLS] ஐ [SEP]")
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# test single special token alone, skip
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pad_id = 0
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decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=True)
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self.assertEqual(decoded_sent, "")
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# test single special token alone, do not skip
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decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=False)
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self.assertEqual(decoded_sent, "[PAD]")
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@require_torch
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def test_padding_accepts_tensors_pt(self):
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import torch
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features = [{"input_ids": torch.tensor([0, 1, 2])}, {"input_ids": torch.tensor([0, 1, 2, 3])}]
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tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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batch = tokenizer.pad(features, padding=True)
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self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
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self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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batch = tokenizer.pad(features, padding=True, return_tensors="pt")
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self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
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self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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@require_tf
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def test_padding_accepts_tensors_tf(self):
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import tensorflow as tf
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features = [{"input_ids": tf.constant([0, 1, 2])}, {"input_ids": tf.constant([0, 1, 2, 3])}]
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tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
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batch = tokenizer.pad(features, padding=True)
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self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
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self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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batch = tokenizer.pad(features, padding=True, return_tensors="tf")
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self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
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self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
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@require_tokenizers
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def test_instantiation_from_tokenizers(self):
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bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
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PreTrainedTokenizerFast(tokenizer_object=bert_tokenizer)
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@require_tokenizers
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def test_instantiation_from_tokenizers_json_file(self):
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bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
|
||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||
bert_tokenizer.save(os.path.join(tmpdirname, "tokenizer.json"))
|
||
PreTrainedTokenizerFast(tokenizer_file=os.path.join(tmpdirname, "tokenizer.json"))
|
||
|
||
def test_len_tokenizer(self):
|
||
for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
|
||
with self.subTest(f"{tokenizer_class}"):
|
||
tokenizer = tokenizer_class.from_pretrained("bert-base-uncased")
|
||
added_tokens_size = len(tokenizer.added_tokens_decoder)
|
||
self.assertEqual(len(tokenizer), tokenizer.vocab_size)
|
||
|
||
tokenizer.add_tokens(["<test_token>"])
|
||
self.assertEqual(len(tokenizer), tokenizer.vocab_size + 1)
|
||
self.assertEqual(len(tokenizer.added_tokens_decoder), added_tokens_size + 1)
|
||
self.assertEqual(len(tokenizer.added_tokens_encoder), added_tokens_size + 1)
|
||
|
||
@require_sentencepiece
|
||
def test_sentencepiece_cohabitation(self):
|
||
from sentencepiece import sentencepiece_model_pb2 as _original_protobuf # noqa: F401
|
||
|
||
from transformers.convert_slow_tokenizer import import_protobuf # noqa: F401
|
||
|
||
# Now this will try to import sentencepiece_model_pb2_new.py. This should not fail even if the protobuf
|
||
# was already imported.
|
||
import_protobuf()
|
||
|
||
def test_training_new_tokenizer_edge_cases(self):
|
||
_tokenizer = Tokenizer(tokenizers.models.BPE(vocab={"a": 1, "b": 2, "ab": 3}, merges=[("a", "b")]))
|
||
_tokenizer.pre_tokenizer = None
|
||
|
||
tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
|
||
toy_text_iterator = ("a" for _ in range(1000))
|
||
tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
|
||
|
||
_tokenizer.normalizer = None
|
||
tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
|
||
toy_text_iterator = ("a" for _ in range(1000))
|
||
tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
|
||
|
||
_tokenizer.post_processor = None
|
||
tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
|
||
toy_text_iterator = ("a" for _ in range(1000))
|
||
tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
|