test_tokenization_utils.py
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Created: Sat, Jul 5, 20:30

test_tokenization_utils.py
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	# coding=utf-8
	# Copyright 2018 HuggingFace Inc..
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import pickle
	import unittest
	from typing import Callable, Optional

	import numpy as np

	from transformers import BatchEncoding, BertTokenizer, BertTokenizerFast, PreTrainedTokenizer, TensorType, TokenSpan
	from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
	from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


	class TokenizerUtilsTest(unittest.TestCase):
	def check_tokenizer_from_pretrained(self, tokenizer_class):
	s3_models = list(tokenizer_class.max_model_input_sizes.keys())
	for model_name in s3_models[:1]:
	tokenizer = tokenizer_class.from_pretrained(model_name)
	self.assertIsNotNone(tokenizer)
	self.assertIsInstance(tokenizer, tokenizer_class)
	self.assertIsInstance(tokenizer, PreTrainedTokenizer)

	for special_tok in tokenizer.all_special_tokens:
	self.assertIsInstance(special_tok, str)
	special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
	self.assertIsInstance(special_tok_id, int)

	def assert_dump_and_restore(self, be_original: BatchEncoding, equal_op: Optional[Callable] = None):
	batch_encoding_str = pickle.dumps(be_original)
	self.assertIsNotNone(batch_encoding_str)

	be_restored = pickle.loads(batch_encoding_str)

	# Ensure is_fast is correctly restored
	self.assertEqual(be_restored.is_fast, be_original.is_fast)

	# Ensure encodings are potentially correctly restored
	if be_original.is_fast:
	self.assertIsNotNone(be_restored.encodings)
	else:
	self.assertIsNone(be_restored.encodings)

	# Ensure the keys are the same
	for original_v, restored_v in zip(be_original.values(), be_restored.values()):
	if equal_op:
	self.assertTrue(equal_op(restored_v, original_v))
	else:
	self.assertEqual(restored_v, original_v)

	@slow
	def test_pretrained_tokenizers(self):
	self.check_tokenizer_from_pretrained(GPT2Tokenizer)

	def test_tensor_type_from_str(self):
	self.assertEqual(TensorType("tf"), TensorType.TENSORFLOW)
	self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
	self.assertEqual(TensorType("np"), TensorType.NUMPY)

	@require_tokenizers
	def test_batch_encoding_pickle(self):
	import numpy as np

	tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
	tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")

	# Python no tensor
	with self.subTest("BatchEncoding (Python, return_tensors=None)"):
	self.assert_dump_and_restore(tokenizer_p("Small example to encode"))

	with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"):
	self.assert_dump_and_restore(
	tokenizer_p("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
	)

	with self.subTest("BatchEncoding (Rust, return_tensors=None)"):
	self.assert_dump_and_restore(tokenizer_r("Small example to encode"))

	with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"):
	self.assert_dump_and_restore(
	tokenizer_r("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
	)

	@require_tf
	@require_tokenizers
	def test_batch_encoding_pickle_tf(self):
	import tensorflow as tf

	def tf_array_equals(t1, t2):
	return tf.reduce_all(tf.equal(t1, t2))

	tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
	tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")

	with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"):
	self.assert_dump_and_restore(
	tokenizer_p("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
	)

	with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"):
	self.assert_dump_and_restore(
	tokenizer_r("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
	)

	@require_torch
	@require_tokenizers
	def test_batch_encoding_pickle_pt(self):
	import torch

	tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
	tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")

	with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"):
	self.assert_dump_and_restore(
	tokenizer_p("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
	)

	with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"):
	self.assert_dump_and_restore(
	tokenizer_r("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
	)

	@require_tokenizers
	def test_batch_encoding_is_fast(self):
	tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
	tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")

	with self.subTest("Python Tokenizer"):
	self.assertFalse(tokenizer_p("Small example to_encode").is_fast)

	with self.subTest("Rust Tokenizer"):
	self.assertTrue(tokenizer_r("Small example to_encode").is_fast)

	@require_tokenizers
	def test_batch_encoding_word_to_tokens(self):
	tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
	encoded = tokenizer_r(["Test", "\xad", "test"], is_split_into_words=True)

	self.assertEqual(encoded.word_to_tokens(0), TokenSpan(start=1, end=2))
	self.assertEqual(encoded.word_to_tokens(1), None)
	self.assertEqual(encoded.word_to_tokens(2), TokenSpan(start=2, end=3))

	def test_batch_encoding_with_labels(self):
	batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
	tensor_batch = batch.convert_to_tensors(tensor_type="np")
	self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
	self.assertEqual(tensor_batch["labels"].shape, (2,))
	# test converting the converted
	with CaptureStderr() as cs:
	tensor_batch = batch.convert_to_tensors(tensor_type="np")
	self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")

	batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
	tensor_batch = batch.convert_to_tensors(tensor_type="np", prepend_batch_axis=True)
	self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
	self.assertEqual(tensor_batch["labels"].shape, (1,))

	@require_torch
	def test_batch_encoding_with_labels_pt(self):
	batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
	tensor_batch = batch.convert_to_tensors(tensor_type="pt")
	self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
	self.assertEqual(tensor_batch["labels"].shape, (2,))
	# test converting the converted
	with CaptureStderr() as cs:
	tensor_batch = batch.convert_to_tensors(tensor_type="pt")
	self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")

	batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
	tensor_batch = batch.convert_to_tensors(tensor_type="pt", prepend_batch_axis=True)
	self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
	self.assertEqual(tensor_batch["labels"].shape, (1,))

	@require_tf
	def test_batch_encoding_with_labels_tf(self):
	batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
	tensor_batch = batch.convert_to_tensors(tensor_type="tf")
	self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
	self.assertEqual(tensor_batch["labels"].shape, (2,))
	# test converting the converted
	with CaptureStderr() as cs:
	tensor_batch = batch.convert_to_tensors(tensor_type="tf")
	self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")

	batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
	tensor_batch = batch.convert_to_tensors(tensor_type="tf", prepend_batch_axis=True)
	self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
	self.assertEqual(tensor_batch["labels"].shape, (1,))

	@require_flax
	def test_batch_encoding_with_labels_jax(self):
	batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
	tensor_batch = batch.convert_to_tensors(tensor_type="jax")
	self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
	self.assertEqual(tensor_batch["labels"].shape, (2,))
	# test converting the converted
	with CaptureStderr() as cs:
	tensor_batch = batch.convert_to_tensors(tensor_type="jax")
	self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")

	batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
	tensor_batch = batch.convert_to_tensors(tensor_type="jax", prepend_batch_axis=True)
	self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
	self.assertEqual(tensor_batch["labels"].shape, (1,))

	def test_padding_accepts_tensors(self):
	features = [{"input_ids": np.array([0, 1, 2])}, {"input_ids": np.array([0, 1, 2, 3])}]
	tokenizer = BertTokenizer.from_pretrained("bert-base-cased")

	batch = tokenizer.pad(features, padding=True)
	self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
	self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
	batch = tokenizer.pad(features, padding=True, return_tensors="np")
	self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
	self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

	@require_torch
	def test_padding_accepts_tensors_pt(self):
	import torch

	features = [{"input_ids": torch.tensor([0, 1, 2])}, {"input_ids": torch.tensor([0, 1, 2, 3])}]
	tokenizer = BertTokenizer.from_pretrained("bert-base-cased")

	batch = tokenizer.pad(features, padding=True)
	self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
	self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
	batch = tokenizer.pad(features, padding=True, return_tensors="pt")
	self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
	self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

	@require_tf
	def test_padding_accepts_tensors_tf(self):
	import tensorflow as tf

	features = [{"input_ids": tf.constant([0, 1, 2])}, {"input_ids": tf.constant([0, 1, 2, 3])}]
	tokenizer = BertTokenizer.from_pretrained("bert-base-cased")

	batch = tokenizer.pad(features, padding=True)
	self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
	self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
	batch = tokenizer.pad(features, padding=True, return_tensors="tf")
	self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
	self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])

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