# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# 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.
"""
isort:skip_file
"""
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

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


if is_tokenizers_available():
    from tokenizers import Tokenizer
    from tokenizers.models import WordPiece


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]])

    @require_tokenizers
    def test_instantiation_from_tokenizers(self):
        bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
        PreTrainedTokenizerFast(tokenizer_object=bert_tokenizer)

    @require_tokenizers
    def test_instantiation_from_tokenizers_json_file(self):
        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"))

# 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.
"""
isort:skip_file
"""
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

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


if is_tokenizers_available():
    from tokenizers import Tokenizer
    from tokenizers.models import WordPiece


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]])

    @require_tokenizers
    def test_instantiation_from_tokenizers(self):
        bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
        PreTrainedTokenizerFast(tokenizer_object=bert_tokenizer)

    @require_tokenizers
    def test_instantiation_from_tokenizers_json_file(self):
        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"))
