Add FNet (#13045)

* Init FNet

* Update config

* Fix config

* Update model classes

* Update tokenizers to use sentencepiece

* Fix errors in model

* Fix defaults in config

* Remove position embedding type completely

* Fix typo and take only real numbers

* Fix type vocab size in configuration

* Add projection layer to embeddings

* Fix position ids bug in embeddings

* Add minor changes

* Add conversion script and remove CausalLM vestiges

* Fix conversion script

* Fix conversion script

* Remove CausalLM Test

* Update checkpoint names to dummy checkpoints

* Add tokenizer mapping

* Fix modeling file and corresponding tests

* Add tokenization test file

* Add PreTraining model test

* Make style and quality

* Make tokenization base tests work

* Update docs

* Add FastTokenizer tests

* Fix fast tokenizer special tokens

* Fix style and quality

* Remove load_tf_weights vestiges

* Add FNet to  main README

* Fix configuration exampl...

tests/test_modeling_fnet.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the PyTorch FNet model. """
+
+
+import unittest
+from typing import Dict, List, Tuple
+
+from transformers import FNetConfig, is_torch_available
+from transformers.models.auto import get_values
+from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        MODEL_FOR_PRETRAINING_MAPPING,
+        FNetForMaskedLM,
+        FNetForMultipleChoice,
+        FNetForNextSentencePrediction,
+        FNetForPreTraining,
+        FNetForQuestionAnswering,
+        FNetForSequenceClassification,
+        FNetForTokenClassification,
+        FNetModel,
+        FNetTokenizerFast,
+    )
+    from transformers.models.fnet.modeling_fnet import (
+        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
+        FNetBasicFourierTransform,
+        is_scipy_available,
+    )
+
+
+# Override ConfigTester
+class FNetConfigTester(ConfigTester):
+    def create_and_test_config_common_properties(self):
+        config = self.config_class(**self.inputs_dict)
+        if self.has_text_modality:
+            self.parent.assertTrue(hasattr(config, "vocab_size"))
+        self.parent.assertTrue(hasattr(config, "hidden_size"))
+        self.parent.assertTrue(hasattr(config, "num_hidden_layers"))
+
+
+class FNetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return FNetConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            tpu_short_seq_length=self.seq_length,
+        )
+
+    @require_torch
+    def create_and_check_fourier_transform(self, config):
+        hidden_states = floats_tensor([self.batch_size, self.seq_length, config.hidden_size])
+        transform = FNetBasicFourierTransform(config)
+        fftn_output = transform(hidden_states)
+
+        config.use_tpu_fourier_optimizations = True
+        if is_scipy_available():
+            transform = FNetBasicFourierTransform(config)
+            dft_output = transform(hidden_states)
+
+        config.max_position_embeddings = 4097
+        transform = FNetBasicFourierTransform(config)
+        fft_output = transform(hidden_states)
+
+        if is_scipy_available():
+            self.parent.assertTrue(torch.allclose(fftn_output[0][0], dft_output[0][0], atol=1e-4))
+            self.parent.assertTrue(torch.allclose(fft_output[0][0], dft_output[0][0], atol=1e-4))
+        self.parent.assertTrue(torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4))
+
+    def create_and_check_model(self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels):
+        model = FNetModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_pretraining(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        model = FNetForPreTraining(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            token_type_ids=token_type_ids,
+            labels=token_labels,
+            next_sentence_label=sequence_labels,
+        )
+        self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+        self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        model = FNetForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_next_sentence_prediction(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        model = FNetForNextSentencePrediction(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            token_type_ids=token_type_ids,
+            next_sentence_label=sequence_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        model = FNetForQuestionAnswering(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            token_type_ids=token_type_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+        )
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = FNetForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = FNetForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_for_multiple_choice(
+        self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = FNetForMultipleChoice(config=config)
+        model.to(torch_device)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        result = model(
+            multiple_choice_inputs_ids,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids}
+        return config, inputs_dict
+
+
+@require_torch
+class FNetModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            FNetModel,
+            FNetForPreTraining,
+            FNetForMaskedLM,
+            FNetForNextSentencePrediction,
+            FNetForMultipleChoice,
+            FNetForQuestionAnswering,
+            FNetForSequenceClassification,
+            FNetForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+
+    # Skip Tests
+    test_pruning = False
+    test_torchscript = False
+    test_head_masking = False
+    test_pruning = False
+
+    # special case for ForPreTraining model
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+                )
+                inputs_dict["next_sentence_label"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+        return inputs_dict
+
+    # Overriden Tests
+    def test_attention_outputs(self):
+        pass
+
+    def test_model_outputs_equivalence(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def set_nan_tensor_to_zero(t):
+            t[t != t] = 0
+            return t
+
+        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
+            with torch.no_grad():
+                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
+                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
+
+                def recursive_check(tuple_object, dict_object):
+                    if isinstance(tuple_object, (List, Tuple)):
+                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif isinstance(tuple_object, Dict):
+                        for tuple_iterable_value, dict_iterable_value in zip(
+                            tuple_object.values(), dict_object.values()
+                        ):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif tuple_object is None:
+                        return
+                    else:
+                        self.assertTrue(
+                            torch.allclose(
+                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
+                            ),
+                            msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
+                        )
+
+                recursive_check(tuple_output, dict_output)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            # tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            # dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            # check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
+
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        # no need to test all models as different heads yield the same functionality
+        model_class = self.all_model_classes[0]
+        model = model_class(config)
+        model.to(torch_device)
+
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+
+        outputs = model(**inputs)
+
+        output = outputs[0]
+
+        hidden_states = outputs.hidden_states[0]
+
+        hidden_states.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        self.assertIsNotNone(hidden_states.grad)
+
+    def setUp(self):
+        self.model_tester = FNetModelTester(self)
+        self.config_tester = FNetConfigTester(self, config_class=FNetConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_pretraining(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FNetModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+@require_torch
+class FNetModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_for_masked_lm(self):
+        """
+        For comparison:
+        1. Modify the pre-training model `__call__` to skip computing metrics and return masked_lm_output like so:
+            ```
+            ...
+            sequence_output, pooled_output = EncoderModel(
+            self.config, random_seed=self.random_seed, name="encoder")(
+                input_ids, input_mask, type_ids, deterministic=deterministic)
+
+            masked_lm_output = nn.Dense(
+                self.config.d_emb,
+                kernel_init=default_kernel_init,
+                name="predictions_dense")(
+                    sequence_output)
+            masked_lm_output = nn.gelu(masked_lm_output)
+            masked_lm_output = nn.LayerNorm(
+                epsilon=LAYER_NORM_EPSILON, name="predictions_layer_norm")(
+                    masked_lm_output)
+            masked_lm_logits = layers.OutputProjection(
+                kernel=self._get_embedding_table(), name="predictions_output")(
+                    masked_lm_output)
+
+            next_sentence_logits = layers.OutputProjection(
+                n_out=2, kernel_init=default_kernel_init, name="classification")(
+                    pooled_output)
+
+            return masked_lm_logits
+            ...
+            ```
+        2. Run the following:
+            >>> import jax.numpy as jnp
+            >>> import sentencepiece as spm
+            >>> from flax.training import checkpoints
+            >>> from f_net.models import PreTrainingModel
+            >>> from f_net.configs.pretraining import get_config, ModelArchitecture
+
+            >>> pretrained_params = checkpoints.restore_checkpoint('./f_net/f_net_checkpoint', None) # Location of original checkpoint
+            >>> pretrained_config  = get_config()
+            >>> pretrained_config.model_arch = ModelArchitecture.F_NET
+
+            >>> vocab_filepath = "./f_net/c4_bpe_sentencepiece.model" # Location of the sentence piece model
+            >>> tokenizer = spm.SentencePieceProcessor()
+            >>> tokenizer.Load(vocab_filepath)
+            >>> with pretrained_config.unlocked():
+            >>>     pretrained_config.vocab_size = tokenizer.GetPieceSize()
+            >>> tokens = jnp.array([[0, 1, 2, 3, 4, 5]])
+            >>> type_ids = jnp.zeros_like(tokens, dtype="i4")
+            >>> attention_mask = jnp.ones_like(tokens) # Dummy. This gets deleted inside the model.
+
+            >>> flax_pretraining_model = PreTrainingModel(pretrained_config)
+            >>> pretrained_model_params = freeze(pretrained_params['target'])
+            >>> flax_model_outputs = flax_pretraining_model.apply({"params": pretrained_model_params}, tokens, attention_mask, type_ids, None, None, None, None, deterministic=True)
+            >>> masked_lm_logits[:, :3, :3]
+        """
+
+        model = FNetForMaskedLM.from_pretrained("google/fnet-base")
+        model.to(torch_device)
+
+        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
+        output = model(input_ids)[0]
+
+        vocab_size = 32000
+
+        expected_shape = torch.Size((1, 6, vocab_size))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[[-1.7819, -7.7384, -7.5002], [-3.4746, -8.5943, -7.7762], [-3.2052, -9.0771, -8.3468]]],
+            device=torch_device,
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
+
+    @slow
+    @require_tokenizers
+    def test_inference_long_sentence(self):
+        model = FNetForMaskedLM.from_pretrained("google/fnet-base")
+        model.to(torch_device)
+        tokenizer = FNetTokenizerFast.from_pretrained("google/fnet-base")
+
+        inputs = tokenizer(
+            "the man worked as a [MASK].",
+            "this is his [MASK].",
+            return_tensors="pt",
+            padding="max_length",
+            max_length=512,
+        )
+        inputs = {k: v.to(torch_device) for k, v in inputs.items()}
+
+        logits = model(**inputs).logits
+        predictions_mask_1 = tokenizer.decode(logits[0, 6].topk(5).indices)
+        predictions_mask_2 = tokenizer.decode(logits[0, 12].topk(5).indices)
+
+        self.assertEqual(predictions_mask_1.split(" "), ["man", "child", "teacher", "woman", "model"])
+        self.assertEqual(predictions_mask_2.split(" "), ["work", "wife", "job", "story", "name"])
+
+    @slow
+    def test_inference_for_next_sentence_prediction(self):
+        model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base")
+        model.to(torch_device)
+
+        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
+        output = model(input_ids)[0]
+
+        expected_shape = torch.Size((1, 2))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = torch.tensor([[-0.2234, -0.0226]], device=torch_device)
+
+        self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
+
+    @slow
+    def test_inference_model(self):
+        model = FNetModel.from_pretrained("google/fnet-base")
+        model.to(torch_device)
+
+        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
+        output = model(input_ids)[0]
+
+        expected_shape = torch.Size((1, 6, model.config.hidden_size))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[[4.1541, -0.1051, -0.1667], [-0.9144, 0.2939, -0.0086], [-0.8472, -0.7281, 0.0256]]], device=torch_device
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

tests/test_tokenization_fnet.py

+# coding=utf-8
+# Copyright 2019 Hugging Face 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 os
+import unittest
+
+from transformers import FNetTokenizer, FNetTokenizerFast
+from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/spiece.model")
+
+
+@require_sentencepiece
+@require_tokenizers
+class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = FNetTokenizer
+    rust_tokenizer_class = FNetTokenizerFast
+    test_rust_tokenizer = True
+    test_sentencepiece = True
+    test_sentencepiece_ignore_case = True
+    test_seq2seq = False
+
+    def setUp(self):
+        super().setUp()
+
+        # We have a SentencePiece fixture for testing
+        tokenizer = FNetTokenizer(SAMPLE_VOCAB)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "this is a test"
+        output_text = "this is a test"
+        return input_text, output_text
+
+    def test_convert_token_and_id(self):
+        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
+        token = "<pad>"
+        token_id = 0
+
+        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
+        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
+
+    def test_get_vocab(self):
+        vocab_keys = list(self.get_tokenizer().get_vocab().keys())
+
+        self.assertEqual(vocab_keys[0], "<pad>")
+        self.assertEqual(vocab_keys[1], "<unk>")
+        self.assertEqual(vocab_keys[-1], "▁eloquent")
+        self.assertEqual(len(vocab_keys), 30_000)
+
+    def test_vocab_size(self):
+        self.assertEqual(self.get_tokenizer().vocab_size, 30_000)
+
+    def test_rust_and_python_full_tokenizers(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        tokenizer = self.get_tokenizer()
+        rust_tokenizer = self.get_rust_tokenizer()
+
+        sequence = "I was born in 92000, and this is falsé."
+
+        tokens = tokenizer.tokenize(sequence)
+        rust_tokens = rust_tokenizer.tokenize(sequence)
+        self.assertListEqual(tokens, rust_tokens)
+
+        ids = tokenizer.encode(sequence, add_special_tokens=False)
+        rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
+        self.assertListEqual(ids, rust_ids)
+
+        rust_tokenizer = self.get_rust_tokenizer()
+        ids = tokenizer.encode(sequence)
+        rust_ids = rust_tokenizer.encode(sequence)
+        self.assertListEqual(ids, rust_ids)
+
+    def test_full_tokenizer(self):
+        tokenizer = FNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
+
+        tokens = tokenizer.tokenize("This is a test")
+        self.assertListEqual(tokens, ["▁", "T", "his", "▁is", "▁a", "▁test"])
+
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [13, 1, 4398, 25, 21, 1289])
+
+        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+        self.assertListEqual(
+            tokens,
+            ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."],
+        )
+        ids = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(ids, [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
+
+        back_tokens = tokenizer.convert_ids_to_tokens(ids)
+        self.assertListEqual(
+            back_tokens,
+            [
+                "▁",
+                "<unk>",
+                "▁was",
+                "▁born",
+                "▁in",
+                "▁9",
+                "2000",
+                ",",
+                "▁and",
+                "▁this",
+                "▁is",
+                "▁fal",
+                "s",
+                "<unk>",
+                ".",
+            ],
+        )
+
+    def test_sequence_builders(self):
+        tokenizer = FNetTokenizer(SAMPLE_VOCAB)
+
+        text = tokenizer.encode("sequence builders")
+        text_2 = tokenizer.encode("multi-sequence build")
+
+        encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
+        encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
+
+        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
+        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
+            tokenizer.sep_token_id
+        ]
+
+    # Overriden Tests
+    def test_padding(self, max_length=50):
+        if not self.test_slow_tokenizer:
+            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
+            return
+
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
+                pad_token_id = tokenizer_p.pad_token_id
+
+                # Encode - Simple input
+                input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
+                input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
+                input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
+                input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+                input_r = tokenizer_r.encode("This is a simple input", padding="longest")
+                input_p = tokenizer_p.encode("This is a simple input", padding=True)
+                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
+
+                # Encode - Pair input
+                input_r = tokenizer_r.encode(
+                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
+                )
+                input_p = tokenizer_p.encode(
+                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
+                )
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
+                input_r = tokenizer_r.encode(
+                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+                )
+                input_p = tokenizer_p.encode(
+                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+                )
+                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
+                input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
+                input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
+                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
+
+                # Encode_plus - Simple input
+                input_r = tokenizer_r.encode_plus(
+                    "This is a simple input", max_length=max_length, pad_to_max_length=True
+                )
+                input_p = tokenizer_p.encode_plus(
+                    "This is a simple input", max_length=max_length, pad_to_max_length=True
+                )
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
+
+                input_r = tokenizer_r.encode_plus(
+                    "This is a simple input", max_length=max_length, padding="max_length"
+                )
+                input_p = tokenizer_p.encode_plus(
+                    "This is a simple input", max_length=max_length, padding="max_length"
+                )
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
+
+                input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
+                input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
+
+                # Encode_plus - Pair input
+                input_r = tokenizer_r.encode_plus(
+                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
+                )
+                input_p = tokenizer_p.encode_plus(
+                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
+                )
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
+
+                input_r = tokenizer_r.encode_plus(
+                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+                )
+                input_p = tokenizer_p.encode_plus(
+                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+                )
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
+
+                input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
+                input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
+
+                # Batch_encode_plus - Simple input
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    pad_to_max_length=True,
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    pad_to_max_length=True,
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    padding="max_length",
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    padding="max_length",
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    padding="longest",
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"],
+                    max_length=max_length,
+                    padding=True,
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
+
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"], padding="longest"
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    ["This is a simple input 1", "This is a simple input 2"], padding=True
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
+
+                # Batch_encode_plus - Pair input
+                input_r = tokenizer_r.batch_encode_plus(
+                    [
+                        ("This is a simple input 1", "This is a simple input 2"),
+                        ("This is a simple pair 1", "This is a simple pair 2"),
+                    ],
+                    max_length=max_length,
+                    truncation=True,
+                    padding="max_length",
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    [
+                        ("This is a simple input 1", "This is a simple input 2"),
+                        ("This is a simple pair 1", "This is a simple pair 2"),
+                    ],
+                    max_length=max_length,
+                    truncation=True,
+                    padding="max_length",
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+                input_r = tokenizer_r.batch_encode_plus(
+                    [
+                        ("This is a simple input 1", "This is a simple input 2"),
+                        ("This is a simple pair 1", "This is a simple pair 2"),
+                    ],
+                    padding=True,
+                )
+                input_p = tokenizer_p.batch_encode_plus(
+                    [
+                        ("This is a simple input 1", "This is a simple input 2"),
+                        ("This is a simple pair 1", "This is a simple pair 2"),
+                    ],
+                    padding="longest",
+                )
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
+
+                # Using pad on single examples after tokenization
+                input_r = tokenizer_r.encode_plus("This is a input 1")
+                input_r = tokenizer_r.pad(input_r)
+
+                input_p = tokenizer_r.encode_plus("This is a input 1")
+                input_p = tokenizer_r.pad(input_p)
+
+                self.assert_padded_input_match(
+                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
+                )
+
+                # Using pad on single examples after tokenization
+                input_r = tokenizer_r.encode_plus("This is a input 1")
+                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
+
+                input_p = tokenizer_r.encode_plus("This is a input 1")
+                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+
+                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
+
+                # Using pad after tokenization
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+                input_r = tokenizer_r.pad(input_r)
+
+                input_p = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+                input_p = tokenizer_r.pad(input_p)
+
+                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
+
+                # Using pad after tokenization
+                input_r = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
+
+                input_p = tokenizer_r.batch_encode_plus(
+                    ["This is a input 1", "This is a much longer input whilch should be padded"]
+                )
+                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+
+                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
+
+    def assert_batch_padded_input_match(
+        self,
+        input_r: dict,
+        input_p: dict,
+        max_length: int,
+        pad_token_id: int,
+        model_main_input_name: str = "input_ids",
+    ):
+        for i_r in input_r.values():
+            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                len(i_r[1]), max_length
+            )
+            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                len(i_r[1]), max_length
+            )
+
+        for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
+            self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
+
+    @slow
+    def test_tokenizer_integration(self):
+        # fmt: off
+        expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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: E501
+        # fmt: on
+
+        self.tokenizer_integration_test_util(
+            expected_encoding=expected_encoding,
+            model_name="google/fnet-base",
+            revision="58e0d1f96af163dc8d0a84a2fddf4bd403e4e802",
+        )