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tests/models/roberta/test_modeling_roberta.py

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+# Copyright 2020 The HuggingFace 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.
+
+import unittest
+
+from transformers import RobertaConfig, is_torch_available
+from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        RobertaForCausalLM,
+        RobertaForMaskedLM,
+        RobertaForMultipleChoice,
+        RobertaForQuestionAnswering,
+        RobertaForSequenceClassification,
+        RobertaForTokenClassification,
+        RobertaModel,
+    )
+    from transformers.models.roberta.modeling_roberta import RobertaEmbeddings
+
+ROBERTA_TINY = "sshleifer/tiny-distilroberta-base"
+
+
+class RobertaModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_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_input_mask = use_input_mask
+        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.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_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)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        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, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return RobertaConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+        )
+
+    def get_pipeline_config(self):
+        config = self.get_config()
+        config.vocab_size = 300
+        return config
+
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        config.is_decoder = True
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RobertaModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        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))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def create_and_check_model_as_decoder(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.add_cross_attention = True
+        model = RobertaModel(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+        )
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        model = RobertaForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, 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_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = RobertaForCausalLM(config=config).to(torch_device).eval()
+
+        # make sure that ids don't start with pad token
+        mask = input_ids.ne(config.pad_token_id).long()
+        input_ids = input_ids * mask
+
+        # first forward pass
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+
+        # make sure that ids don't start with pad token
+        mask = next_tokens.ne(config.pad_token_id).long()
+        next_tokens = next_tokens * mask
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RobertaForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, 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_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = RobertaForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, 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, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = RobertaForMultipleChoice(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()
+        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=multiple_choice_input_mask,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RobertaForQuestionAnswering(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            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 prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            RobertaForCausalLM,
+            RobertaForMaskedLM,
+            RobertaModel,
+            RobertaForSequenceClassification,
+            RobertaForTokenClassification,
+            RobertaForMultipleChoice,
+            RobertaForQuestionAnswering,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": RobertaModel,
+            "fill-mask": RobertaForMaskedLM,
+            "text-classification": RobertaForSequenceClassification,
+            "text-generation": RobertaForCausalLM,
+            "token-classification": RobertaForTokenClassification,
+            "zero-shot": RobertaForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    # Overwriting to add `is_decoder` flag
+    def prepare_config_and_inputs_for_generate(self, batch_size=2):
+        config, inputs = super().prepare_config_and_inputs_for_generate(batch_size)
+        config.is_decoder = True
+        return config, inputs
+
+    def setUp(self):
+        self.model_tester = RobertaModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=32)
+
+    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_model_as_decoder(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
+
+    def test_model_as_decoder_with_default_input_mask(self):
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        ) = self.model_tester.prepare_config_and_inputs_for_decoder()
+
+        input_mask = None
+
+        self.model_tester.create_and_check_model_as_decoder(
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    def test_for_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
+
+    def test_decoder_model_past_with_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_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)
+
+    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)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "FacebookAI/roberta-base"
+        model = RobertaModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    def test_create_position_ids_respects_padding_index(self):
+        """This is a regression test for https://github.com/huggingface/transformers/issues/1761
+
+        The position ids should be masked with the embedding object's padding index. Therefore, the
+        first available non-padding position index is RobertaEmbeddings.padding_idx + 1
+        """
+        config = self.model_tester.prepare_config_and_inputs()[0]
+        model = RobertaEmbeddings(config=config)
+
+        input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
+        expected_positions = torch.as_tensor(
+            [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
+        )
+
+        position_ids = RobertaEmbeddings.create_position_ids_from_input_ids(input_ids, model.padding_idx)
+        self.assertEqual(position_ids.shape, expected_positions.shape)
+        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
+
+    def test_create_position_ids_from_inputs_embeds(self):
+        """This is a regression test for https://github.com/huggingface/transformers/issues/1761
+
+        The position ids should be masked with the embedding object's padding index. Therefore, the
+        first available non-padding position index is RobertaEmbeddings.padding_idx + 1
+        """
+        config = self.model_tester.prepare_config_and_inputs()[0]
+        embeddings = RobertaEmbeddings(config=config)
+
+        inputs_embeds = torch.empty(2, 4, 30)
+        expected_single_positions = [
+            0 + embeddings.padding_idx + 1,
+            1 + embeddings.padding_idx + 1,
+            2 + embeddings.padding_idx + 1,
+            3 + embeddings.padding_idx + 1,
+        ]
+        expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
+        position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds, embeddings.padding_idx)
+        self.assertEqual(position_ids.shape, expected_positions.shape)
+        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
+
+
+@require_torch
+class RobertaModelIntegrationTest(TestCasePlus):
+    @slow
+    def test_inference_masked_lm(self):
+        model = RobertaForMaskedLM.from_pretrained("FacebookAI/roberta-base")
+
+        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 11, 50265))
+        self.assertEqual(output.shape, expected_shape)
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor(
+            [[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
+        )
+
+        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
+        # roberta.eval()
+        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
+
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_no_head(self):
+        model = RobertaModel.from_pretrained("FacebookAI/roberta-base")
+
+        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor(
+            [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
+        )
+
+        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
+        # roberta.eval()
+        # expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()
+
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_classification_head(self):
+        model = RobertaForSequenceClassification.from_pretrained("FacebookAI/roberta-large-mnli")
+
+        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 3))
+        self.assertEqual(output.shape, expected_shape)
+        expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
+
+        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
+        # roberta.eval()
+        # expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()
+
+        torch.testing.assert_close(output, expected_tensor, rtol=1e-4, atol=1e-4)

tests/models/roberta/test_tokenization_roberta.py

@@ -0,0 +1,268 @@
+# Copyright 2020 The HuggingFace 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.
+
+
+import json
+import unittest
+
+from transformers import AutoTokenizer, RobertaTokenizer
+from transformers.testing_utils import require_tokenizers
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        return json.load(reader)
+
+
+def load_merges(merges_file):
+    """Loads a merges file into a list."""
+    merges = []
+    with open(merges_file, "r", encoding="utf-8") as reader:
+        for line in reader:
+            line = line.strip()
+            if line and not line.startswith("#"):
+                merges.append(tuple(line.split()))
+    return merges
+
+
+@require_tokenizers
+class RobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = "FacebookAI/roberta-base"
+    tokenizer_class = RobertaTokenizer
+    rust_tokenizer_class = RobertaTokenizer
+    test_rust_tokenizer = False
+    from_pretrained_kwargs = {"cls_token": "<s>"}
+
+    # Integration test data - expected outputs for the default input string
+    integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<s>', 'Ċ', 'hi', '<s>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing']  # fmt: skip
+    integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 20920, 50118, 0, 50118, 3592, 0, 8585, 50118, 133, 511, 6755, 197, 28, 5083, 45320, 35, 20920, 4, 50118, 1708, 1437, 8602, 8, 1437, 24107, 3726, 24107, 8906, 1437, 1437, 1437, 8602, 1437, 1437, 1437, 24107, 10674, 50118, 13368, 141, 32, 47, 608]  # fmt: skip
+    integration_expected_decoded_text = "This is a test 😊\nI was born in 92000, and this is falsé.\n生活的真谛是\nHi  Hello\nHi   Hello\n\n \n  \n Hello\n<s>\nhi<s>there\nThe following string should be properly encoded: Hello.\nBut ird and ปี   ird   ด\nHey how are you doing"
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        from_pretrained_id = "FacebookAI/roberta-base"
+
+        # Create tokenizer from AutoTokenizer
+        tok_auto = AutoTokenizer.from_pretrained(from_pretrained_id)
+        tok_auto.save_pretrained(cls.tmpdirname)
+
+        # Create tokenizer from vocab and merges
+        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
+        vocab = [
+            "l",
+            "o",
+            "w",
+            "e",
+            "r",
+            "s",
+            "t",
+            "i",
+            "d",
+            "n",
+            "\u0120",
+            "\u0120l",
+            "\u0120n",
+            "\u0120lo",
+            "\u0120low",
+            "er",
+            "\u0120lowest",
+            "\u0120newer",
+            "\u0120wider",
+            "<unk>",
+        ]
+        cls.vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges_raw = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
+        cls.merges = []
+        for line in merges_raw:
+            line = line.strip()
+            if line and not line.startswith("#"):
+                cls.merges.append(tuple(line.split()))
+
+        tok_from_vocab = RobertaTokenizer(vocab=cls.vocab_tokens, merges=cls.merges, unk_token="<unk>")
+
+        cls.tokenizers = [tok_auto, tok_from_vocab]
+        cls.special_tokens_map = {"unk_token": "<unk>"}
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "lower newer"
+        output_text = "lower newer"
+        return input_text, output_text
+
+    def test_full_tokenizer(self):
+        tokenizer = self.tokenizer_class(vocab=self.vocab_tokens, merges=self.merges, **self.special_tokens_map)
+        text = "lower newer"
+        bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
+        tokens = tokenizer.tokenize(text)  # , add_prefix_space=True)
+        self.assertListEqual(tokens, bpe_tokens)
+
+        input_tokens = tokens + [tokenizer.unk_token]
+        input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+    # def roberta_dict_integration_testing(self):
+    #     tokenizer = self.get_tokenizer()
+
+    #     self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [0, 31414, 232, 328, 2])
+    #     self.assertListEqual(
+    #         tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False),
+    #         [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2],
+    #      )
+    # def test_space_encoding(self):
+    #     tokenizer = self.get_tokenizer()
+
+    #     sequence = "Encode this sequence."
+    #     space_encoding = tokenizer.byte_encoder[b" "[0]]
+
+    #     # Testing encoder arguments
+    #     encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=False)
+    #     first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
+    #     self.assertNotEqual(first_char, space_encoding)
+
+    #     encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
+    #     first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
+    #     self.assertEqual(first_char, space_encoding)
+
+    #     tokenizer.add_special_tokens({"bos_token": "<s>"})
+    #     encoded = tokenizer.encode(sequence, add_special_tokens=True)
+    #     first_char = tokenizer.convert_ids_to_tokens(encoded[1])[0]
+    #     self.assertNotEqual(first_char, space_encoding)
+
+    #     # Testing spaces after special tokens
+    #     mask = "<mask>"
+    #     tokenizer.add_special_tokens(
+    #         {"mask_token": AddedToken(mask, lstrip=True, rstrip=False)}
+    #     )  # mask token has a left space
+    #     mask_ind = tokenizer.convert_tokens_to_ids(mask)
+
+    #     sequence = "Encode <mask> sequence"
+    #     sequence_nospace = "Encode <mask>sequence"
+
+    #     encoded = tokenizer.encode(sequence)
+    #     mask_loc = encoded.index(mask_ind)
+    #     first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
+    #     self.assertEqual(first_char, space_encoding)
+
+    #     encoded = tokenizer.encode(sequence_nospace)
+    #     mask_loc = encoded.index(mask_ind)
+    #     first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
+    #     self.assertNotEqual(first_char, space_encoding)
+
+    # def test_change_add_prefix_space_and_trim_offsets_args(self):
+    #     for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2):
+    #         tokenizer_r = self.get_rust_tokenizer(
+    #             self.tmpdirname, use_fast=True, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets
+    #         )
+
+    #         pre_tokenizer_state = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
+    #         post_processor_state = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())
+
+    #         self.assertEqual(pre_tokenizer_state["add_prefix_space"], add_prefix_space)
+
+    #         self.assertEqual(post_processor_state["add_prefix_space"], add_prefix_space)
+    #         self.assertEqual(post_processor_state["trim_offsets"], trim_offsets)
+
+    # def test_offsets_mapping_with_different_add_prefix_space_and_trim_space_arguments(self):
+    #     # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
+    #     # `trim_offsets`
+    #     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+    #         with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+    #             text_of_1_token = "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
+    #             text = f"{text_of_1_token} {text_of_1_token}"
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             text = f" {text}"
+
+    #             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
+    #             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
+    #             # )
+    #             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
+    #             # self.assertEqual(
+    #             #     encoding.offset_mapping[1],
+    #             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             # )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )
+
+    #             tokenizer_r = self.get_rust_tokenizer(
+    #                 pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False
+    #             )
+    #             encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
+    #             self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token)))
+    #             self.assertEqual(
+    #                 encoding.offset_mapping[1],
+    #                 (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
+    #             )