WIP PR to have specific types for tool outputs, which should clarify interaction to and from Agents.

Left to do:
- [x] Remove or complete the video integration
- [x] Add support for remote tools
- [x] Complete documentation
- [x] Test it out with real world use-cases
- [x] Add a test to ensure that the inputs are cast correctly (so far only the outputs are tested)
- [x] Arrange the dependencies so that they don't make all the tests fail
Tool types. WIP PR to have specific types for tool outputs, which should clarify interaction to and from Agents.

Left to do:
- [x] Remove or complete the video integration
- [x] Add support for remote tools
- [x] Complete documentation
- [x] Test it out with real world use-cases
- [x] Add a test to ensure that the inputs are cast correctly (so far only the outputs are tested)
- [x] Arrange the dependencies so that they don't make all the tests fail
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertConfig,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertWithLMHeadModel,
    )


class TFFlaubertModelTester:
    def __init__(
        self,
        parent,
    ):
        self.parent = parent
        self.batch_size = 13
        self.seq_length = 7
        self.is_training = True
        self.use_input_lengths = True
        self.use_token_type_ids = True
        self.use_labels = True
        self.gelu_activation = True
        self.sinusoidal_embeddings = False
        self.causal = False
        self.asm = False
        self.n_langs = 2
        self.vocab_size = 99
        self.n_special = 0
        self.hidden_size = 32
        self.num_hidden_layers = 5
        self.num_attention_heads = 4
        self.hidden_dropout_prob = 0.1
        self.attention_probs_dropout_prob = 0.1
        self.max_position_embeddings = 512
        self.type_vocab_size = 16
        self.type_sequence_label_size = 2
        self.initializer_range = 0.02
        self.num_labels = 3
        self.num_choices = 4
        self.summary_type = "last"
        self.use_proj = True
        self.scope = None
        self.bos_token_id = 0

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length], dtype=tf.float32)

        input_lengths = None
        if self.use_input_lengths:
            input_lengths = (
                ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
            )  # small variation of seq_length

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)

        sequence_labels = None
        token_labels = None
        is_impossible_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)
            is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = FlaubertConfig(
            vocab_size=self.vocab_size,
            n_special=self.n_special,
            emb_dim=self.hidden_size,
            n_layers=self.num_hidden_layers,
            n_heads=self.num_attention_heads,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            gelu_activation=self.gelu_activation,
            sinusoidal_embeddings=self.sinusoidal_embeddings,
            asm=self.asm,
            causal=self.causal,
            n_langs=self.n_langs,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            summary_type=self.summary_type,
            use_proj=self.use_proj,
            bos_token_id=self.bos_token_id,
        )

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )

    def create_and_check_flaubert_model(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertModel(config=config)
        inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        result = model(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_flaubert_lm_head(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertWithLMHeadModel(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        result = model(inputs)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_flaubert_qa(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertForQuestionAnsweringSimple(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths}

        result = model(inputs)

        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_flaubert_sequence_classif(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertForSequenceClassification(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths}

        result = model(inputs)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def create_and_check_flaubert_for_token_classification(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_labels = self.num_labels
        model = TFFlaubertForTokenClassification(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_flaubert_for_multiple_choice(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_choices = self.num_choices
        model = TFFlaubertForMultipleChoice(config=config)
        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
        inputs = {
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        result = model(inputs)
        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,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "langs": token_type_ids,
            "lengths": input_lengths,
        }
        return config, inputs_dict


@require_tf
class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    all_generative_model_classes = (
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    pipeline_model_mapping = (
        {
            "feature-extraction": TFFlaubertModel,
            "fill-mask": TFFlaubertWithLMHeadModel,
            "question-answering": TFFlaubertForQuestionAnsweringSimple,
            "text-classification": TFFlaubertForSequenceClassification,
            "token-classification": TFFlaubertForTokenClassification,
            "zero-shot": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    test_head_masking = False
    test_onnx = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

        return False

    def setUp(self):
        self.model_tester = TFFlaubertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_flaubert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*config_and_inputs)

    def test_flaubert_lm_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)

    def test_flaubert_qa(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)

    def test_flaubert_sequence_classif(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*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_flaubert_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_flaubert_for_multiple_choice(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFFlaubertModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


@require_tf
@require_sentencepiece
@require_tokenizers
class TFFlaubertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_output_embeds_base_model(self):
        model = TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased")

        input_ids = tf.convert_to_tensor(
            [[0, 158, 735, 2592, 1424, 6727, 82, 1]],
            dtype=tf.int32,
        )  # "J'aime flaubert !"

        output = model(input_ids)[0]
        expected_shape = tf.TensorShape((1, 8, 512))
        self.assertEqual(output.shape, expected_shape)
        # compare the actual values for a slice.
        expected_slice = tf.convert_to_tensor(
            [
                [
                    [-1.8768773, -1.566555, 0.27072418],
                    [-1.6920038, -0.5873505, 1.9329599],
                    [-2.9563985, -1.6993835, 1.7972052],
                ]
            ],
            dtype=tf.float32,
        )

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertConfig,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertWithLMHeadModel,
    )


class TFFlaubertModelTester:
    def __init__(
        self,
        parent,
    ):
        self.parent = parent
        self.batch_size = 13
        self.seq_length = 7
        self.is_training = True
        self.use_input_lengths = True
        self.use_token_type_ids = True
        self.use_labels = True
        self.gelu_activation = True
        self.sinusoidal_embeddings = False
        self.causal = False
        self.asm = False
        self.n_langs = 2
        self.vocab_size = 99
        self.n_special = 0
        self.hidden_size = 32
        self.num_hidden_layers = 5
        self.num_attention_heads = 4
        self.hidden_dropout_prob = 0.1
        self.attention_probs_dropout_prob = 0.1
        self.max_position_embeddings = 512
        self.type_vocab_size = 16
        self.type_sequence_label_size = 2
        self.initializer_range = 0.02
        self.num_labels = 3
        self.num_choices = 4
        self.summary_type = "last"
        self.use_proj = True
        self.scope = None
        self.bos_token_id = 0

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length], dtype=tf.float32)

        input_lengths = None
        if self.use_input_lengths:
            input_lengths = (
                ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
            )  # small variation of seq_length

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)

        sequence_labels = None
        token_labels = None
        is_impossible_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)
            is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = FlaubertConfig(
            vocab_size=self.vocab_size,
            n_special=self.n_special,
            emb_dim=self.hidden_size,
            n_layers=self.num_hidden_layers,
            n_heads=self.num_attention_heads,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            gelu_activation=self.gelu_activation,
            sinusoidal_embeddings=self.sinusoidal_embeddings,
            asm=self.asm,
            causal=self.causal,
            n_langs=self.n_langs,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            summary_type=self.summary_type,
            use_proj=self.use_proj,
            bos_token_id=self.bos_token_id,
        )

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )

    def create_and_check_flaubert_model(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertModel(config=config)
        inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        result = model(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_flaubert_lm_head(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertWithLMHeadModel(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        result = model(inputs)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_flaubert_qa(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertForQuestionAnsweringSimple(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths}

        result = model(inputs)

        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_flaubert_sequence_classif(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = TFFlaubertForSequenceClassification(config)

        inputs = {"input_ids": input_ids, "lengths": input_lengths}

        result = model(inputs)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def create_and_check_flaubert_for_token_classification(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_labels = self.num_labels
        model = TFFlaubertForTokenClassification(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_flaubert_for_multiple_choice(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_choices = self.num_choices
        model = TFFlaubertForMultipleChoice(config=config)
        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
        inputs = {
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        result = model(inputs)
        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,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "langs": token_type_ids,
            "lengths": input_lengths,
        }
        return config, inputs_dict


@require_tf
class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    all_generative_model_classes = (
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    pipeline_model_mapping = (
        {
            "feature-extraction": TFFlaubertModel,
            "fill-mask": TFFlaubertWithLMHeadModel,
            "question-answering": TFFlaubertForQuestionAnsweringSimple,
            "text-classification": TFFlaubertForSequenceClassification,
            "token-classification": TFFlaubertForTokenClassification,
            "zero-shot": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    test_head_masking = False
    test_onnx = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

        return False

    def setUp(self):
        self.model_tester = TFFlaubertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_flaubert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*config_and_inputs)

    def test_flaubert_lm_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)

    def test_flaubert_qa(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)

    def test_flaubert_sequence_classif(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*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_flaubert_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_flaubert_for_multiple_choice(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFFlaubertModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


@require_tf
@require_sentencepiece
@require_tokenizers
class TFFlaubertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_output_embeds_base_model(self):
        model = TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased")

        input_ids = tf.convert_to_tensor(
            [[0, 158, 735, 2592, 1424, 6727, 82, 1]],
            dtype=tf.int32,
        )  # "J'aime flaubert !"

        output = model(input_ids)[0]
        expected_shape = tf.TensorShape((1, 8, 512))
        self.assertEqual(output.shape, expected_shape)
        # compare the actual values for a slice.
        expected_slice = tf.convert_to_tensor(
            [
                [
                    [-1.8768773, -1.566555, 0.27072418],
                    [-1.6920038, -0.5873505, 1.9329599],
                    [-2.9563985, -1.6993835, 1.7972052],
                ]
            ],
            dtype=tf.float32,
        )

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
