# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# 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.
from __future__ import annotations

import os
import random
import tempfile
import unittest
from typing import List

import numpy as np
import paddle
from parameterized import parameterized, parameterized_class

from paddlenlp import __version__ as current_version
from paddlenlp.transformers import (
    AutoModel,
    AutoModelForQuestionAnswering,
    AutoModelForTokenClassification,
    BertForMaskedLM,
    BertForMultipleChoice,
    BertForPretraining,
    BertForQuestionAnswering,
    BertForSequenceClassification,
    BertForTokenClassification,
    BertModel,
)
from paddlenlp.transformers.bert.configuration import BertConfig
from paddlenlp.transformers.model_utils import PretrainedModel
from paddlenlp.utils import install_package, uninstall_package

from ...testing_utils import require_package, slow
from ..test_configuration_common import ConfigTester
from ..test_modeling_common import (
    ModelTesterMixin,
    ModelTesterPretrainedMixin,
    ids_tensor,
    random_attention_mask,
)


class BertModelTester:
    def __init__(
        self,
        parent: BertModelTest,
        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=5,
        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,
        initializer_range=0.02,
        pad_token_id=0,
        pool_act="tanh",
        fuse=False,
        type_sequence_label_size=2,
        num_labels=3,
        num_choices=4,
        scope=None,
        dropout=0.56,
        return_dict=False,
    ):
        self.parent: BertModelTest = 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.initializer_range = initializer_range
        self.pad_token_id = pad_token_id
        self.pool_act = pool_act
        self.fuse = fuse
        self.type_sequence_label_size = type_sequence_label_size
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope
        self.dropout = dropout
        self.return_dict = return_dict

    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) -> BertConfig:
        return BertConfig(
            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,
            pad_token_id=self.pad_token_id,
            pool_act=self.pool_act,
            fuse=self.fuse,
            num_labels=self.num_labels,
            num_choices=self.num_choices,
        )

    def create_and_check_model(
        self, config: BertConfig, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertModel(config)
        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[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size])

    def create_and_check_for_masked_lm(
        self,
        config: BertConfig,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = BertForMaskedLM(config)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])

    def create_and_check_model_past_large_inputs(
        self,
        config: BertConfig,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = BertModel(config)
        model.eval()

        # first forward pass
        outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.return_dict)
        past_key_values = outputs.past_key_values if self.return_dict else outputs[2]

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        # append to next input_ids and
        next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
        next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1)

        outputs = model(
            next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, return_dict=self.return_dict
        )

        output_from_no_past = outputs[2][0]

        outputs = model(
            next_tokens,
            attention_mask=next_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
            return_dict=self.return_dict,
        )

        output_from_past = outputs[2][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(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_for_pretraining(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = BertForPretraining(config)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
            next_sentence_label=sequence_labels,
        )
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])
        self.parent.assertEqual(result[2].shape, [self.batch_size, 2])

    def create_and_check_for_multiple_choice(
        self,
        config: BertConfig,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = BertForMultipleChoice(config)
        model.eval()
        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
        multiple_choice_input_mask = input_mask.unsqueeze(1).expand([-1, self.num_choices, -1])
        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[1].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 = BertForQuestionAnswering(config)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            return_dict=self.return_dict,
        )
        if sequence_labels is not None:
            result = result[1:]

        self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])

    def create_and_check_for_sequence_classification(
        self,
        config: BertConfig,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):

        model = BertForSequenceClassification(config)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.num_labels])

    def create_and_check_for_token_classification(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = BertForTokenClassification(config)

        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.num_labels])

    def test_addition_params(self, config: BertConfig, *args, **kwargs):
        config.num_labels = 7
        config.classifier_dropout = 0.98

        model = BertForTokenClassification(config)
        model.eval()

        self.parent.assertEqual(model.classifier.weight.shape, [config.hidden_size, 7])
        self.parent.assertEqual(model.dropout.p, 0.98)

    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


@parameterized_class(
    ("return_dict", "use_labels"),
    [
        [False, False],
        [False, True],
        [True, False],
        [True, True],
    ],
)
class BertModelTest(ModelTesterMixin, unittest.TestCase):
    base_model_class = BertModel
    return_dict = False
    use_labels = False
    test_tie_weights = True

    all_model_classes = (
        BertModel,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForPretraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
    )

    def setUp(self):
        super().setUp()

        self.model_tester = BertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=BertConfig, vocab_size=256, hidden_size=24)

    def test_config(self):
        # self.config_tester.create_and_test_config_from_and_save_pretrained()
        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_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_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_past_large_inputs(*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_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_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)

    def test_for_custom_params(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.test_addition_params(*config_and_inputs)

    def test_model_name_list(self):
        config = self.model_tester.get_config()
        model = self.base_model_class(config)
        self.assertTrue(len(model.model_name_list) != 0)

    @slow
    def test_params_compatibility_of_init_method(self):
        """test initing model with different params"""
        model: BertForTokenClassification = BertForTokenClassification.from_pretrained(
            "bert-base-uncased", num_classes=4, dropout=0.3
        )
        assert model.num_labels == 4
        assert model.dropout.p == 0.3


class BertCompatibilityTest(unittest.TestCase):
    test_model_id = "hf-internal-testing/tiny-random-BertModel"

    @classmethod
    @require_package("transformers", "torch")
    def setUpClass(cls) -> None:
        from transformers import BertModel

        # when python application is done, `TemporaryDirectory` will be free
        cls.torch_model_path = tempfile.TemporaryDirectory().name
        model = BertModel.from_pretrained(cls.test_model_id)
        model.save_pretrained(cls.torch_model_path)

    def test_model_config_mapping(self):
        config = BertConfig(num_labels=22, hidden_dropout_prob=0.99)
        self.assertEqual(config.hidden_dropout_prob, 0.99)
        self.assertEqual(config.num_labels, 22)

    def setUp(self) -> None:
        self.tempdirs: List[tempfile.TemporaryDirectory] = []

    def tearDown(self) -> None:
        for tempdir in self.tempdirs:
            tempdir.cleanup()

    def get_tempdir(self) -> str:
        tempdir = tempfile.TemporaryDirectory()
        self.tempdirs.append(tempdir)
        return tempdir.name

    def run_token_for_classification(self, version: str):
        install_package("paddlenlp", version=version)

        from paddlenlp import __version__

        self.assertEqual(__version__, version)
        from paddlenlp.transformers import BertForTokenClassification, BertModel

        tempdir = self.get_tempdir()

        # prepare the old version of model
        old_model = BertModel.from_pretrained("bert-base-uncased")
        old_model_path = os.path.join(tempdir, "old-model")
        old_model.save_pretrained(old_model_path)

        old_model_for_token = BertForTokenClassification.from_pretrained(
            "bert-base-uncased", num_classes=4, dropout=0.3
        )
        old_model_for_token_path = os.path.join(tempdir, "old-model-for-token")
        old_model_for_token.save_pretrained(old_model_for_token_path)

        uninstall_package("paddlenlp")
        from paddlenlp import __version__

        self.assertEqual(__version__, current_version)

        from paddlenlp.transformers import BertForTokenClassification, BertModel

        # bert: from old bert
        model = BertModel.from_pretrained(old_model_path)
        self.compare_two_model(old_model, model)

        # bert: from old bert-for-token
        model = BertModel.from_pretrained(old_model_for_token_path)
        self.compare_two_model(old_model, model)

        # bert-for-token: from old bert
        model = BertForTokenClassification.from_pretrained(old_model_path)
        self.compare_two_model(old_model_for_token, model)
        self.assertNotEqual(model.num_labels, 4)
        self.assertNotEqual(model.dropout.p, 0.3)

        # bert-for-token: from old bert-for-token
        model = BertForTokenClassification.from_pretrained(old_model_for_token_path)
        self.compare_two_model(old_model_for_token, model)
        self.assertEqual(model.num_labels, 4)
        self.assertEqual(model.dropout.p, 0.3)

    def compare_two_model(self, first_model: PretrainedModel, second_model: PretrainedModel):

        first_weight_name = "encoder.layers.8.linear2.weight"
        if first_model.__class__.__name__ != "BertModel":
            first_weight_name = "bert." + first_weight_name

        second_weight_name = "encoder.layers.8.linear2.weight"
        if second_model.__class__.__name__ != "BertModel":
            second_weight_name = "bert." + second_weight_name

        first_tensor = first_model.state_dict()[first_weight_name]
        second_tensor = second_model.state_dict()[second_weight_name]
        self.compare_two_weight(first_tensor, second_tensor)

    def compare_two_weight(self, first_tensor, second_tensor):
        diff = paddle.sum(first_tensor - second_tensor).item()
        self.assertEqual(diff, 0.0)

    @slow
    def test_paddlenlp_token_classification(self):
        versions = ["3.0.0b4"]
        for version in versions:
            install_package("paddlenlp", version=version)
            self.run_token_for_classification(version)
            uninstall_package("paddlenlp")

    @slow
    def test_bert_save_token_load(self):
        """bert -> token"""
        from paddlenlp.transformers import BertForTokenClassification, BertModel

        saved_dir = os.path.join(self.get_tempdir(), "bert-saved")
        bert: BertModel = BertModel.from_pretrained("bert-base-uncased")
        bert.save_pretrained(saved_dir)

        bert_for_token = BertForTokenClassification.from_pretrained(saved_dir)
        self.compare_two_model(bert, bert_for_token)

    @slow
    def test_bert_save_bert_load(self):
        """bert -> bert"""
        saved_dir = os.path.join(self.get_tempdir(), "bert-saved")
        bert: BertModel = BertModel.from_pretrained("bert-base-uncased")
        bert.save_pretrained(saved_dir)

        bert_loaded = BertModel.from_pretrained(saved_dir)
        self.compare_two_model(bert, bert_loaded)

    @slow
    def test_token_saved_bert_load(self):
        """token -> bert"""
        from paddlenlp.transformers import BertForTokenClassification, BertModel

        saved_dir = os.path.join(self.get_tempdir(), "bert-token-saved")
        bert_for_token = BertForTokenClassification.from_pretrained("bert-base-uncased")
        bert_for_token.save_pretrained(saved_dir)

        bert = BertModel.from_pretrained(saved_dir)
        self.compare_two_model(bert, bert_for_token)

    @slow
    def test_token_saved_token_load(self):
        """token -> token"""
        saved_dir = os.path.join(self.get_tempdir(), "bert-token-saved")
        bert_for_token = BertForTokenClassification.from_pretrained("bert-base-uncased")
        bert_for_token.save_pretrained(saved_dir)

        bert_for_token_loaded = BertForTokenClassification.from_pretrained(saved_dir)
        self.compare_two_model(bert_for_token, bert_for_token_loaded)

    @slow
    def test_auto_model(self):
        AutoModel.from_pretrained("bert-base-uncased")
        model = AutoModelForTokenClassification.from_pretrained("bert-base-uncased", num_classes=4, dropout=0.3)
        self.assertEqual(model.num_labels, 4)
        self.assertEqual(model.dropout.p, 0.3)

        model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased", dropout=0.3)
        self.assertEqual(model.dropout.p, 0.3)

    @require_package("transformers", "torch")
    def test_bert_converter(self):
        with tempfile.TemporaryDirectory() as tempdir:

            # 1. create common input
            input_ids = np.random.randint(100, 200, [1, 20])

            # 2. forward the paddle model
            from paddlenlp.transformers import BertModel

            paddle_model = BertModel.from_pretrained(
                "hf-internal-testing/tiny-random-BertModel", from_hf_hub=True, cache_dir=tempdir
            )
            paddle_model.eval()
            paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]

            # 3. forward the torch  model
            import torch
            from transformers import BertModel

            torch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel", cache_dir=tempdir)
            torch_model.eval()
            torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]

            self.assertTrue(
                np.allclose(
                    paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    rtol=1e-4,
                )
            )

    @require_package("transformers", "torch")
    def test_bert_converter_from_local_dir(self):
        with tempfile.TemporaryDirectory() as tempdir:

            # 1. create common input
            input_ids = np.random.randint(100, 200, [1, 20])

            # 2. forward the torch  model
            import torch
            from transformers import BertModel

            torch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
            torch_model.eval()
            torch_model.save_pretrained(tempdir)
            torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]

            # 2. forward the paddle model
            from paddlenlp.transformers import BertModel

            paddle_model = BertModel.from_pretrained(tempdir, convert_from_torch=True)
            paddle_model.eval()
            paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]

            self.assertTrue(
                np.allclose(
                    paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    rtol=1e-4,
                )
            )

    @parameterized.expand(
        [
            ("BertModel",),
            # ("BertForMaskedLM",),   TODO: need to tie weights
            # ("BertForPretraining", "BertForPreTraining"),   TODO: need to tie weights
            ("BertForMultipleChoice",),
            ("BertForQuestionAnswering",),
            ("BertForSequenceClassification",),
            ("BertForTokenClassification",),
        ]
    )
    @require_package("transformers", "torch")
    def test_bert_classes_from_local_dir(self, class_name, pytorch_class_name: str | None = None):
        pytorch_class_name = pytorch_class_name or class_name
        with tempfile.TemporaryDirectory() as tempdir:

            # 1. create common input
            input_ids = np.random.randint(100, 200, [1, 20])

            # 2. forward the torch model
            import torch
            import transformers

            torch_model_class = getattr(transformers, pytorch_class_name)
            torch_model = torch_model_class.from_pretrained(self.torch_model_path)
            torch_model.eval()

            if "MultipleChoice" in class_name:
                # construct input for MultipleChoice Model
                torch_model.config.num_choices = random.randint(2, 10)
                input_ids = (
                    paddle.to_tensor(input_ids)
                    .unsqueeze(1)
                    .expand([-1, torch_model.config.num_choices, -1])
                    .cpu()
                    .numpy()
                )

            torch_model.save_pretrained(tempdir)
            torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]

            # 3. forward the paddle model
            from paddlenlp import transformers

            paddle_model_class = getattr(transformers, class_name)
            paddle_model = paddle_model_class.from_pretrained(tempdir, convert_from_torch=True)
            paddle_model.eval()

            paddle_logit = paddle_model(paddle.to_tensor(input_ids), return_dict=False)[0]

            self.assertTrue(
                np.allclose(
                    paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
                    atol=1e-3,
                )
            )


class BertModelIntegrationTest(ModelTesterPretrainedMixin, unittest.TestCase):
    base_model_class = BertModel
    hf_remote_test_model_path = "PaddleCI/tiny-random-bert"
    paddlehub_remote_test_model_path = "__internal_testing__/tiny-random-bert"

    @slow
    def test_inference_no_attention(self):
        model = BertModel.from_pretrained("bert-base-uncased")
        model.eval()
        input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
        attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        with paddle.no_grad():
            output = model(input_ids, attention_mask=attention_mask)[0]
        expected_shape = [1, 11, 768]
        self.assertEqual(output.shape, expected_shape)

        expected_slice = paddle.to_tensor(
            [[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]
        )
        self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

    @slow
    def test_inference_with_attention(self):
        model = BertModel.from_pretrained("bert-base-uncased")
        model.eval()
        input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
        attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        with paddle.no_grad():
            output = model(input_ids, attention_mask=attention_mask)[0]
        expected_shape = [1, 11, 768]
        self.assertEqual(output.shape, expected_shape)

        expected_slice = paddle.to_tensor(
            [[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]
        )
        self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))


if __name__ == "__main__":
    unittest.main()
