codingutf8 2020 the huggingface team 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 orglicenseslicense2 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 inputs inputids inputids attentionmask inputmask tokentypeids tokentypeids sequenceoutput pooledoutput modelinputs special case for forpretraining model coding utf 8 2020 the huggingface team 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 inputs input_ids input_ids attention_mask input_mask token_type_ids token_type_ids sequence_output pooled_output model inputs special case for forpretraining model
from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, 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 tensorflow as tf

    from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
    from transformers.models.albert.modeling_tf_albert import (
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAlbertForMaskedLM,
        TFAlbertForMultipleChoice,
        TFAlbertForPreTraining,
        TFAlbertForQuestionAnswering,
        TFAlbertForSequenceClassification,
        TFAlbertForTokenClassification,
        TFAlbertModel,
    )


class TFAlbertModelTester:
    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,
        embedding_size=16,
        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 = 13
        self.seq_length = 7
        self.is_training = True
        self.use_input_mask = True
        self.use_token_type_ids = True
        self.use_labels = True
        self.vocab_size = 99
        self.embedding_size = 16
        self.hidden_size = 32
        self.num_hidden_layers = 2
        self.num_attention_heads = 4
        self.intermediate_size = 37
        self.hidden_act = "gelu"
        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.scope = None

    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 = AlbertConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            embedding_size=self.embedding_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,
        )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def create_and_check_albert_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = TFAlbertModel(config=config)
        
        
        
        
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs)

        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_albert_for_pretraining(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = TFAlbertForPreTraining(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)
        self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertEqual(result.sop_logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_albert_for_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = TFAlbertForMaskedLM(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.vocab_size))

    def create_and_check_albert_for_sequence_classification(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = TFAlbertForSequenceClassification(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.num_labels))

    def create_and_check_albert_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = TFAlbertForQuestionAnswering(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        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_albert_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 = TFAlbertForMultipleChoice(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.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])

    def create_and_check_albert_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 = TFAlbertForTokenClassification(config=config)
        inputs = {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        result = model(inputs)
        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels])

    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_tf
class TFAlbertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFAlbertModel,
            TFAlbertForPreTraining,
            TFAlbertForMaskedLM,
            TFAlbertForSequenceClassification,
            TFAlbertForQuestionAnswering,
            TFAlbertForTokenClassification,
            TFAlbertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": TFAlbertModel,
            "fill-mask": TFAlbertForMaskedLM,
            "question-answering": TFAlbertForQuestionAnswering,
            "text-classification": TFAlbertForSequenceClassification,
            "token-classification": TFAlbertForTokenClassification,
            "zero-shot": TFAlbertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    test_head_masking = False
    test_onnx = False

    
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_PRETRAINING_MAPPING):
                inputs_dict["sentence_order_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)

        return inputs_dict

    def setUp(self):
        self.model_tester = TFAlbertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)

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

    def test_albert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_model(*config_and_inputs)

    def test_for_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_pretraining(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs)

    def test_for_multiple_choice(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_multiple_choice(*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_albert_for_sequence_classification(*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_albert_for_question_answering(*config_and_inputs)

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


@require_tf
class TFAlbertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = TFAlbertForPreTraining.from_pretrained("albert-base-v2")
        input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
        output = model(input_ids)[0]

        expected_shape = [1, 6, 30000]
        self.assertEqual(output.shape, expected_shape)

        expected_slice = tf.constant(
            [
                [
                    [4.595668, 0.74462754, -1.818147],
                    [4.5954347, 0.7454184, -1.8188258],
                    [4.5954905, 0.7448235, -1.8182316],
                ]
            ]
        )
        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)