# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch REALM model. """

import copy
import unittest

import numpy as np

from transformers import RealmConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
        RealmEmbedder,
        RealmForOpenQA,
        RealmKnowledgeAugEncoder,
        RealmReader,
        RealmRetriever,
        RealmScorer,
        RealmTokenizer,
    )


class RealmModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        retriever_proj_size=128,
        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,
        type_sequence_label_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        span_hidden_size=50,
        max_span_width=10,
        reader_layer_norm_eps=1e-3,
        reader_beam_size=4,
        reader_seq_len=288 + 32,
        num_block_records=13353718,
        searcher_beam_size=8,
        searcher_seq_len=64,
        num_labels=3,
        num_choices=4,
        num_candidates=10,
        scope=None,
    ):
        # General config
        self.parent = parent
        self.batch_size = batch_size
        self.retriever_proj_size = retriever_proj_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.layer_norm_eps = layer_norm_eps

        # Reader config
        self.span_hidden_size = span_hidden_size
        self.max_span_width = max_span_width
        self.reader_layer_norm_eps = reader_layer_norm_eps
        self.reader_beam_size = reader_beam_size
        self.reader_seq_len = reader_seq_len

        # Searcher config
        self.num_block_records = num_block_records
        self.searcher_beam_size = searcher_beam_size
        self.searcher_seq_len = searcher_seq_len

        self.num_labels = num_labels
        self.num_choices = num_choices
        self.num_candidates = num_candidates
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        candiate_input_ids = ids_tensor([self.batch_size, self.num_candidates, self.seq_length], self.vocab_size)
        reader_input_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.vocab_size)

        input_mask = None
        candiate_input_mask = None
        reader_input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])
            candiate_input_mask = random_attention_mask([self.batch_size, self.num_candidates, self.seq_length])
            reader_input_mask = random_attention_mask([self.reader_beam_size, self.reader_seq_len])

        token_type_ids = None
        candidate_token_type_ids = None
        reader_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)
            candidate_token_type_ids = ids_tensor(
                [self.batch_size, self.num_candidates, self.seq_length], self.type_vocab_size
            )
            reader_token_type_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], 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()

        # inputs with additional num_candidates axis.
        scorer_encoder_inputs = (candiate_input_ids, candiate_input_mask, candidate_token_type_ids)
        # reader inputs
        reader_inputs = (reader_input_ids, reader_input_mask, reader_token_type_ids)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            scorer_encoder_inputs,
            reader_inputs,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def get_config(self):
        return RealmConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            retriever_proj_size=self.retriever_proj_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            num_candidates=self.num_candidates,
            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 create_and_check_embedder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmEmbedder(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        self.parent.assertEqual(result.projected_score.shape, (self.batch_size, self.retriever_proj_size))

    def create_and_check_encoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmKnowledgeAugEncoder(config=config)
        model.to(torch_device)
        model.eval()
        relevance_score = floats_tensor([self.batch_size, self.num_candidates])
        result = model(
            scorer_encoder_inputs[0],
            attention_mask=scorer_encoder_inputs[1],
            token_type_ids=scorer_encoder_inputs[2],
            relevance_score=relevance_score,
            labels=token_labels,
        )
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size * self.num_candidates, self.seq_length, self.vocab_size)
        )

    def create_and_check_reader(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmReader(config=config)
        model.to(torch_device)
        model.eval()
        relevance_score = floats_tensor([self.reader_beam_size])
        result = model(
            reader_inputs[0],
            attention_mask=reader_inputs[1],
            token_type_ids=reader_inputs[2],
            relevance_score=relevance_score,
        )
        self.parent.assertEqual(result.block_idx.shape, ())
        self.parent.assertEqual(result.candidate.shape, ())
        self.parent.assertEqual(result.start_pos.shape, ())
        self.parent.assertEqual(result.end_pos.shape, ())

    def create_and_check_scorer(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmScorer(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            candidate_input_ids=scorer_encoder_inputs[0],
            candidate_attention_mask=scorer_encoder_inputs[1],
            candidate_token_type_ids=scorer_encoder_inputs[2],
        )
        self.parent.assertEqual(result.relevance_score.shape, (self.batch_size, self.num_candidates))
        self.parent.assertEqual(result.query_score.shape, (self.batch_size, self.retriever_proj_size))
        self.parent.assertEqual(
            result.candidate_score.shape, (self.batch_size, self.num_candidates, self.retriever_proj_size)
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            scorer_encoder_inputs,
            reader_inputs,
            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 RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            RealmEmbedder,
            RealmKnowledgeAugEncoder,
            # RealmScorer is excluded from common tests as it is a container model
            # consisting of two RealmEmbedders & a simple inner product calculation.
            # RealmScorer
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = ()
    pipeline_model_mapping = {} if is_torch_available() else {}

    # disable these tests because there is no base_model in Realm
    test_save_load_fast_init_from_base = False
    test_save_load_fast_init_to_base = False

    def setUp(self):
        self.test_pruning = False
        self.model_tester = RealmModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RealmConfig)

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

    def test_embedder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_embedder(*config_and_inputs)

    def test_encoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_encoder(*config_and_inputs)

    def test_model_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config_and_inputs[0].position_embedding_type = type
            self.model_tester.create_and_check_embedder(*config_and_inputs)
            self.model_tester.create_and_check_encoder(*config_and_inputs)

    def test_scorer(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_scorer(*config_and_inputs)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        config, *inputs = self.model_tester.prepare_config_and_inputs()
        input_ids, token_type_ids, input_mask, scorer_encoder_inputs = inputs[0:4]
        config.return_dict = True

        tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")

        # RealmKnowledgeAugEncoder training
        model = RealmKnowledgeAugEncoder(config)
        model.to(torch_device)
        model.train()

        inputs_dict = {
            "input_ids": scorer_encoder_inputs[0].to(torch_device),
            "attention_mask": scorer_encoder_inputs[1].to(torch_device),
            "token_type_ids": scorer_encoder_inputs[2].to(torch_device),
            "relevance_score": floats_tensor([self.model_tester.batch_size, self.model_tester.num_candidates]),
        }
        inputs_dict["labels"] = torch.zeros(
            (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
        )
        inputs = inputs_dict
        loss = model(**inputs).loss
        loss.backward()

        # RealmForOpenQA training
        openqa_config = copy.deepcopy(config)
        openqa_config.vocab_size = 30522  # the retrieved texts will inevitably have more than 99 vocabs.
        openqa_config.num_block_records = 5
        openqa_config.searcher_beam_size = 2

        block_records = np.array(
            [
                b"This is the first record.",
                b"This is the second record.",
                b"This is the third record.",
                b"This is the fourth record.",
                b"This is the fifth record.",
            ],
            dtype=object,
        )
        retriever = RealmRetriever(block_records, tokenizer)
        model = RealmForOpenQA(openqa_config, retriever)
        model.to(torch_device)
        model.train()

        inputs_dict = {
            "input_ids": input_ids[:1].to(torch_device),
            "attention_mask": input_mask[:1].to(torch_device),
            "token_type_ids": token_type_ids[:1].to(torch_device),
            "answer_ids": input_ids[:1].tolist(),
        }
        inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA)
        loss = model(**inputs).reader_output.loss
        loss.backward()

        # Test model.block_embedding_to
        device = torch.device("cpu")
        model.block_embedding_to(device)
        loss = model(**inputs).reader_output.loss
        loss.backward()
        self.assertEqual(model.block_emb.device.type, device.type)

    @slow
    def test_embedder_from_pretrained(self):
        model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
        self.assertIsNotNone(model)

    @slow
    def test_encoder_from_pretrained(self):
        model = RealmKnowledgeAugEncoder.from_pretrained("google/realm-cc-news-pretrained-encoder")
        self.assertIsNotNone(model)

    @slow
    def test_open_qa_from_pretrained(self):
        model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa")
        self.assertIsNotNone(model)

    @slow
    def test_reader_from_pretrained(self):
        model = RealmReader.from_pretrained("google/realm-orqa-nq-reader")
        self.assertIsNotNone(model)

    @slow
    def test_scorer_from_pretrained(self):
        model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer")
        self.assertIsNotNone(model)


@require_torch
class RealmModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_embedder(self):
        retriever_projected_size = 128

        model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
        output = model(input_ids)[0]

        expected_shape = torch.Size((1, retriever_projected_size))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[-0.0714, -0.0837, -0.1314]])
        self.assertTrue(torch.allclose(output[:, :3], expected_slice, atol=1e-4))

    @slow
    def test_inference_encoder(self):
        num_candidates = 2
        vocab_size = 30522

        model = RealmKnowledgeAugEncoder.from_pretrained(
            "google/realm-cc-news-pretrained-encoder", num_candidates=num_candidates
        )
        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
        relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32)
        output = model(input_ids, relevance_score=relevance_score)[0]

        expected_shape = torch.Size((2, 6, vocab_size))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[[-11.0888, -11.2544], [-10.2170, -10.3874]]])

        self.assertTrue(torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4))

    @slow
    def test_inference_open_qa(self):
        from transformers.models.realm.retrieval_realm import RealmRetriever

        tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")
        retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa")

        model = RealmForOpenQA.from_pretrained(
            "google/realm-orqa-nq-openqa",
            retriever=retriever,
        )

        question = "Who is the pioneer in modern computer science?"

        question = tokenizer(
            [question],
            padding=True,
            truncation=True,
            max_length=model.config.searcher_seq_len,
            return_tensors="pt",
        ).to(model.device)

        predicted_answer_ids = model(**question).predicted_answer_ids

        predicted_answer = tokenizer.decode(predicted_answer_ids)
        self.assertEqual(predicted_answer, "alan mathison turing")

    @slow
    def test_inference_reader(self):
        config = RealmConfig(reader_beam_size=2, max_span_width=3)
        model = RealmReader.from_pretrained("google/realm-orqa-nq-reader", config=config)

        concat_input_ids = torch.arange(10).view((2, 5))
        concat_token_type_ids = torch.tensor([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1]], dtype=torch.int64)
        concat_block_mask = torch.tensor([[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]], dtype=torch.int64)
        relevance_score = torch.tensor([0.3, 0.7], dtype=torch.float32)

        output = model(
            concat_input_ids,
            token_type_ids=concat_token_type_ids,
            relevance_score=relevance_score,
            block_mask=concat_block_mask,
            return_dict=True,
        )

        block_idx_expected_shape = torch.Size(())
        start_pos_expected_shape = torch.Size((1,))
        end_pos_expected_shape = torch.Size((1,))
        self.assertEqual(output.block_idx.shape, block_idx_expected_shape)
        self.assertEqual(output.start_pos.shape, start_pos_expected_shape)
        self.assertEqual(output.end_pos.shape, end_pos_expected_shape)

        expected_block_idx = torch.tensor(1)
        expected_start_pos = torch.tensor(3)
        expected_end_pos = torch.tensor(3)

        self.assertTrue(torch.allclose(output.block_idx, expected_block_idx, atol=1e-4))
        self.assertTrue(torch.allclose(output.start_pos, expected_start_pos, atol=1e-4))
        self.assertTrue(torch.allclose(output.end_pos, expected_end_pos, atol=1e-4))

    @slow
    def test_inference_scorer(self):
        num_candidates = 2

        model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=num_candidates)

        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
        candidate_input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
        output = model(input_ids, candidate_input_ids=candidate_input_ids)[0]

        expected_shape = torch.Size((1, 2))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[0.7410, 0.7170]])
        self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch REALM model. """

import copy
import unittest

import numpy as np

from transformers import RealmConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
        RealmEmbedder,
        RealmForOpenQA,
        RealmKnowledgeAugEncoder,
        RealmReader,
        RealmRetriever,
        RealmScorer,
        RealmTokenizer,
    )


class RealmModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        retriever_proj_size=128,
        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,
        type_sequence_label_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        span_hidden_size=50,
        max_span_width=10,
        reader_layer_norm_eps=1e-3,
        reader_beam_size=4,
        reader_seq_len=288 + 32,
        num_block_records=13353718,
        searcher_beam_size=8,
        searcher_seq_len=64,
        num_labels=3,
        num_choices=4,
        num_candidates=10,
        scope=None,
    ):
        # General config
        self.parent = parent
        self.batch_size = batch_size
        self.retriever_proj_size = retriever_proj_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.layer_norm_eps = layer_norm_eps

        # Reader config
        self.span_hidden_size = span_hidden_size
        self.max_span_width = max_span_width
        self.reader_layer_norm_eps = reader_layer_norm_eps
        self.reader_beam_size = reader_beam_size
        self.reader_seq_len = reader_seq_len

        # Searcher config
        self.num_block_records = num_block_records
        self.searcher_beam_size = searcher_beam_size
        self.searcher_seq_len = searcher_seq_len

        self.num_labels = num_labels
        self.num_choices = num_choices
        self.num_candidates = num_candidates
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        candiate_input_ids = ids_tensor([self.batch_size, self.num_candidates, self.seq_length], self.vocab_size)
        reader_input_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.vocab_size)

        input_mask = None
        candiate_input_mask = None
        reader_input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])
            candiate_input_mask = random_attention_mask([self.batch_size, self.num_candidates, self.seq_length])
            reader_input_mask = random_attention_mask([self.reader_beam_size, self.reader_seq_len])

        token_type_ids = None
        candidate_token_type_ids = None
        reader_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)
            candidate_token_type_ids = ids_tensor(
                [self.batch_size, self.num_candidates, self.seq_length], self.type_vocab_size
            )
            reader_token_type_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], 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()

        # inputs with additional num_candidates axis.
        scorer_encoder_inputs = (candiate_input_ids, candiate_input_mask, candidate_token_type_ids)
        # reader inputs
        reader_inputs = (reader_input_ids, reader_input_mask, reader_token_type_ids)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            scorer_encoder_inputs,
            reader_inputs,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def get_config(self):
        return RealmConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            retriever_proj_size=self.retriever_proj_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            num_candidates=self.num_candidates,
            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 create_and_check_embedder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmEmbedder(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        self.parent.assertEqual(result.projected_score.shape, (self.batch_size, self.retriever_proj_size))

    def create_and_check_encoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmKnowledgeAugEncoder(config=config)
        model.to(torch_device)
        model.eval()
        relevance_score = floats_tensor([self.batch_size, self.num_candidates])
        result = model(
            scorer_encoder_inputs[0],
            attention_mask=scorer_encoder_inputs[1],
            token_type_ids=scorer_encoder_inputs[2],
            relevance_score=relevance_score,
            labels=token_labels,
        )
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size * self.num_candidates, self.seq_length, self.vocab_size)
        )

    def create_and_check_reader(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmReader(config=config)
        model.to(torch_device)
        model.eval()
        relevance_score = floats_tensor([self.reader_beam_size])
        result = model(
            reader_inputs[0],
            attention_mask=reader_inputs[1],
            token_type_ids=reader_inputs[2],
            relevance_score=relevance_score,
        )
        self.parent.assertEqual(result.block_idx.shape, ())
        self.parent.assertEqual(result.candidate.shape, ())
        self.parent.assertEqual(result.start_pos.shape, ())
        self.parent.assertEqual(result.end_pos.shape, ())

    def create_and_check_scorer(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        scorer_encoder_inputs,
        reader_inputs,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RealmScorer(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            candidate_input_ids=scorer_encoder_inputs[0],
            candidate_attention_mask=scorer_encoder_inputs[1],
            candidate_token_type_ids=scorer_encoder_inputs[2],
        )
        self.parent.assertEqual(result.relevance_score.shape, (self.batch_size, self.num_candidates))
        self.parent.assertEqual(result.query_score.shape, (self.batch_size, self.retriever_proj_size))
        self.parent.assertEqual(
            result.candidate_score.shape, (self.batch_size, self.num_candidates, self.retriever_proj_size)
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            scorer_encoder_inputs,
            reader_inputs,
            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 RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            RealmEmbedder,
            RealmKnowledgeAugEncoder,
            # RealmScorer is excluded from common tests as it is a container model
            # consisting of two RealmEmbedders & a simple inner product calculation.
            # RealmScorer
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = ()
    pipeline_model_mapping = {} if is_torch_available() else {}

    # disable these tests because there is no base_model in Realm
    test_save_load_fast_init_from_base = False
    test_save_load_fast_init_to_base = False

    def setUp(self):
        self.test_pruning = False
        self.model_tester = RealmModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RealmConfig)

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

    def test_embedder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_embedder(*config_and_inputs)

    def test_encoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_encoder(*config_and_inputs)

    def test_model_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config_and_inputs[0].position_embedding_type = type
            self.model_tester.create_and_check_embedder(*config_and_inputs)
            self.model_tester.create_and_check_encoder(*config_and_inputs)

    def test_scorer(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_scorer(*config_and_inputs)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        config, *inputs = self.model_tester.prepare_config_and_inputs()
        input_ids, token_type_ids, input_mask, scorer_encoder_inputs = inputs[0:4]
        config.return_dict = True

        tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")

        # RealmKnowledgeAugEncoder training
        model = RealmKnowledgeAugEncoder(config)
        model.to(torch_device)
        model.train()

        inputs_dict = {
            "input_ids": scorer_encoder_inputs[0].to(torch_device),
            "attention_mask": scorer_encoder_inputs[1].to(torch_device),
            "token_type_ids": scorer_encoder_inputs[2].to(torch_device),
            "relevance_score": floats_tensor([self.model_tester.batch_size, self.model_tester.num_candidates]),
        }
        inputs_dict["labels"] = torch.zeros(
            (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
        )
        inputs = inputs_dict
        loss = model(**inputs).loss
        loss.backward()

        # RealmForOpenQA training
        openqa_config = copy.deepcopy(config)
        openqa_config.vocab_size = 30522  # the retrieved texts will inevitably have more than 99 vocabs.
        openqa_config.num_block_records = 5
        openqa_config.searcher_beam_size = 2

        block_records = np.array(
            [
                b"This is the first record.",
                b"This is the second record.",
                b"This is the third record.",
                b"This is the fourth record.",
                b"This is the fifth record.",
            ],
            dtype=object,
        )
        retriever = RealmRetriever(block_records, tokenizer)
        model = RealmForOpenQA(openqa_config, retriever)
        model.to(torch_device)
        model.train()

        inputs_dict = {
            "input_ids": input_ids[:1].to(torch_device),
            "attention_mask": input_mask[:1].to(torch_device),
            "token_type_ids": token_type_ids[:1].to(torch_device),
            "answer_ids": input_ids[:1].tolist(),
        }
        inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA)
        loss = model(**inputs).reader_output.loss
        loss.backward()

        # Test model.block_embedding_to
        device = torch.device("cpu")
        model.block_embedding_to(device)
        loss = model(**inputs).reader_output.loss
        loss.backward()
        self.assertEqual(model.block_emb.device.type, device.type)

    @slow
    def test_embedder_from_pretrained(self):
        model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
        self.assertIsNotNone(model)

    @slow
    def test_encoder_from_pretrained(self):
        model = RealmKnowledgeAugEncoder.from_pretrained("google/realm-cc-news-pretrained-encoder")
        self.assertIsNotNone(model)

    @slow
    def test_open_qa_from_pretrained(self):
        model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa")
        self.assertIsNotNone(model)

    @slow
    def test_reader_from_pretrained(self):
        model = RealmReader.from_pretrained("google/realm-orqa-nq-reader")
        self.assertIsNotNone(model)

    @slow
    def test_scorer_from_pretrained(self):
        model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer")
        self.assertIsNotNone(model)


@require_torch
class RealmModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_embedder(self):
        retriever_projected_size = 128

        model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
        output = model(input_ids)[0]

        expected_shape = torch.Size((1, retriever_projected_size))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[-0.0714, -0.0837, -0.1314]])
        self.assertTrue(torch.allclose(output[:, :3], expected_slice, atol=1e-4))

    @slow
    def test_inference_encoder(self):
        num_candidates = 2
        vocab_size = 30522

        model = RealmKnowledgeAugEncoder.from_pretrained(
            "google/realm-cc-news-pretrained-encoder", num_candidates=num_candidates
        )
        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
        relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32)
        output = model(input_ids, relevance_score=relevance_score)[0]

        expected_shape = torch.Size((2, 6, vocab_size))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[[-11.0888, -11.2544], [-10.2170, -10.3874]]])

        self.assertTrue(torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4))

    @slow
    def test_inference_open_qa(self):
        from transformers.models.realm.retrieval_realm import RealmRetriever

        tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")
        retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa")

        model = RealmForOpenQA.from_pretrained(
            "google/realm-orqa-nq-openqa",
            retriever=retriever,
        )

        question = "Who is the pioneer in modern computer science?"

        question = tokenizer(
            [question],
            padding=True,
            truncation=True,
            max_length=model.config.searcher_seq_len,
            return_tensors="pt",
        ).to(model.device)

        predicted_answer_ids = model(**question).predicted_answer_ids

        predicted_answer = tokenizer.decode(predicted_answer_ids)
        self.assertEqual(predicted_answer, "alan mathison turing")

    @slow
    def test_inference_reader(self):
        config = RealmConfig(reader_beam_size=2, max_span_width=3)
        model = RealmReader.from_pretrained("google/realm-orqa-nq-reader", config=config)

        concat_input_ids = torch.arange(10).view((2, 5))
        concat_token_type_ids = torch.tensor([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1]], dtype=torch.int64)
        concat_block_mask = torch.tensor([[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]], dtype=torch.int64)
        relevance_score = torch.tensor([0.3, 0.7], dtype=torch.float32)

        output = model(
            concat_input_ids,
            token_type_ids=concat_token_type_ids,
            relevance_score=relevance_score,
            block_mask=concat_block_mask,
            return_dict=True,
        )

        block_idx_expected_shape = torch.Size(())
        start_pos_expected_shape = torch.Size((1,))
        end_pos_expected_shape = torch.Size((1,))
        self.assertEqual(output.block_idx.shape, block_idx_expected_shape)
        self.assertEqual(output.start_pos.shape, start_pos_expected_shape)
        self.assertEqual(output.end_pos.shape, end_pos_expected_shape)

        expected_block_idx = torch.tensor(1)
        expected_start_pos = torch.tensor(3)
        expected_end_pos = torch.tensor(3)

        self.assertTrue(torch.allclose(output.block_idx, expected_block_idx, atol=1e-4))
        self.assertTrue(torch.allclose(output.start_pos, expected_start_pos, atol=1e-4))
        self.assertTrue(torch.allclose(output.end_pos, expected_end_pos, atol=1e-4))

    @slow
    def test_inference_scorer(self):
        num_candidates = 2

        model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=num_candidates)

        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
        candidate_input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
        output = model(input_ids, candidate_input_ids=candidate_input_ids)[0]

        expected_shape = torch.Size((1, 2))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[0.7410, 0.7170]])
        self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
