# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# 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 RoCBert model. """

import unittest

from transformers import RoCBertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
        MODEL_FOR_PRETRAINING_MAPPING,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertModel,
    )
    from transformers.models.roc_bert.modeling_roc_bert import ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST


class RoCBertModelTester:
    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,
        pronunciation_vocab_size=99,
        shape_vocab_size=99,
        pronunciation_embed_dim=32,
        shape_embed_dim=32,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.pronunciation_vocab_size = pronunciation_vocab_size
        self.shape_vocab_size = shape_vocab_size
        self.pronunciation_embed_dim = pronunciation_embed_dim
        self.shape_embed_dim = shape_embed_dim
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_shape_ids = ids_tensor([self.batch_size, self.seq_length], self.shape_vocab_size)
        input_pronunciation_ids = ids_tensor([self.batch_size, self.seq_length], self.pronunciation_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,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def get_config(self):
        return RoCBertConfig(
            vocab_size=self.vocab_size,
            shape_vocab_size=self.shape_vocab_size,
            pronunciation_vocab_size=self.pronunciation_vocab_size,
            shape_embed_dim=self.shape_embed_dim,
            pronunciation_embed_dim=self.pronunciation_embed_dim,
            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,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = self.prepare_config_and_inputs()

        config.is_decoder = True
        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

    def create_and_check_model(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            token_type_ids=token_type_ids,
        )
        result = model(input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_model_as_decoder(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = RoCBertModel(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
        )
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = RoCBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_for_masked_lm(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = RoCBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        outputs = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

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

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_input_shape_ids = torch.cat([input_shape_ids, next_shape_tokens], dim=-1)
        next_input_pronunciation_ids = torch.cat([input_pronunciation_ids, next_pronunciation_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            input_shape_ids=next_input_shape_ids,
            input_pronunciation_ids=next_input_pronunciation_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            input_shape_ids=next_shape_tokens,
            input_pronunciation_ids=next_pronunciation_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_for_question_answering(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    def create_and_check_for_sequence_classification(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_labels = self.num_labels
        model = RoCBertForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_labels = self.num_labels
        model = RoCBertForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_for_multiple_choice(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_choices = self.num_choices
        model = RoCBertForMultipleChoice(config=config)
        model.to(torch_device)
        model.eval()
        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_inputs_shape_ids = input_shape_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_inputs_pronunciation_ids = (
            input_pronunciation_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        )
        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        result = model(
            multiple_choice_inputs_ids,
            input_shape_ids=multiple_choice_inputs_shape_ids,
            input_pronunciation_ids=multiple_choice_inputs_pronunciation_ids,
            attention_mask=multiple_choice_input_mask,
            token_type_ids=multiple_choice_token_type_ids,
            labels=choice_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "input_shape_ids": input_shape_ids,
            "input_pronunciation_ids": input_pronunciation_ids,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict

    def create_and_check_for_pretraining(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForPreTraining(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            attack_input_ids=input_ids,
            attack_input_shape_ids=input_shape_ids,
            attack_input_pronunciation_ids=input_pronunciation_ids,
            attack_attention_mask=input_mask,
            attack_token_type_ids=token_type_ids,
            labels_input_ids=token_labels,
            labels_input_shape_ids=input_shape_ids,
            labels_input_pronunciation_ids=input_pronunciation_ids,
            labels_attention_mask=input_mask,
            labels_token_type_ids=token_type_ids,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))


@require_torch
class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            RoCBertModel,
            RoCBertForMaskedLM,
            RoCBertForCausalLM,
            RoCBertForMultipleChoice,
            RoCBertForQuestionAnswering,
            RoCBertForSequenceClassification,
            RoCBertForTokenClassification,
            RoCBertForPreTraining,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (RoCBertForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": RoCBertModel,
            "fill-mask": RoCBertForMaskedLM,
            "question-answering": RoCBertForQuestionAnswering,
            "text-classification": RoCBertForSequenceClassification,
            "text-generation": RoCBertForCausalLM,
            "token-classification": RoCBertForTokenClassification,
            "zero-shot": RoCBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    # TODO: Fix the failed tests when this model gets more usage
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name in [
            "FillMaskPipelineTests",
            "FeatureExtractionPipelineTests",
            "TextClassificationPipelineTests",
            "TokenClassificationPipelineTests",
        ]:
            # Get error: IndexError: index out of range in self.
            # `word_shape_file` and `word_pronunciation_file` should be shrunk during tiny model creation,
            # otherwise `IndexError` could occur in some embedding layers. Skip for now until this model has
            # more usage.
            return True

        return False

    # special case for ForPreTraining model
    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(MODEL_FOR_PRETRAINING_MAPPING):
                inputs_dict["labels_input_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["labels_input_shape_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["labels_input_pronunciation_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_shape_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_pronunciation_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
        return inputs_dict

    def setUp(self):
        self.model_tester = RoCBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RoCBertConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_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_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_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_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        config_and_inputs[0].position_embedding_type = "relative_key"
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_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_model_as_decoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

    def test_model_as_decoder_with_default_input_mask(self):
        # This regression test was failing with PyTorch < 1.3
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = self.model_tester.prepare_config_and_inputs_for_decoder()

        input_mask = None

        self.model_tester.create_and_check_model_as_decoder(
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

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


@require_torch
class RoCBertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh")

        # input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text
        # of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means
        # "Paris is the [MASK] of France" in English
        input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]])
        input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]])
        input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]])

        output = model(input_ids, input_shape_ids, input_pronunciation_ids)
        output_ids = torch.argmax(output.logits, dim=2)

        # convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]']
        expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]])

        assert torch.allclose(output_ids, expected_output)

# 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 RoCBert model. """

import unittest

from transformers import RoCBertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
        MODEL_FOR_PRETRAINING_MAPPING,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertModel,
    )
    from transformers.models.roc_bert.modeling_roc_bert import ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST


class RoCBertModelTester:
    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,
        pronunciation_vocab_size=99,
        shape_vocab_size=99,
        pronunciation_embed_dim=32,
        shape_embed_dim=32,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.pronunciation_vocab_size = pronunciation_vocab_size
        self.shape_vocab_size = shape_vocab_size
        self.pronunciation_embed_dim = pronunciation_embed_dim
        self.shape_embed_dim = shape_embed_dim
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_shape_ids = ids_tensor([self.batch_size, self.seq_length], self.shape_vocab_size)
        input_pronunciation_ids = ids_tensor([self.batch_size, self.seq_length], self.pronunciation_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,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def get_config(self):
        return RoCBertConfig(
            vocab_size=self.vocab_size,
            shape_vocab_size=self.shape_vocab_size,
            pronunciation_vocab_size=self.pronunciation_vocab_size,
            shape_embed_dim=self.shape_embed_dim,
            pronunciation_embed_dim=self.pronunciation_embed_dim,
            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,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = self.prepare_config_and_inputs()

        config.is_decoder = True
        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

    def create_and_check_model(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            token_type_ids=token_type_ids,
        )
        result = model(input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_model_as_decoder(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = RoCBertModel(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
        )
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = RoCBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_for_masked_lm(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = RoCBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        outputs = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

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

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_input_shape_ids = torch.cat([input_shape_ids, next_shape_tokens], dim=-1)
        next_input_pronunciation_ids = torch.cat([input_pronunciation_ids, next_pronunciation_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            input_shape_ids=next_input_shape_ids,
            input_pronunciation_ids=next_input_pronunciation_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            input_shape_ids=next_shape_tokens,
            input_pronunciation_ids=next_pronunciation_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_for_question_answering(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    def create_and_check_for_sequence_classification(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_labels = self.num_labels
        model = RoCBertForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_labels = self.num_labels
        model = RoCBertForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids=input_shape_ids,
            input_pronunciation_ids=input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_for_multiple_choice(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.num_choices = self.num_choices
        model = RoCBertForMultipleChoice(config=config)
        model.to(torch_device)
        model.eval()
        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_inputs_shape_ids = input_shape_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_inputs_pronunciation_ids = (
            input_pronunciation_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        )
        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        result = model(
            multiple_choice_inputs_ids,
            input_shape_ids=multiple_choice_inputs_shape_ids,
            input_pronunciation_ids=multiple_choice_inputs_pronunciation_ids,
            attention_mask=multiple_choice_input_mask,
            token_type_ids=multiple_choice_token_type_ids,
            labels=choice_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "input_shape_ids": input_shape_ids,
            "input_pronunciation_ids": input_pronunciation_ids,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict

    def create_and_check_for_pretraining(
        self,
        config,
        input_ids,
        input_shape_ids,
        input_pronunciation_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = RoCBertForPreTraining(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            attack_input_ids=input_ids,
            attack_input_shape_ids=input_shape_ids,
            attack_input_pronunciation_ids=input_pronunciation_ids,
            attack_attention_mask=input_mask,
            attack_token_type_ids=token_type_ids,
            labels_input_ids=token_labels,
            labels_input_shape_ids=input_shape_ids,
            labels_input_pronunciation_ids=input_pronunciation_ids,
            labels_attention_mask=input_mask,
            labels_token_type_ids=token_type_ids,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))


@require_torch
class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            RoCBertModel,
            RoCBertForMaskedLM,
            RoCBertForCausalLM,
            RoCBertForMultipleChoice,
            RoCBertForQuestionAnswering,
            RoCBertForSequenceClassification,
            RoCBertForTokenClassification,
            RoCBertForPreTraining,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (RoCBertForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": RoCBertModel,
            "fill-mask": RoCBertForMaskedLM,
            "question-answering": RoCBertForQuestionAnswering,
            "text-classification": RoCBertForSequenceClassification,
            "text-generation": RoCBertForCausalLM,
            "token-classification": RoCBertForTokenClassification,
            "zero-shot": RoCBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    # TODO: Fix the failed tests when this model gets more usage
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name in [
            "FillMaskPipelineTests",
            "FeatureExtractionPipelineTests",
            "TextClassificationPipelineTests",
            "TokenClassificationPipelineTests",
        ]:
            # Get error: IndexError: index out of range in self.
            # `word_shape_file` and `word_pronunciation_file` should be shrunk during tiny model creation,
            # otherwise `IndexError` could occur in some embedding layers. Skip for now until this model has
            # more usage.
            return True

        return False

    # special case for ForPreTraining model
    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(MODEL_FOR_PRETRAINING_MAPPING):
                inputs_dict["labels_input_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["labels_input_shape_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["labels_input_pronunciation_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_shape_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
                inputs_dict["attack_input_pronunciation_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
        return inputs_dict

    def setUp(self):
        self.model_tester = RoCBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RoCBertConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_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_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_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_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        config_and_inputs[0].position_embedding_type = "relative_key"
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_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_model_as_decoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

    def test_model_as_decoder_with_default_input_mask(self):
        # This regression test was failing with PyTorch < 1.3
        (
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = self.model_tester.prepare_config_and_inputs_for_decoder()

        input_mask = None

        self.model_tester.create_and_check_model_as_decoder(
            config,
            input_ids,
            input_shape_ids,
            input_pronunciation_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

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


@require_torch
class RoCBertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh")

        # input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text
        # of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means
        # "Paris is the [MASK] of France" in English
        input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]])
        input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]])
        input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]])

        output = model(input_ids, input_shape_ids, input_pronunciation_ids)
        output_ids = torch.argmax(output.logits, dim=2)

        # convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]']
        expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]])

        assert torch.allclose(output_ids, expected_output)
