# 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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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      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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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.
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<!-- 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 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import random
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
    )
    from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST


class XLNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=14,
        seq_length=7,
        mem_len=10,
        clamp_len=-1,
        reuse_len=15,
        is_training=True,
        use_labels=True,
        vocab_size=99,
        cutoffs=[10, 50, 80],
        hidden_size=32,
        num_attention_heads=4,
        d_inner=128,
        num_hidden_layers=2,
        type_sequence_label_size=2,
        untie_r=True,
        bi_data=False,
        same_length=False,
        initializer_range=0.05,
        seed=1,
        type_vocab_size=2,
        bos_token_id=1,
        eos_token_id=2,
        pad_token_id=5,
        num_choices=4,
    ):
        self.parent = parent
        self.batch_size = 14
        self.seq_length = 7
        self.mem_len = 10
        # self.key_len = seq_length + mem_len
        self.clamp_len = -1
        self.reuse_len = 15
        self.is_training = True
        self.use_labels = True
        self.vocab_size = 99
        self.cutoffs = [10, 50, 80]
        self.hidden_size = 32
        self.num_attention_heads = 4
        self.d_inner = 128
        self.num_hidden_layers = 5
        self.type_sequence_label_size = 2
        self.untie_r = True
        self.bi_data = False
        self.same_length = False
        self.initializer_range = 0.05
        self.seed = 1
        self.type_vocab_size = 2
        self.bos_token_id = 1
        self.eos_token_id = 2
        self.pad_token_id = 5
        self.num_choices = 4

    def prepare_config_and_inputs(self):
        input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length])

        input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
        perm_mask = torch.zeros(
            self.batch_size,
            self.seq_length + 1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
        target_mapping = torch.zeros(
            self.batch_size,
            1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        target_mapping[:, 0, -1] = 1.0  # predict last token

        sequence_labels = None
        lm_labels = None
        is_impossible_labels = None
        token_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            is_impossible_labels = ids_tensor([self.batch_size], 2).float()
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        )

    def get_config(self):
        return XLNetConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            n_head=self.num_attention_heads,
            d_inner=self.d_inner,
            n_layer=self.num_hidden_layers,
            untie_r=self.untie_r,
            mem_len=self.mem_len,
            clamp_len=self.clamp_len,
            same_length=self.same_length,
            reuse_len=self.reuse_len,
            bi_data=self.bi_data,
            initializer_range=self.initializer_range,
            num_labels=self.type_sequence_label_size,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            eos_token_id=self.eos_token_id,
        )

    def set_seed(self):
        random.seed(self.seed)
        torch.manual_seed(self.seed)

    def create_and_check_xlnet_base_model(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1, input_mask=input_mask)
        result = model(input_ids_1, attention_mask=input_mask)
        result = model(input_ids_1, token_type_ids=segment_ids)
        result = model(input_ids_1)

        config.mem_len = 0
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()
        base_model_output = model(input_ids_1)
        self.parent.assertEqual(len(base_model_output), 2)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_use_mems_train(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.train()

        train_size = input_ids_1.shape[0]

        batch_size = 4
        for i in range(train_size // batch_size + 1):
            input_ids = input_ids_1[i : (i + 1) * batch_size]
            labels = sequence_labels[i : (i + 1) * batch_size]
            outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
            self.parent.assertIsNone(outputs.mems)
            self.parent.assertIsNotNone(outputs.loss)

    def create_and_check_xlnet_model_use_mems(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1],
            input_ids_1.shape[1],
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
        outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
        outputs_conf = model(input_ids_1)

        self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
        self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)

        output, mems = outputs_cache.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # append to next input_ids and token_type_ids
        next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)

        # causal mask
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1] + 1,
            input_ids_1.shape[1] + 1,
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)

        # second forward pass
        output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # 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_xlnet_base_model_with_att_output(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]

        self.parent.assertEqual(len(attentions), config.n_layer)
        self.parent.assertIsInstance(attentions[0], tuple)
        self.parent.assertEqual(len(attentions[0]), 2)
        self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)

    def create_and_check_xlnet_lm_head(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)

        result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)

        _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)

        self.parent.assertEqual(result1.loss.shape, ())
        self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result1.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

        self.parent.assertEqual(result2.loss.shape, ())
        self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result2.mems],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_qa(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForQuestionAnswering(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
            p_mask=input_mask,
        )

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
        )

        total_loss, mems = result_with_labels.to_tuple()

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )

        total_loss, mems = result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape, ())
        self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(
            result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(
            result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_token_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForTokenClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=token_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_sequence_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=sequence_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids_1}
        return config, inputs_dict


@require_torch
class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            XLNetModel,
            XLNetLMHeadModel,
            XLNetForTokenClassification,
            XLNetForSequenceClassification,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (
        (XLNetLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    pipeline_model_mapping = (
        {
            "feature-extraction": XLNetModel,
            "question-answering": XLNetForQuestionAnsweringSimple,
            "text-classification": XLNetForSequenceClassification,
            "text-generation": XLNetLMHeadModel,
            "token-classification": XLNetForTokenClassification,
            "zero-shot": XLNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_pruning = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
            return True

        return False

    # XLNet has 2 QA models -> need to manually set the correct labels for one of them here
    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.__name__ == "XLNetForQuestionAnswering":
                inputs_dict["start_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
                inputs_dict["end_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = XLNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)

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

    def test_xlnet_base_model(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)

    def test_xlnet_base_model_use_mems(self):
        # checking that in auto-regressive mode, `use_mems` gives the same results
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)

    def test_seq_classification_use_mems_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_use_mems_train(*config_and_inputs)

    def test_xlnet_base_model_with_att_output(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)

    def test_xlnet_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)

    def test_xlnet_sequence_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)

    def test_xlnet_token_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)

    def test_xlnet_qa(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)

    def test_retain_grad_hidden_states_attentions(self):
        # xlnet cannot keep gradients in attentions or hidden states
        return

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)

        for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
            if hasattr(module, param) and getattr(module, param) is not None:
                weight = getattr(module, param)
                weight.data.fill_(3)

    def _check_hidden_states_for_generate(
        self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
            [True] * len(hidden_states),
        )
        self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

        for idx, iter_hidden_states in enumerate(hidden_states):
            # check hidden size
            for i, layer_hidden_states in enumerate(iter_hidden_states):
                # every 2nd tensor is from extra stream
                if i % 2 != 0:
                    seq_len = 1
                else:
                    # for first item dummy PAD token is appended so need one more
                    seq_len = (min_length + 1) if idx == 0 else min_length

                expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
                self.assertEqual(layer_hidden_states.shape, expected_shape)

    def _check_attentions_for_generate(
        self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
        )
        self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

        for idx, attentions_item in enumerate(attentions):
            for iter_attentions in attentions_item:
                tgt_len = min_length

                # for first item dummy PAD token is appended so need one more
                if idx == 0:
                    tgt_len += 1

                src_len = min_length + idx + 1

                expected_shape = (
                    batch_size * num_beam_groups,
                    config.num_attention_heads,
                    tgt_len,
                    src_len,
                )
                # check attn size
                self.assertListEqual(
                    [layer_attention.shape for layer_attention in iter_attentions],
                    [expected_shape] * len(iter_attentions),
                )

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


@require_torch
class XLNetModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_xlnet_base_cased(self):
        model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
        model.to(torch_device)
        # fmt: off
        input_ids = torch.tensor(
            [
                [
                    67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3,
                ]
            ],
            dtype=torch.long,
            device=torch_device,
        )
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family
        #  (except for Alexei and Maria) are discovered.
        #  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
        #  remainder of the story. 1883 Western Siberia,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic.
        #  Rasputin has a vision and denounces one of the men as a horse thief. Although his
        #  father initially slaps him for making such an accusation, Rasputin watches as the
        #  man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
        #  the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
        #  with people, even a bishop, begging for his blessing. """

        # fmt: off
        expected_output_ids = [
            67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771,
        ]
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
        #  are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
        #  narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
        #  is asked by his father and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially slaps
        #  him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
        #  Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
        #  <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
        #  He is asked to perform a ritual of the Virgin Mary. He is asked to perform

        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import random
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
    )
    from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST


class XLNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=14,
        seq_length=7,
        mem_len=10,
        clamp_len=-1,
        reuse_len=15,
        is_training=True,
        use_labels=True,
        vocab_size=99,
        cutoffs=[10, 50, 80],
        hidden_size=32,
        num_attention_heads=4,
        d_inner=128,
        num_hidden_layers=2,
        type_sequence_label_size=2,
        untie_r=True,
        bi_data=False,
        same_length=False,
        initializer_range=0.05,
        seed=1,
        type_vocab_size=2,
        bos_token_id=1,
        eos_token_id=2,
        pad_token_id=5,
        num_choices=4,
    ):
        self.parent = parent
        self.batch_size = 14
        self.seq_length = 7
        self.mem_len = 10
        # self.key_len = seq_length + mem_len
        self.clamp_len = -1
        self.reuse_len = 15
        self.is_training = True
        self.use_labels = True
        self.vocab_size = 99
        self.cutoffs = [10, 50, 80]
        self.hidden_size = 32
        self.num_attention_heads = 4
        self.d_inner = 128
        self.num_hidden_layers = 5
        self.type_sequence_label_size = 2
        self.untie_r = True
        self.bi_data = False
        self.same_length = False
        self.initializer_range = 0.05
        self.seed = 1
        self.type_vocab_size = 2
        self.bos_token_id = 1
        self.eos_token_id = 2
        self.pad_token_id = 5
        self.num_choices = 4

    def prepare_config_and_inputs(self):
        input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length])

        input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
        perm_mask = torch.zeros(
            self.batch_size,
            self.seq_length + 1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
        target_mapping = torch.zeros(
            self.batch_size,
            1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        target_mapping[:, 0, -1] = 1.0  # predict last token

        sequence_labels = None
        lm_labels = None
        is_impossible_labels = None
        token_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            is_impossible_labels = ids_tensor([self.batch_size], 2).float()
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        )

    def get_config(self):
        return XLNetConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            n_head=self.num_attention_heads,
            d_inner=self.d_inner,
            n_layer=self.num_hidden_layers,
            untie_r=self.untie_r,
            mem_len=self.mem_len,
            clamp_len=self.clamp_len,
            same_length=self.same_length,
            reuse_len=self.reuse_len,
            bi_data=self.bi_data,
            initializer_range=self.initializer_range,
            num_labels=self.type_sequence_label_size,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            eos_token_id=self.eos_token_id,
        )

    def set_seed(self):
        random.seed(self.seed)
        torch.manual_seed(self.seed)

    def create_and_check_xlnet_base_model(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1, input_mask=input_mask)
        result = model(input_ids_1, attention_mask=input_mask)
        result = model(input_ids_1, token_type_ids=segment_ids)
        result = model(input_ids_1)

        config.mem_len = 0
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()
        base_model_output = model(input_ids_1)
        self.parent.assertEqual(len(base_model_output), 2)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_use_mems_train(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.train()

        train_size = input_ids_1.shape[0]

        batch_size = 4
        for i in range(train_size // batch_size + 1):
            input_ids = input_ids_1[i : (i + 1) * batch_size]
            labels = sequence_labels[i : (i + 1) * batch_size]
            outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
            self.parent.assertIsNone(outputs.mems)
            self.parent.assertIsNotNone(outputs.loss)

    def create_and_check_xlnet_model_use_mems(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1],
            input_ids_1.shape[1],
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
        outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
        outputs_conf = model(input_ids_1)

        self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
        self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)

        output, mems = outputs_cache.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # append to next input_ids and token_type_ids
        next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)

        # causal mask
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1] + 1,
            input_ids_1.shape[1] + 1,
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)

        # second forward pass
        output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # 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_xlnet_base_model_with_att_output(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]

        self.parent.assertEqual(len(attentions), config.n_layer)
        self.parent.assertIsInstance(attentions[0], tuple)
        self.parent.assertEqual(len(attentions[0]), 2)
        self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)

    def create_and_check_xlnet_lm_head(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)

        result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)

        _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)

        self.parent.assertEqual(result1.loss.shape, ())
        self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result1.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

        self.parent.assertEqual(result2.loss.shape, ())
        self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result2.mems],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_qa(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForQuestionAnswering(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
            p_mask=input_mask,
        )

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
        )

        total_loss, mems = result_with_labels.to_tuple()

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )

        total_loss, mems = result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape, ())
        self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(
            result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(
            result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_token_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForTokenClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=token_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_sequence_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=sequence_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids_1}
        return config, inputs_dict


@require_torch
class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            XLNetModel,
            XLNetLMHeadModel,
            XLNetForTokenClassification,
            XLNetForSequenceClassification,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (
        (XLNetLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    pipeline_model_mapping = (
        {
            "feature-extraction": XLNetModel,
            "question-answering": XLNetForQuestionAnsweringSimple,
            "text-classification": XLNetForSequenceClassification,
            "text-generation": XLNetLMHeadModel,
            "token-classification": XLNetForTokenClassification,
            "zero-shot": XLNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_pruning = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
            return True

        return False

    # XLNet has 2 QA models -> need to manually set the correct labels for one of them here
    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.__name__ == "XLNetForQuestionAnswering":
                inputs_dict["start_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
                inputs_dict["end_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = XLNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)

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

    def test_xlnet_base_model(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)

    def test_xlnet_base_model_use_mems(self):
        # checking that in auto-regressive mode, `use_mems` gives the same results
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)

    def test_seq_classification_use_mems_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_use_mems_train(*config_and_inputs)

    def test_xlnet_base_model_with_att_output(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)

    def test_xlnet_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)

    def test_xlnet_sequence_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)

    def test_xlnet_token_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)

    def test_xlnet_qa(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)

    def test_retain_grad_hidden_states_attentions(self):
        # xlnet cannot keep gradients in attentions or hidden states
        return

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)

        for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
            if hasattr(module, param) and getattr(module, param) is not None:
                weight = getattr(module, param)
                weight.data.fill_(3)

    def _check_hidden_states_for_generate(
        self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
            [True] * len(hidden_states),
        )
        self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

        for idx, iter_hidden_states in enumerate(hidden_states):
            # check hidden size
            for i, layer_hidden_states in enumerate(iter_hidden_states):
                # every 2nd tensor is from extra stream
                if i % 2 != 0:
                    seq_len = 1
                else:
                    # for first item dummy PAD token is appended so need one more
                    seq_len = (min_length + 1) if idx == 0 else min_length

                expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
                self.assertEqual(layer_hidden_states.shape, expected_shape)

    def _check_attentions_for_generate(
        self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
        )
        self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

        for idx, attentions_item in enumerate(attentions):
            for iter_attentions in attentions_item:
                tgt_len = min_length

                # for first item dummy PAD token is appended so need one more
                if idx == 0:
                    tgt_len += 1

                src_len = min_length + idx + 1

                expected_shape = (
                    batch_size * num_beam_groups,
                    config.num_attention_heads,
                    tgt_len,
                    src_len,
                )
                # check attn size
                self.assertListEqual(
                    [layer_attention.shape for layer_attention in iter_attentions],
                    [expected_shape] * len(iter_attentions),
                )

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


@require_torch
class XLNetModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_xlnet_base_cased(self):
        model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
        model.to(torch_device)
        # fmt: off
        input_ids = torch.tensor(
            [
                [
                    67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3,
                ]
            ],
            dtype=torch.long,
            device=torch_device,
        )
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family
        #  (except for Alexei and Maria) are discovered.
        #  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
        #  remainder of the story. 1883 Western Siberia,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic.
        #  Rasputin has a vision and denounces one of the men as a horse thief. Although his
        #  father initially slaps him for making such an accusation, Rasputin watches as the
        #  man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
        #  the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
        #  with people, even a bishop, begging for his blessing. """

        # fmt: off
        expected_output_ids = [
            67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771,
        ]
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
        #  are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
        #  narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
        #  is asked by his father and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially slaps
        #  him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
        #  Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
        #  <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
        #  He is asked to perform a ritual of the Virgin Mary. He is asked to perform

        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
