# 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).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      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).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

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 2021 HuggingFace Inc.
#
# 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 numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
    # to overwrite at feature extractactor specific tests
    feat_extract_tester = None
    feature_extraction_class = None

    @property
    def feat_extract_dict(self):
        return self.feat_extract_tester.prepare_feat_extract_dict()

    def test_feat_extract_common_properties(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(feat_extract, "feature_size"))
        self.assertTrue(hasattr(feat_extract, "sampling_rate"))
        self.assertTrue(hasattr(feat_extract, "padding_value"))

    def test_batch_feature(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name])))

        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    @require_torch
    def test_batch_feature_pt(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    @require_tf
    def test_batch_feature_tf(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="tf")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    def _check_padding(self, numpify=False):
        def _inputs_have_equal_length(input):
            length = len(input[0])
            for input_slice in input[1:]:
                if len(input_slice) != length:
                    return False
            return True

        def _inputs_are_equal(input_1, input_2):
            if len(input_1) != len(input_2):
                return False

            for input_slice_1, input_slice_2 in zip(input_1, input_2):
                if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
                    return False
            return True

        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        pad_diff = self.feat_extract_tester.seq_length_diff
        pad_max_length = self.feat_extract_tester.max_seq_length + pad_diff
        pad_min_length = self.feat_extract_tester.min_seq_length
        batch_size = self.feat_extract_tester.batch_size
        feature_size = self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        input_1 = feat_extract.pad(processed_features, padding=False)
        input_1 = input_1[input_name]

        input_2 = feat_extract.pad(processed_features, padding="longest")
        input_2 = input_2[input_name]

        input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))
        input_3 = input_3[input_name]

        input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")
        input_4 = input_4[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="max_length")[input_name]

        input_5 = feat_extract.pad(
            processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np"
        )
        input_5 = input_5[input_name]

        self.assertFalse(_inputs_have_equal_length(input_1))
        self.assertTrue(_inputs_have_equal_length(input_2))
        self.assertTrue(_inputs_have_equal_length(input_3))
        self.assertTrue(_inputs_are_equal(input_2, input_3))
        self.assertTrue(len(input_1[0]) == pad_min_length)
        self.assertTrue(len(input_1[1]) == pad_min_length + pad_diff)
        self.assertTrue(input_4.shape[:2] == (batch_size, len(input_3[0])))
        self.assertTrue(input_5.shape[:2] == (batch_size, pad_max_length))

        if feature_size > 1:
            self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
        input_6 = input_6[input_name]

        input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)
        input_7 = input_7[input_name]

        input_8 = feat_extract.pad(
            processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length
        )
        input_8 = input_8[input_name]

        input_9 = feat_extract.pad(
            processed_features,
            padding="max_length",
            pad_to_multiple_of=10,
            max_length=pad_max_length,
            return_tensors="np",
        )
        input_9 = input_9[input_name]

        self.assertTrue(all(len(x) % 10 == 0 for x in input_6))
        self.assertTrue(_inputs_are_equal(input_6, input_7))

        expected_mult_pad_length = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(x) == expected_mult_pad_length for x in input_8))
        self.assertEqual(input_9.shape[:2], (batch_size, expected_mult_pad_length))

        if feature_size > 1:
            self.assertTrue(input_9.shape[2] == feature_size)

        # Check padding value is correct
        padding_vector_sum = (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_2[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
            < 1e-3
        )
        self.assertTrue(
            abs(
                np.asarray(input_2[1])[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)
            )
            < 1e-3
        )
        self.assertTrue(
            abs(
                np.asarray(input_2[2])[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)
            )
            < 1e-3
        )
        self.assertTrue(
            abs(input_5[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3
        )
        self.assertTrue(
            abs(input_9[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
            < 1e-3
        )

    def _check_truncation(self, numpify=False):
        def _inputs_have_equal_length(input):
            length = len(input[0])
            for input_slice in input[1:]:
                if len(input_slice) != length:
                    return False
            return True

        def _inputs_are_equal(input_1, input_2):
            if len(input_1) != len(input_2):
                return False

            for input_slice_1, input_slice_2 in zip(input_1, input_2):
                if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
                    return False
            return True

        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        # truncate to smallest
        input_1 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True
        )
        input_1 = input_1[input_name]

        input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0]))
        input_2 = input_2[input_name]

        self.assertTrue(_inputs_have_equal_length(input_1))
        self.assertFalse(_inputs_have_equal_length(input_2))

        # truncate to smallest with np
        input_3 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            return_tensors="np",
            truncation=True,
        )
        input_3 = input_3[input_name]

        input_4 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np"
        )
        input_4 = input_4[input_name]

        self.assertTrue(_inputs_have_equal_length(input_3))
        self.assertTrue(input_3.shape[1] == len(speech_inputs[0]))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(input_4))

        # truncate to middle
        input_5 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[1]),
            truncation=True,
            return_tensors="np",
        )
        input_5 = input_5[input_name]

        input_6 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True
        )
        input_6 = input_6[input_name]

        input_7 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np"
        )
        input_7 = input_7[input_name]

        self.assertTrue(input_5.shape[1] == len(speech_inputs[1]))
        self.assertTrue(_inputs_have_equal_length(input_5))
        self.assertTrue(_inputs_have_equal_length(input_6))
        self.assertTrue(_inputs_are_equal(input_5, input_6))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(input_7))
        self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1]))

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, truncation=True)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        pad_to_multiple_of = 12
        input_8 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            pad_to_multiple_of=pad_to_multiple_of,
            truncation=True,
        )
        input_8 = input_8[input_name]

        input_9 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            pad_to_multiple_of=pad_to_multiple_of,
        )
        input_9 = input_9[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        expected_length = len(speech_inputs[0])
        if expected_length % pad_to_multiple_of != 0:
            expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_8[0]) == expected_length)
        self.assertTrue(_inputs_have_equal_length(input_8))
        self.assertFalse(_inputs_have_equal_length(input_9))

    def test_padding_from_list(self):
        self._check_padding(numpify=False)

    def test_padding_from_array(self):
        self._check_padding(numpify=True)

    def test_truncation_from_list(self):
        self._check_truncation(numpify=False)

    def test_truncation_from_array(self):
        self._check_truncation(numpify=True)

    @require_torch
    def test_padding_accepts_tensors_pt(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)

    @require_tf
    def test_padding_accepts_tensors_tf(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_tf = feat_extract.pad(processed_features, padding="longest", return_tensors="tf")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_tf.numpy().astype(np.float32).sum()) < 1e-2)

    def test_attention_mask(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})

        processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
        self.assertIn("attention_mask", processed)
        self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)

    def test_attention_mask_with_truncation(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})
        max_length = min(input_lenghts)

        processed_pad = feat_extract.pad(
            processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
        )
        self.assertIn("attention_mask", processed_pad)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length]
        )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
        )

# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# 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 numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
    # to overwrite at feature extractactor specific tests
    feat_extract_tester = None
    feature_extraction_class = None

    @property
    def feat_extract_dict(self):
        return self.feat_extract_tester.prepare_feat_extract_dict()

    def test_feat_extract_common_properties(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(feat_extract, "feature_size"))
        self.assertTrue(hasattr(feat_extract, "sampling_rate"))
        self.assertTrue(hasattr(feat_extract, "padding_value"))

    def test_batch_feature(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name])))

        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    @require_torch
    def test_batch_feature_pt(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    @require_tf
    def test_batch_feature_tf(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="tf")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
        )

    def _check_padding(self, numpify=False):
        def _inputs_have_equal_length(input):
            length = len(input[0])
            for input_slice in input[1:]:
                if len(input_slice) != length:
                    return False
            return True

        def _inputs_are_equal(input_1, input_2):
            if len(input_1) != len(input_2):
                return False

            for input_slice_1, input_slice_2 in zip(input_1, input_2):
                if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
                    return False
            return True

        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        pad_diff = self.feat_extract_tester.seq_length_diff
        pad_max_length = self.feat_extract_tester.max_seq_length + pad_diff
        pad_min_length = self.feat_extract_tester.min_seq_length
        batch_size = self.feat_extract_tester.batch_size
        feature_size = self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        input_1 = feat_extract.pad(processed_features, padding=False)
        input_1 = input_1[input_name]

        input_2 = feat_extract.pad(processed_features, padding="longest")
        input_2 = input_2[input_name]

        input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))
        input_3 = input_3[input_name]

        input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")
        input_4 = input_4[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="max_length")[input_name]

        input_5 = feat_extract.pad(
            processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np"
        )
        input_5 = input_5[input_name]

        self.assertFalse(_inputs_have_equal_length(input_1))
        self.assertTrue(_inputs_have_equal_length(input_2))
        self.assertTrue(_inputs_have_equal_length(input_3))
        self.assertTrue(_inputs_are_equal(input_2, input_3))
        self.assertTrue(len(input_1[0]) == pad_min_length)
        self.assertTrue(len(input_1[1]) == pad_min_length + pad_diff)
        self.assertTrue(input_4.shape[:2] == (batch_size, len(input_3[0])))
        self.assertTrue(input_5.shape[:2] == (batch_size, pad_max_length))

        if feature_size > 1:
            self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
        input_6 = input_6[input_name]

        input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)
        input_7 = input_7[input_name]

        input_8 = feat_extract.pad(
            processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length
        )
        input_8 = input_8[input_name]

        input_9 = feat_extract.pad(
            processed_features,
            padding="max_length",
            pad_to_multiple_of=10,
            max_length=pad_max_length,
            return_tensors="np",
        )
        input_9 = input_9[input_name]

        self.assertTrue(all(len(x) % 10 == 0 for x in input_6))
        self.assertTrue(_inputs_are_equal(input_6, input_7))

        expected_mult_pad_length = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(x) == expected_mult_pad_length for x in input_8))
        self.assertEqual(input_9.shape[:2], (batch_size, expected_mult_pad_length))

        if feature_size > 1:
            self.assertTrue(input_9.shape[2] == feature_size)

        # Check padding value is correct
        padding_vector_sum = (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_2[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
            < 1e-3
        )
        self.assertTrue(
            abs(
                np.asarray(input_2[1])[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)
            )
            < 1e-3
        )
        self.assertTrue(
            abs(
                np.asarray(input_2[2])[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)
            )
            < 1e-3
        )
        self.assertTrue(
            abs(input_5[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3
        )
        self.assertTrue(
            abs(input_9[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
            < 1e-3
        )

    def _check_truncation(self, numpify=False):
        def _inputs_have_equal_length(input):
            length = len(input[0])
            for input_slice in input[1:]:
                if len(input_slice) != length:
                    return False
            return True

        def _inputs_are_equal(input_1, input_2):
            if len(input_1) != len(input_2):
                return False

            for input_slice_1, input_slice_2 in zip(input_1, input_2):
                if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
                    return False
            return True

        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        # truncate to smallest
        input_1 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True
        )
        input_1 = input_1[input_name]

        input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0]))
        input_2 = input_2[input_name]

        self.assertTrue(_inputs_have_equal_length(input_1))
        self.assertFalse(_inputs_have_equal_length(input_2))

        # truncate to smallest with np
        input_3 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            return_tensors="np",
            truncation=True,
        )
        input_3 = input_3[input_name]

        input_4 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np"
        )
        input_4 = input_4[input_name]

        self.assertTrue(_inputs_have_equal_length(input_3))
        self.assertTrue(input_3.shape[1] == len(speech_inputs[0]))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(input_4))

        # truncate to middle
        input_5 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[1]),
            truncation=True,
            return_tensors="np",
        )
        input_5 = input_5[input_name]

        input_6 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True
        )
        input_6 = input_6[input_name]

        input_7 = feat_extract.pad(
            processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np"
        )
        input_7 = input_7[input_name]

        self.assertTrue(input_5.shape[1] == len(speech_inputs[1]))
        self.assertTrue(_inputs_have_equal_length(input_5))
        self.assertTrue(_inputs_have_equal_length(input_6))
        self.assertTrue(_inputs_are_equal(input_5, input_6))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(input_7))
        self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1]))

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, truncation=True)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(ValueError):
            feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        pad_to_multiple_of = 12
        input_8 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            pad_to_multiple_of=pad_to_multiple_of,
            truncation=True,
        )
        input_8 = input_8[input_name]

        input_9 = feat_extract.pad(
            processed_features,
            padding="max_length",
            max_length=len(speech_inputs[0]),
            pad_to_multiple_of=pad_to_multiple_of,
        )
        input_9 = input_9[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        expected_length = len(speech_inputs[0])
        if expected_length % pad_to_multiple_of != 0:
            expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_8[0]) == expected_length)
        self.assertTrue(_inputs_have_equal_length(input_8))
        self.assertFalse(_inputs_have_equal_length(input_9))

    def test_padding_from_list(self):
        self._check_padding(numpify=False)

    def test_padding_from_array(self):
        self._check_padding(numpify=True)

    def test_truncation_from_list(self):
        self._check_truncation(numpify=False)

    def test_truncation_from_array(self):
        self._check_truncation(numpify=True)

    @require_torch
    def test_padding_accepts_tensors_pt(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)

    @require_tf
    def test_padding_accepts_tensors_tf(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_tf = feat_extract.pad(processed_features, padding="longest", return_tensors="tf")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_tf.numpy().astype(np.float32).sum()) < 1e-2)

    def test_attention_mask(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})

        processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
        self.assertIn("attention_mask", processed)
        self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)

    def test_attention_mask_with_truncation(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})
        max_length = min(input_lenghts)

        processed_pad = feat_extract.pad(
            processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
        )
        self.assertIn("attention_mask", processed_pad)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length]
        )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
        )
