# What does this PR do?

Until recently, there was only one TTS model in Transformers. Recent ([Bark](https://huggingface.co/docs/transformers/model_doc/bark)) and future ([FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439)) additions have and will further enrich the number of TTS models in Transformers. 
This may be the best time to add a text-to-speech pipeline to Transformers.

This PR tentatively proposes:

- The addition of a text-to-speech pipeline whose design could be modified in line with future TTS additions.
- Add a class AutoModelForTextToSpeech
- Add a `processor` task to the pipeline code to facilitate use of the `processor`.

My conception of the architecture for now:

- Backward compatibility with [FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439), retaining the ability to use its hacked `generate_speech` method.
- Future compatibility with future TTS models, counting on the fact that these models will use a `generate` method to generate audio.
- Possible compatibility with other TTA (text-to-audio) models such as [MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen).

What I'm counting on:
- future models should have a `generate` method, even if they are not AR models per se (for the moment, [FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439) is not AR and has no such method) or counts on an additional head model ([FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439) needs a vocoder on top to pass from a spectrogram to an audio - see [discussion here](https://github.com/huggingface/transformers/pull/23439#discussion_r1258660411)).
- future models will use a `Processor` even if they only use a tokenizer, to allow easy use of other conditional inputs such as audio or speaker embeddings.  And the processor must be added to `PROCESSOR_MAPPING` (not the case of [MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen) atm).

I'm open to further discuss the architecture and to make some changes!

EDIT: for reference, I've made another design choice following internal discussions. It is discussed [here](https://github.com/huggingface/transformers/pull/24952/#pullrequestreview-1556507240).

Fixes #22487

*Note:* I was inspired by @LysandreJik draft of a [TTS pipeline](https://huggingface.co/lysandre/text-to-speech-pipeline/blob/main/tts.py).

## 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/)? [LINK](https://github.com/huggingface/transformers/issues/22487#issuecomment-1496312713)
- [ ] 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?

Hey @sanchit-gandhi and @Narsil, I think you're the right people to talk to before the core review!

Add Text-To-Speech pipeline. # What does this PR do?

Until recently, there was only one TTS model in Transformers. Recent ([Bark](https://huggingface.co/docs/transformers/model_doc/bark)) and future ([FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439)) additions have and will further enrich the number of TTS models in Transformers. 
This may be the best time to add a text-to-speech pipeline to Transformers.

This PR tentatively proposes:

- The addition of a text-to-speech pipeline whose design could be modified in line with future TTS additions.
- Add a class AutoModelForTextToSpeech
- Add a `processor` task to the pipeline code to facilitate use of the `processor`.

My conception of the architecture for now:

- Backward compatibility with [FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439), retaining the ability to use its hacked `generate_speech` method.
- Future compatibility with future TTS models, counting on the fact that these models will use a `generate` method to generate audio.
- Possible compatibility with other TTA (text-to-audio) models such as [MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen).

What I'm counting on:
- future models should have a `generate` method, even if they are not AR models per se (for the moment, [FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439) is not AR and has no such method) or counts on an additional head model ([FastSpeechConformer2](https://github.com/huggingface/transformers/pull/23439) needs a vocoder on top to pass from a spectrogram to an audio - see [discussion here](https://github.com/huggingface/transformers/pull/23439#discussion_r1258660411)).
- future models will use a `Processor` even if they only use a tokenizer, to allow easy use of other conditional inputs such as audio or speaker embeddings.  And the processor must be added to `PROCESSOR_MAPPING` (not the case of [MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen) atm).

I'm open to further discuss the architecture and to make some changes!

EDIT: for reference, I've made another design choice following internal discussions. It is discussed [here](https://github.com/huggingface/transformers/pull/24952/#pullrequestreview-1556507240).

Fixes #22487

*Note:* I was inspired by @LysandreJik draft of a [TTS pipeline](https://huggingface.co/lysandre/text-to-speech-pipeline/blob/main/tts.py).

## 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/)? [LINK](https://github.com/huggingface/transformers/issues/22487#issuecomment-1496312713)
- [ ] 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?

Hey @sanchit-gandhi and @Narsil, I think you're the right people to talk to before the core review!

# Copyright 2023 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    is_torch_greater_or_equal_than_1_11 = False

if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        Pix2StructImageProcessor,
        Pix2StructProcessor,
        PreTrainedTokenizerFast,
        T5Tokenizer,
    )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_vision
@require_torch
class Pix2StructProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        image_processor = Pix2StructImageProcessor()
        tokenizer = T5Tokenizer.from_pretrained("t5-small")

        processor = Pix2StructProcessor(image_processor, tokenizer)

        processor.save_pretrained(self.tmpdirname)

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """
        This function prepares a list of random PIL images of the same fixed size.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_additional_features(self):
        processor = Pix2StructProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = Pix2StructProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, Pix2StructImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str, return_token_type_ids=False, add_special_tokens=True)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
        )

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_processor_max_patches(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        max_patches = [512, 1024, 2048, 4096]
        expected_hidden_size = [770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(max_patches):
            inputs = processor(text=input_str, images=image_input, max_patches=max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])

        # without text input
        for i, max_patch in enumerate(max_patches):
            inputs = processor(images=image_input, max_patches=max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
        )

        inputs = processor(text=input_str)

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])

# Copyright 2023 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    is_torch_greater_or_equal_than_1_11 = False

if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        Pix2StructImageProcessor,
        Pix2StructProcessor,
        PreTrainedTokenizerFast,
        T5Tokenizer,
    )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_vision
@require_torch
class Pix2StructProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        image_processor = Pix2StructImageProcessor()
        tokenizer = T5Tokenizer.from_pretrained("t5-small")

        processor = Pix2StructProcessor(image_processor, tokenizer)

        processor.save_pretrained(self.tmpdirname)

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """
        This function prepares a list of random PIL images of the same fixed size.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_additional_features(self):
        processor = Pix2StructProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = Pix2StructProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, Pix2StructImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str, return_token_type_ids=False, add_special_tokens=True)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
        )

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_processor_max_patches(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        max_patches = [512, 1024, 2048, 4096]
        expected_hidden_size = [770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(max_patches):
            inputs = processor(text=input_str, images=image_input, max_patches=max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])

        # without text input
        for i, max_patch in enumerate(max_patches):
            inputs = processor(images=image_input, max_patches=max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
            self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = Pix2StructProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
        )

        inputs = processor(text=input_str)

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
