# 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?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

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

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

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

Models:

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

Library:

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

Integrations:

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

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

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

Maintained examples (not research project or legacy):

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

 -->

add VITS model. # What does this PR do?

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

<!--
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 2023 The HuggingFace Team 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 clone 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 unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM


@require_torch
class StreamerTester(unittest.TestCase):
    def test_text_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, greedy_text)

    def test_iterator_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        streamer = TextIteratorStreamer(tokenizer)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
        streamer_text = ""
        for new_text in streamer:
            streamer_text += new_text

        self.assertEqual(streamer_text, greedy_text)

    def test_text_streamer_skip_prompt(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        new_greedy_ids = greedy_ids[:, input_ids.shape[1] :]
        new_greedy_text = tokenizer.decode(new_greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_prompt=True)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, new_greedy_text)

    def test_text_streamer_decode_kwargs(self):
        # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
        # with actual models -- the dummy models' tokenizers are not aligned with their models, and
        # `skip_special_tokens=True` has no effect on them
        tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
        model = AutoModelForCausalLM.from_pretrained("distilgpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = torch.ones((1, 5), device=torch_device).long() * model.config.bos_token_id
        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_special_tokens=True)
            model.generate(input_ids, max_new_tokens=1, do_sample=False, streamer=streamer)

        # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
        # re-tokenized, must only contain one token
        streamer_text = cs.out[:-1]  # Remove the final "\n"
        streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pt")
        self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1))

    def test_iterator_streamer_timeout(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        # The streamer will timeout after 0.001 seconds, so an exception will be raised
        with self.assertRaises(Empty):
            streamer_text = ""
            for new_text in streamer:
                streamer_text += new_text

# coding=utf-8
# Copyright 2023 The HuggingFace Team 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 clone 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 unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM


@require_torch
class StreamerTester(unittest.TestCase):
    def test_text_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, greedy_text)

    def test_iterator_streamer_matches_non_streaming(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        greedy_text = tokenizer.decode(greedy_ids[0])

        streamer = TextIteratorStreamer(tokenizer)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
        streamer_text = ""
        for new_text in streamer:
            streamer_text += new_text

        self.assertEqual(streamer_text, greedy_text)

    def test_text_streamer_skip_prompt(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
        new_greedy_ids = greedy_ids[:, input_ids.shape[1] :]
        new_greedy_text = tokenizer.decode(new_greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_prompt=True)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        # The greedy text should be printed to stdout, except for the final "\n" in the streamer
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, new_greedy_text)

    def test_text_streamer_decode_kwargs(self):
        # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
        # with actual models -- the dummy models' tokenizers are not aligned with their models, and
        # `skip_special_tokens=True` has no effect on them
        tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
        model = AutoModelForCausalLM.from_pretrained("distilgpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = torch.ones((1, 5), device=torch_device).long() * model.config.bos_token_id
        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_special_tokens=True)
            model.generate(input_ids, max_new_tokens=1, do_sample=False, streamer=streamer)

        # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
        # re-tokenized, must only contain one token
        streamer_text = cs.out[:-1]  # Remove the final "\n"
        streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pt")
        self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1))

    def test_iterator_streamer_timeout(self):
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
        streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        # The streamer will timeout after 0.001 seconds, so an exception will be raised
        with self.assertRaises(Empty):
            streamer_text = ""
            for new_text in streamer:
                streamer_text += new_text
