# What does this PR do?

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

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

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

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

<!-- Remove if not applicable -->

Fixes # (issue)


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


## Who can review?

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

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

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

Models:

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

Library:

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

Integrations:

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

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

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

Maintained examples (not research project or legacy):

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

 -->

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 2020 The HuggingFace Inc. team.
#
# 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.
"""
Utility that checks the big table in the file docs/source/en/index.md and potentially updates it.

Use from the root of the repo with:

```bash
python utils/check_inits.py
```

for a check that will error in case of inconsistencies (used by `make repo-consistency`).

To auto-fix issues run:

```bash
python utils/check_inits.py --fix_and_overwrite
```

which is used by `make fix-copies`.
"""
import argparse
import collections
import os
import re
from typing import List

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
TRANSFORMERS_PATH = "src/transformers"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."


def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str:
    """
    Find the text in filename between two prompts.

    Args:
        filename (`str`): The file to search into.
        start_prompt (`str`): A string to look for at the start of the content searched.
        end_prompt (`str`): A string that will mark the end of the content to look for.

    Returns:
        `str`: The content between the prompts.
    """
    with open(filename, "r", encoding="utf-8", newline="\n") as f:
        lines = f.readlines()

    # Find the start prompt.
    start_index = 0
    while not lines[start_index].startswith(start_prompt):
        start_index += 1
    start_index += 1

    # Now go until the end prompt.
    end_index = start_index
    while not lines[end_index].startswith(end_prompt):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]) <= 1:
        start_index += 1
    while len(lines[end_index]) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]), start_index, end_index, lines


# Regexes that match TF/Flax/PT model names. Add here suffixes that are used to identify models, separated by |
_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch after the two previous regexes.
_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
transformers_module = direct_transformers_import(TRANSFORMERS_PATH)


def camel_case_split(identifier: str) -> List[str]:
    """
    Split a camel-cased name into words.

    Args:
        identifier (`str`): The camel-cased name to parse.

    Returns:
        `List[str]`: The list of words in the identifier (as seprated by capital letters).

    Example:

    ```py
    >>> camel_case_split("CamelCasedClass")
    ["Camel", "Cased", "Class"]
    ```
    """
    # Regex thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
    matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
    return [m.group(0) for m in matches]


def _center_text(text: str, width: int) -> str:
    """
    Utility that will add spaces on the left and right of a text to make it centered for a given width.

    Args:
        text (`str`): The text to center.
        width (`int`): The desired length of the result.

    Returns:
        `str`: A text of length `width` with the original `text` in the middle.
    """
    text_length = 2 if text == "✅" or text == "❌" else len(text)
    left_indent = (width - text_length) // 2
    right_indent = width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent


def get_model_table_from_auto_modules() -> str:
    """
    Generates an up-to-date model table from the content of the auto modules.
    """
    # Dictionary model names to config.
    config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    model_name_to_config = {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    pt_models = collections.defaultdict(bool)
    tf_models = collections.defaultdict(bool)
    flax_models = collections.defaultdict(bool)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(transformers_module):
        lookup_dict = None
        if _re_tf_models.match(attr_name) is not None:
            lookup_dict = tf_models
            attr_name = _re_tf_models.match(attr_name).groups()[0]
        elif _re_flax_models.match(attr_name) is not None:
            lookup_dict = flax_models
            attr_name = _re_flax_models.match(attr_name).groups()[0]
        elif _re_pt_models.match(attr_name) is not None:
            lookup_dict = pt_models
            attr_name = _re_pt_models.match(attr_name).groups()[0]

        if lookup_dict is not None:
            while len(attr_name) > 0:
                if attr_name in model_name_to_prefix.values():
                    lookup_dict[attr_name] = True
                    break
                # Try again after removing the last word in the name
                attr_name = "".join(camel_case_split(attr_name)[:-1])

    # Let's build that table!
    model_names = list(model_name_to_config.keys())
    model_names.sort(key=str.lower)
    columns = ["Model", "PyTorch support", "TensorFlow support", "Flax Support"]
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    widths = [len(c) + 2 for c in columns]
    widths[0] = max([len(name) for name in model_names]) + 2

    # Build the table per se
    table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n"
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"

    check = {True: "✅", False: "❌"}
    for name in model_names:
        prefix = model_name_to_prefix[name]
        line = [
            name,
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
    return table


def check_model_table(overwrite=False):
    """
    Check the model table in the index.md is consistent with the state of the lib and potentially fix it.

    Args:
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the table when it's not up to date.
    """
    current_table, start_index, end_index, lines = _find_text_in_file(
        filename=os.path.join(PATH_TO_DOCS, "index.md"),
        start_prompt="<!--This table is updated automatically from the auto modules",
        end_prompt="<!-- End table-->",
    )
    new_table = get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
        else:
            raise ValueError(
                "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."
            )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
    args = parser.parse_args()

    check_model_table(args.fix_and_overwrite)

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
"""
Utility that checks the big table in the file docs/source/en/index.md and potentially updates it.

Use from the root of the repo with:

```bash
python utils/check_inits.py
```

for a check that will error in case of inconsistencies (used by `make repo-consistency`).

To auto-fix issues run:

```bash
python utils/check_inits.py --fix_and_overwrite
```

which is used by `make fix-copies`.
"""
import argparse
import collections
import os
import re
from typing import List

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
TRANSFORMERS_PATH = "src/transformers"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."


def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str:
    """
    Find the text in filename between two prompts.

    Args:
        filename (`str`): The file to search into.
        start_prompt (`str`): A string to look for at the start of the content searched.
        end_prompt (`str`): A string that will mark the end of the content to look for.

    Returns:
        `str`: The content between the prompts.
    """
    with open(filename, "r", encoding="utf-8", newline="\n") as f:
        lines = f.readlines()

    # Find the start prompt.
    start_index = 0
    while not lines[start_index].startswith(start_prompt):
        start_index += 1
    start_index += 1

    # Now go until the end prompt.
    end_index = start_index
    while not lines[end_index].startswith(end_prompt):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]) <= 1:
        start_index += 1
    while len(lines[end_index]) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]), start_index, end_index, lines


# Regexes that match TF/Flax/PT model names. Add here suffixes that are used to identify models, separated by |
_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch after the two previous regexes.
_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
transformers_module = direct_transformers_import(TRANSFORMERS_PATH)


def camel_case_split(identifier: str) -> List[str]:
    """
    Split a camel-cased name into words.

    Args:
        identifier (`str`): The camel-cased name to parse.

    Returns:
        `List[str]`: The list of words in the identifier (as seprated by capital letters).

    Example:

    ```py
    >>> camel_case_split("CamelCasedClass")
    ["Camel", "Cased", "Class"]
    ```
    """
    # Regex thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
    matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
    return [m.group(0) for m in matches]


def _center_text(text: str, width: int) -> str:
    """
    Utility that will add spaces on the left and right of a text to make it centered for a given width.

    Args:
        text (`str`): The text to center.
        width (`int`): The desired length of the result.

    Returns:
        `str`: A text of length `width` with the original `text` in the middle.
    """
    text_length = 2 if text == "✅" or text == "❌" else len(text)
    left_indent = (width - text_length) // 2
    right_indent = width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent


def get_model_table_from_auto_modules() -> str:
    """
    Generates an up-to-date model table from the content of the auto modules.
    """
    # Dictionary model names to config.
    config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    model_name_to_config = {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    pt_models = collections.defaultdict(bool)
    tf_models = collections.defaultdict(bool)
    flax_models = collections.defaultdict(bool)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(transformers_module):
        lookup_dict = None
        if _re_tf_models.match(attr_name) is not None:
            lookup_dict = tf_models
            attr_name = _re_tf_models.match(attr_name).groups()[0]
        elif _re_flax_models.match(attr_name) is not None:
            lookup_dict = flax_models
            attr_name = _re_flax_models.match(attr_name).groups()[0]
        elif _re_pt_models.match(attr_name) is not None:
            lookup_dict = pt_models
            attr_name = _re_pt_models.match(attr_name).groups()[0]

        if lookup_dict is not None:
            while len(attr_name) > 0:
                if attr_name in model_name_to_prefix.values():
                    lookup_dict[attr_name] = True
                    break
                # Try again after removing the last word in the name
                attr_name = "".join(camel_case_split(attr_name)[:-1])

    # Let's build that table!
    model_names = list(model_name_to_config.keys())
    model_names.sort(key=str.lower)
    columns = ["Model", "PyTorch support", "TensorFlow support", "Flax Support"]
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    widths = [len(c) + 2 for c in columns]
    widths[0] = max([len(name) for name in model_names]) + 2

    # Build the table per se
    table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n"
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"

    check = {True: "✅", False: "❌"}
    for name in model_names:
        prefix = model_name_to_prefix[name]
        line = [
            name,
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
    return table


def check_model_table(overwrite=False):
    """
    Check the model table in the index.md is consistent with the state of the lib and potentially fix it.

    Args:
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the table when it's not up to date.
    """
    current_table, start_index, end_index, lines = _find_text_in_file(
        filename=os.path.join(PATH_TO_DOCS, "index.md"),
        start_prompt="<!--This table is updated automatically from the auto modules",
        end_prompt="<!-- End table-->",
    )
    new_table = get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
        else:
            raise ValueError(
                "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."
            )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
    args = parser.parse_args()

    check_model_table(args.fix_and_overwrite)
