# 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 2023 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.

import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
PATH_TO_TRANSFORMERS = "src/transformers"


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

CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING

SPECIAL_CASES_TO_ALLOW = {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "Pop2PianoConfig": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        # For backward compatibility with trust remote code models
        "MptConfig": True,
        "MptAttentionConfig": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
        "IdeficsConfig": True,
        "IdeficsVisionConfig": True,
        "IdeficsPerceiverConfig": True,
    }
)


def check_attribute_being_used(config_class, attributes, default_value, source_strings):
    """Check if any name in `attributes` is used in one of the strings in `source_strings`

    Args:
        config_class (`type`):
            The configuration class for which the arguments in its `__init__` will be checked.
        attributes (`List[str]`):
            The name of an argument (or attribute) and its variant names if any.
        default_value (`Any`):
            A default value for the attribute in `attributes` assigned in the `__init__` of `config_class`.
        source_strings (`List[str]`):
            The python source code strings in the same modeling directory where `config_class` is defined. The file
            containing the definition of `config_class` should be excluded.
    """
    attribute_used = False
    for attribute in attributes:
        for modeling_source in source_strings:
            # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
            if (
                f"config.{attribute}" in modeling_source
                or f'getattr(config, "{attribute}"' in modeling_source
                or f'getattr(self.config, "{attribute}"' in modeling_source
            ):
                attribute_used = True
            # Deal with multi-line cases
            elif (
                re.search(
                    rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"',
                    modeling_source,
                )
                is not None
            ):
                attribute_used = True
            # `SequenceSummary` is called with `SequenceSummary(config)`
            elif attribute in [
                "summary_type",
                "summary_use_proj",
                "summary_activation",
                "summary_last_dropout",
                "summary_proj_to_labels",
                "summary_first_dropout",
            ]:
                if "SequenceSummary" in modeling_source:
                    attribute_used = True
            if attribute_used:
                break
        if attribute_used:
            break

    # common and important attributes, even if they do not always appear in the modeling files
    attributes_to_allow = [
        "bos_index",
        "eos_index",
        "pad_index",
        "unk_index",
        "mask_index",
        "image_size",
        "use_cache",
        "out_features",
        "out_indices",
    ]
    attributes_used_in_generation = ["encoder_no_repeat_ngram_size"]

    # Special cases to be allowed
    case_allowed = True
    if not attribute_used:
        case_allowed = False
        for attribute in attributes:
            # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
            if attribute in ["is_encoder_decoder"] and default_value is True:
                case_allowed = True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                case_allowed = True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                case_allowed = True
            elif attribute.endswith("_token_id"):
                case_allowed = True

            # configuration class specific cases
            if not case_allowed:
                allowed_cases = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__, [])
                case_allowed = allowed_cases is True or attribute in allowed_cases

    return attribute_used or case_allowed


def check_config_attributes_being_used(config_class):
    """Check the arguments in `__init__` of `config_class` are used in the modeling files in the same directory

    Args:
        config_class (`type`):
            The configuration class for which the arguments in its `__init__` will be checked.
    """
    # Get the parameters in `__init__` of the configuration class, and the default values if any
    signature = dict(inspect.signature(config_class.__init__).parameters)
    parameter_names = [x for x in list(signature.keys()) if x not in ["self", "kwargs"]]
    parameter_defaults = [signature[param].default for param in parameter_names]

    # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
    # as one variant is used, the test should pass
    reversed_attribute_map = {}
    if len(config_class.attribute_map) > 0:
        reversed_attribute_map = {v: k for k, v in config_class.attribute_map.items()}

    # Get the path to modeling source files
    config_source_file = inspect.getsourcefile(config_class)
    model_dir = os.path.dirname(config_source_file)
    # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
    modeling_paths = [os.path.join(model_dir, fn) for fn in os.listdir(model_dir) if fn.startswith("modeling_")]

    # Get the source code strings
    modeling_sources = []
    for path in modeling_paths:
        if os.path.isfile(path):
            with open(path, encoding="utf8") as fp:
                modeling_sources.append(fp.read())

    unused_attributes = []
    for config_param, default_value in zip(parameter_names, parameter_defaults):
        # `attributes` here is all the variant names for `config_param`
        attributes = [config_param]
        # some configuration classes have non-empty `attribute_map`, and both names could be used in the
        # corresponding modeling files. As long as one of them appears, it is fine.
        if config_param in reversed_attribute_map:
            attributes.append(reversed_attribute_map[config_param])

        if not check_attribute_being_used(config_class, attributes, default_value, modeling_sources):
            unused_attributes.append(attributes[0])

    return sorted(unused_attributes)


def check_config_attributes():
    """Check the arguments in `__init__` of all configuration classes are used in  python files"""
    configs_with_unused_attributes = {}
    for _config_class in list(CONFIG_MAPPING.values()):
        # Skip deprecated models
        if "models.deprecated" in _config_class.__module__:
            continue
        # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
        config_classes_in_module = [
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class),
                lambda x: inspect.isclass(x)
                and issubclass(x, PretrainedConfig)
                and inspect.getmodule(x) == inspect.getmodule(_config_class),
            )
        ]
        for config_class in config_classes_in_module:
            unused_attributes = check_config_attributes_being_used(config_class)
            if len(unused_attributes) > 0:
                configs_with_unused_attributes[config_class.__name__] = unused_attributes

    if len(configs_with_unused_attributes) > 0:
        error = "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
        for name, attributes in configs_with_unused_attributes.items():
            error += f"{name}: {attributes}\n"

        raise ValueError(error)


if __name__ == "__main__":
    check_config_attributes()

# coding=utf-8
# Copyright 2023 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.

import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
PATH_TO_TRANSFORMERS = "src/transformers"


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

CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING

SPECIAL_CASES_TO_ALLOW = {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "Pop2PianoConfig": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        # For backward compatibility with trust remote code models
        "MptConfig": True,
        "MptAttentionConfig": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
        "IdeficsConfig": True,
        "IdeficsVisionConfig": True,
        "IdeficsPerceiverConfig": True,
    }
)


def check_attribute_being_used(config_class, attributes, default_value, source_strings):
    """Check if any name in `attributes` is used in one of the strings in `source_strings`

    Args:
        config_class (`type`):
            The configuration class for which the arguments in its `__init__` will be checked.
        attributes (`List[str]`):
            The name of an argument (or attribute) and its variant names if any.
        default_value (`Any`):
            A default value for the attribute in `attributes` assigned in the `__init__` of `config_class`.
        source_strings (`List[str]`):
            The python source code strings in the same modeling directory where `config_class` is defined. The file
            containing the definition of `config_class` should be excluded.
    """
    attribute_used = False
    for attribute in attributes:
        for modeling_source in source_strings:
            # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
            if (
                f"config.{attribute}" in modeling_source
                or f'getattr(config, "{attribute}"' in modeling_source
                or f'getattr(self.config, "{attribute}"' in modeling_source
            ):
                attribute_used = True
            # Deal with multi-line cases
            elif (
                re.search(
                    rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"',
                    modeling_source,
                )
                is not None
            ):
                attribute_used = True
            # `SequenceSummary` is called with `SequenceSummary(config)`
            elif attribute in [
                "summary_type",
                "summary_use_proj",
                "summary_activation",
                "summary_last_dropout",
                "summary_proj_to_labels",
                "summary_first_dropout",
            ]:
                if "SequenceSummary" in modeling_source:
                    attribute_used = True
            if attribute_used:
                break
        if attribute_used:
            break

    # common and important attributes, even if they do not always appear in the modeling files
    attributes_to_allow = [
        "bos_index",
        "eos_index",
        "pad_index",
        "unk_index",
        "mask_index",
        "image_size",
        "use_cache",
        "out_features",
        "out_indices",
        "sampling_rate",
    ]
    attributes_used_in_generation = ["encoder_no_repeat_ngram_size"]

    # Special cases to be allowed
    case_allowed = True
    if not attribute_used:
        case_allowed = False
        for attribute in attributes:
            # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
            if attribute in ["is_encoder_decoder"] and default_value is True:
                case_allowed = True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                case_allowed = True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                case_allowed = True
            elif attribute.endswith("_token_id"):
                case_allowed = True

            # configuration class specific cases
            if not case_allowed:
                allowed_cases = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__, [])
                case_allowed = allowed_cases is True or attribute in allowed_cases

    return attribute_used or case_allowed


def check_config_attributes_being_used(config_class):
    """Check the arguments in `__init__` of `config_class` are used in the modeling files in the same directory

    Args:
        config_class (`type`):
            The configuration class for which the arguments in its `__init__` will be checked.
    """
    # Get the parameters in `__init__` of the configuration class, and the default values if any
    signature = dict(inspect.signature(config_class.__init__).parameters)
    parameter_names = [x for x in list(signature.keys()) if x not in ["self", "kwargs"]]
    parameter_defaults = [signature[param].default for param in parameter_names]

    # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
    # as one variant is used, the test should pass
    reversed_attribute_map = {}
    if len(config_class.attribute_map) > 0:
        reversed_attribute_map = {v: k for k, v in config_class.attribute_map.items()}

    # Get the path to modeling source files
    config_source_file = inspect.getsourcefile(config_class)
    model_dir = os.path.dirname(config_source_file)
    # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
    modeling_paths = [os.path.join(model_dir, fn) for fn in os.listdir(model_dir) if fn.startswith("modeling_")]

    # Get the source code strings
    modeling_sources = []
    for path in modeling_paths:
        if os.path.isfile(path):
            with open(path, encoding="utf8") as fp:
                modeling_sources.append(fp.read())

    unused_attributes = []
    for config_param, default_value in zip(parameter_names, parameter_defaults):
        # `attributes` here is all the variant names for `config_param`
        attributes = [config_param]
        # some configuration classes have non-empty `attribute_map`, and both names could be used in the
        # corresponding modeling files. As long as one of them appears, it is fine.
        if config_param in reversed_attribute_map:
            attributes.append(reversed_attribute_map[config_param])

        if not check_attribute_being_used(config_class, attributes, default_value, modeling_sources):
            unused_attributes.append(attributes[0])

    return sorted(unused_attributes)


def check_config_attributes():
    """Check the arguments in `__init__` of all configuration classes are used in  python files"""
    configs_with_unused_attributes = {}
    for _config_class in list(CONFIG_MAPPING.values()):
        # Skip deprecated models
        if "models.deprecated" in _config_class.__module__:
            continue
        # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
        config_classes_in_module = [
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class),
                lambda x: inspect.isclass(x)
                and issubclass(x, PretrainedConfig)
                and inspect.getmodule(x) == inspect.getmodule(_config_class),
            )
        ]
        for config_class in config_classes_in_module:
            unused_attributes = check_config_attributes_being_used(config_class)
            if len(unused_attributes) > 0:
                configs_with_unused_attributes[config_class.__name__] = unused_attributes

    if len(configs_with_unused_attributes) > 0:
        error = "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
        for name, attributes in configs_with_unused_attributes.items():
            error += f"{name}: {attributes}\n"

        raise ValueError(error)


if __name__ == "__main__":
    check_config_attributes()
