# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# coding=utf-8
# Copyright 2022 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 re

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

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_re_checkpoint = re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}


def get_checkpoint_from_config_class(config_class):
    checkpoint = None

    # source code of `config_class`
    config_source = inspect.getsource(config_class)
    checkpoints = _re_checkpoint.findall(config_source)

    # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
    # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
    for ckpt_name, ckpt_link in checkpoints:
        # allow the link to end with `/`
        if ckpt_link.endswith("/"):
            ckpt_link = ckpt_link[:-1]

        # verify the checkpoint name corresponds to the checkpoint link
        ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
        if ckpt_link == ckpt_link_from_name:
            checkpoint = ckpt_name
            break

    return checkpoint


def check_config_docstrings_have_checkpoints():
    configs_without_checkpoint = []

    for config_class in list(CONFIG_MAPPING.values()):
        checkpoint = get_checkpoint_from_config_class(config_class)

        name = config_class.__name__
        if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
            configs_without_checkpoint.append(name)

    if len(configs_without_checkpoint) > 0:
        message = "\n".join(sorted(configs_without_checkpoint))
        raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()

# coding=utf-8
# Copyright 2022 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 re

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

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_re_checkpoint = re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}


def get_checkpoint_from_config_class(config_class):
    checkpoint = None

    # source code of `config_class`
    config_source = inspect.getsource(config_class)
    checkpoints = _re_checkpoint.findall(config_source)

    # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
    # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
    for ckpt_name, ckpt_link in checkpoints:
        # allow the link to end with `/`
        if ckpt_link.endswith("/"):
            ckpt_link = ckpt_link[:-1]

        # verify the checkpoint name corresponds to the checkpoint link
        ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
        if ckpt_link == ckpt_link_from_name:
            checkpoint = ckpt_name
            break

    return checkpoint


def check_config_docstrings_have_checkpoints():
    configs_without_checkpoint = []

    for config_class in list(CONFIG_MAPPING.values()):
        # Skip deprecated models
        if "models.deprecated" in config_class.__module__:
            continue
        checkpoint = get_checkpoint_from_config_class(config_class)

        name = config_class.__name__
        if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
            configs_without_checkpoint.append(name)

    if len(configs_without_checkpoint) > 0:
        message = "\n".join(sorted(configs_without_checkpoint))
        raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()
