# 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 (c) Facebook, Inc. and its affiliates.
# Copyright (c) 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.
""" MMBT configuration"""

from ...utils import logging


logger = logging.get_logger(__name__)


class MMBTConfig(object):
    """
    This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT
    model according to the specified arguments, defining the model architecture.

    Args:
        config ([`PreTrainedConfig`]):
            Config of the underlying Transformer models. Its values are copied over to use a single config.
        num_labels (`int`, *optional*):
            Size of final Linear layer for classification.
        modal_hidden_size (`int`, *optional*, defaults to 2048):
            Embedding dimension of the non-text modality encoder.
    """

    def __init__(self, config, num_labels=None, modal_hidden_size=2048):
        self.__dict__ = config.__dict__
        self.modal_hidden_size = modal_hidden_size
        if num_labels:
            self.num_labels = num_labels

# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) 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.
""" MMBT configuration"""

from ...utils import logging


logger = logging.get_logger(__name__)


class MMBTConfig(object):
    """
    This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT
    model according to the specified arguments, defining the model architecture.

    Args:
        config ([`PreTrainedConfig`]):
            Config of the underlying Transformer models. Its values are copied over to use a single config.
        num_labels (`int`, *optional*):
            Size of final Linear layer for classification.
        modal_hidden_size (`int`, *optional*, defaults to 2048):
            Embedding dimension of the non-text modality encoder.
    """

    def __init__(self, config, num_labels=None, modal_hidden_size=2048):
        self.__dict__ = config.__dict__
        self.modal_hidden_size = modal_hidden_size
        if num_labels:
            self.num_labels = num_labels
