# 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. All rights reserved.
#
# 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.
""" MobileNetV1 model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class MobileNetV1Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
    MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the MobileNetV1
    [google/mobilenet_v1_1.0_224](https://huggingface.co/google/mobilenet_v1_1.0_224) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        depth_multiplier (`float`, *optional*, defaults to 1.0):
            Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
            channels. This is sometimes also called "alpha" or "width multiplier".
        min_depth (`int`, *optional*, defaults to 8):
            All layers will have at least this many channels.
        hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
            The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
        tf_padding (`bool`, `optional`, defaults to `True`):
            Whether to use TensorFlow padding rules on the convolution layers.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
            The dropout ratio for attached classifiers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 0.001):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import MobileNetV1Config, MobileNetV1Model

    >>> # Initializing a "mobilenet_v1_1.0_224" style configuration
    >>> configuration = MobileNetV1Config()

    >>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
    >>> model = MobileNetV1Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "mobilenet_v1"

    def __init__(
        self,
        num_channels=3,
        image_size=224,
        depth_multiplier=1.0,
        min_depth=8,
        hidden_act="relu6",
        tf_padding=True,
        classifier_dropout_prob=0.999,
        initializer_range=0.02,
        layer_norm_eps=0.001,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if depth_multiplier <= 0:
            raise ValueError("depth_multiplier must be greater than zero.")

        self.num_channels = num_channels
        self.image_size = image_size
        self.depth_multiplier = depth_multiplier
        self.min_depth = min_depth
        self.hidden_act = hidden_act
        self.tf_padding = tf_padding
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps


class MobileNetV1OnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})])

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
            return OrderedDict([("logits", {0: "batch"})])
        else:
            return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])

    @property
    def atol_for_validation(self) -> float:
        return 1e-4

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" MobileNetV1 model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class MobileNetV1Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
    MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the MobileNetV1
    [google/mobilenet_v1_1.0_224](https://huggingface.co/google/mobilenet_v1_1.0_224) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        depth_multiplier (`float`, *optional*, defaults to 1.0):
            Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
            channels. This is sometimes also called "alpha" or "width multiplier".
        min_depth (`int`, *optional*, defaults to 8):
            All layers will have at least this many channels.
        hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
            The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
        tf_padding (`bool`, `optional`, defaults to `True`):
            Whether to use TensorFlow padding rules on the convolution layers.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
            The dropout ratio for attached classifiers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 0.001):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import MobileNetV1Config, MobileNetV1Model

    >>> # Initializing a "mobilenet_v1_1.0_224" style configuration
    >>> configuration = MobileNetV1Config()

    >>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
    >>> model = MobileNetV1Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "mobilenet_v1"

    def __init__(
        self,
        num_channels=3,
        image_size=224,
        depth_multiplier=1.0,
        min_depth=8,
        hidden_act="relu6",
        tf_padding=True,
        classifier_dropout_prob=0.999,
        initializer_range=0.02,
        layer_norm_eps=0.001,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if depth_multiplier <= 0:
            raise ValueError("depth_multiplier must be greater than zero.")

        self.num_channels = num_channels
        self.image_size = image_size
        self.depth_multiplier = depth_multiplier
        self.min_depth = min_depth
        self.hidden_act = hidden_act
        self.tf_padding = tf_padding
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps


class MobileNetV1OnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})])

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
            return OrderedDict([("logits", {0: "batch"})])
        else:
            return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])

    @property
    def atol_for_validation(self) -> float:
        return 1e-4
