# 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.
""" MobileViT 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__)

MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class MobileViTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a
    MobileViT 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 MobileViT
    [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) 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 256):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 2):
            The size (resolution) of each patch.
        hidden_sizes (`List[int]`, *optional*, defaults to `[144, 192, 240]`):
            Dimensionality (hidden size) of the Transformer encoders at each stage.
        neck_hidden_sizes (`List[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`):
            The number of channels for the feature maps of the backbone.
        num_attention_heads (`int`, *optional*, defaults to 4):
            Number of attention heads for each attention layer in the Transformer encoder.
        mlp_ratio (`float`, *optional*, defaults to 2.0):
            The ratio of the number of channels in the output of the MLP to the number of channels in the input.
        expand_ratio (`float`, *optional*, defaults to 4.0):
            Expansion factor for the MobileNetv2 layers.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
        conv_kernel_size (`int`, *optional*, defaults to 3):
            The size of the convolutional kernel in the MobileViT layer.
        output_stride (`int`, `optional`, defaults to 32):
            The ratio of the spatial resolution of the output to the resolution of the input image.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probabilitiy for all fully connected layers in the Transformer encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            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 1e-5):
            The epsilon used by the layer normalization layers.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        aspp_out_channels (`int`, `optional`, defaults to 256):
            Number of output channels used in the ASPP layer for semantic segmentation.
        atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`):
            Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
        aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the ASPP layer for semantic segmentation.
        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function of the semantic segmentation model.

    Example:

    ```python
    >>> from transformers import MobileViTConfig, MobileViTModel

    >>> # Initializing a mobilevit-small style configuration
    >>> configuration = MobileViTConfig()

    >>> # Initializing a model from the mobilevit-small style configuration
    >>> model = MobileViTModel(configuration)

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

    def __init__(
        self,
        num_channels=3,
        image_size=256,
        patch_size=2,
        hidden_sizes=[144, 192, 240],
        neck_hidden_sizes=[16, 32, 64, 96, 128, 160, 640],
        num_attention_heads=4,
        mlp_ratio=2.0,
        expand_ratio=4.0,
        hidden_act="silu",
        conv_kernel_size=3,
        output_stride=32,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        qkv_bias=True,
        aspp_out_channels=256,
        atrous_rates=[6, 12, 18],
        aspp_dropout_prob=0.1,
        semantic_loss_ignore_index=255,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_sizes = hidden_sizes
        self.neck_hidden_sizes = neck_hidden_sizes
        self.num_attention_heads = num_attention_heads
        self.mlp_ratio = mlp_ratio
        self.expand_ratio = expand_ratio
        self.hidden_act = hidden_act
        self.conv_kernel_size = conv_kernel_size
        self.output_stride = output_stride
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.qkv_bias = qkv_bias

        # decode head attributes for semantic segmentation
        self.aspp_out_channels = aspp_out_channels
        self.atrous_rates = atrous_rates
        self.aspp_dropout_prob = aspp_dropout_prob
        self.semantic_loss_ignore_index = semantic_loss_ignore_index


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

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])

    @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.
""" MobileViT 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__)

MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class MobileViTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a
    MobileViT 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 MobileViT
    [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) 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 256):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 2):
            The size (resolution) of each patch.
        hidden_sizes (`List[int]`, *optional*, defaults to `[144, 192, 240]`):
            Dimensionality (hidden size) of the Transformer encoders at each stage.
        neck_hidden_sizes (`List[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`):
            The number of channels for the feature maps of the backbone.
        num_attention_heads (`int`, *optional*, defaults to 4):
            Number of attention heads for each attention layer in the Transformer encoder.
        mlp_ratio (`float`, *optional*, defaults to 2.0):
            The ratio of the number of channels in the output of the MLP to the number of channels in the input.
        expand_ratio (`float`, *optional*, defaults to 4.0):
            Expansion factor for the MobileNetv2 layers.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
        conv_kernel_size (`int`, *optional*, defaults to 3):
            The size of the convolutional kernel in the MobileViT layer.
        output_stride (`int`, `optional`, defaults to 32):
            The ratio of the spatial resolution of the output to the resolution of the input image.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probabilitiy for all fully connected layers in the Transformer encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            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 1e-5):
            The epsilon used by the layer normalization layers.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        aspp_out_channels (`int`, `optional`, defaults to 256):
            Number of output channels used in the ASPP layer for semantic segmentation.
        atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`):
            Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
        aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the ASPP layer for semantic segmentation.
        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function of the semantic segmentation model.

    Example:

    ```python
    >>> from transformers import MobileViTConfig, MobileViTModel

    >>> # Initializing a mobilevit-small style configuration
    >>> configuration = MobileViTConfig()

    >>> # Initializing a model from the mobilevit-small style configuration
    >>> model = MobileViTModel(configuration)

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

    def __init__(
        self,
        num_channels=3,
        image_size=256,
        patch_size=2,
        hidden_sizes=[144, 192, 240],
        neck_hidden_sizes=[16, 32, 64, 96, 128, 160, 640],
        num_attention_heads=4,
        mlp_ratio=2.0,
        expand_ratio=4.0,
        hidden_act="silu",
        conv_kernel_size=3,
        output_stride=32,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        qkv_bias=True,
        aspp_out_channels=256,
        atrous_rates=[6, 12, 18],
        aspp_dropout_prob=0.1,
        semantic_loss_ignore_index=255,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_sizes = hidden_sizes
        self.neck_hidden_sizes = neck_hidden_sizes
        self.num_attention_heads = num_attention_heads
        self.mlp_ratio = mlp_ratio
        self.expand_ratio = expand_ratio
        self.hidden_act = hidden_act
        self.conv_kernel_size = conv_kernel_size
        self.output_stride = output_stride
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.qkv_bias = qkv_bias

        # decode head attributes for semantic segmentation
        self.aspp_out_channels = aspp_out_channels
        self.atrous_rates = atrous_rates
        self.aspp_dropout_prob = aspp_dropout_prob
        self.semantic_loss_ignore_index = semantic_loss_ignore_index


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

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])

    @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
