# 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.
""" CvT model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

CVT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}


class CvtConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT 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 CvT
    [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) 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.
        patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`):
            The kernel size of each encoder's patch embedding.
        patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`):
            The stride size of each encoder's patch embedding.
        patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
            The padding size of each encoder's patch embedding.
        embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`):
            Dimension of each of the encoder blocks.
        num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`):
            Number of attention heads for each attention layer in each block of the Transformer encoder.
        depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`):
            The number of layers in each encoder block.
        mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`):
            Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
            encoder blocks.
        attention_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
            The dropout ratio for the attention probabilities.
        drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
            The dropout ratio for the patch embeddings probabilities.
        drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`):
            The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
        qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`):
            The bias bool for query, key and value in attentions
        cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`):
            Whether or not to add a classification token to the output of each of the last 3 stages.
        qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`):
            The projection method for query, key and value Default is depth-wise convolutions with batch norm. For
            Linear projection use "avg".
        kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`):
            The kernel size for query, key and value in attention layer
        padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The padding size for key and value in attention layer
        stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
            The stride size for key and value in attention layer
        padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The padding size for query in attention layer
        stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The stride size for query in attention layer
        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-6):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import CvtConfig, CvtModel

    >>> # Initializing a Cvt msft/cvt style configuration
    >>> configuration = CvtConfig()

    >>> # Initializing a model (with random weights) from the msft/cvt style configuration
    >>> model = CvtModel(configuration)

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

    def __init__(
        self,
        num_channels=3,
        patch_sizes=[7, 3, 3],
        patch_stride=[4, 2, 2],
        patch_padding=[2, 1, 1],
        embed_dim=[64, 192, 384],
        num_heads=[1, 3, 6],
        depth=[1, 2, 10],
        mlp_ratio=[4.0, 4.0, 4.0],
        attention_drop_rate=[0.0, 0.0, 0.0],
        drop_rate=[0.0, 0.0, 0.0],
        drop_path_rate=[0.0, 0.0, 0.1],
        qkv_bias=[True, True, True],
        cls_token=[False, False, True],
        qkv_projection_method=["dw_bn", "dw_bn", "dw_bn"],
        kernel_qkv=[3, 3, 3],
        padding_kv=[1, 1, 1],
        stride_kv=[2, 2, 2],
        padding_q=[1, 1, 1],
        stride_q=[1, 1, 1],
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.num_channels = num_channels
        self.patch_sizes = patch_sizes
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.depth = depth
        self.mlp_ratio = mlp_ratio
        self.attention_drop_rate = attention_drop_rate
        self.drop_rate = drop_rate
        self.drop_path_rate = drop_path_rate
        self.qkv_bias = qkv_bias
        self.cls_token = cls_token
        self.qkv_projection_method = qkv_projection_method
        self.kernel_qkv = kernel_qkv
        self.padding_kv = padding_kv
        self.stride_kv = stride_kv
        self.padding_q = padding_q
        self.stride_q = stride_q
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps

# 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.
""" CvT model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

CVT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}


class CvtConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT 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 CvT
    [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) 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.
        patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`):
            The kernel size of each encoder's patch embedding.
        patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`):
            The stride size of each encoder's patch embedding.
        patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
            The padding size of each encoder's patch embedding.
        embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`):
            Dimension of each of the encoder blocks.
        num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`):
            Number of attention heads for each attention layer in each block of the Transformer encoder.
        depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`):
            The number of layers in each encoder block.
        mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`):
            Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
            encoder blocks.
        attention_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
            The dropout ratio for the attention probabilities.
        drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
            The dropout ratio for the patch embeddings probabilities.
        drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`):
            The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
        qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`):
            The bias bool for query, key and value in attentions
        cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`):
            Whether or not to add a classification token to the output of each of the last 3 stages.
        qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`):
            The projection method for query, key and value Default is depth-wise convolutions with batch norm. For
            Linear projection use "avg".
        kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`):
            The kernel size for query, key and value in attention layer
        padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The padding size for key and value in attention layer
        stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
            The stride size for key and value in attention layer
        padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The padding size for query in attention layer
        stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
            The stride size for query in attention layer
        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-6):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import CvtConfig, CvtModel

    >>> # Initializing a Cvt msft/cvt style configuration
    >>> configuration = CvtConfig()

    >>> # Initializing a model (with random weights) from the msft/cvt style configuration
    >>> model = CvtModel(configuration)

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

    def __init__(
        self,
        num_channels=3,
        patch_sizes=[7, 3, 3],
        patch_stride=[4, 2, 2],
        patch_padding=[2, 1, 1],
        embed_dim=[64, 192, 384],
        num_heads=[1, 3, 6],
        depth=[1, 2, 10],
        mlp_ratio=[4.0, 4.0, 4.0],
        attention_drop_rate=[0.0, 0.0, 0.0],
        drop_rate=[0.0, 0.0, 0.0],
        drop_path_rate=[0.0, 0.0, 0.1],
        qkv_bias=[True, True, True],
        cls_token=[False, False, True],
        qkv_projection_method=["dw_bn", "dw_bn", "dw_bn"],
        kernel_qkv=[3, 3, 3],
        padding_kv=[1, 1, 1],
        stride_kv=[2, 2, 2],
        padding_q=[1, 1, 1],
        stride_q=[1, 1, 1],
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.num_channels = num_channels
        self.patch_sizes = patch_sizes
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.depth = depth
        self.mlp_ratio = mlp_ratio
        self.attention_drop_rate = attention_drop_rate
        self.drop_rate = drop_rate
        self.drop_path_rate = drop_path_rate
        self.qkv_bias = qkv_bias
        self.cls_token = cls_token
        self.qkv_projection_method = qkv_projection_method
        self.kernel_qkv = kernel_qkv
        self.padding_kv = padding_kv
        self.stride_kv = stride_kv
        self.padding_q = padding_q
        self.stride_q = stride_q
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
