# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2023 Google Research, Inc. and 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.
""" EfficientNet model configuration"""

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


logger = logging.get_logger(__name__)

EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}


class EfficientNetConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`EfficientNetModel`]. It is used to instantiate an
    EfficientNet 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 EfficientNet
    [google/efficientnet-b7](https://huggingface.co/google/efficientnet-b7) 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 600):
            The input image size.
        width_coefficient (`float`, *optional*, defaults to 2.0):
            Scaling coefficient for network width at each stage.
        depth_coefficient (`float`, *optional*, defaults to 3.1):
            Scaling coefficient for network depth at each stage.
        depth_divisor `int`, *optional*, defaults to 8):
            A unit of network width.
        kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
            List of kernel sizes to be used in each block.
        in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
            List of input channel sizes to be used in each block for convolutional layers.
        out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
            List of output channel sizes to be used in each block for convolutional layers.
        depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
            List of block indices with square padding.
        strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
            List of stride sizes to be used in each block for convolutional layers.
        num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
            List of the number of times each block is to repeated.
        expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
            List of scaling coefficient of each block.
        squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
            Squeeze expansion ratio.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
        hiddem_dim (`int`, *optional*, defaults to 1280):
            The hidden dimension of the layer before the classification head.
        pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
            Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
            `"max"`]
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        batch_norm_eps (`float`, *optional*, defaults to 1e-3):
            The epsilon used by the batch normalization layers.
        batch_norm_momentum (`float`, *optional*, defaults to 0.99):
            The momentum used by the batch normalization layers.
        dropout_rate (`float`, *optional*, defaults to 0.5):
            The dropout rate to be applied before final classifier layer.
        drop_connect_rate (`float`, *optional*, defaults to 0.2):
            The drop rate for skip connections.

    Example:
    ```python
    >>> from transformers import EfficientNetConfig, EfficientNetModel

    >>> # Initializing a EfficientNet efficientnet-b7 style configuration
    >>> configuration = EfficientNetConfig()

    >>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration
    >>> model = EfficientNetModel(configuration)

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

    def __init__(
        self,
        num_channels: int = 3,
        image_size: int = 600,
        width_coefficient: float = 2.0,
        depth_coefficient: float = 3.1,
        depth_divisor: int = 8,
        kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
        in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
        out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
        depthwise_padding: List[int] = [],
        strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
        num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
        expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
        squeeze_expansion_ratio: float = 0.25,
        hidden_act: str = "swish",
        hidden_dim: int = 2560,
        pooling_type: str = "mean",
        initializer_range: float = 0.02,
        batch_norm_eps: float = 0.001,
        batch_norm_momentum: float = 0.99,
        dropout_rate: float = 0.5,
        drop_connect_rate: float = 0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.width_coefficient = width_coefficient
        self.depth_coefficient = depth_coefficient
        self.depth_divisor = depth_divisor
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.depthwise_padding = depthwise_padding
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.squeeze_expansion_ratio = squeeze_expansion_ratio
        self.hidden_act = hidden_act
        self.hidden_dim = hidden_dim
        self.pooling_type = pooling_type
        self.initializer_range = initializer_range
        self.batch_norm_eps = batch_norm_eps
        self.batch_norm_momentum = batch_norm_momentum
        self.dropout_rate = dropout_rate
        self.drop_connect_rate = drop_connect_rate
        self.num_hidden_layers = sum(num_block_repeats) * 4


class EfficientNetOnnxConfig(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 atol_for_validation(self) -> float:
        return 1e-5

# coding=utf-8
# Copyright 2023 Google Research, Inc. and 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.
""" EfficientNet model configuration"""

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


logger = logging.get_logger(__name__)

EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}


class EfficientNetConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`EfficientNetModel`]. It is used to instantiate an
    EfficientNet 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 EfficientNet
    [google/efficientnet-b7](https://huggingface.co/google/efficientnet-b7) 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 600):
            The input image size.
        width_coefficient (`float`, *optional*, defaults to 2.0):
            Scaling coefficient for network width at each stage.
        depth_coefficient (`float`, *optional*, defaults to 3.1):
            Scaling coefficient for network depth at each stage.
        depth_divisor `int`, *optional*, defaults to 8):
            A unit of network width.
        kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
            List of kernel sizes to be used in each block.
        in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
            List of input channel sizes to be used in each block for convolutional layers.
        out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
            List of output channel sizes to be used in each block for convolutional layers.
        depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
            List of block indices with square padding.
        strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
            List of stride sizes to be used in each block for convolutional layers.
        num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
            List of the number of times each block is to repeated.
        expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
            List of scaling coefficient of each block.
        squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
            Squeeze expansion ratio.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
        hiddem_dim (`int`, *optional*, defaults to 1280):
            The hidden dimension of the layer before the classification head.
        pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
            Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
            `"max"`]
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        batch_norm_eps (`float`, *optional*, defaults to 1e-3):
            The epsilon used by the batch normalization layers.
        batch_norm_momentum (`float`, *optional*, defaults to 0.99):
            The momentum used by the batch normalization layers.
        dropout_rate (`float`, *optional*, defaults to 0.5):
            The dropout rate to be applied before final classifier layer.
        drop_connect_rate (`float`, *optional*, defaults to 0.2):
            The drop rate for skip connections.

    Example:
    ```python
    >>> from transformers import EfficientNetConfig, EfficientNetModel

    >>> # Initializing a EfficientNet efficientnet-b7 style configuration
    >>> configuration = EfficientNetConfig()

    >>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration
    >>> model = EfficientNetModel(configuration)

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

    def __init__(
        self,
        num_channels: int = 3,
        image_size: int = 600,
        width_coefficient: float = 2.0,
        depth_coefficient: float = 3.1,
        depth_divisor: int = 8,
        kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
        in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
        out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
        depthwise_padding: List[int] = [],
        strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
        num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
        expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
        squeeze_expansion_ratio: float = 0.25,
        hidden_act: str = "swish",
        hidden_dim: int = 2560,
        pooling_type: str = "mean",
        initializer_range: float = 0.02,
        batch_norm_eps: float = 0.001,
        batch_norm_momentum: float = 0.99,
        dropout_rate: float = 0.5,
        drop_connect_rate: float = 0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.width_coefficient = width_coefficient
        self.depth_coefficient = depth_coefficient
        self.depth_divisor = depth_divisor
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.depthwise_padding = depthwise_padding
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.squeeze_expansion_ratio = squeeze_expansion_ratio
        self.hidden_act = hidden_act
        self.hidden_dim = hidden_dim
        self.pooling_type = pooling_type
        self.initializer_range = initializer_range
        self.batch_norm_eps = batch_norm_eps
        self.batch_norm_momentum = batch_norm_momentum
        self.dropout_rate = dropout_rate
        self.drop_connect_rate = drop_connect_rate
        self.num_hidden_layers = sum(num_block_repeats) * 4


class EfficientNetOnnxConfig(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 atol_for_validation(self) -> float:
        return 1e-5
