# What does this PR do?

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<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Set `TF32` flag for PyTorch cuDNN backend. # What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# coding=utf-8
# Copyright 2022 Meta Platforms, 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.
""" Mask2Former model configuration"""
import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

logger = logging.get_logger(__name__)


class Mask2FormerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Mask2FormerModel`]. It is used to instantiate a
    Mask2Former 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 Mask2Former
    [facebook/mask2former-swin-small-coco-instance](https://huggingface.co/facebook/mask2former-swin-small-coco-instance)
    architecture.

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

    Currently, Mask2Former only supports the [Swin Transformer](swin) as backbone.

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `SwinConfig()`):
            The configuration of the backbone model. If unset, the configuration corresponding to
            `swin-base-patch4-window12-384` will be used.
        feature_size (`int`, *optional*, defaults to 256):
            The features (channels) of the resulting feature maps.
        mask_feature_size (`int`, *optional*, defaults to 256):
            The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
            size.
        hidden_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the encoder layers.
        encoder_feedforward_dim (`int`, *optional*, defaults to 1024):
            Dimension of feedforward network for deformable detr encoder used as part of pixel decoder.
        encoder_layers (`int`, *optional*, defaults to 6):
            Number of layers in the deformable detr encoder used as part of pixel decoder.
        decoder_layers (`int`, *optional*, defaults to 10):
            Number of layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder.
        dim_feedforward (`int`, *optional*, defaults to 2048):
            Feature dimension in feedforward network for transformer decoder.
        pre_norm (`bool`, *optional*, defaults to `False`):
            Whether to use pre-LayerNorm or not for transformer decoder.
        enforce_input_projection (`bool`, *optional*, defaults to `False`):
            Whether to add an input projection 1x1 convolution even if the input channels and hidden dim are identical
            in the Transformer decoder.
        common_stride (`int`, *optional*, defaults to 4):
            Parameter used for determining number of FPN levels used as part of pixel decoder.
        ignore_value (`int`, *optional*, defaults to 255):
            Category id to be ignored during training.
        num_queries (`int`, *optional*, defaults to 100):
            Number of queries for the decoder.
        no_object_weight (`int`, *optional*, defaults to 0.1):
            The weight to apply to the null (no object) class.
        class_weight (`int`, *optional*, defaults to 2.0):
            The weight for the cross entropy loss.
        mask_weight (`int`, *optional*, defaults to 5.0):
            The weight for the mask loss.
        dice_weight (`int`, *optional*, defaults to 5.0):
            The weight for the dice loss.
        train_num_points (`str` or `function`, *optional*, defaults to 12544):
            Number of points used for sampling during loss calculation.
        oversample_ratio (`float`, *optional*, defaults to 3.0):
            Oversampling parameter used for calculating no. of sampled points
        importance_sample_ratio (`float`, *optional*, defaults to 0.75):
            Ratio of points that are sampled via importance sampling.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        init_xavier_std (`float``, *optional*, defaults to 1.0):
            The scaling factor used for the Xavier initialization gain in the HM Attention map module.
        use_auxiliary_loss (`boolean``, *optional*, defaults to `True`):
            If `True` [`Mask2FormerForUniversalSegmentationOutput`] will contain the auxiliary losses computed using
            the logits from each decoder's stage.
        feature_strides (`List[int]`, *optional*, defaults to `[4, 8, 16, 32]`):
            Feature strides corresponding to features generated from backbone network.
        output_auxiliary_logits (`bool`, *optional*):
            Should the model output its `auxiliary_logits` or not.

    Examples:

    ```python
    >>> from transformers import Mask2FormerConfig, Mask2FormerModel

    >>> # Initializing a Mask2Former facebook/mask2former-swin-small-coco-instance configuration
    >>> configuration = Mask2FormerConfig()

    >>> # Initializing a model (with random weights) from the facebook/mask2former-swin-small-coco-instance style configuration
    >>> model = Mask2FormerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```

    """
    model_type = "mask2former"
    backbones_supported = ["swin"]
    attribute_map = {"hidden_size": "hidden_dim"}

    def __init__(
        self,
        backbone_config: Optional[Dict] = None,
        feature_size: int = 256,
        mask_feature_size: int = 256,
        hidden_dim: int = 256,
        encoder_feedforward_dim: int = 1024,
        activation_function: str = "relu",
        encoder_layers: int = 6,
        decoder_layers: int = 10,
        num_attention_heads: int = 8,
        dropout: float = 0.0,
        dim_feedforward: int = 2048,
        pre_norm: bool = False,
        enforce_input_projection: bool = False,
        common_stride: int = 4,
        ignore_value: int = 255,
        num_queries: int = 100,
        no_object_weight: float = 0.1,
        class_weight: float = 2.0,
        mask_weight: float = 5.0,
        dice_weight: float = 5.0,
        train_num_points: int = 12544,
        oversample_ratio: float = 3.0,
        importance_sample_ratio: float = 0.75,
        init_std: float = 0.02,
        init_xavier_std: float = 1.0,
        use_auxiliary_loss: bool = True,
        feature_strides: List[int] = [4, 8, 16, 32],
        output_auxiliary_logits: bool = None,
        **kwargs,
    ):
        if backbone_config is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
            backbone_config = CONFIG_MAPPING["swin"](
                image_size=224,
                in_channels=3,
                patch_size=4,
                embed_dim=96,
                depths=[2, 2, 18, 2],
                num_heads=[3, 6, 12, 24],
                window_size=7,
                drop_path_rate=0.3,
                use_absolute_embeddings=False,
                out_features=["stage1", "stage2", "stage3", "stage4"],
            )

        if isinstance(backbone_config, dict):
            backbone_model_type = backbone_config.pop("model_type")
            config_class = CONFIG_MAPPING[backbone_model_type]
            backbone_config = config_class.from_dict(backbone_config)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                f"Supported model types: {','.join(self.backbones_supported)}"
            )

        self.backbone_config = backbone_config
        self.feature_size = feature_size
        self.mask_feature_size = mask_feature_size
        self.hidden_dim = hidden_dim
        self.encoder_feedforward_dim = encoder_feedforward_dim
        self.activation_function = activation_function
        self.encoder_layers = encoder_layers
        self.decoder_layers = decoder_layers
        self.num_attention_heads = num_attention_heads
        self.dropout = dropout
        self.dim_feedforward = dim_feedforward
        self.pre_norm = pre_norm
        self.enforce_input_projection = enforce_input_projection
        self.common_stride = common_stride
        self.ignore_value = ignore_value
        self.num_queries = num_queries
        self.no_object_weight = no_object_weight
        self.class_weight = class_weight
        self.mask_weight = mask_weight
        self.dice_weight = dice_weight
        self.train_num_points = train_num_points
        self.oversample_ratio = oversample_ratio
        self.importance_sample_ratio = importance_sample_ratio
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.use_auxiliary_loss = use_auxiliary_loss
        self.feature_strides = feature_strides
        self.output_auxiliary_logits = output_auxiliary_logits
        self.num_hidden_layers = decoder_layers

        super().__init__(**kwargs)

    @classmethod
    def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
        """Instantiate a [`Mask2FormerConfig`] (or a derived class) from a pre-trained backbone model configuration.

        Args:
            backbone_config ([`PretrainedConfig`]):
                The backbone configuration.

        Returns:
            [`Mask2FormerConfig`]: An instance of a configuration object
        """
        return cls(
            backbone_config=backbone_config,
            **kwargs,
        )

    def to_dict(self) -> Dict[str, any]:
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["backbone_config"] = self.backbone_config.to_dict()
        output["model_type"] = self.__class__.model_type
        return output

# coding=utf-8
# Copyright 2022 Meta Platforms, 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.
""" Mask2Former model configuration"""
import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

logger = logging.get_logger(__name__)


class Mask2FormerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Mask2FormerModel`]. It is used to instantiate a
    Mask2Former 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 Mask2Former
    [facebook/mask2former-swin-small-coco-instance](https://huggingface.co/facebook/mask2former-swin-small-coco-instance)
    architecture.

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

    Currently, Mask2Former only supports the [Swin Transformer](swin) as backbone.

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `SwinConfig()`):
            The configuration of the backbone model. If unset, the configuration corresponding to
            `swin-base-patch4-window12-384` will be used.
        feature_size (`int`, *optional*, defaults to 256):
            The features (channels) of the resulting feature maps.
        mask_feature_size (`int`, *optional*, defaults to 256):
            The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
            size.
        hidden_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the encoder layers.
        encoder_feedforward_dim (`int`, *optional*, defaults to 1024):
            Dimension of feedforward network for deformable detr encoder used as part of pixel decoder.
        encoder_layers (`int`, *optional*, defaults to 6):
            Number of layers in the deformable detr encoder used as part of pixel decoder.
        decoder_layers (`int`, *optional*, defaults to 10):
            Number of layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder.
        dim_feedforward (`int`, *optional*, defaults to 2048):
            Feature dimension in feedforward network for transformer decoder.
        pre_norm (`bool`, *optional*, defaults to `False`):
            Whether to use pre-LayerNorm or not for transformer decoder.
        enforce_input_projection (`bool`, *optional*, defaults to `False`):
            Whether to add an input projection 1x1 convolution even if the input channels and hidden dim are identical
            in the Transformer decoder.
        common_stride (`int`, *optional*, defaults to 4):
            Parameter used for determining number of FPN levels used as part of pixel decoder.
        ignore_value (`int`, *optional*, defaults to 255):
            Category id to be ignored during training.
        num_queries (`int`, *optional*, defaults to 100):
            Number of queries for the decoder.
        no_object_weight (`int`, *optional*, defaults to 0.1):
            The weight to apply to the null (no object) class.
        class_weight (`int`, *optional*, defaults to 2.0):
            The weight for the cross entropy loss.
        mask_weight (`int`, *optional*, defaults to 5.0):
            The weight for the mask loss.
        dice_weight (`int`, *optional*, defaults to 5.0):
            The weight for the dice loss.
        train_num_points (`str` or `function`, *optional*, defaults to 12544):
            Number of points used for sampling during loss calculation.
        oversample_ratio (`float`, *optional*, defaults to 3.0):
            Oversampling parameter used for calculating no. of sampled points
        importance_sample_ratio (`float`, *optional*, defaults to 0.75):
            Ratio of points that are sampled via importance sampling.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        init_xavier_std (`float``, *optional*, defaults to 1.0):
            The scaling factor used for the Xavier initialization gain in the HM Attention map module.
        use_auxiliary_loss (`boolean``, *optional*, defaults to `True`):
            If `True` [`Mask2FormerForUniversalSegmentationOutput`] will contain the auxiliary losses computed using
            the logits from each decoder's stage.
        feature_strides (`List[int]`, *optional*, defaults to `[4, 8, 16, 32]`):
            Feature strides corresponding to features generated from backbone network.
        output_auxiliary_logits (`bool`, *optional*):
            Should the model output its `auxiliary_logits` or not.

    Examples:

    ```python
    >>> from transformers import Mask2FormerConfig, Mask2FormerModel

    >>> # Initializing a Mask2Former facebook/mask2former-swin-small-coco-instance configuration
    >>> configuration = Mask2FormerConfig()

    >>> # Initializing a model (with random weights) from the facebook/mask2former-swin-small-coco-instance style configuration
    >>> model = Mask2FormerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```

    """
    model_type = "mask2former"
    backbones_supported = ["swin"]
    attribute_map = {"hidden_size": "hidden_dim"}

    def __init__(
        self,
        backbone_config: Optional[Dict] = None,
        feature_size: int = 256,
        mask_feature_size: int = 256,
        hidden_dim: int = 256,
        encoder_feedforward_dim: int = 1024,
        activation_function: str = "relu",
        encoder_layers: int = 6,
        decoder_layers: int = 10,
        num_attention_heads: int = 8,
        dropout: float = 0.0,
        dim_feedforward: int = 2048,
        pre_norm: bool = False,
        enforce_input_projection: bool = False,
        common_stride: int = 4,
        ignore_value: int = 255,
        num_queries: int = 100,
        no_object_weight: float = 0.1,
        class_weight: float = 2.0,
        mask_weight: float = 5.0,
        dice_weight: float = 5.0,
        train_num_points: int = 12544,
        oversample_ratio: float = 3.0,
        importance_sample_ratio: float = 0.75,
        init_std: float = 0.02,
        init_xavier_std: float = 1.0,
        use_auxiliary_loss: bool = True,
        feature_strides: List[int] = [4, 8, 16, 32],
        output_auxiliary_logits: bool = None,
        **kwargs,
    ):
        if backbone_config is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
            backbone_config = CONFIG_MAPPING["swin"](
                image_size=224,
                in_channels=3,
                patch_size=4,
                embed_dim=96,
                depths=[2, 2, 18, 2],
                num_heads=[3, 6, 12, 24],
                window_size=7,
                drop_path_rate=0.3,
                use_absolute_embeddings=False,
                out_features=["stage1", "stage2", "stage3", "stage4"],
            )

        if isinstance(backbone_config, dict):
            backbone_model_type = backbone_config.pop("model_type")
            config_class = CONFIG_MAPPING[backbone_model_type]
            backbone_config = config_class.from_dict(backbone_config)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                f"Supported model types: {','.join(self.backbones_supported)}"
            )

        self.backbone_config = backbone_config
        self.feature_size = feature_size
        self.mask_feature_size = mask_feature_size
        self.hidden_dim = hidden_dim
        self.encoder_feedforward_dim = encoder_feedforward_dim
        self.activation_function = activation_function
        self.encoder_layers = encoder_layers
        self.decoder_layers = decoder_layers
        self.num_attention_heads = num_attention_heads
        self.dropout = dropout
        self.dim_feedforward = dim_feedforward
        self.pre_norm = pre_norm
        self.enforce_input_projection = enforce_input_projection
        self.common_stride = common_stride
        self.ignore_value = ignore_value
        self.num_queries = num_queries
        self.no_object_weight = no_object_weight
        self.class_weight = class_weight
        self.mask_weight = mask_weight
        self.dice_weight = dice_weight
        self.train_num_points = train_num_points
        self.oversample_ratio = oversample_ratio
        self.importance_sample_ratio = importance_sample_ratio
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.use_auxiliary_loss = use_auxiliary_loss
        self.feature_strides = feature_strides
        self.output_auxiliary_logits = output_auxiliary_logits
        self.num_hidden_layers = decoder_layers

        super().__init__(**kwargs)

    @classmethod
    def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
        """Instantiate a [`Mask2FormerConfig`] (or a derived class) from a pre-trained backbone model configuration.

        Args:
            backbone_config ([`PretrainedConfig`]):
                The backbone configuration.

        Returns:
            [`Mask2FormerConfig`]: An instance of a configuration object
        """
        return cls(
            backbone_config=backbone_config,
            **kwargs,
        )

    def to_dict(self) -> Dict[str, any]:
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["backbone_config"] = self.backbone_config.to_dict()
        output["model_type"] = self.__class__.model_type
        return output
