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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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"""VitPose model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig, SubConfigSpec
from ...utils import auto_docstring
from ..auto.configuration_auto import AutoConfig


@auto_docstring(checkpoint="usyd-community/vitpose-base-simple")
@strict
class VitPoseConfig(PreTrainedConfig):
    r"""
    scale_factor (`int`, *optional*, defaults to 4):
        Factor to upscale the feature maps coming from the ViT backbone.
    use_simple_decoder (`bool`, *optional*, defaults to `True`):
        Whether to use a `VitPoseSimpleDecoder` to decode the feature maps from the backbone into heatmaps. Otherwise it uses `VitPoseClassicDecoder`.

    Example:

    ```python
    >>> from transformers import VitPoseConfig, VitPoseForPoseEstimation

    >>> # Initializing a VitPose configuration
    >>> configuration = VitPoseConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = VitPoseForPoseEstimation(configuration)

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

    model_type = "vitpose"
    sub_configs_defaults = {
        "backbone_config": SubConfigSpec(
            config_class=AutoConfig,
            model_type="vitpose_backbone",
            init_kwargs={"out_indices": [4]},
        ),
    }

    backbone_config: dict | PreTrainedConfig | None = None
    initializer_range: float = 0.02
    scale_factor: int = 4
    use_simple_decoder: bool = True


__all__ = ["VitPoseConfig"]