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"""VitPose model configuration"""

from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto.configuration_auto import CONFIG_MAPPING


logger = logging.get_logger(__name__)


class VitPoseConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`VitPoseForPoseEstimation`]. It is used to instantiate a
    VitPose 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 VitPose
    [usyd-community/vitpose-base-simple](https://huggingface.co/usyd-community/vitpose-base-simple) architecture.

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

    Args:
        backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitPoseBackboneConfig()`):
            The configuration of the backbone model. Currently, only `backbone_config` with `vitpose_backbone` as `model_type` is supported.
        backbone (`str`, *optional*):
            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
        use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
            Whether to use pretrained weights for the backbone.
        use_timm_backbone (`bool`, *optional*, defaults to `False`):
            Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
            library.
        backbone_kwargs (`dict`, *optional*):
            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        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"

    def __init__(
        self,
        backbone_config: Optional[PretrainedConfig] = None,
        backbone: Optional[str] = None,
        use_pretrained_backbone: bool = False,
        use_timm_backbone: bool = False,
        backbone_kwargs: Optional[dict] = None,
        initializer_range: float = 0.02,
        scale_factor: int = 4,
        use_simple_decoder: bool = True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if use_pretrained_backbone:
            logger.info(
                "`use_pretrained_backbone` is `True`. For the pure inference purpose of VitPose weight do not set this value."
            )
        if use_timm_backbone:
            raise ValueError("use_timm_backbone set `True` is not supported at the moment.")

        if backbone_config is None and backbone is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `VitPose` backbone.")
            backbone_config = CONFIG_MAPPING["vitpose_backbone"](out_indices=[4])
        elif isinstance(backbone_config, dict):
            backbone_model_type = backbone_config.get("model_type")
            config_class = CONFIG_MAPPING[backbone_model_type]
            backbone_config = config_class.from_dict(backbone_config)

        verify_backbone_config_arguments(
            use_timm_backbone=use_timm_backbone,
            use_pretrained_backbone=use_pretrained_backbone,
            backbone=backbone,
            backbone_config=backbone_config,
            backbone_kwargs=backbone_kwargs,
        )

        self.backbone_config = backbone_config
        self.backbone = backbone
        self.use_pretrained_backbone = use_pretrained_backbone
        self.use_timm_backbone = use_timm_backbone
        self.backbone_kwargs = backbone_kwargs

        self.initializer_range = initializer_range
        self.scale_factor = scale_factor
        self.use_simple_decoder = use_simple_decoder


__all__ = ["VitPoseConfig"]