# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" UperNet model configuration"""

import copy

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


logger = logging.get_logger(__name__)


class UperNetConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to
    instantiate an UperNet 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 UperNet
    [openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) 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 `ResNetConfig()`):
            The configuration of the backbone model.
        hidden_size (`int`, *optional*, defaults to 512):
            The number of hidden units in the convolutional layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
            Pooling scales used in Pooling Pyramid Module applied on the last feature map.
        use_auxiliary_head (`bool`, *optional*, defaults to `True`):
            Whether to use an auxiliary head during training.
        auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
            Weight of the cross-entropy loss of the auxiliary head.
        auxiliary_channels (`int`, *optional*, defaults to 256):
            Number of channels to use in the auxiliary head.
        auxiliary_num_convs (`int`, *optional*, defaults to 1):
            Number of convolutional layers to use in the auxiliary head.
        auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
            Whether to concatenate the output of the auxiliary head with the input before the classification layer.
        loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function.

    Examples:

    ```python
    >>> from transformers import UperNetConfig, UperNetForSemanticSegmentation

    >>> # Initializing a configuration
    >>> configuration = UperNetConfig()

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

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

    def __init__(
        self,
        backbone_config=None,
        hidden_size=512,
        initializer_range=0.02,
        pool_scales=[1, 2, 3, 6],
        use_auxiliary_head=True,
        auxiliary_loss_weight=0.4,
        auxiliary_in_channels=384,
        auxiliary_channels=256,
        auxiliary_num_convs=1,
        auxiliary_concat_input=False,
        loss_ignore_index=255,
        **kwargs
    ):
        super().__init__(**kwargs)

        if backbone_config is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
            backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
        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)

        self.backbone_config = backbone_config
        self.hidden_size = hidden_size
        self.initializer_range = initializer_range
        self.pool_scales = pool_scales
        self.use_auxiliary_head = use_auxiliary_head
        self.auxiliary_loss_weight = auxiliary_loss_weight
        self.auxiliary_in_channels = auxiliary_in_channels
        self.auxiliary_channels = auxiliary_channels
        self.auxiliary_num_convs = auxiliary_num_convs
        self.auxiliary_concat_input = auxiliary_concat_input
        self.loss_ignore_index = loss_ignore_index

    def to_dict(self):
        """
        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
