





'''simple docstring'''


import copy
from typing import Dict, List, Optional

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


lowerCAmelCase :					Tuple 							={
    '''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
}

lowerCAmelCase :					Optional[Any] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "mask2former"
     __A								=      ["swin"]
     __A								=      {"hidden_size": "hidden_dim"}





     def __init__(			self		: Union[str, Any]     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: int = 1_024     ,				lowercase		: str = "relu"     ,				lowercase		: int = 6     ,				lowercase		: int = 10     ,				lowercase		: int = 8     ,				lowercase		: float = 0.0     ,				lowercase		: int = 2_048     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: int = 4     ,				lowercase		: int = 255     ,				lowercase		: int = 100     ,				lowercase		: float = 0.1     ,				lowercase		: float = 2.0     ,				lowercase		: float = 5.0     ,				lowercase		: float = 5.0     ,				lowercase		: int = 12_544     ,				lowercase		: float = 3.0     ,				lowercase		: float = 0.75     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: bool = True     ,				lowercase		: List[int] = [4, 8, 16, 32]     ,				lowercase		: bool = None     ,				**lowercase		: Tuple     ,				):





        """simple docstring"""




        if backbone_config is None:
           logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."						)
           lowercase_      :Optional[Any]         =			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=lowercase     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Any         =			backbone_config.pop("model_type"						)
           lowercase_      :Dict         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :str         =			config_class.from_dict(lowercase						)

        # 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						)}'						)

        lowercase_      :List[Any]         =			backbone_config
        lowercase_      :str         =			feature_size
        lowercase_      :Union[str, Any]         =			mask_feature_size
        lowercase_      :Tuple         =			hidden_dim
        lowercase_      :Union[str, Any]         =			encoder_feedforward_dim
        lowercase_      :Dict         =			activation_function
        lowercase_      :Union[str, Any]         =			encoder_layers
        lowercase_      :List[Any]         =			decoder_layers
        lowercase_      :Optional[int]         =			num_attention_heads
        lowercase_      :Optional[Any]         =			dropout
        lowercase_      :str         =			dim_feedforward
        lowercase_      :Optional[Any]         =			pre_norm
        lowercase_      :int         =			enforce_input_projection
        lowercase_      :str         =			common_stride
        lowercase_      :List[Any]         =			ignore_value
        lowercase_      :List[Any]         =			num_queries
        lowercase_      :Any         =			no_object_weight
        lowercase_      :Any         =			class_weight
        lowercase_      :Union[str, Any]         =			mask_weight
        lowercase_      :str         =			dice_weight
        lowercase_      :Optional[Any]         =			train_num_points
        lowercase_      :Optional[Any]         =			oversample_ratio
        lowercase_      :Dict         =			importance_sample_ratio
        lowercase_      :List[str]         =			init_std
        lowercase_      :Any         =			init_xavier_std
        lowercase_      :int         =			use_auxiliary_loss
        lowercase_      :Union[str, Any]         =			feature_strides
        lowercase_      :int         =			output_auxiliary_logits
        lowercase_      :int         =			decoder_layers

        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: str     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :Tuple         =			self.backbone_config.to_dict()
        lowercase_      :Tuple         =			self.__class__.model_type
        return output









'''simple docstring'''


from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 				a_       (    _lowerCAmelCase							):





     @staticmethod
     @abstractmethod
     def 							lowercase__				(			lowercase		: ArgumentParser						):





        """simple docstring"""




        raise NotImplementedError()







     @abstractmethod
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        raise NotImplementedError()


