




'''simple docstring'''






import copy
from typing import Dict, List, Optional

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


A_			      =		{
    "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
}

A_			      =		logging.get_logger(__name__)






class        lowercase_      (						_SCREAMING_SNAKE_CASE							):
     A_											=			"mask2former"
     A_											=			["swin"]
     A_											=			{"hidden_size": "hidden_dim"}





     def __init__(     self     :					int     ,							__lowerCamelCase     :					Optional[Dict] = None     ,							__lowerCamelCase     :					int = 256     ,							__lowerCamelCase     :					int = 256     ,							__lowerCamelCase     :					int = 256     ,							__lowerCamelCase     :					int = 1024     ,							__lowerCamelCase     :					str = "relu"     ,							__lowerCamelCase     :					int = 6     ,							__lowerCamelCase     :					int = 10     ,							__lowerCamelCase     :					int = 8     ,							__lowerCamelCase     :					float = 0.0     ,							__lowerCamelCase     :					int = 2048     ,							__lowerCamelCase     :					bool = False     ,							__lowerCamelCase     :					bool = False     ,							__lowerCamelCase     :					int = 4     ,							__lowerCamelCase     :					int = 255     ,							__lowerCamelCase     :					int = 100     ,							__lowerCamelCase     :					float = 0.1     ,							__lowerCamelCase     :					float = 2.0     ,							__lowerCamelCase     :					float = 5.0     ,							__lowerCamelCase     :					float = 5.0     ,							__lowerCamelCase     :					int = 12544     ,							__lowerCamelCase     :					float = 3.0     ,							__lowerCamelCase     :					float = 0.7_5     ,							__lowerCamelCase     :					float = 0.0_2     ,							__lowerCamelCase     :					float = 1.0     ,							__lowerCamelCase     :					bool = True     ,							__lowerCamelCase     :					List[int] = [4, 8, 16, 32]     ,							__lowerCamelCase     :					bool = None     ,							**__lowerCamelCase     :					str     ,							):
           if backbone_config is None:
                 logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'			)
                 snake_case__       :   List[str]	      =    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=__lowerCamelCase     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

           if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   List[Any]	      =    backbone_config.pop('model_type'			)
                 snake_case__       :   Dict	      =    CONFIG_MAPPING[backbone_model_type]
                 snake_case__       :   int	      =    config_class.from_dict(__lowerCamelCase			)

           # 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			)}"			)

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

           super().__init__(**__lowerCamelCase			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[str]     ,							__lowerCamelCase     :					PretrainedConfig     ,							**__lowerCamelCase     :					Dict			):
           return cls(
               backbone_config=__lowerCamelCase     ,							**__lowerCamelCase     ,							)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Any	      =    copy.deepcopy(self.__dict__			)
           snake_case__       :   List[str]	      =    self.backbone_config.to_dict()
           snake_case__       :   Union[str, Any]	      =    self.__class__.model_type
           return output




'''simple docstring'''






def 							UpperCamelCase_     (       A__   :  bytes   ):
  '''simple docstring'''





  return "".join([hex(A__   )[2:].zfill(2   ).upper() for byte in list(A__   )]   )







def 							UpperCamelCase_     (       A__   :  str   ):
  '''simple docstring'''





  if (len(A__   ) % 2) != 0:
    raise ValueError(
        """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""   )
  # Check the character set - the standard base16 alphabet
  # is uppercase according to RFC3548 section 6
  if not set(A__   ) <= set("""0123456789ABCDEF"""   ):
    raise ValueError(
        """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""   )
  # For every two hexadecimal digits (= a byte), turn it into an integer.
  # Then, string the result together into bytes, and return it.
  return bytes(int(data[i] + data[i + 1]				,       16   ) for i in range(0				,       len(A__   )				,       2   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



