






"""simple docstring"""
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCamelCase_       			=   {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCamelCase_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''maskformer'''
	__magic_name__			   =       {'''hidden_size''': '''mask_feature_size'''}
	__magic_name__			   =       ['''resnet''', '''swin''']
	__magic_name__			   =       ['''detr''']





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int = 256			,			lowerCAmelCase_      :				int = 256			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				Optional[Dict] = None			,			lowerCAmelCase_      :				Optional[Dict] = None			,			lowerCAmelCase_      :				float = 0.0_2			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 2_0.0			,			lowerCAmelCase_      :				Optional[bool] = None			,			**lowerCAmelCase_      :				Optional[Any]			,			)		->    Optional[int]:
				if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							UpperCAmelCase_  :				int      =      SwinConfig(
							    image_size=384			,			in_channels=3			,			patch_size=4			,			embed_dim=128			,			depths=[2, 2, 18, 2]			,			num_heads=[4, 8, 16, 32]			,			window_size=12			,			drop_path_rate=0.3			,			out_features=["stage1", "stage2", "stage3", "stage4"]			,			)

				if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				Tuple      =      backbone_config.pop("model_type"		)
							UpperCAmelCase_  :				Any      =      CONFIG_MAPPING[backbone_model_type]
							UpperCAmelCase_  :				Dict      =      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 MaskFormer. """
							    f"""Supported model types: {','.join(self.backbones_supported		)}"""		)

				if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							UpperCAmelCase_  :				List[str]      =      DetrConfig()
				else:
							# verify that the decoder is supported
							UpperCAmelCase_  :				Any      =      (
							    decoder_config.pop("model_type"		) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
										raise ValueError(
										    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
										    f""" {','.join(self.decoders_supported		)}"""		)
							if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
										UpperCAmelCase_  :				Tuple      =      CONFIG_MAPPING[decoder_type]
										UpperCAmelCase_  :				Any      =      config_class.from_dict(lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      backbone_config
				UpperCAmelCase_  :				Any      =      decoder_config
				# main feature dimension for the model
				UpperCAmelCase_  :				List[Any]      =      fpn_feature_size
				UpperCAmelCase_  :				Union[str, Any]      =      mask_feature_size
				# initializer
				UpperCAmelCase_  :				Dict      =      init_std
				UpperCAmelCase_  :				Optional[Any]      =      init_xavier_std
				# Hungarian matcher && loss
				UpperCAmelCase_  :				int      =      cross_entropy_weight
				UpperCAmelCase_  :				Any      =      dice_weight
				UpperCAmelCase_  :				Any      =      mask_weight
				UpperCAmelCase_  :				List[str]      =      use_auxiliary_loss
				UpperCAmelCase_  :				str      =      no_object_weight
				UpperCAmelCase_  :				Optional[Any]      =      output_auxiliary_logits

				UpperCAmelCase_  :				Any      =      self.decoder_config.encoder_attention_heads
				UpperCAmelCase_  :				Optional[int]      =      self.decoder_config.num_hidden_layers
				super().__init__(**lowerCAmelCase_		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			lowerCAmelCase_      :				PretrainedConfig			,			lowerCAmelCase_      :				PretrainedConfig			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				return cls(
				    backbone_config=lowerCAmelCase_			,			decoder_config=lowerCAmelCase_			,			**lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict[str, any]:
				UpperCAmelCase_  :				Union[str, Any]      =      copy.deepcopy(self.__dict__		)
				UpperCAmelCase_  :				Dict      =      self.backbone_config.to_dict()
				UpperCAmelCase_  :				Optional[int]      =      self.decoder_config.to_dict()
				UpperCAmelCase_  :				Dict      =      self.__class__.model_type
				return output











"""simple docstring"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



