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


SCREAMING_SNAKE_CASE		:     Any         =	{
    "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
}

SCREAMING_SNAKE_CASE		:     Any         =	logging.get_logger(__name__)

class        UpperCamelCase						(				UpperCamelCase_				):
						a__		:List[str]      =			'maskformer'
						a__		:Dict      =			{'hidden_size': 'mask_feature_size'}
						a__		:Union[str, Any]      =			['resnet', 'swin']
						a__		:Any      =			['detr']





						def __init__(self			,     __UpperCamelCase = 256			,     __UpperCamelCase = 256			,     __UpperCamelCase = 0.1			,     __UpperCamelCase = False			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = 0.02			,     __UpperCamelCase = 1.0			,     __UpperCamelCase = 1.0			,     __UpperCamelCase = 1.0			,     __UpperCamelCase = 20.0			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				Dict:




												if backbone_config is None:
																		# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																		UpperCamelCase_		:							Optional[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(lowercase_			,     lowercase_						):
																		UpperCamelCase_		:							str  =		backbone_config.pop("""model_type"""						)
																		UpperCamelCase_		:							int  =		CONFIG_MAPPING[backbone_model_type]
																		UpperCamelCase_		:							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 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_		:							Union[str, Any]  =		DetrConfig()
												else:
																		# verify that the decoder is supported
																		UpperCamelCase_		:							List[Any]  =		(
																		    decoder_config.pop("""model_type"""						) if isinstance(lowercase_			,     lowercase_						) 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(lowercase_			,     lowercase_						):
																								UpperCamelCase_		:							List[Any]  =		CONFIG_MAPPING[decoder_type]
																								UpperCamelCase_		:							List[str]  =		config_class.from_dict(lowercase_						)

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

												UpperCamelCase_		:							List[Any]  =		self.decoder_config.encoder_attention_heads
												UpperCamelCase_		:							Dict  =		self.decoder_config.num_hidden_layers
												super().__init__(**lowercase_						)





						@classmethod
						def        A_							(cls			,     __UpperCamelCase			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				Any:
												return cls(
												    backbone_config=lowercase_			,     decoder_config=lowercase_			,     **lowercase_			,     )





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










import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	List[Any]					)   ->		None:
             """simple docstring"""




             warnings.warn(
                 'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileViTImageProcessor instead.'     ,  lowercase_     ,  )
             super().__init__(*lowercase_     ,  **lowercase_					)


