



'''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_     :       Optional[int]								=						{
    '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_     :       Union[str, Any]								=						logging.get_logger(__name__)
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   List[Any]	     =				'maskformer'
						_lowerCAmelCase     :   str	     =				{'hidden_size': 'mask_feature_size'}
						_lowerCAmelCase     :   Any	     =				['resnet', 'swin']
						_lowerCAmelCase     :   Optional[int]	     =				['detr']







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      int = 2_56				,    lowerCAmelCase__   :      int = 2_56				,    lowerCAmelCase__   :      float = 0.1				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      Optional[Dict] = None				,    lowerCAmelCase__   :      Optional[Dict] = None				,    lowerCAmelCase__   :      float = 0.02				,    lowerCAmelCase__   :      float = 1.0				,    lowerCAmelCase__   :      float = 1.0				,    lowerCAmelCase__   :      float = 1.0				,    lowerCAmelCase__   :      float = 20.0				,    lowerCAmelCase__   :      Optional[bool] = None				,    **lowerCAmelCase__   :      Dict				,    ):

													"""simple docstring"""
													if backbone_config is None:
																				# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																				SCREAMING_SNAKE_CASE					:      Dict     					=			SwinConfig(
																				    image_size=3_84				,    in_channels=3				,    patch_size=4				,    embed_dim=1_28				,    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__				):
																				SCREAMING_SNAKE_CASE					:      Dict     					=			backbone_config.pop('''model_type'''				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			CONFIG_MAPPING[backbone_model_type]
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			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
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			DetrConfig()
													else:
																				# verify that the decoder is supported
																				SCREAMING_SNAKE_CASE					:      int     					=			(
																				    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__				):
																											SCREAMING_SNAKE_CASE					:      str     					=			CONFIG_MAPPING[decoder_type]
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			config_class.from_dict(lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.decoder_config.encoder_attention_heads
													SCREAMING_SNAKE_CASE					:      Any     					=			self.decoder_config.num_hidden_layers
													super().__init__(**lowerCAmelCase__				)







						@classmethod
						def 					__lowercase    (  cls   :      Any				,    lowerCAmelCase__   :      PretrainedConfig				,    lowerCAmelCase__   :      PretrainedConfig				,    **lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													return cls(
													    backbone_config=lowerCAmelCase__				,    decoder_config=lowerCAmelCase__				,    **lowerCAmelCase__				,    )







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			copy.deepcopy(self.__dict__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.backbone_config.to_dict()
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.decoder_config.to_dict()
													SCREAMING_SNAKE_CASE					:      Any     					=			self.__class__.model_type
													return output









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :       Optional[int]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   str	     =				'swinv2'

						_lowerCAmelCase     :   Optional[int]	     =				{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Any=2_24				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=3				,    lowerCAmelCase__   :      Tuple=96				,    lowerCAmelCase__   :      Optional[int]=[2, 2, 6, 2]				,    lowerCAmelCase__   :      Tuple=[3, 6, 12, 24]				,    lowerCAmelCase__   :      List[str]=7				,    lowerCAmelCase__   :      Tuple=4.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[int]=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      Dict=1e-5				,    lowerCAmelCase__   :      List[str]=32				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      int     					=			embed_dim
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			depths
													SCREAMING_SNAKE_CASE					:      str     					=			len(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_heads
													SCREAMING_SNAKE_CASE					:      Tuple     					=			window_size
													SCREAMING_SNAKE_CASE					:      Any     					=			mlp_ratio
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			drop_path_rate
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			use_absolute_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_stride
													# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(embed_dim * 2 ** (len(lowerCAmelCase__				) - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			(0, 0, 0, 0)




