





'''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_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					'maskformer'
							lowerCamelCase_           =					{'hidden_size': 'mask_feature_size'}
							lowerCamelCase_           =					['resnet', 'swin']
							lowerCamelCase_           =					['detr']


							def __init__(							self    :List[str]							,				__A    :int = 256							,				__A    :int = 256							,				__A    :float = 0.1							,				__A    :bool = False							,				__A    :Optional[Dict] = None							,				__A    :Optional[Dict] = None							,				__A    :float = 0.0_2							,				__A    :float = 1.0							,				__A    :float = 1.0							,				__A    :float = 1.0							,				__A    :float = 2_0.0							,				__A    :Optional[bool] = None							,				**__A    :Tuple							,				)   ->   str:



									"""simple docstring"""



									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE__			=      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(__A							,				__A		):
											SCREAMING_SNAKE_CASE__			=      backbone_config.pop("""model_type"""		)
											SCREAMING_SNAKE_CASE__			=      CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE__			=      config_class.from_dict(__A		)

									# 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__			=      DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE__			=      (
											    decoder_config.pop("""model_type"""		) if isinstance(__A							,				__A		) 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(__A							,				__A		):
													SCREAMING_SNAKE_CASE__			=      CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE__			=      config_class.from_dict(__A		)

									SCREAMING_SNAKE_CASE__			=      backbone_config
									SCREAMING_SNAKE_CASE__			=      decoder_config
									# main feature dimension for the model
									SCREAMING_SNAKE_CASE__			=      fpn_feature_size
									SCREAMING_SNAKE_CASE__			=      mask_feature_size
									# initializer
									SCREAMING_SNAKE_CASE__			=      init_std
									SCREAMING_SNAKE_CASE__			=      init_xavier_std
									# Hungarian matcher && loss
									SCREAMING_SNAKE_CASE__			=      cross_entropy_weight
									SCREAMING_SNAKE_CASE__			=      dice_weight
									SCREAMING_SNAKE_CASE__			=      mask_weight
									SCREAMING_SNAKE_CASE__			=      use_auxiliary_loss
									SCREAMING_SNAKE_CASE__			=      no_object_weight
									SCREAMING_SNAKE_CASE__			=      output_auxiliary_logits

									SCREAMING_SNAKE_CASE__			=      self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE__			=      self.decoder_config.num_hidden_layers
									super().__init__(**__A		)


							@classmethod
							def      _snake_case  (							cls    :Optional[int]							,				__A    :PretrainedConfig							,				__A    :PretrainedConfig							,				**__A    :Union[str, Any]		)   ->   List[str]:



									"""simple docstring"""



									return cls(
									    backbone_config=__A							,				decoder_config=__A							,				**__A							,				)


							def      _snake_case  (							self    :List[Any]		)   ->   Dict[str, any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.decoder_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):
							lowerCamelCase_           =					"nat"

							lowerCamelCase_           =					{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :List[Any]							,				__A    :Optional[Any]=4							,				__A    :Any=3							,				__A    :Optional[int]=64							,				__A    :Optional[int]=[3, 4, 6, 5]							,				__A    :Union[str, Any]=[2, 4, 8, 16]							,				__A    :Optional[Any]=7							,				__A    :Optional[Any]=3.0							,				__A    :List[Any]=True							,				__A    :int=0.0							,				__A    :Dict=0.0							,				__A    :Optional[Any]=0.1							,				__A    :str="gelu"							,				__A    :Optional[Any]=0.0_2							,				__A    :Optional[int]=1E-5							,				__A    :Optional[int]=0.0							,				__A    :Optional[Any]=None							,				__A    :Union[str, Any]=None							,				**__A    :Union[str, Any]							,				)   ->   Optional[int]:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__			=      patch_size
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      embed_dim
									SCREAMING_SNAKE_CASE__			=      depths
									SCREAMING_SNAKE_CASE__			=      len(__A		)
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      kernel_size
									SCREAMING_SNAKE_CASE__			=      mlp_ratio
									SCREAMING_SNAKE_CASE__			=      qkv_bias
									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      drop_path_rate
									SCREAMING_SNAKE_CASE__			=      hidden_act
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      initializer_range
									# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									SCREAMING_SNAKE_CASE__			=      int(embed_dim * 2 ** (len(__A		) - 1)		)
									SCREAMING_SNAKE_CASE__			=      layer_scale_init_value
									SCREAMING_SNAKE_CASE__			=      ["""stem"""] + [f'''stage{idx}''' for idx in range(1							,				len(__A		) + 1		)]
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      get_aligned_output_features_output_indices(
									    out_features=__A							,				out_indices=__A							,				stage_names=self.stage_names		)