






"""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	:	List[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
}

_lowerCAmelCase	:	List[Any]				=      logging.get_logger(__name__)




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''maskformer'''
   SCREAMING_SNAKE_CASE_    ={'''hidden_size''': '''mask_feature_size'''}
   SCREAMING_SNAKE_CASE_    =['''resnet''', '''swin''']
   SCREAMING_SNAKE_CASE_    =['''detr''']
   def __init__(		self      :	List[Any]							,			snake_case__      :	int = 2_5_6							,			snake_case__      :	int = 2_5_6							,			snake_case__      :	float = 0.1							,			snake_case__      :	bool = False							,			snake_case__      :	Optional[Dict] = None							,			snake_case__      :	Optional[Dict] = None							,			snake_case__      :	float = 0.02							,			snake_case__      :	float = 1.0							,			snake_case__      :	float = 1.0							,			snake_case__      :	float = 1.0							,			snake_case__      :	float = 20.0							,			snake_case__      :	Optional[bool] = None							,			**snake_case__      :	Dict							,			):


        '''simple docstring'''
        if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             UpperCAmelCase__						:    Any							=							SwinConfig(
                 image_size=3_8_4							,			in_channels=3							,			patch_size=4							,			embed_dim=1_2_8							,			depths=[2, 2, 1_8, 2]							,			num_heads=[4, 8, 1_6, 3_2]							,			window_size=1_2							,			drop_path_rate=0.3							,			out_features=["stage1", "stage2", "stage3", "stage4"]							,			)

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

        # 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__						:    str							=							DetrConfig()
        else:
             # verify that the decoder is supported
             UpperCAmelCase__						:    Optional[int]							=							(
                 decoder_config.pop("model_type"							) if isinstance(snake_case__							,			snake_case__							) 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(snake_case__							,			snake_case__							):
                  UpperCAmelCase__						:    Any							=							CONFIG_MAPPING[decoder_type]
                  UpperCAmelCase__						:    Union[str, Any]							=							config_class.from_dict(snake_case__							)

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

        UpperCAmelCase__						:    Union[str, Any]							=							self.decoder_config.encoder_attention_heads
        UpperCAmelCase__						:    Tuple							=							self.decoder_config.num_hidden_layers
        super().__init__(**snake_case__							)
   @classmethod
   def       __a							(		cls      :	Union[str, Any]							,			snake_case__      :	PretrainedConfig							,			snake_case__      :	PretrainedConfig							,			**snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return cls(
            backbone_config=snake_case__							,			decoder_config=snake_case__							,			**snake_case__							,			)




   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							copy.deepcopy(self.__dict__							)
        UpperCAmelCase__						:    Tuple							=							self.backbone_config.to_dict()
        UpperCAmelCase__						:    Union[str, Any]							=							self.decoder_config.to_dict()
        UpperCAmelCase__						:    List[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	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


