



import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case__					:     List[str]    		=   {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

snake_case__					:     Dict    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Optional[Any]      							=      """mask2former"""
						_snake_case			:     Union[str, Any]      							=      ["""swin"""]
						_snake_case			:     Union[str, Any]      							=      {"""hidden_size""": """hidden_dim"""}



						def __init__(	self	:							Tuple							,      lowerCamelCase	:							Optional[Dict] = None							,      lowerCamelCase	:							int = 256							,      lowerCamelCase	:							int = 256							,      lowerCamelCase	:							int = 256							,      lowerCamelCase	:							int = 1_024							,      lowerCamelCase	:							str = "relu"							,      lowerCamelCase	:							int = 6							,      lowerCamelCase	:							int = 10							,      lowerCamelCase	:							int = 8							,      lowerCamelCase	:							float = 0.0							,      lowerCamelCase	:							int = 2_048							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 255							,      lowerCamelCase	:							int = 100							,      lowerCamelCase	:							float = 0.1							,      lowerCamelCase	:							float = 2.0							,      lowerCamelCase	:							float = 5.0							,      lowerCamelCase	:							float = 5.0							,      lowerCamelCase	:							int = 12_544							,      lowerCamelCase	:							float = 3.0							,      lowerCamelCase	:							float = 0.75							,      lowerCamelCase	:							float = 0.02							,      lowerCamelCase	:							float = 1.0							,      lowerCamelCase	:							bool = True							,      lowerCamelCase	:							List[int] = [4, 8, 16, 32]							,      lowerCamelCase	:							bool = None							,      **lowerCamelCase	:							List[Any]							,      ):






							'''simple docstring'''



							if backbone_config is None:
								logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
								__lowercase		       =			CONFIG_MAPPING["swin"](
								    image_size=224							,      in_channels=3							,      patch_size=4							,      embed_dim=96							,      depths=[2, 2, 18, 2]							,      num_heads=[3, 6, 12, 24]							,      window_size=7							,      drop_path_rate=0.3							,      use_absolute_embeddings=lowerCamelCase							,      out_features=["stage1", "stage2", "stage3", "stage4"]							,      )

							if isinstance(lowerCamelCase							,      lowerCamelCase	):
								__lowercase		       =			backbone_config.pop("model_type"	)
								__lowercase		       =			CONFIG_MAPPING[backbone_model_type]
								__lowercase		       =			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 Mask2Former. """
								    f"""Supported model types: {','.join(self.backbones_supported	)}"""	)

							__lowercase		       =			backbone_config
							__lowercase		       =			feature_size
							__lowercase		       =			mask_feature_size
							__lowercase		       =			hidden_dim
							__lowercase		       =			encoder_feedforward_dim
							__lowercase		       =			activation_function
							__lowercase		       =			encoder_layers
							__lowercase		       =			decoder_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			dropout
							__lowercase		       =			dim_feedforward
							__lowercase		       =			pre_norm
							__lowercase		       =			enforce_input_projection
							__lowercase		       =			common_stride
							__lowercase		       =			ignore_value
							__lowercase		       =			num_queries
							__lowercase		       =			no_object_weight
							__lowercase		       =			class_weight
							__lowercase		       =			mask_weight
							__lowercase		       =			dice_weight
							__lowercase		       =			train_num_points
							__lowercase		       =			oversample_ratio
							__lowercase		       =			importance_sample_ratio
							__lowercase		       =			init_std
							__lowercase		       =			init_xavier_std
							__lowercase		       =			use_auxiliary_loss
							__lowercase		       =			feature_strides
							__lowercase		       =			output_auxiliary_logits
							__lowercase		       =			decoder_layers

							super().__init__(**lowerCamelCase	)



						@classmethod
						def     _snake_case	(	cls	:							Optional[int]							,      lowerCamelCase	:							PretrainedConfig							,      **lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							return cls(
							    backbone_config=lowerCamelCase							,      **lowerCamelCase							,      )



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



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








from ....utils import logging


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

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



