






"""simple docstring"""

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A__			)
class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				str            =							field(default="""summarization""" ,							metadata={"""include_in_asdict_even_if_is_default""": True}			)
		a_	:				ClassVar[Features]            =							Features({"""text""": Value("""string"""			)}			)
		a_	:				ClassVar[Features]            =							Features({"""summary""": Value("""string"""			)}			)
		a_	:				str            =							"text"
		a_	:				str            =							"summary"





		@property
		def    lowerCamelCase       (   self							:	int	):
				return {self.text_column: "text", self.summary_column: "summary"}








"""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


lowercase__          = {
    """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
}

lowercase__          = logging.get_logger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				Optional[int]            =							"""maskformer"""
		a_	:				Optional[int]            =							{"""hidden_size""": """mask_feature_size"""}
		a_	:				Optional[int]            =							["""resnet""", """swin"""]
		a_	:				int            =							["""detr"""]





		def __init__(   self							:	str						,			a_							:	int = 2_56						,			a_							:	int = 2_56						,			a_							:	float = 0.1						,			a_							:	bool = False						,			a_							:	Optional[Dict] = None						,			a_							:	Optional[Dict] = None						,			a_							:	float = 0.02						,			a_							:	float = 1.0						,			a_							:	float = 1.0						,			a_							:	float = 1.0						,			a_							:	float = 20.0						,			a_							:	Optional[bool] = None						,			**a_							:	str						,			):
				if backbone_config is None:
						# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
						lowerCAmelCase_   :							Tuple									=			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(a_						,			a_	):
						lowerCAmelCase_   :							Optional[Any]									=			backbone_config.pop("model_type"	)
						lowerCAmelCase_   :							Any									=			CONFIG_MAPPING[backbone_model_type]
						lowerCAmelCase_   :							str									=			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
						lowerCAmelCase_   :							Union[str, Any]									=			DetrConfig()
				else:
						# verify that the decoder is supported
						lowerCAmelCase_   :							Optional[Any]									=			(
						    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_	):
								lowerCAmelCase_   :							Optional[int]									=			CONFIG_MAPPING[decoder_type]
								lowerCAmelCase_   :							List[Any]									=			config_class.from_dict(a_	)

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

				lowerCAmelCase_   :							int									=			self.decoder_config.encoder_attention_heads
				lowerCAmelCase_   :							str									=			self.decoder_config.num_hidden_layers
				super().__init__(**a_	)





		@classmethod
		def    lowerCamelCase       (   cls							:	int						,			a_							:	PretrainedConfig						,			a_							:	PretrainedConfig						,			**a_							:	Tuple	):
				return cls(
				    backbone_config=a_						,			decoder_config=a_						,			**a_						,			)





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							Optional[int]									=			copy.deepcopy(self.__dict__	)
				lowerCAmelCase_   :							Optional[Any]									=			self.backbone_config.to_dict()
				lowerCAmelCase_   :							Union[str, Any]									=			self.decoder_config.to_dict()
				lowerCAmelCase_   :							List[str]									=			self.__class__.model_type
				return output
