






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


__UpperCamelCase					:		Union[str, 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
}

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


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "maskformer"
							_UpperCAmelCase     =       {"hidden_size": "mask_feature_size"}
							_UpperCAmelCase     =       ["resnet", "swin"]
							_UpperCAmelCase     =       ["detr"]
							def __init__(     self:      str       ,	UpperCamelCase:      int = 2_56       ,	UpperCamelCase:      int = 2_56       ,	UpperCamelCase:      float = 0.1       ,	UpperCamelCase:      bool = False       ,	UpperCamelCase:      Optional[Dict] = None       ,	UpperCamelCase:      Optional[Dict] = None       ,	UpperCamelCase:      float = 0.02       ,	UpperCamelCase:      float = 1.0       ,	UpperCamelCase:      float = 1.0       ,	UpperCamelCase:      float = 1.0       ,	UpperCamelCase:      float = 20.0       ,	UpperCamelCase:      Optional[bool] = None       ,	**UpperCamelCase:      Optional[int]       ,	)		->		Tuple:
											if backbone_config is None:
															# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
															snake_case__									=     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(UpperCamelCase       ,	UpperCamelCase						):
															snake_case__									=     backbone_config.pop('model_type'						)
															snake_case__									=     CONFIG_MAPPING[backbone_model_type]
															snake_case__									=     config_class.from_dict(UpperCamelCase						)

											# 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
															snake_case__									=     DetrConfig()
											else:
															# verify that the decoder is supported
															snake_case__									=     (
															    decoder_config.pop('model_type'						) if isinstance(UpperCamelCase       ,	UpperCamelCase						) 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(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     CONFIG_MAPPING[decoder_type]
																			snake_case__									=     config_class.from_dict(UpperCamelCase						)

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

											snake_case__									=     self.decoder_config.encoder_attention_heads
											snake_case__									=     self.decoder_config.num_hidden_layers
											super().__init__(**UpperCamelCase						)
							@classmethod
							def lowerCAmelCase_ (     cls:      Dict       ,	UpperCamelCase:      PretrainedConfig       ,	UpperCamelCase:      PretrainedConfig       ,	**UpperCamelCase:      List[Any]						)		->		Optional[int]:
											return cls(
											    backbone_config=UpperCamelCase       ,	decoder_config=UpperCamelCase       ,	**UpperCamelCase       ,	)


							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Dict[str, any]:
											snake_case__									=     copy.deepcopy(self.__dict__						)
											snake_case__									=     self.backbone_config.to_dict()
											snake_case__									=     self.decoder_config.to_dict()
											snake_case__									=     self.__class__.model_type
											return output












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCamelCase					:		str	  =						{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""BartphoTokenizer"""]

if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bartpho import BartphoTokenizer

else:
							import sys

							__UpperCamelCase					:		Any	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




