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					:	Tuple       =  {
    '''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					:	Tuple       =  logging.get_logger(__name__)






class 		a__						(					_lowerCAmelCase					):
					A	       =							'maskformer'
					A	       =							{'hidden_size': 'mask_feature_size'}
					A	       =							['resnet', 'swin']
					A	       =							['detr']

					def __init__(    self      :       Tuple,_A      :       int = 256,_A      :       int = 256,_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      :       Tuple,):







							"""simple docstring"""






							if backbone_config is None:
									# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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_  :		List[str]				      =    backbone_config.pop("model_type"    )
									SCREAMING_SNAKE_CASE_  :		int				      =    CONFIG_MAPPING[backbone_model_type]
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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_  :		Tuple				      =    DetrConfig()
							else:
									# verify that the decoder is supported
									SCREAMING_SNAKE_CASE_  :		List[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    ):
											SCREAMING_SNAKE_CASE_  :		Any				      =    CONFIG_MAPPING[decoder_type]
											SCREAMING_SNAKE_CASE_  :		Any				      =    config_class.from_dict(_A    )

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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.decoder_config.encoder_attention_heads
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.decoder_config.num_hidden_layers
							super().__init__(**_A    )

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Union[str, Any],_A      :       PretrainedConfig,_A      :       PretrainedConfig,**_A      :       str    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    copy.deepcopy(self.__dict__    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.backbone_config.to_dict()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.decoder_config.to_dict()
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.__class__.model_type
							return output







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     int												={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Any												=[
     '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BigBirdPegasusForCausalLM''',
     '''BigBirdPegasusForConditionalGeneration''',
     '''BigBirdPegasusForQuestionAnswering''',
     '''BigBirdPegasusForSequenceClassification''',
     '''BigBirdPegasusModel''',
     '''BigBirdPegasusPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase					:     Dict												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


