"""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


_a      :		str           =   {
    '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
}

_a      :		Optional[int]           =   logging.get_logger(__name__)
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		"maskformer"
     _UpperCamelCase							:			Optional[Any]							=		{"hidden_size": "mask_feature_size"}
     _UpperCamelCase							:			Any							=		["resnet", "swin"]
     _UpperCamelCase							:			Dict							=		["detr"]






     def __init__(		self				,     a__ = 256				,     a__ = 256				,     a__ = 0.1				,     a__ = False				,     a__ = None				,     a__ = None				,     a__ = 0.0_2				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 2_0.0				,     a__ = None				,     **a__				,     ):
       if backbone_config is None:
         # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
         _lowerCAmelCase						:       Union[str, Any]  			=     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__  ):
         _lowerCAmelCase						:       str  			=     backbone_config.pop("""model_type"""  )
         _lowerCAmelCase						:       List[str]  			=     CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase						:       Dict  			=     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						:       int  			=     DetrConfig()
       else:
         # verify that the decoder is supported
         _lowerCAmelCase						:       Union[str, 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						:       int  			=     CONFIG_MAPPING[decoder_type]
           _lowerCAmelCase						:       Tuple  			=     config_class.from_dict(a__  )

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

       _lowerCAmelCase						:       str  			=     self.decoder_config.encoder_attention_heads
       _lowerCAmelCase						:       int  			=     self.decoder_config.num_hidden_layers
       super().__init__(**a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__				,     **a__  ):
       return cls(
           backbone_config=a__				,     decoder_config=a__				,     **a__				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     copy.deepcopy(self.__dict__  )
       _lowerCAmelCase						:       str  			=     self.backbone_config.to_dict()
       _lowerCAmelCase						:       Dict  			=     self.decoder_config.to_dict()
       _lowerCAmelCase						:       List[Any]  			=     self.__class__.model_type
       return output








"""simple docstring"""



from typing import Dict

from .base import GenericTensor, Pipeline







class _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       if truncation is not None:
           if "truncation" in tokenize_kwargs:
               raise ValueError(
                   "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       if return_tensors:
           return model_outputs[0]
       if self.framework == "pt":
           return model_outputs[0].tolist()
       elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





