





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class 				_UpperCamelCase       (						enum.Enum  ):
       _UpperCamelCase					:       Tuple       = '''timm'''
       _UpperCamelCase					:       Union[str, Any]       = '''transformers'''




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"	)

       if out_features is not None:
              if not isinstance(UpperCamelCase_       ,		(list,)	):
                     raise ValueError(F'''out_features must be a list {type(UpperCamelCase_	)}'''	)
              if any(feat not in stage_names for feat in out_features	):
                     raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''	)

       if out_indices is not None:
              if not isinstance(UpperCamelCase_       ,		(list, tuple)	):
                     raise ValueError(F'''out_indices must be a list or tuple, got {type(UpperCamelCase_	)}'''	)
              if any(idx >= len(UpperCamelCase_	) for idx in out_indices	):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"	)

       if out_features is not None and out_indices is not None:
              if len(UpperCamelCase_	) != len(UpperCamelCase_	):
                     raise ValueError("out_features and out_indices should have the same length if both are set"	)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		int:
       if out_indices is None and out_features is None:
              UpperCamelCase_ 							=	[len(UpperCamelCase_	) - 1]
              UpperCamelCase_ 							=	[stage_names[-1]]
       elif out_indices is None and out_features is not None:
              UpperCamelCase_ 							=	[stage_names.index(UpperCamelCase_	) for layer in out_features]
       elif out_features is None and out_indices is not None:
              UpperCamelCase_ 							=	[stage_names[idx] for idx in out_indices]
       return out_features, out_indices




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	_align_output_features_output_indices(
           out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       return output_features, output_indices



class 				_UpperCamelCase       :
       _UpperCamelCase					:       Optional[BackboneType]       = None

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Dict		)							->			None:




              """simple docstring"""


              if getattr(self       , "_backbone"       , _SCREAMING_SNAKE_CASE		) is None:
                     raise ValueError("self._backbone must be set before calling _init_timm_backbone"		)

              # These will diagree with the defaults for the transformers models e.g. for resnet50
              # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
              # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
              UpperCamelCase_ 							=	[stage["module"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	[stage["num_chs"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	self._backbone.feature_info.out_indices
              UpperCamelCase_ 							=	self._backbone.feature_info.module_name()

              # We verify the out indices and out features are valid
              verify_out_features_out_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	out_features, out_indices

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "stage_names"		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_features"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_indices"       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	stage_names
              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=_SCREAMING_SNAKE_CASE		)
              # Number of channels for each stage. This is set in the transformer backbone model init
              UpperCamelCase_ 							=	None

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	config

              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "use_timm_backbone"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

              if self.backbone_type == BackboneType.TIMM:
                     self._init_timm_backbone(_SCREAMING_SNAKE_CASE		)
              elif self.backbone_type == BackboneType.TRANSFORMERS:
                     self._init_transformers_backbone(_SCREAMING_SNAKE_CASE		)
              else:
                     raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''		)

       @property
       def lowercase			(		self:			str		)							->			List[str]:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			List[str]		)							->			str:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names		)}

       @property
       def lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              return [self.out_feature_channels[name] for name in self.out_features]

       def lowercase			(		self:			Optional[int]       , *_SCREAMING_SNAKE_CASE:			Dict       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(inspect.signature(self.forward		).parameters		)
              UpperCamelCase_ 							=	{k: v for k, v in kwargs.items() if k in signature}
              return self(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , )							->			List[Any]:




              """simple docstring"""


              raise NotImplementedError("This method should be implemented by the derived class."		)



       def lowercase			(		self:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output



class 				_UpperCamelCase       :

       @property
       def lowercase			(		self:			List[str]		)							->			Any:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Any		)							->			Dict:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)



       def lowercase			(		self:			Tuple		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''qdqbert'''

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=30522       , _SCREAMING_SNAKE_CASE:			List[str]=768       , _SCREAMING_SNAKE_CASE:			int=12       , _SCREAMING_SNAKE_CASE:			List[Any]=12       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=3072       , _SCREAMING_SNAKE_CASE:			List[str]="gelu"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Dict=512       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , _SCREAMING_SNAKE_CASE:			int=0.02       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1e-12       , _SCREAMING_SNAKE_CASE:			str=True       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			int=0       , _SCREAMING_SNAKE_CASE:			Tuple=2       , **_SCREAMING_SNAKE_CASE:			List[str]       , )							->			Tuple:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	num_attention_heads
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	layer_norm_eps
              UpperCamelCase_ 							=	use_cache

