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



class     snake_case_			(						enum.Enum			):
   __A      :						str   =							"timm"
   __A      :						Union[str, Any]   =							"transformers"



def      lowercase_							(     _lowerCamelCase				:			Optional[Iterable[str]]						,      _lowerCamelCase				:			Optional[Iterable[int]]						,      _lowerCamelCase				:			Optional[Iterable[str]]):
    if stage_names is None:
        raise ValueError("Stage_names must be set for transformers backbones")

    if out_features is not None:
        if not isinstance(_lowerCamelCase						,      (list,)):
            raise ValueError(f'''out_features must be a list {type(_lowerCamelCase)}''')
        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(_lowerCamelCase						,      (list, tuple)):
            raise ValueError(f'''out_indices must be a list or tuple, got {type(_lowerCamelCase)}''')
        if any(idx >= len(_lowerCamelCase) 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(_lowerCamelCase) != len(_lowerCamelCase):
            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      lowercase_							(     _lowerCamelCase				:			Optional[List[str]]						,      _lowerCamelCase				:			Optional[Union[List[int], Tuple[int]]]						,      _lowerCamelCase				:			List[str]						,      ):
    if out_indices is None and out_features is None:
        lowercase__					:  int          =							[len(_lowerCamelCase) - 1]
        lowercase__					:  Optional[int]          =							[stage_names[-1]]
    elif out_indices is None and out_features is not None:
        lowercase__					:  Dict          =							[stage_names.index(_lowerCamelCase) for layer in out_features]
    elif out_features is None and out_indices is not None:
        lowercase__					:  List[Any]          =							[stage_names[idx] for idx in out_indices]
    return out_features, out_indices

def      lowercase_							(     _lowerCamelCase				:			Optional[List[str]]						,      _lowerCamelCase				:			Optional[Union[List[int], Tuple[int]]]						,      _lowerCamelCase				:			List[str]						,      ):
    verify_out_features_out_indices(out_features=_lowerCamelCase						,      out_indices=_lowerCamelCase						,      stage_names=_lowerCamelCase)
    lowercase__   ,  lowercase__					:  str          =							_align_output_features_output_indices(
        out_features=_lowerCamelCase						,      out_indices=_lowerCamelCase						,      stage_names=_lowerCamelCase)
    # Verify that the aligned out_features and out_indices are valid
    verify_out_features_out_indices(out_features=_lowerCamelCase						,      out_indices=_lowerCamelCase						,      stage_names=_lowerCamelCase)
    return output_features, output_indices



class     snake_case_			:
   __A      :						Optional[BackboneType]   =							None






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Optional[Any]    )   ->		None:
       if getattr(self      ,		"_backbone"      ,		lowercase_    ) 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']
       lowercase__					:  List[Any]          =							[stage["module"] for stage in self._backbone.feature_info.info]
       lowercase__					:  Dict          =							[stage["num_chs"] for stage in self._backbone.feature_info.info]
       lowercase__					:  List[Any]          =							self._backbone.feature_info.out_indices
       lowercase__					:  str          =							self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=self.stage_names    )
       lowercase__   ,  lowercase__					:  Optional[int]          =							out_features, out_indices






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Tuple    )   ->		None:
       lowercase__					:  Dict          =							getattr(lowercase_      ,		"stage_names"    )
       lowercase__					:  Any          =							getattr(lowercase_      ,		"out_features"      ,		lowercase_    )
       lowercase__					:  Optional[int]          =							getattr(lowercase_      ,		"out_indices"      ,		lowercase_    )

       lowercase__					:  Dict          =							stage_names
       lowercase__   ,  lowercase__					:  str          =							get_aligned_output_features_output_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=lowercase_    )
       # Number of channels for each stage. This is set in the transformer backbone model init
       lowercase__					:  Union[str, Any]          =							None






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	Union[str, Any]    )   ->		None:
       lowercase__					:  Optional[Any]          =							config

       lowercase__					:  Tuple          =							getattr(lowercase_      ,		"use_timm_backbone"      ,		lowercase_    )
       lowercase__					:  List[str]          =							BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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






   @property
   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		List[str]:
       return self._out_features






   @out_features.setter
   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	List[str]    )   ->		List[str]:
       lowercase__   ,  lowercase__					:  List[str]          =							get_aligned_output_features_output_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=self.stage_names    )






   @property
   def  __UpperCamelCase	( self							:	Any    )   ->		Union[str, Any]:
       return self._out_indices






   @out_indices.setter
   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Union[Tuple[int], List[int]]    )   ->		List[str]:
       lowercase__   ,  lowercase__					:  str          =							get_aligned_output_features_output_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=self.stage_names    )






   @property
   def  __UpperCamelCase	( self							:	Tuple    )   ->		Optional[Any]:
       # the current backbones will output the number of channels for each stage
       # even if that stage is not in the out_features list.
       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names    )}






   @property
   def  __UpperCamelCase	( self							:	str    )   ->		Optional[Any]:
       return [self.out_feature_channels[name] for name in self.out_features]






   def  __UpperCamelCase	( self							:	Tuple      ,		*lowercase_							:	List[str]      ,		**lowercase_							:	Optional[int]    )   ->		str:
       lowercase__					:  Tuple          =							dict(inspect.signature(self.forward    ).parameters    )
       lowercase__					:  Tuple          =							{k: v for k, v in kwargs.items() if k in signature}
       return self(*lowercase_      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	List[str]      ,		lowercase_							:	Optional[bool] = None      ,		lowercase_							:	Optional[bool] = None      ,		lowercase_							:	Optional[bool] = None      ,		)   ->		Tuple:
       raise NotImplementedError("This method should be implemented by the derived class."    )







   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Optional[Any]:
       lowercase__					:  Any          =							super().to_dict()
       lowercase__					:  int          =							output.pop("_out_features"    )
       lowercase__					:  List[Any]          =							output.pop("_out_indices"    )
       return output



class     snake_case_			:






   @property
   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Optional[int]:
       return self._out_features






   @out_features.setter
   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	List[str]    )   ->		List[Any]:
       lowercase__   ,  lowercase__					:  Dict          =							get_aligned_output_features_output_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=self.stage_names    )






   @property
   def  __UpperCamelCase	( self							:	int    )   ->		Dict:
       return self._out_indices






   @out_indices.setter
   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Union[Tuple[int], List[int]]    )   ->		Dict:
       lowercase__   ,  lowercase__					:  Union[str, Any]          =							get_aligned_output_features_output_indices(
           out_features=lowercase_      ,		out_indices=lowercase_      ,		stage_names=self.stage_names    )







   def  __UpperCamelCase	( self							:	Tuple    )   ->		Optional[Any]:
       lowercase__					:  Any          =							super().to_dict()
       lowercase__					:  Union[str, Any]          =							output.pop("_out_features"    )
       lowercase__					:  str          =							output.pop("_out_indices"    )
       return output




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



