


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





class UpperCAmelCase_							(   enum.Enum   ):
  __lowerCAmelCase      :	Dict  	=      '''timm'''
  __lowerCAmelCase      :	str  	=      '''transformers'''




def     UpperCAmelCase_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :Optional[Any]							)  ->							Any:
     '''simple docstring'''


     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     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :Tuple     ,	_UpperCAmelCase       :int     ,	)  ->							Union[str, Any]:
     '''simple docstring'''


     if out_indices is None and out_features is None:
          A_					      =					[len(UpperCamelCase__							) - 1]
          A_					      =					[stage_names[-1]]
     elif out_indices is None and out_features is not None:
          A_					      =					[stage_names.index(UpperCamelCase__							) for layer in out_features]
     elif out_features is None and out_indices is not None:
          A_					      =					[stage_names[idx] for idx in out_indices]
     return out_features, out_indices

def     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :str     ,	_UpperCAmelCase       :Optional[Any]     ,	)  ->							Dict:
     '''simple docstring'''


     verify_out_features_out_indices(out_features=UpperCamelCase__     ,	out_indices=UpperCamelCase__     ,	stage_names=UpperCamelCase__							)
     A_      ,						A_					      =					_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_							:
  __lowerCAmelCase      :	Optional[BackboneType]  	=      None


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       if getattr(self   ,'''_backbone'''   ,a_   ) 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']
       A_					      =					[stage['''module'''] for stage in self._backbone.feature_info.info]
       A_					      =					[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
       A_					      =					self._backbone.feature_info.out_indices
       A_					      =					self._backbone.feature_info.module_name()

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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					getattr(a_   ,'''stage_names'''   )
       A_					      =					getattr(a_   ,'''out_features'''   ,a_   )
       A_					      =					getattr(a_   ,'''out_indices'''   ,a_   )

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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					config

       A_					      =					getattr(a_   ,'''use_timm_backbone'''   ,a_   )
       A_					      =					BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self._out_features


  @out_features.setter
  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_      ,						A_					      =					get_aligned_output_features_output_indices(
           out_features=a_   ,out_indices=a_   ,stage_names=self.stage_names   )


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self._out_indices


  @out_indices.setter
  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_      ,						A_					      =					get_aligned_output_features_output_indices(
           out_features=a_   ,out_indices=a_   ,stage_names=self.stage_names   )


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,*__snake_case   ,**__snake_case   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,):



       """simple docstring"""





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




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					super().to_dict()
       A_					      =					output.pop('''_out_features'''   )
       A_					      =					output.pop('''_out_indices'''   )
       return output





class UpperCAmelCase_							:


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self._out_features


  @out_features.setter
  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_      ,						A_					      =					get_aligned_output_features_output_indices(
           out_features=a_   ,out_indices=a_   ,stage_names=self.stage_names   )


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self._out_indices


  @out_indices.setter
  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_      ,						A_					      =					get_aligned_output_features_output_indices(
           out_features=a_   ,out_indices=a_   ,stage_names=self.stage_names   )




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					super().to_dict()
       A_					      =					output.pop('''_out_features'''   )
       A_					      =					output.pop('''_out_indices'''   )
       return output










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



