






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor







class 			_lowercase ( lowerCAmelCase__    ,					lowerCAmelCase__    ):
      _a				:    int				      =				'pixel_values'
      _a				:    Optional[Any]				      =				False
      _a				:    List[str]				      =				TimmBackboneConfig
      def __init__(  self					,      a					,      **a ):
       requires_backends(self					,      """timm""" )
       super().__init__(__UpperCAmelCase )
       snake_case__	:     Dict											=config

       if config.backbone is None:
        raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""" )

       if config.backbone not in timm.list_models():
        raise ValueError(F"backbone {config.backbone} is not supported by timm." )

       if hasattr(__UpperCAmelCase					,      """out_features""" ) and config.out_features is not None:
        raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )

       snake_case__	:     Union[str, Any]											=getattr(__UpperCAmelCase					,      """use_pretrained_backbone"""					,      __UpperCAmelCase )
       if pretrained is None:
        raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""" )

       # We just take the final layer by default. This matches the default for the transformers models.
       snake_case__	:     Dict											=config.out_indices if getattr(__UpperCAmelCase					,      """out_indices"""					,      __UpperCAmelCase ) is not None else (-1,)

       snake_case__	:     List[Any]											=timm.create_model(
           config.backbone					,      pretrained=__UpperCAmelCase					,      features_only=config.features_only					,      in_chans=config.num_channels					,      out_indices=__UpperCAmelCase					,      **__UpperCAmelCase					,      )
       # These are used to control the output of the model when called. If output_hidden_states is True, then
       # return_layers is modified to include all layers.
       snake_case__	:     Union[str, Any]											=self._backbone.return_layers
       snake_case__	:     Dict											={layer["""module"""]: str(__UpperCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
       super()._init_backbone(__UpperCAmelCase )
      @classmethod
      def     lowercase__       (  cls					,      a					,      *a					,      **a ):
       requires_backends(cls					,      ["""vision""", """timm"""] )
       from ...models.timm_backbone import TimmBackboneConfig

       snake_case__	:     Any											=kwargs.pop("""config"""					,      TimmBackboneConfig() )

       snake_case__	:     Optional[Any]											=kwargs.pop("""use_timm_backbone"""					,      __UpperCAmelCase )
       if not use_timm:
        raise ValueError("""use_timm_backbone must be True for timm backbones""" )

       snake_case__	:     int											=kwargs.pop("""num_channels"""					,      config.num_channels )
       snake_case__	:     Optional[Any]											=kwargs.pop("""features_only"""					,      config.features_only )
       snake_case__	:     List[str]											=kwargs.pop("""use_pretrained_backbone"""					,      config.use_pretrained_backbone )
       snake_case__	:     Union[str, Any]											=kwargs.pop("""out_indices"""					,      config.out_indices )
       snake_case__	:     Union[str, Any]											=TimmBackboneConfig(
           backbone=__UpperCAmelCase					,      num_channels=__UpperCAmelCase					,      features_only=__UpperCAmelCase					,      use_pretrained_backbone=__UpperCAmelCase					,      out_indices=__UpperCAmelCase					,      )
       return super()._from_config(__UpperCAmelCase					,      **__UpperCAmelCase )
      def     lowercase__       (  self					,      a ):
       pass



      def     lowercase__       (  self					,      a					,      a=None					,      a=None					,      a=None					,      **a ):
       snake_case__	:     str											=return_dict if return_dict is not None else self.config.use_return_dict
       snake_case__	:     int											=(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       snake_case__	:     List[Any]											=output_attentions if output_attentions is not None else self.config.output_attentions

       if output_attentions:
        raise ValueError("""Cannot output attentions for timm backbones at the moment""" )

       if output_hidden_states:
        # We modify the return layers to include all the stages of the backbone
        snake_case__	:     Optional[Any]											=self._all_layers
        snake_case__	:     Any											=self._backbone(__UpperCAmelCase					,      **__UpperCAmelCase )
        snake_case__	:     Tuple											=self._return_layers
        snake_case__	:     List[str]											=tuple(hidden_states[i] for i in self.out_indices )
       else:
        snake_case__	:     Tuple											=self._backbone(__UpperCAmelCase					,      **__UpperCAmelCase )
        snake_case__	:     List[Any]											=None

       snake_case__	:     Any											=tuple(__UpperCAmelCase )
       snake_case__	:     Optional[int]											=tuple(__UpperCAmelCase ) if hidden_states is not None else None

       if not return_dict:
        snake_case__	:     List[Any]											=(feature_maps,)
        if output_hidden_states:
         snake_case__	:     Dict											=output + (hidden_states,)
        return output

       return BackboneOutput(feature_maps=__UpperCAmelCase					,      hidden_states=__UpperCAmelCase					,      attentions=__UpperCAmelCase )










def 		a__      (		_UpperCamelCase      :			str				):
   if not all(x.isalpha() for x in string				):
      raise ValueError('''String must only contain alphabetic characters.'''				)

   __lowerCamelCase    				=     sorted(string.lower()				)
   return len(_UpperCamelCase				) == len(set(_UpperCamelCase				)				)


if __name__ == "__main__":
  a_								=       input("""Enter a string """).strip()

  a_								=       is_isogram(input_str)
  print(f"{input_str} is {'an' if isogram else 'not an'} isogram.")






