'''simple docstring'''
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 lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''pixel_values'''
  lowerCAmelCase__										=	False
  lowerCAmelCase__										=	TimmBackboneConfig






  def __init__(      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							**UpperCAmelCase__			:       Any  )				->List[Any]:
        requires_backends(self  ,							'''timm'''  )
        super().__init__(UpperCAmelCase__  )
        UpperCAmelCase_            =  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.'''  )

        UpperCAmelCase_            =  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.
        UpperCAmelCase_            =  config.out_indices if getattr(UpperCAmelCase__  ,							'''out_indices'''  ,							UpperCAmelCase__  ) is not None else (-1,)

        UpperCAmelCase_            =  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.
        UpperCAmelCase_            =  self._backbone.return_layers
        UpperCAmelCase_            =  {layer['''module''']: str(UpperCAmelCase__  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
        super()._init_backbone(UpperCAmelCase__  )






  @classmethod
  def  lowerCAmelCase__ (      cls			:       List[str]  ,							UpperCAmelCase__			:       Optional[int]  ,							*UpperCAmelCase__			:       Union[str, Any]  ,							**UpperCAmelCase__			:       List[Any]  )				->Union[str, Any]:
        requires_backends(cls  ,							['''vision''', '''timm''']  )
        from ...models.timm_backbone import TimmBackboneConfig

        UpperCAmelCase_            =  kwargs.pop('''config'''  ,							TimmBackboneConfig()  )

        UpperCAmelCase_            =  kwargs.pop('''use_timm_backbone'''  ,							UpperCAmelCase__  )
        if not use_timm:
              raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

        UpperCAmelCase_            =  kwargs.pop('''num_channels'''  ,							config.num_channels  )
        UpperCAmelCase_            =  kwargs.pop('''features_only'''  ,							config.features_only  )
        UpperCAmelCase_            =  kwargs.pop('''use_pretrained_backbone'''  ,							config.use_pretrained_backbone  )
        UpperCAmelCase_            =  kwargs.pop('''out_indices'''  ,							config.out_indices  )
        UpperCAmelCase_            =  TimmBackboneConfig(
            backbone=UpperCAmelCase__  ,							num_channels=UpperCAmelCase__  ,							features_only=UpperCAmelCase__  ,							use_pretrained_backbone=UpperCAmelCase__  ,							out_indices=UpperCAmelCase__  ,							)
        return super()._from_config(UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       int  )				->List[str]:
        pass






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       int=None  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       Any=None  ,							**UpperCAmelCase__			:       List[str]  )				->Union[BackboneOutput, Tuple[Tensor, ...]]:
        UpperCAmelCase_            =  return_dict if return_dict is not None else self.config.use_return_dict
        UpperCAmelCase_            =  (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        UpperCAmelCase_            =  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
              UpperCAmelCase_            =  self._all_layers
              UpperCAmelCase_            =  self._backbone(UpperCAmelCase__  ,							**UpperCAmelCase__  )
              UpperCAmelCase_            =  self._return_layers
              UpperCAmelCase_            =  tuple(hidden_states[i] for i in self.out_indices  )
        else:
              UpperCAmelCase_            =  self._backbone(UpperCAmelCase__  ,							**UpperCAmelCase__  )
              UpperCAmelCase_            =  None

        UpperCAmelCase_            =  tuple(UpperCAmelCase__  )
        UpperCAmelCase_            =  tuple(UpperCAmelCase__  ) if hidden_states is not None else None

        if not return_dict:
              UpperCAmelCase_            =  (feature_maps,)
              if output_hidden_states:
                    UpperCAmelCase_            =  output + (hidden_states,)
              return output

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






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





