





from __future__ import annotations

from typing import TypedDict

class      A       (	__lowercase						):
      _snake_case							     =42
      _snake_case							     =42




def     a__ (					lowercase__  ):






   '''simple docstring'''






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

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




def     a__ (					lowercase__  ):






   '''simple docstring'''






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

   UpperCAmelCase_											=all_rotations(lowercase__  )
   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(lowercase__  ),
   }
   return response




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   if not isinstance(lowercase__  ,    lowercase__  ):
      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(lowercase__  )
   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(lowercase__  ):
      raise ValueError(
          "The parameter idx_original_string must be lower than" " len(bwt_string)."  )

   UpperCAmelCase_											=[""] * len(lowercase__  )
   for _ in range(len(lowercase__  )  ):
      for i in range(len(lowercase__  )  ):
         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[str]						      =input(entry_msg).strip()
     __lowercase  :   str						      =bwt_transform(s)
     print(
         f"""Burrows Wheeler transform for string '{s}' results """
         f"""in '{result['bwt_string']}'"""
     )
     __lowercase  :   List[Any]						      =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}'"""
     )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




