






'''simple docstring'''



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						np.ndarray     )	->      float:

   """simple docstring"""
   return math.sqrt(sum(pow(a - b,       2     ) for a, b in zip(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						np.ndarray     )	->      list[list[list[float] | float]]:

   """simple docstring"""

   if dataset.ndim != value_array.ndim:
      __a					  =     (
          'Wrong input data\'s dimensions... '
          f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
      )
      raise ValueError(SCREAMING_SNAKE_CASE__     )

   try:
      if dataset.shape[1] != value_array.shape[1]:
         __a					  =     (
             'Wrong input data\'s shape... '
             f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
         )
         raise ValueError(SCREAMING_SNAKE_CASE__     )
   except IndexError:
      if dataset.ndim != value_array.ndim:
         raise TypeError('Wrong shape'     )

   if dataset.dtype != value_array.dtype:
      __a					  =     (
          'Input data have different datatype... '
          f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
      )
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   __a					  =     []

   for value in value_array:
      __a					  =     euclidean(SCREAMING_SNAKE_CASE__,       dataset[0]     )
      __a					  =     dataset[0].tolist()

      for dataset_value in dataset[1:]:
         __a					  =     euclidean(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

         if dist > temp_dist:
            __a					  =     temp_dist
            __a					  =     dataset_value.tolist()

      answer.append([vector, dist]     )

   return answer



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						np.ndarray     )	->      float:

   """simple docstring"""
   return np.dot(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) / (norm(SCREAMING_SNAKE_CASE__     ) * norm(SCREAMING_SNAKE_CASE__     ))


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

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

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      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:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   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(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

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


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							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}'"""
 )