





"""simple docstring"""




from __future__ import annotations

from typing import TypedDict


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str
							lowerCamelCase   :					int






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->list[str]:






	'''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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->BWTTransformDict:






	'''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."					)

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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''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:
		a      :		Tuple	=				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)."					)

	a      :		Any	=				[""] * len(_lowercase					)
	for _ in range(len(_lowercase					)					):
		for i in range(len(_lowercase					)					):
			a      :		Tuple	=				bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


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









"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(_lowercase    ,       _lowercase					)					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

	try:
		if dataset.shape[1] != value_array.shape[1]:
			a      :		int	=				(
			    "Wrong input data's shape... "
			    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
			)
			raise ValueError(_lowercase					)
	except IndexError:
		if dataset.ndim != value_array.ndim:
			raise TypeError("Wrong shape"					)

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return np.dot(_lowercase    ,       _lowercase					) / (norm(_lowercase					) * norm(_lowercase					))


if __name__ == "__main__":
		import doctest

		doctest.testmod()


