






'''simple docstring'''







def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase							)				->	list[list[float]]:
							lowerCamelCase__ :     Optional[int]	  =	[]
							for data in source_data:
														for i, el in enumerate(a_							):
																					if len(a_							) < i + 1:
																												data_lists.append([]							)
																					data_lists[i].append(float(a_							)							)
							return data_lists





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase							)				->	list[list[float]]:
							lowerCamelCase__ :     Union[str, Any]	  =	[]
							for dlist, weight in zip(a_    ,      a_							):
														lowerCamelCase__ :     Tuple	  =	min(a_							)
														lowerCamelCase__ :     List[Any]	  =	max(a_							)

														lowerCamelCase__ :     Dict	  =	[]
														# for weight 0 score is 1 - actual score
														if weight == 0:
																					for item in dlist:
																												try:
																																			score.append(1 - ((item - mind) / (maxd - mind))							)
																												except ZeroDivisionError:
																																			score.append(1							)

														elif weight == 1:
																					for item in dlist:
																												try:
																																			score.append((item - mind) / (maxd - mind)							)
																												except ZeroDivisionError:
																																			score.append(0							)

        # weight not 0 or 1
														else:
																					lowerCamelCase__ :     str	  =	f'''Invalid weight of {weight:f} provided'''
																					raise ValueError(a_							)

														score_lists.append(a_							)

							return score_lists





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase							)				->	list[float]:
							lowerCamelCase__ :     Optional[Any]	  =	[0 for i in range(len(score_lists[0]							)							)]

							for slist in score_lists:
														for j, ele in enumerate(a_							):
																					lowerCamelCase__ :     str	  =	final_scores[j] + ele

							return final_scores





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase							)				->	list[list[float]]:
							lowerCamelCase__ :     str	  =	get_data(a_							)
							lowerCamelCase__ :     Dict	  =	calculate_each_score(a_    ,      a_							)
							lowerCamelCase__ :     Any	  =	generate_final_scores(a_							)

							# append scores to source data
							for i, ele in enumerate(a_							):
														source_data[i].append(a_							)

							return source_data









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

