






"""simple docstring"""







def 						lowercase__(A ,		A						)    ->Any:




						"""simple docstring"""



						return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2





def 						lowercase__(A ,		A=0						)    ->str:




						"""simple docstring"""



						return sorted(A ,		key=lambda A						: x[column]						)





def 						lowercase__(A ,		A ,		A=float("inf"						)						)    ->Optional[Any]:




						"""simple docstring"""



						for i in range(points_counts - 1						):
												for j in range(i + 1 ,		A						):
																		lowercase__    :				str=	euclidean_distance_sqr(points[i] ,		points[j]						)
																		if current_dis < min_dis:
																								lowercase__    :				Dict=	current_dis
						return min_dis





def 						lowercase__(A ,		A ,		A=float("inf"						)						)    ->Dict:




						"""simple docstring"""



						for i in range(min(6 ,		points_counts - 1						) ,		A						):
												for j in range(max(0 ,		i - 6						) ,		A						):
																		lowercase__    :				Any=	euclidean_distance_sqr(points[i] ,		points[j]						)
																		if current_dis < min_dis:
																								lowercase__    :				List[str]=	current_dis
						return min_dis





def 						lowercase__(A ,		A ,		A						)    ->Optional[int]:




						"""simple docstring"""



						if points_counts <= 3:
												return dis_between_closest_pair(A ,		A						)

						# recursion
						lowercase__    :				Union[str, Any]=	points_counts // 2
						lowercase__    :				Optional[Any]=	closest_pair_of_points_sqr(
						    A ,		points_sorted_on_y[:mid] ,		A						)
						lowercase__    :				Tuple=	closest_pair_of_points_sqr(
						    A ,		points_sorted_on_y[mid:] ,		points_counts - mid						)
						lowercase__    :				Optional[Any]=	min(A ,		A						)
						lowercase__    :				Optional[Any]=	[]
						for point in points_sorted_on_x:
												if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
																		cross_strip.append(A						)

						lowercase__    :				int=	dis_between_closest_in_strip(
						    A ,		len(A						) ,		A						)
						return min(A ,		A						)





def 						lowercase__(A ,		A						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	column_based_sort(A ,		column=0						)
						lowercase__    :				Any=	column_based_sort(A ,		column=1						)
						return (
						    closest_pair_of_points_sqr(
						        A ,		A ,		A						)
						) ** 0.5


if __name__ == "__main__":
			a			:    List[str]				 =		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
			print("""Distance:""", closest_pair_of_points(points, len(points)))














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				str=	Path(A						)
						save_dir.mkdir(exist_ok=A						)

						for split in ds.keys():
												print(f'''Splitting {split} with {ds[split].num_rows} records'''						)

												# to save to val.source, val.target like summary datasets
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				Any=	tgt_path.open("w+"						)

												# reader is the bottleneck so writing one record at a time doesn't slow things down
												for x in tqdm(ds[split]						):
																		lowercase__    :				int=	x["translation"]
																		src_fp.write(ex[src_lang] + "\n"						)
																		tgt_fp.write(ex[tgt_lang] + "\n"						)

						print(f'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
			fire.Fire(download_wmt_dataset)






