
"""simple docstring"""


def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Tuple:







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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=0				)->							List[Any]:







       """simple docstring"""
       return sorted(UpperCAmelCase_		,      key=lambda UpperCAmelCase_				: x[column]				)



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=float("inf"				)				)->							Optional[int]:







       """simple docstring"""

       for i in range(points_counts - 1				):
              for j in range(i + 1		,      UpperCAmelCase_				):
                     UpperCamelCase					 =		euclidean_distance_sqr(points[i]		,      points[j]				)
                     if current_dis < min_dis:
                            UpperCamelCase					 =		current_dis
       return min_dis



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=float("inf"				)				)->							Optional[int]:







       """simple docstring"""

       for i in range(min(6		,      points_counts - 1				)		,      UpperCAmelCase_				):
              for j in range(max(0		,      i - 6				)		,      UpperCAmelCase_				):
                     UpperCamelCase					 =		euclidean_distance_sqr(points[i]		,      points[j]				)
                     if current_dis < min_dis:
                            UpperCamelCase					 =		current_dis
       return min_dis



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""

       # base case
       if points_counts <= 3:
              return dis_between_closest_pair(UpperCAmelCase_		,      UpperCAmelCase_				)

       # recursion
       UpperCamelCase					 =		points_counts // 2
       UpperCamelCase					 =		closest_pair_of_points_sqr(
           UpperCAmelCase_		,      points_sorted_on_y[:mid]		,      UpperCAmelCase_				)
       UpperCamelCase					 =		closest_pair_of_points_sqr(
           UpperCAmelCase_		,      points_sorted_on_y[mid:]		,      points_counts - mid				)
       UpperCamelCase					 =		min(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		[]
       for point in points_sorted_on_x:
              if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
                     cross_strip.append(UpperCAmelCase_				)

       UpperCamelCase					 =		dis_between_closest_in_strip(
           UpperCAmelCase_		,      len(UpperCAmelCase_				)		,      UpperCAmelCase_				)
       return min(UpperCAmelCase_		,      UpperCAmelCase_				)



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       UpperCamelCase					 =		column_based_sort(UpperCAmelCase_		,      column=0				)
       UpperCamelCase					 =		column_based_sort(UpperCAmelCase_		,      column=1				)
       return (
           closest_pair_of_points_sqr(
               UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)
       ) ** 0.5


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







"""simple docstring"""


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


SCREAMING_SNAKE_CASE      =     4
SCREAMING_SNAKE_CASE      =     3



class   __a  (      _lowerCAmelCase	):
       pass





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       for shard in shards:
              for i in range(UpperCAmelCase_				):
                     yield {"i": i, "shard": shard}



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







       """simple docstring"""
       UpperCamelCase					 =		int(os.environ["RANK"]				)
       UpperCamelCase					 =		int(os.environ["WORLD_SIZE"]				)

       UpperCamelCase					 =		ArgumentParser()
       parser.add_argument("--streaming"		,      type=UpperCAmelCase_				)
       parser.add_argument("--local_rank"		,      type=UpperCAmelCase_				)
       parser.add_argument("--num_workers"		,      type=UpperCAmelCase_		,      default=0				)
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		args.streaming
       UpperCamelCase					 =		args.num_workers

       UpperCamelCase					 =		{"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_				)]}
       UpperCamelCase					 =		IterableDataset.from_generator(UpperCAmelCase_		,      gen_kwargs=UpperCAmelCase_				)
       if not streaming:
              UpperCamelCase					 =		Dataset.from_list(list(UpperCAmelCase_				)				)

       UpperCamelCase					 =		split_dataset_by_node(UpperCAmelCase_		,      rank=UpperCAmelCase_		,      world_size=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.utils.data.DataLoader(UpperCAmelCase_		,      num_workers=UpperCAmelCase_				)

       UpperCamelCase					 =		NUM_SHARDS * NUM_ITEMS_PER_SHARD
       UpperCamelCase					 =		full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)				)

       UpperCamelCase					 =		sum(1 for _ in dataloader				)
       if local_size != expected_local_size:
              raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}"				)


if __name__ == "__main__":
      main()





