


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


__UpperCamelCase       :       Tuple						       =				4
__UpperCamelCase       :       Union[str, Any]						       =				3

class       __magic_name__	(     __lowerCAmelCase):
     pass



def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 for shard in shards:
  for i in range(SCREAMING_SNAKE_CASE     ):
   yield {"i": i, "shard": shard}

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    List[Any]          =   int(os.environ['''RANK''']     )
 UpperCamelCase__					:    Optional[Any]          =   int(os.environ['''WORLD_SIZE''']     )

 UpperCamelCase__					:    List[str]          =   ArgumentParser()
 parser.add_argument('''--streaming'''    , type=SCREAMING_SNAKE_CASE     )
 parser.add_argument('''--local_rank'''    , type=SCREAMING_SNAKE_CASE     )
 parser.add_argument('''--num_workers'''    , type=SCREAMING_SNAKE_CASE    , default=0     )
 UpperCamelCase__					:    int          =   parser.parse_args()
 UpperCamelCase__					:    Tuple          =   args.streaming
 UpperCamelCase__					:    Tuple          =   args.num_workers

 UpperCamelCase__					:    Optional[Any]          =   {'''shards''': [F"shard_{shard_idx}" for shard_idx in range(SCREAMING_SNAKE_CASE     )]}
 UpperCamelCase__					:    Union[str, Any]          =   IterableDataset.from_generator(SCREAMING_SNAKE_CASE    , gen_kwargs=SCREAMING_SNAKE_CASE     )
 if not streaming:
  UpperCamelCase__					:    int          =   Dataset.from_list(list(SCREAMING_SNAKE_CASE     )     )

 UpperCamelCase__					:    Dict          =   split_dataset_by_node(SCREAMING_SNAKE_CASE    , rank=SCREAMING_SNAKE_CASE    , world_size=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE    , num_workers=SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    List[Any]          =   NUM_SHARDS * NUM_ITEMS_PER_SHARD
 UpperCamelCase__					:    List[Any]          =   full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 UpperCamelCase__					:    Any          =   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()






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


