




"""simple docstring"""




from __future__ import annotations

lowerCAmelCase							:    Dict  							=							"""#"""






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self				):





							"""simple docstring"""
							lowerCamelCase       =      {}





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self._trie
							for char in text:
										if char not in trie:
													lowerCamelCase       =      {}
										lowerCamelCase       =      trie[char]
							lowerCamelCase       =      True





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self._trie
							for char in prefix:
										if char in trie:
													lowerCamelCase       =      trie[char]
										else:
													return []
							return self._elements(_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      []
							for c, v in d.items():
										lowerCamelCase       =      [""" """] if c == END else [(c + s) for s in self._elements(_a				)]
										result.extend(_a				)
							return tuple(_a				)




lowerCAmelCase							:    Optional[Any]  							=							Trie()
lowerCAmelCase							:    Dict  							=							("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
					trie.insert_word(word)



def 					a__				(       snake_case__       )		->      tuple:
			lowerCamelCase       =      trie.find_word(snake_case__       )
			return tuple(string + word for word in suffixes       )



def 					a__				(       )		->      None:
			print(autocomplete_using_trie("""de"""       )       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()









"""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


lowerCAmelCase							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




def 					a__				(       snake_case__       )		->      Dict:
			for shard in shards:
						for i in range(snake_case__       ):
									yield {"i": i, "shard": shard}



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

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

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

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

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

			lowerCamelCase       =      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()



