






"""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


__A		:       Union[str, Any]  				=		4
__A		:       List[Any]  				=		3





class 					_UpperCAmelCase       ( _A							):
					pass



def 						lowercase    (				__snake_case				: List[str]	):
		for shard in shards:
				for i in range(__snake_case	):
						yield {"i": i, "shard": shard}



def 						lowercase    (				):
		lowercase_ : List[Any]							       =   int(os.environ['''RANK''']	)
		lowercase_ : Dict							       =   int(os.environ['''WORLD_SIZE''']	)

		lowercase_ : List[Any]							       =   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	)
		lowercase_ : List[Any]							       =   parser.parse_args()
		lowercase_ : List[Any]							       =   args.streaming
		lowercase_ : Optional[int]							       =   args.num_workers

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

		lowercase_ : List[str]							       =   split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case	)
		lowercase_ : List[str]							       =   torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case	)

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

		lowercase_ : 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()











"""simple docstring"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



