





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 SCREAMING_SNAKE_CASE_						(					snake_case_					):
						pass


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	for shard in shards:
		for i in range(__a   ):
			yield {"i": i, "shard": shard}


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			List[str]											=	int(os.environ['RANK']   )
	snake_case_		:			int											=	int(os.environ['WORLD_SIZE']   )

	snake_case_		:			str											=	ArgumentParser()
	parser.add_argument('--streaming'   , type=__a   )
	parser.add_argument('--local_rank'   , type=__a   )
	parser.add_argument('--num_workers'   , type=__a   , default=0   )
	snake_case_		:			Any											=	parser.parse_args()
	snake_case_		:			List[Any]											=	args.streaming
	snake_case_		:			Optional[int]											=	args.num_workers

	snake_case_		:			Union[str, Any]											=	{'shards': [f"""shard_{shard_idx}""" for shard_idx in range(__a   )]}
	snake_case_		:			int											=	IterableDataset.from_generator(__a   , gen_kwargs=__a   )
	if not streaming:
		snake_case_		:			List[str]											=	Dataset.from_list(list(__a   )   )

	snake_case_		:			int											=	split_dataset_by_node(__a   , rank=__a   , world_size=__a   )
	snake_case_		:			List[str]											=	torch.utils.data.DataLoader(__a   , num_workers=__a   )

	snake_case_		:			str											=	NUM_SHARDS * NUM_ITEMS_PER_SHARD
	snake_case_		:			int											=	full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)   )

	snake_case_		:			List[str]											=	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()








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

