





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						=   4
_a						=   3






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			pass





def lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







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





def lowerCAmelCase__()       ->   List[str]:







	'''simple docstring'''







	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()










import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a						=   namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	return (abs(source - target							) / target) < 0.0_1





@pytest.mark.integration
def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	_TestCommandArgs(dataset=__snake_case ,all_configs=__snake_case ,save_infos=__snake_case							)
	lowerCamelCase__              =	TestCommand(*__snake_case							)
	test_command.run()
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	assert os.path.exists(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict.from_directory(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict(
	    {
	        '''default''': DatasetInfo(
	            features=Features(
	                {
	                    '''tokens''': Sequence(Value('''string'''							)							),
	                    '''ner_tags''': Sequence(
	                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']							)							),
	                    '''langs''': Sequence(Value('''string'''							)							),
	                    '''spans''': Sequence(Value('''string'''							)							),
	                }							) ,splits=[
	                {
	                    '''name''': '''train''',
	                    '''num_bytes''': 2351563,
	                    '''num_examples''': 10000,
	                },
	                {
	                    '''name''': '''validation''',
	                    '''num_bytes''': 238418,
	                    '''num_examples''': 1000,
	                },
	            ] ,download_size=3940680 ,dataset_size=2589981 ,)
	    }							)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		lowerCamelCase__       ,			lowerCamelCase__              =	getattr(dataset_infos['''default'''] ,__snake_case							), getattr(expected_dataset_infos['''default'''] ,__snake_case							)
		if key == "num_bytes":
			assert is_apercent_close(__snake_case ,__snake_case							)
		elif key == "splits":
			assert list(__snake_case							) == list(__snake_case							)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes ,expected[split].num_bytes							)
		else:
			result == expected



