

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













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


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				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_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

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

							snake_case_           =				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()





