




'''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


_lowercase  =			4
_lowercase  =			3
class 						a_				(			UpperCAmelCase__						):
	pass





def 						lowerCamelCase__   (				a							):
						for shard in shards:
												for i in range(a							):
																		yield {"i": i, "shard": shard}
def 						lowerCamelCase__   (				):
						__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()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

