






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    				=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   [
	    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ClapModel",
	    "ClapPreTrainedModel",
	    "ClapTextModel",
	    "ClapTextModelWithProjection",
	    "ClapAudioModel",
	    "ClapAudioModelWithProjection",
	]
	lowercase    				=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
	from .configuration_clap import (
	    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
	    ClapAudioConfig,
	    ClapConfig,
	    ClapTextConfig,
	)
	from .processing_clap import ClapProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clap import ClapFeatureExtractor
		from .modeling_clap import (
		    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ClapAudioModel,
		    ClapAudioModelWithProjection,
		    ClapModel,
		    ClapPreTrainedModel,
		    ClapTextModel,
		    ClapTextModelWithProjection,
		)


else:
	import sys

	lowercase    				=   _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()





