



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 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					pass







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Any:
		for shard in shards:
				for i in range(UpperCAmelCase__ ):
						yield {"i": i, "shard": shard}







def snake_case__  (			)		->							List[str]:
		UpperCAmelCase_						=				int(os.environ["RANK"] )
		UpperCAmelCase_						=				int(os.environ["WORLD_SIZE"] )

		UpperCAmelCase_						=				ArgumentParser()
		parser.add_argument("--streaming"			,     type=UpperCAmelCase__ )
		parser.add_argument("--local_rank"			,     type=UpperCAmelCase__ )
		parser.add_argument("--num_workers"			,     type=UpperCAmelCase__			,     default=0 )
		UpperCAmelCase_						=				parser.parse_args()
		UpperCAmelCase_						=				args.streaming
		UpperCAmelCase_						=				args.num_workers

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

		UpperCAmelCase_						=				split_dataset_by_node(UpperCAmelCase__			,     rank=UpperCAmelCase__			,     world_size=UpperCAmelCase__ )
		UpperCAmelCase_						=				torch.utils.data.DataLoader(UpperCAmelCase__			,     num_workers=UpperCAmelCase__ )

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

		UpperCAmelCase_						=				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

