"""simple docstring"""





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands


def    lowercase_   (   ) ->      Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     ArgumentParser("Transformers CLI tool"						,   usage="transformers-cli <command> [<args>]"      )
							__lowerCamelCase					:		List[Any]		   =     parser.add_subparsers(help="transformers-cli command helpers"      )

							# Register commands
							ConvertCommand.register_subcommand(__lowercase      )
							DownloadCommand.register_subcommand(__lowercase      )
							EnvironmentCommand.register_subcommand(__lowercase      )
							RunCommand.register_subcommand(__lowercase      )
							ServeCommand.register_subcommand(__lowercase      )
							UserCommands.register_subcommand(__lowercase      )
							AddNewModelCommand.register_subcommand(__lowercase      )
							AddNewModelLikeCommand.register_subcommand(__lowercase      )
							LfsCommands.register_subcommand(__lowercase      )
							PTtoTFCommand.register_subcommand(__lowercase      )

							# Let's go
							__lowerCamelCase					:		List[str]		   =     parser.parse_args()

							if not hasattr(__lowercase						,   "func"      ):
														parser.print_help()
														exit(1      )

							# Run
							__lowerCamelCase					:		Dict		   =     args.func(__lowercase      )
							service.run()


if __name__ == "__main__":
				main()
"""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


__A  =				4
__A  =				3
class  _snake_case     (					a__		):
						pass


def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

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


def    lowercase_   (   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     int(os.environ["RANK"]      )
							__lowerCamelCase					:		Optional[int]		   =     int(os.environ["WORLD_SIZE"]      )

							__lowerCamelCase					:		Any		   =     ArgumentParser()
							parser.add_argument("--streaming"						,   type=_lowerCamelCase      )
							parser.add_argument("--local_rank"						,   type=_lowerCamelCase      )
							parser.add_argument("--num_workers"						,   type=_lowerCamelCase						,   default=0      )
							__lowerCamelCase					:		Dict		   =     parser.parse_args()
							__lowerCamelCase					:		str		   =     args.streaming
							__lowerCamelCase					:		List[Any]		   =     args.num_workers

							__lowerCamelCase					:		Optional[Any]		   =     {"shards": [F"""shard_{shard_idx}""" for shard_idx in range(_lowerCamelCase      )]}
							__lowerCamelCase					:		int		   =     IterableDataset.from_generator(_lowerCamelCase						,   gen_kwargs=_lowerCamelCase      )
							if not streaming:
														__lowerCamelCase					:		Optional[int]		   =     Dataset.from_list(list(_lowerCamelCase      )      )

							__lowerCamelCase					:		Union[str, Any]		   =     split_dataset_by_node(_lowerCamelCase						,   rank=_lowerCamelCase						,   world_size=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     torch.utils.data.DataLoader(_lowerCamelCase						,   num_workers=_lowerCamelCase      )

							__lowerCamelCase					:		List[Any]		   =     NUM_SHARDS * NUM_ITEMS_PER_SHARD
							__lowerCamelCase					:		Optional[Any]		   =     full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)      )

							__lowerCamelCase					:		Optional[Any]		   =     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()