
"""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  __lowerCamelCase  ( lowerCAmelCase			):
				pass






def  lowercase						(	lowerCAmelCase__						):
			for shard in shards:
						for i in range(lowerCAmelCase__						):
									yield {"i": i, "shard": shard}






def  lowercase						(	):
			lowerCamelCase_					=  int(os.environ['''RANK''']						)
			lowerCamelCase_					=  int(os.environ['''WORLD_SIZE''']						)

			lowerCamelCase_					=  ArgumentParser()
			parser.add_argument('''--streaming'''	,type=lowerCAmelCase__						)
			parser.add_argument('''--local_rank'''	,type=lowerCAmelCase__						)
			parser.add_argument('''--num_workers'''	,type=lowerCAmelCase__	,default=0						)
			lowerCamelCase_					=  parser.parse_args()
			lowerCamelCase_					=  args.streaming
			lowerCamelCase_					=  args.num_workers

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

			lowerCamelCase_					=  split_dataset_by_node(lowerCAmelCase__	,rank=lowerCAmelCase__	,world_size=lowerCAmelCase__						)
			lowerCamelCase_					=  torch.utils.data.DataLoader(lowerCAmelCase__	,num_workers=lowerCAmelCase__						)

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

			lowerCamelCase_					=  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"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



