

'''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


_snake_case         =  4
_snake_case         =  3


class      a__  (							lowerCamelCase_  ):
   pass







def  _A		(     snake_case					)      ->     List[Any]:
   for shard in shards:
      for i in range(snake_case					):
         yield {"i": i, "shard": shard}







def  _A		(     )      ->     Tuple:
   _lowercase      :				int      					=       int(os.environ["RANK"]					)
   _lowercase      :				List[Any]      					=       int(os.environ["WORLD_SIZE"]					)

   _lowercase      :				Union[str, Any]      					=       ArgumentParser()
   parser.add_argument("--streaming"		,							type=snake_case					)
   parser.add_argument("--local_rank"		,							type=snake_case					)
   parser.add_argument("--num_workers"		,							type=snake_case		,							default=0					)
   _lowercase      :				Tuple      					=       parser.parse_args()
   _lowercase      :				Dict      					=       args.streaming
   _lowercase      :				int      					=       args.num_workers

   _lowercase      :				int      					=       {"shards": [F'''shard_{shard_idx}''' for shard_idx in range(snake_case					)]}
   _lowercase      :				Union[str, Any]      					=       IterableDataset.from_generator(snake_case		,							gen_kwargs=snake_case					)
   if not streaming:
      _lowercase      :				Union[str, Any]      					=       Dataset.from_list(list(snake_case					)					)

   _lowercase      :				List[str]      					=       split_dataset_by_node(snake_case		,							rank=snake_case		,							world_size=snake_case					)
   _lowercase      :				Optional[Any]      					=       torch.utils.data.DataLoader(snake_case		,							num_workers=snake_case					)

   _lowercase      :				Tuple      					=       NUM_SHARDS * NUM_ITEMS_PER_SHARD
   _lowercase      :				List[str]      					=       full_size // world_size
   expected_local_size += int(rank < (full_size % world_size)					)

   _lowercase      :				Tuple      					=       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 transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






