



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__   =       logging.get_logger(__name__)

UpperCAmelCase__   =       {
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}







class 			__lowerCAmelCase    (			a__						):
			UpperCamelCase   					=     '''roberta-prelayernorm'''



			def __init__(						self   :  int ,		A   :  List[str]=5_02_65 ,		A   :  Tuple=7_68 ,		A   :  Optional[Any]=12 ,		A   :  str=12 ,		A   :  Any=30_72 ,		A   :  int="gelu" ,		A   :  Tuple=0.1 ,		A   :  Dict=0.1 ,		A   :  int=5_12 ,		A   :  Dict=2 ,		A   :  Union[str, Any]=0.0_2 ,		A   :  Tuple=1E-12 ,		A   :  Dict=1 ,		A   :  Tuple=0 ,		A   :  str=2 ,		A   :  Optional[Any]="absolute" ,		A   :  Union[str, Any]=True ,		A   :  List[str]=None ,		**A   :  Any ,		)   ->    Union[str, Any]:



						"""simple docstring"""






						super().__init__(pad_token_id=__lowerCAmelCase ,		bos_token_id=__lowerCAmelCase ,		eos_token_id=__lowerCAmelCase ,		**__lowerCAmelCase)

						_UpperCAmelCase		       =   vocab_size
						_UpperCAmelCase		       =   hidden_size
						_UpperCAmelCase		       =   num_hidden_layers
						_UpperCAmelCase		       =   num_attention_heads
						_UpperCAmelCase		       =   hidden_act
						_UpperCAmelCase		       =   intermediate_size
						_UpperCAmelCase		       =   hidden_dropout_prob
						_UpperCAmelCase		       =   attention_probs_dropout_prob
						_UpperCAmelCase		       =   max_position_embeddings
						_UpperCAmelCase		       =   type_vocab_size
						_UpperCAmelCase		       =   initializer_range
						_UpperCAmelCase		       =   layer_norm_eps
						_UpperCAmelCase		       =   position_embedding_type
						_UpperCAmelCase		       =   use_cache
						_UpperCAmelCase		       =   classifier_dropout







class 			__lowerCAmelCase    (			a__						):



			@property
			def     _lowerCamelCase       (						self   :  List[Any])   ->    Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






						if self.task == "multiple-choice":
									_UpperCAmelCase		       =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
									_UpperCAmelCase		       =   {0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ])

'''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: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

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

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

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

 lowercase__				:    Union[str, 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()

