






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__		:							List[Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							List[str] 				=				{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'roc_bert'




     def __init__(				self	,						_lowerCAmelCase=30522	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=True	,						_lowerCAmelCase=0	,						_lowerCAmelCase="absolute"	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						_lowerCAmelCase=True	,						_lowerCAmelCase=768	,						_lowerCAmelCase=910	,						_lowerCAmelCase=512	,						_lowerCAmelCase=24858	,						_lowerCAmelCase=True	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			vocab_size
          UpperCAmelCase__      :  Tuple				      =			max_position_embeddings
          UpperCAmelCase__      :  Any				      =			hidden_size
          UpperCAmelCase__      :  int				      =			num_hidden_layers
          UpperCAmelCase__      :  Tuple				      =			num_attention_heads
          UpperCAmelCase__      :  Dict				      =			intermediate_size
          UpperCAmelCase__      :  Tuple				      =			hidden_act
          UpperCAmelCase__      :  List[str]				      =			hidden_dropout_prob
          UpperCAmelCase__      :  Dict				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  int				      =			initializer_range
          UpperCAmelCase__      :  List[Any]				      =			type_vocab_size
          UpperCAmelCase__      :  List[str]				      =			layer_norm_eps
          UpperCAmelCase__      :  List[str]				      =			use_cache
          UpperCAmelCase__      :  int				      =			enable_pronunciation
          UpperCAmelCase__      :  Tuple				      =			enable_shape
          UpperCAmelCase__      :  str				      =			pronunciation_embed_dim
          UpperCAmelCase__      :  Tuple				      =			pronunciation_vocab_size
          UpperCAmelCase__      :  Optional[Any]				      =			shape_embed_dim
          UpperCAmelCase__      :  Optional[int]				      =			shape_vocab_size
          UpperCAmelCase__      :  List[Any]				      =			concat_input
          UpperCAmelCase__      :  Optional[int]				      =			position_embedding_type
          UpperCAmelCase__      :  Any				      =			classifier_dropout
          super().__init__(pad_token_id=_lowerCAmelCase	,						**_lowerCAmelCase		)









"""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


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

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

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

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

	lowerCAmelCase__      :		int    							=      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()




