

'''simple docstring'''




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


_a			:	str								=  logging.get_logger(__name__)

_a			:	List[str]								=  {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Union[str, Any]      					=				"qdqbert"
  def __init__(       self				,	a_=3_0_5_2_2				,	a_=7_6_8				,	a_=1_2				,	a_=1_2				,	a_=3_0_7_2				,	a_="gelu"				,	a_=0.1				,	a_=0.1				,	a_=5_1_2				,	a_=2				,	a_=0.02				,	a_=1E-12				,	a_=True				,	a_=1				,	a_=0				,	a_=2				,	**a_				,	)							->			Optional[Any]:


    """simple docstring"""
    super().__init__(pad_token_id=a_				,	bos_token_id=a_				,	eos_token_id=a_				,	**a_       )

    UpperCAmelCase  							=		vocab_size
    UpperCAmelCase  							=		max_position_embeddings
    UpperCAmelCase  							=		hidden_size
    UpperCAmelCase  							=		num_hidden_layers
    UpperCAmelCase  							=		num_attention_heads
    UpperCAmelCase  							=		intermediate_size
    UpperCAmelCase  							=		hidden_act
    UpperCAmelCase  							=		hidden_dropout_prob
    UpperCAmelCase  							=		attention_probs_dropout_prob
    UpperCAmelCase  							=		initializer_range
    UpperCAmelCase  							=		type_vocab_size
    UpperCAmelCase  							=		layer_norm_eps
    UpperCAmelCase  							=		use_cache





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


