"""simple docstring"""




from ...configuration_utils import PretrainedConfig
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		"bert-generation"






     def __init__(		self    ,					a__=50358    ,					a__=1024    ,					a__=24    ,					a__=16    ,					a__=4096    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=0    ,					a__=2    ,					a__=1    ,					a__="absolute"    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=snake_case__    ,					bos_token_id=snake_case__    ,					eos_token_id=snake_case__    ,					**snake_case__  )

       _lowerCAmelCase						:	List[str]        =					vocab_size
       _lowerCAmelCase						:	int        =					hidden_size
       _lowerCAmelCase						:	Any        =					num_hidden_layers
       _lowerCAmelCase						:	Optional[Any]        =					num_attention_heads
       _lowerCAmelCase						:	str        =					hidden_act
       _lowerCAmelCase						:	str        =					intermediate_size
       _lowerCAmelCase						:	Tuple        =					hidden_dropout_prob
       _lowerCAmelCase						:	List[str]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Optional[int]        =					max_position_embeddings
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	Optional[int]        =					layer_norm_eps
       _lowerCAmelCase						:	List[str]        =					position_embedding_type
       _lowerCAmelCase						:	Any        =					use_cache





"""simple docstring"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




