






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       NestedDataStructureLike[PathLike]						,_SCREAMING_SNAKE_CASE		:       Optional[NamedSplit] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Features] = None						,_SCREAMING_SNAKE_CASE		:       str = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,**_SCREAMING_SNAKE_CASE		:       Dict						,)    ->   Optional[Any]:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,split=_SCREAMING_SNAKE_CASE						,features=_SCREAMING_SNAKE_CASE						,cache_dir=_SCREAMING_SNAKE_CASE						,keep_in_memory=_SCREAMING_SNAKE_CASE						,streaming=_SCREAMING_SNAKE_CASE						,num_proc=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)
						A	 =      path_or_paths if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
						A	 =      Text(
						    cache_dir=_SCREAMING_SNAKE_CASE						,data_files=_SCREAMING_SNAKE_CASE						,features=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)







		def       A(  self		:       int		)    ->   Any:


						'''simple docstring'''
						# Build iterable dataset
						if self.streaming:
										A	 =      self.builder.as_streaming_dataset(split=self.split		)
						# Build regular (map-style) dataset
						else:
										A	 =      None
										A	 =      None
										A	 =      None
										A	 =      None

										self.builder.download_and_prepare(
										    download_config=_SCREAMING_SNAKE_CASE						,download_mode=_SCREAMING_SNAKE_CASE						,verification_mode=_SCREAMING_SNAKE_CASE						,base_path=_SCREAMING_SNAKE_CASE						,num_proc=self.num_proc						,)
										A	 =      self.builder.as_dataset(
										    split=self.split						,verification_mode=_SCREAMING_SNAKE_CASE						,in_memory=self.keep_in_memory		)
						return dataset













from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache





