



"""simple docstring"""






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	Callable							,       __lowerCamelCase    :	Optional[Features] = None							,       __lowerCamelCase    :	str = None							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	Optional[dict] = None							,       __lowerCamelCase    :	Optional[int] = None							,       **__lowerCamelCase    :	Union[str, Any]							,       ):



     """simple docstring"""
     super().__init__(
         features=__lowerCamelCase							,       cache_dir=__lowerCamelCase							,       keep_in_memory=__lowerCamelCase							,       streaming=__lowerCamelCase							,       num_proc=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      Generator(
         cache_dir=__lowerCamelCase							,       features=__lowerCamelCase							,       generator=__lowerCamelCase							,       gen_kwargs=__lowerCamelCase							,       **__lowerCamelCase							,       )





   def    __UpperCAmelCase    (       self    :	Optional[int]				):



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

       self.builder.download_and_prepare(
           download_config=__lowerCamelCase							,       download_mode=__lowerCamelCase							,       verification_mode=__lowerCamelCase							,       base_path=__lowerCamelCase							,       num_proc=self.num_proc							,       )
       _snake_case   	=      self.builder.as_dataset(
           split='''train'''							,       verification_mode=__lowerCamelCase							,       in_memory=self.keep_in_memory				)
     return dataset










"""simple docstring"""






from ...configuration_utils import PretrainedConfig



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''bert-generation'''



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Optional[int]=5_0_3_5_8							,       __lowerCamelCase    :	List[str]=1_0_2_4							,       __lowerCamelCase    :	Optional[Any]=2_4							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Union[str, Any]=4_0_9_6							,       __lowerCamelCase    :	Optional[Any]="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	List[str]=0.1							,       __lowerCamelCase    :	int=5_1_2							,       __lowerCamelCase    :	Dict=0.0_2							,       __lowerCamelCase    :	Tuple=1E-12							,       __lowerCamelCase    :	Any=0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]="absolute"							,       __lowerCamelCase    :	str=True							,       **__lowerCamelCase    :	List[Any]							,       ):



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

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





