
"""simple docstring"""



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 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	split=UpperCamelCase_     ,	features=UpperCamelCase_     ,	cache_dir=UpperCamelCase_     ,	keep_in_memory=UpperCamelCase_     ,	streaming=UpperCamelCase_     ,	num_proc=UpperCamelCase_     ,	**UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Dict     							=					path_or_paths if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ) else {self.split: path_or_paths}
          UpperCAmelCase__	:      int     							=					Text(
              cache_dir=UpperCamelCase_     ,	data_files=UpperCamelCase_     ,	features=UpperCamelCase_     ,	**UpperCamelCase_     ,	)



      def  __snake_case							(   self      ):
          # Build iterable dataset
          if self.streaming:
              UpperCAmelCase__	:      Any     							=					self.builder.as_streaming_dataset(split=self.split      )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__	:      Tuple     							=					None
              UpperCAmelCase__	:      List[str]     							=					None
              UpperCAmelCase__	:      Any     							=					None
              UpperCAmelCase__	:      Tuple     							=					None

              self.builder.download_and_prepare(
                  download_config=UpperCamelCase_     ,	download_mode=UpperCamelCase_     ,	verification_mode=UpperCamelCase_     ,	base_path=UpperCamelCase_     ,	num_proc=self.num_proc     ,	)
              UpperCAmelCase__	:      Dict     							=					self.builder.as_dataset(
                  split=self.split     ,	verification_mode=UpperCamelCase_     ,	in_memory=self.keep_in_memory      )
          return dataset



"""simple docstring"""



from ...configuration_utils import PretrainedConfig





class 				a			(					lowercase      ):
      UpperCamelCase							:   Union[str, Any]           =     """bert-generation"""



      def __init__(   self     ,	UpperCamelCase_=50_358     ,	UpperCamelCase_=1_024     ,	UpperCamelCase_=24     ,	UpperCamelCase_=16     ,	UpperCamelCase_=4_096     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=512     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=1E-12     ,	UpperCamelCase_=0     ,	UpperCamelCase_=2     ,	UpperCamelCase_=1     ,	UpperCamelCase_="absolute"     ,	UpperCamelCase_=True     ,	**UpperCamelCase_     ,	):
          super().__init__(pad_token_id=UpperCamelCase_     ,	bos_token_id=UpperCamelCase_     ,	eos_token_id=UpperCamelCase_     ,	**UpperCamelCase_      )

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

