






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			(_UpperCamelCase			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	List[Any]:
           super().__init__(
               __UpperCamelCase					,						split=__UpperCamelCase					,						features=__UpperCamelCase					,						cache_dir=__UpperCamelCase					,						keep_in_memory=__UpperCamelCase					,						streaming=__UpperCamelCase					,						num_proc=__UpperCamelCase					,						**__UpperCamelCase					,						)
           __lowerCAmelCase								=					path_or_paths if isinstance(__UpperCamelCase					,						__UpperCamelCase	) else {self.split: path_or_paths}
           __lowerCAmelCase								=					Text(
               cache_dir=__UpperCamelCase					,						data_files=__UpperCamelCase					,						features=__UpperCamelCase					,						**__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self	)->	Any:
           # Build iterable dataset
           if self.streaming:
                __lowerCAmelCase								=					self.builder.as_streaming_dataset(split=self.split	)
           # Build regular (map-style) dataset
           else:
                __lowerCAmelCase								=					None
                __lowerCAmelCase								=					None
                __lowerCAmelCase								=					None
                __lowerCAmelCase								=					None

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














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


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






