


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :    str									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    List[str]									=				{
    """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				):






		'''simple docstring'''

		lowerCamelCase__				="biogpt"



		def __init__(self				,       a_=4_23_84				,       a_=10_24				,       a_=24				,       a_=16				,       a_=40_96				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=10_24				,       a_=0.02				,       a_=1E-12				,       a_=True				,       a_=True				,       a_=0.0				,       a_=0.0				,       a_=1				,       a_=0				,       a_=2				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Tuple						=				vocab_size
			__snake_case	:						Optional[int]						=				max_position_embeddings
			__snake_case	:						Tuple						=				hidden_size
			__snake_case	:						Any						=				num_hidden_layers
			__snake_case	:						Optional[int]						=				num_attention_heads
			__snake_case	:						Tuple						=				intermediate_size
			__snake_case	:						Optional[int]						=				hidden_act
			__snake_case	:						List[str]						=				hidden_dropout_prob
			__snake_case	:						List[Any]						=				attention_probs_dropout_prob
			__snake_case	:						List[str]						=				initializer_range
			__snake_case	:						int						=				layer_norm_eps
			__snake_case	:						Dict						=				scale_embedding
			__snake_case	:						Optional[Any]						=				use_cache
			__snake_case	:						Union[str, Any]						=				layerdrop
			__snake_case	:						int						=				activation_dropout
			super().__init__(pad_token_id=_UpperCamelCase				,       bos_token_id=_UpperCamelCase				,       eos_token_id=_UpperCamelCase				,       **_UpperCamelCase					)








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 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)