

'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy
class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case			,_snake_case			,_snake_case = None			,_snake_case = None			,_snake_case = False			,**_snake_case			,):
				super().__init__(features=_snake_case			,cache_dir=_snake_case			,keep_in_memory=_snake_case			,**_snake_case     )
				UpperCAmelCase_     :      str						=  Sql(
				    cache_dir=_snake_case			,features=_snake_case			,sql=_snake_case			,con=_snake_case			,**_snake_case			,)






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  None
				UpperCAmelCase_     :      Tuple						=  None
				UpperCAmelCase_     :      Union[str, Any]						=  None
				UpperCAmelCase_     :      Tuple						=  None

				self.builder.download_and_prepare(
				    download_config=_snake_case			,download_mode=_snake_case			,verification_mode=_snake_case			,base_path=_snake_case			,)

				# Build dataset for splits
				UpperCAmelCase_     :      Union[str, Any]						=  self.builder.as_dataset(
				    split="train"			,verification_mode=_snake_case			,in_memory=self.keep_in_memory     )
				return dataset




class 					_snake_case		:






			def __init__(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case = None			,_snake_case = None			,**_snake_case			,):
				if num_proc is not None and num_proc <= 0:
					raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

				UpperCAmelCase_     :      Any						=  dataset
				UpperCAmelCase_     :      Union[str, Any]						=  name
				UpperCAmelCase_     :      List[Any]						=  con
				UpperCAmelCase_     :      Any						=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				UpperCAmelCase_     :      Dict						=  num_proc
				UpperCAmelCase_     :      Tuple						=  to_sql_kwargs






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  self.to_sql_kwargs.pop("sql"			,_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.to_sql_kwargs.pop("con"			,_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.to_sql_kwargs.pop("index"			,_snake_case     )

				UpperCAmelCase_     :      Tuple						=  self._write(index=_snake_case			,**self.to_sql_kwargs     )
				return written






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  args
				UpperCAmelCase_     :      Optional[int]						=  {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
				UpperCAmelCase_     :      Optional[int]						=  query_table(
				    table=self.dataset.data			,key=slice(_snake_case			,offset + self.batch_size     )			,indices=self.dataset._indices			,)
				UpperCAmelCase_     :      List[Any]						=  batch.to_pandas()
				UpperCAmelCase_     :      str						=  df.to_sql(self.name			,self.con			,index=_snake_case			,**_snake_case     )
				return num_rows or len(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,**_snake_case     ):
				UpperCAmelCase_     :      Dict						=  0

				if self.num_proc is None or self.num_proc == 1:
					for offset in logging.tqdm(
					    range(0			,len(self.dataset     )			,self.batch_size     )			,unit="ba"			,disable=not logging.is_progress_bar_enabled()			,desc="Creating SQL from Arrow format"			,):
						written += self._batch_sql((offset, index, to_sql_kwargs)     )
				else:
					UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  len(self.dataset     ), self.batch_size
					with multiprocessing.Pool(self.num_proc     ) as pool:
						for num_rows in logging.tqdm(
						    pool.imap(
						        self._batch_sql			,[(offset, index, to_sql_kwargs) for offset in range(0			,_snake_case			,_snake_case     )]			,)			,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,unit="ba"			,disable=not logging.is_progress_bar_enabled()			,desc="Creating SQL from Arrow format"			,):
							written += num_rows

				return written






"""simple docstring"""





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


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_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_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




