





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  __lowerCAmelCase	(      __snake_case					):






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							**__magic_name__ ,							)					->       List[Any]:


							'''simple docstring'''



							super().__init__(features=a_ ,							cache_dir=a_ ,							keep_in_memory=a_ ,							**a_   )
							snake_case_						:   Union[str, Any]           =  Sql(
							    cache_dir=a_ ,							features=a_ ,							sql=a_ ,							con=a_ ,							**a_ ,							)






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  None
							snake_case_						:   Dict           =  None
							snake_case_						:   Dict           =  None
							snake_case_						:   List[str]           =  None

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

							# Build dataset for splits
							snake_case_						:   Any           =  self.builder.as_dataset(
							    split='''train''' ,							verification_mode=a_ ,							in_memory=self.keep_in_memory   )
							return dataset




class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       str:


							'''simple docstring'''



							if num_proc is not None and num_proc <= 0:
											raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''   )

							snake_case_						:   List[str]           =  dataset
							snake_case_						:   Tuple           =  name
							snake_case_						:   Optional[int]           =  con
							snake_case_						:   int           =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							snake_case_						:   Dict           =  num_proc
							snake_case_						:   Dict           =  to_sql_kwargs






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.to_sql_kwargs.pop('''sql''' ,							a_   )
							snake_case_						:   Union[str, Any]           =  self.to_sql_kwargs.pop('''con''' ,							a_   )
							snake_case_						:   Any           =  self.to_sql_kwargs.pop('''index''' ,							a_   )

							snake_case_						:   Optional[Any]           =  self._write(index=a_ ,							**self.to_sql_kwargs   )
							return written






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Any:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   int           =  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:
											snake_case_						:   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 ,							a_ ,							a_   )] ,							) ,							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







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if number > 0:
								raise ValueError('''input must be a negative integer'''			)
				snake_case_						:   List[str]           =  len(bin(_UpperCamelCase			)[3:]			)
				snake_case_						:   str           =  bin(abs(_UpperCamelCase			) - (1 << binary_number_length)			)[3:]
				snake_case_						:   Dict           =  (
				    (
				        '''1'''
				        + '''0''' * (binary_number_length - len(_UpperCamelCase			))
				        + twos_complement_number
				    )
				    if number < 0
				    else '''0'''
				)
				return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()
