




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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  **UpperCamelCase__     ,  )     ->      List[Any]:
           super().__init__(features=UpperCamelCase__     ,  cache_dir=UpperCamelCase__     ,  keep_in_memory=UpperCamelCase__     ,  **UpperCamelCase__     )
           lowerCamelCase   :      str			 =					Sql(
               cache_dir=UpperCamelCase__     ,  features=UpperCamelCase__     ,  sql=UpperCamelCase__     ,  con=UpperCamelCase__     ,  **UpperCamelCase__     ,  )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[Any]			 =					None
           lowerCamelCase   :      List[str]			 =					None
           lowerCamelCase   :      int			 =					None
           lowerCamelCase   :      Optional[Any]			 =					None

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

           # Build dataset for splits
           lowerCamelCase   :      Any			 =					self.builder.as_dataset(
               split="train"     ,  verification_mode=UpperCamelCase__     ,  in_memory=self.keep_in_memory     )
           return dataset




class 		UpperCamelCase__	:

    '''simple docstring'''



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

           lowerCamelCase   :      int			 =					dataset
           lowerCamelCase   :      int			 =					name
           lowerCamelCase   :      Optional[int]			 =					con
           lowerCamelCase   :      List[Any]			 =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           lowerCamelCase   :      int			 =					num_proc
           lowerCamelCase   :      int			 =					to_sql_kwargs







    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Optional[Any]			 =					self.to_sql_kwargs.pop("sql"     ,  UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					self.to_sql_kwargs.pop("con"     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					self.to_sql_kwargs.pop("index"     ,  UpperCamelCase__     )

           lowerCamelCase   :      str			 =					self._write(index=UpperCamelCase__     ,  **self.to_sql_kwargs     )
           return written







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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Tuple			 =					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:
                  lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					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     ,  UpperCamelCase__     ,  UpperCamelCase__     )]     ,  )     ,  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"""





import baseaa




def   _A  (  UpperCamelCase_     :       str)	->						bytes:
   '''simple docstring'''


   return baseaa.baaencode(string.encode("utf-8"))




def   _A  (  UpperCamelCase_     :       bytes)	->						str:
   '''simple docstring'''


   return baseaa.baadecode(UpperCamelCase_).decode("utf-8")


if __name__ == "__main__":
   _a       =							'Hello World!'
   _a       =							baseaa_encode(test)
   print(encoded)

   _a       =							baseaa_decode(encoded)
   print(decoded)




