
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		(						A_ ):



   def __init__(self		:					Tuple    ,						snake_case__		:					Union[str, "sqlalchemy.sql.Selectable"]    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[Features] = None    ,						snake_case__		:					str = None    ,						snake_case__		:					bool = False    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(features=snake_case__    ,						cache_dir=snake_case__    ,						keep_in_memory=snake_case__    ,						**snake_case__ )
          snake_case       :   Tuple							=      Sql(
              cache_dir=snake_case__    ,						features=snake_case__    ,						sql=snake_case__    ,						con=snake_case__    ,						**snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      None
          snake_case       :   int							=      None
          snake_case       :   List[str]							=      None
          snake_case       :   int							=      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
          snake_case       :   Optional[int]							=      self.builder.as_dataset(
              split="train"    ,						verification_mode=snake_case__    ,						in_memory=self.keep_in_memory )
          return dataset






class   UpperCAmelCase		:



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					Dataset    ,						snake_case__		:					str    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None    ,						**snake_case__		:					Dict    ,						)		->	Optional[Any]:


          '''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       :   Any							=      dataset
          snake_case       :   Dict							=      name
          snake_case       :   Optional[Any]							=      con
          snake_case       :   Any							=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          snake_case       :   Optional[int]							=      num_proc
          snake_case       :   str							=      to_sql_kwargs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   int							=      self.to_sql_kwargs.pop("sql"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("con"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("index"    ,						snake_case__ )

          snake_case       :   Union[str, Any]							=      self._write(index=snake_case__    ,						**self.to_sql_kwargs )
          return written



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str    ,						**snake_case__		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          snake_case       :   List[str]							=      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				,					snake_case       :   List[str]							=      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








from timeit import timeit
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        number &= number - 1
        result += 1
    return result
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result
def 						_snake_case(    )							->      None:





    '''simple docstring'''






    def do_benchmark(SCREAMING_SNAKE_CASE__			:	int      ) -> None:
        A__          =   'import __main__ as z'
        print(f'Benchmark when {number = }:'      )
        print(f'{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,			setup=SCREAMING_SNAKE_CASE__      )
        print(f'timeit() runs in {timing} seconds'      )
        print(f'{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit(
            'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,			setup=SCREAMING_SNAKE_CASE__    ,			)
        print(f'timeit() runs in {timing} seconds'      )

    for number in (25, 37, 58, 0):
        do_benchmark(SCREAMING_SNAKE_CASE__      )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

