




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








def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




