

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    _a (       SCREAMING_SNAKE_CASE__							):




      """simple docstring"""

      def __init__(					self				:							Tuple		,       UpperCAmelCase				:							Any		,       UpperCAmelCase				:							List[str]		,       UpperCAmelCase				:							Dict = None		,       UpperCAmelCase				:							Optional[int] = None		,       UpperCAmelCase				:							Any = False		,       **UpperCAmelCase				:							Any		,       ):
            super().__init__(features=A__		,       cache_dir=A__		,       keep_in_memory=A__		,       **A__     )
            A_		   =							Sql(
                cache_dir=A__		,       features=A__		,       sql=A__		,       con=A__		,       **A__		,       )







      def 						__A     (					self				:							str     ):
            A_		   =							None
            A_		   =							None
            A_		   =							None
            A_		   =							None

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

            # Build dataset for splits
            A_		   =							self.builder.as_dataset(
                split="train"		,       verification_mode=A__		,       in_memory=self.keep_in_memory     )
            return dataset




class    _a :




      """simple docstring"""

      def __init__(					self				:							List[str]		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Any		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Union[str, Any] = None		,       UpperCAmelCase				:							int = None		,       **UpperCAmelCase				:							List[str]		,       ):
            if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

            A_		   =							dataset
            A_		   =							name
            A_		   =							con
            A_		   =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            A_		   =							num_proc
            A_		   =							to_sql_kwargs





      def 						__A     (					self				:							Tuple     ):
            A_		   =							self.to_sql_kwargs.pop("sql"		,       A__     )
            A_		   =							self.to_sql_kwargs.pop("con"		,       A__     )
            A_		   =							self.to_sql_kwargs.pop("index"		,       A__     )

            A_		   =							self._write(index=A__		,       **self.to_sql_kwargs     )
            return written





      def 						__A     (					self				:							Tuple		,       UpperCAmelCase				:							Dict     ):
            A_   ,			A_   ,			A_		   =							args
            A_		   =							{**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
            A_		   =							query_table(
                table=self.dataset.data		,       key=slice(A__		,       offset + self.batch_size     )		,       indices=self.dataset._indices		,       )
            A_		   =							batch.to_pandas()
            A_		   =							df.to_sql(self.name		,       self.con		,       index=A__		,       **A__     )
            return num_rows or len(A__     )







      def 						__A     (					self				:							List[str]		,       UpperCAmelCase				:							Optional[int]		,       **UpperCAmelCase				:							Any     ):
            A_		   =							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:
                  A_   ,			A_		   =							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

from functools import lru_cache



@lru_cache
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if num < 0:
										raise ValueError('''Number should not be negative.'''		)

					return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




