




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_							(      SCREAMING_SNAKE_CASE__					):


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]				,				_lowerCAmelCase  :		Union[str, "sqlalchemy.sql.Selectable"]				,				_lowerCAmelCase  :		Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,				_lowerCAmelCase  :		Optional[Features] = None				,				_lowerCAmelCase  :		str = None				,				_lowerCAmelCase  :		bool = False				,				**_lowerCAmelCase  :		Dict				,				):
       super().__init__(features=_lowerCAmelCase				,				cache_dir=_lowerCAmelCase				,				keep_in_memory=_lowerCAmelCase				,				**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			Sql(
           cache_dir=_lowerCAmelCase				,				features=_lowerCAmelCase				,				sql=_lowerCAmelCase				,				con=_lowerCAmelCase				,				**_lowerCAmelCase				,				)




 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None

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

       # Build dataset for splits
       SCREAMING_SNAKE_CASE_							=			self.builder.as_dataset(
           split='train'				,				verification_mode=_lowerCAmelCase				,				in_memory=self.keep_in_memory						)
       return dataset





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		str				,				_lowerCAmelCase  :		Dataset				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,				_lowerCAmelCase  :		Optional[int] = None				,				_lowerCAmelCase  :		Optional[int] = None				,				**_lowerCAmelCase  :		Dict				,				):
       if num_proc is not None and num_proc <= 0:
             raise ValueError(F"num_proc {num_proc} must be an integer > 0."						)

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



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			self.to_sql_kwargs.pop('sql'				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.to_sql_kwargs.pop('con'				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.to_sql_kwargs.pop('index'				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			self._write(index=_lowerCAmelCase				,				**self.to_sql_kwargs						)
       return written



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			args
       SCREAMING_SNAKE_CASE_							=			{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
       SCREAMING_SNAKE_CASE_							=			query_table(
           table=self.dataset.data				,				key=slice(_lowerCAmelCase				,				offset + self.batch_size						)				,				indices=self.dataset._indices				,				)
       SCREAMING_SNAKE_CASE_							=			batch.to_pandas()
       SCREAMING_SNAKE_CASE_							=			df.to_sql(self.name				,				self.con				,				index=_lowerCAmelCase				,				**_lowerCAmelCase						)
       return num_rows or len(_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				**_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			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:
             SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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				,				_lowerCAmelCase				,				_lowerCAmelCase						)]				,				)				,				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    a__       (						__UpperCamelCase ):
      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()


