


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   __lowercase				(  __snake_case  ):





      def __init__(		self						:					List[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						:					Any					,       )    ->   str:






         """simple docstring"""







         super().__init__(features=__lowerCamelCase					,       cache_dir=__lowerCamelCase					,       keep_in_memory=__lowerCamelCase					,       **__lowerCamelCase       )
         UpperCAmelCase							=     Sql(
             cache_dir=__lowerCamelCase					,       features=__lowerCamelCase					,       sql=__lowerCamelCase					,       con=__lowerCamelCase					,       **__lowerCamelCase					,       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     None
         UpperCAmelCase							=     None
         UpperCAmelCase							=     None
         UpperCAmelCase							=     None

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

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






class   __lowercase				:





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

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





      def        _lowercase				(		self						:					Any       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.to_sql_kwargs.pop("""sql"""					,       __lowerCamelCase       )
         UpperCAmelCase							=     self.to_sql_kwargs.pop("""con"""					,       __lowerCamelCase       )
         UpperCAmelCase							=     self.to_sql_kwargs.pop("""index"""					,       __lowerCamelCase       )

         UpperCAmelCase							=     self._write(index=__lowerCamelCase					,       **self.to_sql_kwargs       )
         return written





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					List[Any]       )    ->   Dict:






         """simple docstring"""







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





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






         """simple docstring"""







         UpperCAmelCase							=     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:
            UpperCAmelCase				,  UpperCAmelCase							=     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 __future__ import annotations
def   _UpperCamelCase				(						lowerCAmelCase_   )						->None:
   create_state_space_tree(lowerCAmelCase_  ,						[]  ,						0  ,						[0 for i in range(len(lowerCAmelCase_   )   )]   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->None:

   if index == len(lowerCAmelCase_   ):
      print(lowerCAmelCase_   )
      return

   for i in range(len(lowerCAmelCase_   )   ):
      if not index_used[i]:
         current_sequence.append(sequence[i]   )
         UpperCAmelCase							=     True
         create_state_space_tree(lowerCAmelCase_  ,						lowerCAmelCase_  ,						index + 1  ,						lowerCAmelCase_   )
         current_sequence.pop()
         UpperCAmelCase							=     False
__a     =			[3, 1, 2, 4]
generate_all_permutations(sequence)

__a     =			["A", "B", "C"]







generate_all_permutations(sequence_a)



