

from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

A_						:	Optional[Any]   =      TypeVar('T')
class     A_      (					Generic[T]  ):


    '''simple docstring'''






    def __init__(self   ,					lowercase__     )   ->					str:
        __UpperCAmelCase     =	data
        __UpperCAmelCase     =	None






    def __str__(self     )   ->					str:
        return F'''{self.data}'''






class     A_      (					Generic[T]  ):


    '''simple docstring'''






    def __init__(self     )   ->					None:
        __UpperCAmelCase     =	None


    def __iter__(self     )   ->					Iterator[T]:
        __UpperCAmelCase     =	self.top
        while node:
            yield node.data
            __UpperCAmelCase     =	node.next


    def __str__(self     )   ->					str:
        return "->".join([str(_a     ) for item in self]     )


    def __len__(self     )   ->					int:
        return len(tuple(iter(self     )     )     )


    def    lowerCAmelCase_  (self     )   ->					bool:
        return self.top is None


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					None:
        __UpperCAmelCase     =	Node(_a     )
        if not self.is_empty():
            __UpperCAmelCase     =	self.top
        __UpperCAmelCase     =	node


    def    lowerCAmelCase_  (self     )   ->					T:
        if self.is_empty():
            raise IndexError('''pop from empty stack'''     )
        assert isinstance(self.top   ,					_a     )
        __UpperCAmelCase     =	self.top
        __UpperCAmelCase     =	self.top.next
        return pop_node.data


    def    lowerCAmelCase_  (self     )   ->					T:
        if self.is_empty():
            raise IndexError('''peek from empty stack'''     )

        assert self.top is not None
        return self.top.data






    def    lowerCAmelCase_  (self     )   ->					None:
        __UpperCAmelCase     =	None


if __name__ == "__main__":
       from doctest import testmod

       testmod()










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							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a = None		,     _a = None		,     _a = False		,     **_a		,     )						->							int:
           super().__init__(features=_a		,     cache_dir=_a		,     keep_in_memory=_a		,     **_a				)
           _A							:     Optional[int]			=						Sql(
               cache_dir=_a		,     features=_a		,     sql=_a		,     con=_a		,     **_a		,     )



     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						None
           _A							:     int			=						None
           _A							:     Tuple			=						None
           _A							:     Union[str, Any]			=						None

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

           # Build dataset for splits
           _A							:     int			=						self.builder.as_dataset(
               split="""train"""		,     verification_mode=_a		,     in_memory=self.keep_in_memory				)
           return dataset







class 						lowercase							:

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

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

     def 					a__	(  self				)						->							int:
           _A							:     Any			=						self.to_sql_kwargs.pop("""sql"""		,     _a				)
           _A							:     List[str]			=						self.to_sql_kwargs.pop("""con"""		,     _a				)
           _A							:     int			=						self.to_sql_kwargs.pop("""index"""		,     _a				)

           _A							:     List[str]			=						self._write(index=_a		,     **self.to_sql_kwargs				)
           return written

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



     def 					a__	(  self		,     _a		,     **_a				)						->							int:
           _A							:     Any			=						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							:     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		,     _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

