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 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	List[Any]		,	_lowercase       :	Union[str, "sqlalchemy.sql.Selectable"]		,	_lowercase       :	Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		,	_lowercase       :	Optional[Features] = None		,	_lowercase       :	str = None		,	_lowercase       :	bool = False		,	**_lowercase       :	int		,	):



    """simple docstring"""



    super().__init__(features=_lowercase		,	cache_dir=_lowercase		,	keep_in_memory=_lowercase		,	**_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			Sql(
        cache_dir=_lowercase		,	features=_lowercase		,	sql=_lowercase		,	con=_lowercase		,	**_lowercase		,	)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			None
    _UpperCamelCase:  Optional[Any]    						=			None
    _UpperCamelCase:  Dict    						=			None
    _UpperCamelCase:  List[str]    						=			None

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

    # Build dataset for splits
    _UpperCamelCase:  List[Any]    						=			self.builder.as_dataset(
        split='''train'''		,	verification_mode=_lowercase		,	in_memory=self.keep_in_memory				)
    return dataset




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Dataset		,	_lowercase       :	str		,	_lowercase       :	Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	Optional[int] = None		,	**_lowercase       :	int		,	):



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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self.to_sql_kwargs.pop('''sql'''		,	_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			self.to_sql_kwargs.pop('''con'''		,	_lowercase				)
    _UpperCamelCase:  str    						=			self.to_sql_kwargs.pop('''index'''		,	_lowercase				)

    _UpperCamelCase:  Optional[int]    						=			self._write(index=_lowercase		,	**self.to_sql_kwargs				)
    return written



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			args
    _UpperCamelCase:  str    						=			{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
    _UpperCamelCase:  List[Any]    						=			query_table(
        table=self.dataset.data		,	key=slice(_lowercase		,	offset + self.batch_size				)		,	indices=self.dataset._indices		,	)
    _UpperCamelCase:  Dict    						=			batch.to_pandas()
    _UpperCamelCase:  List[str]    						=			df.to_sql(self.name		,	self.con		,	index=_lowercase		,	**_lowercase				)
    return num_rows or len(_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	Optional[Any]		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  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:
     _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			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		,	_lowercase		,	_lowercase				)]		,	)		,	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 pathlib import Path

import fire




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				str	,       lowercase:				int		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			Path(lowercase		)
 _UpperCamelCase:  int    						=			Path(lowercase		)
 dest_dir.mkdir(exist_ok=lowercase		)
 for path in src_dir.iterdir():
  _UpperCamelCase:  List[str]    						=			[x.rstrip() for x in list(path.open().readlines()		)][:n]
  _UpperCamelCase:  int    						=			dest_dir.joinpath(path.name		)
  print(lowercase		)
  dest_path.open('''w'''		).write('''\n'''.join(lowercase		)		)


if __name__ == "__main__":
  fire.Fire(minify)