




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      (			UpperCamelCase__			):

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

										super().__init__(features=__snake_case    , cache_dir=__snake_case    , keep_in_memory=__snake_case    , **__snake_case )
										__magic_name__	:	int								=Sql(
										    cache_dir=__snake_case    , features=__snake_case    , sql=__snake_case    , con=__snake_case    , **__snake_case    , )


						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=None
										__magic_name__	:	Optional[Any]								=None
										__magic_name__	:	Union[str, Any]								=None
										__magic_name__	:	int								=None

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

										# Build dataset for splits
										__magic_name__	:	Optional[int]								=self.builder.as_dataset(
										    split="""train"""    , verification_mode=__snake_case    , in_memory=self.keep_in_memory )
										return dataset




class 		__A      :

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

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

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.to_sql_kwargs.pop("""sql"""    , __snake_case )
										__magic_name__	:	List[Any]								=self.to_sql_kwargs.pop("""con"""    , __snake_case )
										__magic_name__	:	Any								=self.to_sql_kwargs.pop("""index"""    , __snake_case )

										__magic_name__	:	Dict								=self._write(index=__snake_case    , **self.to_sql_kwargs )
										return written

						def     A__						(		self   :Tuple    , __snake_case   :Any ):
										'''simple docstring'''

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


						def     A__						(		self   :Any    , __snake_case   :Optional[Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, 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:
														__magic_name__				, __magic_name__	:	Union[str, Any]								=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    , __snake_case    , __snake_case )]    , )    , 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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




