






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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a			,     a = None			,     a = None			,     a = False			,     **a			,     )			->       List[str]:
								super().__init__(features=a			,     cache_dir=a			,     keep_in_memory=a			,     **a   )
								snake_case_           =				Sql(
								    cache_dir=a			,     features=a			,     sql=a			,     con=a			,     **a			,     )






	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				None
								snake_case_           =				None
								snake_case_           =				None
								snake_case_           =				None

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

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






class 					UpperCamelCase_ :



	'''simple docstring'''




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

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




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				self.to_sql_kwargs.pop('sql'			,     a   )
								snake_case_           =				self.to_sql_kwargs.pop('con'			,     a   )
								snake_case_           =				self.to_sql_kwargs.pop('index'			,     a   )

								snake_case_           =				self._write(index=a			,     **self.to_sql_kwargs   )
								return written




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






	def 				_UpperCamelCase     (			self			,     a			,     **a   )			->       int:
								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:
															snake_case_     ,						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			,     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













import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class 					UpperCamelCase_ :



	'''simple docstring'''




	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					True
	lowerCAmelCase						=					None
	lowerCAmelCase						=					1
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None




	def 				_UpperCamelCase     (			self   )			->       "DownloadConfig":
								return self.__class__(**{k: copy.deepcopy(a   ) for k, v in self.__dict__.items()}   )





