
'''simple docstring'''


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			,   ):
								super().__init__(features=__A			,   cache_dir=__A			,   keep_in_memory=__A			,   **__A      )
								lowerCamelCase_					:      Dict     =		Sql(
								    cache_dir=__A			,   features=__A			,   sql=__A			,   con=__A			,   **__A			,   )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		None
								lowerCamelCase_					:      int     =		None
								lowerCamelCase_					:      Dict     =		None
								lowerCamelCase_					:      Tuple     =		None

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

								# Build dataset for splits
								lowerCamelCase_					:      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			,   ):



								if num_proc is not None and num_proc <= 0:
													raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""      )

								lowerCamelCase_					:      List[Any]     =		dataset
								lowerCamelCase_					:      Any     =		name
								lowerCamelCase_					:      str     =		con
								lowerCamelCase_					:      Dict     =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								lowerCamelCase_					:      Any     =		num_proc
								lowerCamelCase_					:      Any     =		to_sql_kwargs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		self.to_sql_kwargs.pop('''sql'''			,   __A      )
								lowerCamelCase_					:      Tuple     =		self.to_sql_kwargs.pop('''con'''			,   __A      )
								lowerCamelCase_					:      int     =		self.to_sql_kwargs.pop('''index'''			,   __A      )

								lowerCamelCase_					:      Any     =		self._write(index=__A			,   **self.to_sql_kwargs      )
								return written

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      str     =		args
								lowerCamelCase_					:      Optional[int]     =		{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
								lowerCamelCase_					:      str     =		query_table(
								    table=self.dataset.data			,   key=slice(__A			,   offset + self.batch_size      )			,   indices=self.dataset._indices			,   )
								lowerCamelCase_					:      Dict     =		batch.to_pandas()
								lowerCamelCase_					:      int     =		df.to_sql(self.name			,   self.con			,   index=__A			,   **__A      )
								return num_rows or len(__A      )






			def        UpperCAmelCase__ (self			,   A			,   **A      ):
								lowerCamelCase_					:      List[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:
													lowerCamelCase_,      lowerCamelCase_					:      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			,   __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




'''simple docstring'''


import os
from math import logaa




def lowercase_	(   _lowercase = "base_exp.txt"				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      float     =		0
					lowerCamelCase_					:      Dict     =		0
					for i, line in enumerate(open(os.path.join(os.path.dirname(_lowercase				)			,       _lowercase				)				)				):
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		list(map(_lowercase			,       line.split(''','''				)				)				)
										if x * logaa(_lowercase				) > largest:
															lowerCamelCase_					:      List[str]     =		x * logaa(_lowercase				)
															lowerCamelCase_					:      Union[str, Any]     =		i + 1
					return result


if __name__ == "__main__":
					print(solution())


