




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    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None    ,		_SCREAMING_SNAKE_CASE = None    ,		_SCREAMING_SNAKE_CASE = False    ,		**_SCREAMING_SNAKE_CASE    ,		):
								super().__init__(features=__SCREAMING_SNAKE_CASE    ,		cache_dir=__SCREAMING_SNAKE_CASE    ,		keep_in_memory=__SCREAMING_SNAKE_CASE    ,		**__SCREAMING_SNAKE_CASE      )
								_UpperCAmelCase	     =      Sql(
								    cache_dir=__SCREAMING_SNAKE_CASE    ,		features=__SCREAMING_SNAKE_CASE    ,		sql=__SCREAMING_SNAKE_CASE    ,		con=__SCREAMING_SNAKE_CASE    ,		**__SCREAMING_SNAKE_CASE    ,		)






		def 					UpperCAmelCase			(     self      ):
								_UpperCAmelCase	     =      None
								_UpperCAmelCase	     =      None
								_UpperCAmelCase	     =      None
								_UpperCAmelCase	     =      None

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

								# Build dataset for splits
								_UpperCAmelCase	     =      self.builder.as_dataset(
								    split="""train"""    ,		verification_mode=__SCREAMING_SNAKE_CASE    ,		in_memory=self.keep_in_memory      )
								return dataset




class    _A					:






		def __init__(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None    ,		_SCREAMING_SNAKE_CASE = None    ,		**_SCREAMING_SNAKE_CASE    ,		):







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

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






		def 					UpperCAmelCase			(     self      ):
								_UpperCAmelCase	     =      self.to_sql_kwargs.pop("""sql"""    ,		__SCREAMING_SNAKE_CASE      )
								_UpperCAmelCase	     =      self.to_sql_kwargs.pop("""con"""    ,		__SCREAMING_SNAKE_CASE      )
								_UpperCAmelCase	     =      self.to_sql_kwargs.pop("""index"""    ,		__SCREAMING_SNAKE_CASE      )

								_UpperCAmelCase	     =      self._write(index=__SCREAMING_SNAKE_CASE    ,		**self.to_sql_kwargs      )
								return written






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






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE    ,		**_SCREAMING_SNAKE_CASE      ):
								_UpperCAmelCase	     =      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	     =      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    ,		__SCREAMING_SNAKE_CASE    ,		__SCREAMING_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

def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):

      def get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		) -> str:
            UpperCamelCase__     : Union[str, Any]											=					[]
            UpperCamelCase__     : Union[str, Any]											=					min(len(_stra		)			,		len(_stra		)		) // 2
            for i, l in enumerate(_stra		):
                  UpperCamelCase__     : Optional[int]											=					int(max(0			,		i - limit		)		)
                  UpperCamelCase__     : List[str]											=					int(min(i + limit + 1			,		len(_stra		)		)		)
                  if l in _stra[left:right]:
                        matched.append(UpperCamelCase__		)
                        UpperCamelCase__     : List[str]											=					f'''{_stra[0:_stra.index(UpperCamelCase__		)]} {_stra[_stra.index(UpperCamelCase__		) + 1:]}'''

            return "".join(UpperCamelCase__		)

      # matching characters
      UpperCamelCase__     : Any											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : List[Any]											=					len(UpperCamelCase__		)

      # transposition
      UpperCamelCase__     : str											=					(
          len([(ca, ca) for ca, ca in zip(UpperCamelCase__			,		UpperCamelCase__		) if ca != ca]		) // 2
      )

      if not match_count:
            UpperCamelCase__     : Union[str, Any]											=					0.0
      else:
            UpperCamelCase__     : Optional[int]											=					(
                1
                / 3
                * (
                    match_count / len(UpperCamelCase__		)
                    + match_count / len(UpperCamelCase__		)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCamelCase__     : Dict											=					0
      for ca, ca in zip(stra[:4]			,		stra[:4]		):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(jaro_winkler("hello", "world"))


