




'''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   __UpperCamelCase       ( lowerCamelCase__				):






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = False,		**lowerCAmelCase,		):



              """simple docstring"""







              super().__init__(features=lowerCAmelCase,		cache_dir=lowerCAmelCase,		keep_in_memory=lowerCAmelCase,		**lowerCAmelCase  )
              lowerCamelCase_							=Sql(
                  cache_dir=lowerCAmelCase,		features=lowerCAmelCase,		sql=lowerCAmelCase,		con=lowerCAmelCase,		**lowerCAmelCase,		)






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=None
              lowerCamelCase_							=None
              lowerCamelCase_							=None
              lowerCamelCase_							=None

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

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





class   __UpperCamelCase       :






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



              """simple docstring"""







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

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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.to_sql_kwargs.pop('''sql''',		lowerCAmelCase  )
              lowerCamelCase_							=self.to_sql_kwargs.pop('''con''',		lowerCAmelCase  )
              lowerCamelCase_							=self.to_sql_kwargs.pop('''index''',		lowerCAmelCase  )

              lowerCamelCase_							=self._write(index=lowerCAmelCase,		**self.to_sql_kwargs  )
              return written






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=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_							=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,		lowerCAmelCase,		lowerCAmelCase  )],		),		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 functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



