
'''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 				_lowerCamelCase (      lowercase__ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[Any]:




           """simple docstring"""







           super().__init__(features=__lowercase						,						cache_dir=__lowercase						,						keep_in_memory=__lowercase						,						**__lowercase  )
           UpperCamelCase__    :   Optional[Any]					=				Sql(
               cache_dir=__lowercase						,						features=__lowercase						,						sql=__lowercase						,						con=__lowercase						,						**__lowercase						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				None
           UpperCamelCase__    :   Optional[Any]					=				None
           UpperCamelCase__    :   List[str]					=				None
           UpperCamelCase__    :   List[Any]					=				None

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

           # Build dataset for splits
           UpperCamelCase__    :   int					=				self.builder.as_dataset(
               split='''train'''						,						verification_mode=__lowercase						,						in_memory=self.keep_in_memory  )
           return dataset




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Tuple:




           """simple docstring"""







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

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.to_sql_kwargs.pop('''sql'''						,						__lowercase  )
           UpperCamelCase__    :   Dict					=				self.to_sql_kwargs.pop('''con'''						,						__lowercase  )
           UpperCamelCase__    :   List[str]					=				self.to_sql_kwargs.pop('''index'''						,						__lowercase  )

           UpperCamelCase__    :   List[Any]					=				self._write(index=__lowercase						,						**self.to_sql_kwargs  )
           return written






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   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:
                 UpperCamelCase__				,UpperCamelCase__    :   List[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						,						__lowercase						,						__lowercase  )]						,						)						,						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




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


