




'''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 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Optional[Any]    ,		a       :  Dict    ,		a       :  List[Any]    ,		a       :  List[Any] = None    ,		a       :  Dict = None    ,		a       :  str = False    ,		**a       :  Dict    ,		)       ->   List[Any]:






    """simple docstring"""


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






 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     None
    A__       =     None
    A__       =     None
    A__       =     None

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

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




class 	_snake_case:






 def __init__(self       :  Optional[Any]    ,		a       :  List[Any]    ,		a       :  List[Any]    ,		a       :  Union[str, Any]    ,		a       :  Any = None    ,		a       :  Optional[Any] = None    ,		**a       :  List[str]    ,		)       ->   List[Any]:






    """simple docstring"""


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

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






 def 						_UpperCamelCase (self       :  int    )       ->   int:






    """simple docstring"""


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

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






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


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






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Optional[Any]    ,		**a       :  Dict    )       ->   int:






    """simple docstring"""


    A__       =     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:
       A__  ,			A__       =     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





"""simple docstring"""


UpperCAmelCase													=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

      while queue:
            A				  =		queue.pop(0				)
            for ind in range(len(graph[u]				)				):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a				)
                        A				  =		True
                        A				  =		u

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

            while s != source:
                  # Find the minimum value in select path
                  A				  =		min(_a     ,   graph[parent[s]][s]				)
                  A				  =		parent[s]

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            for j in range(len(graph[0]				)				):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)				)

      return res


if __name__ == "__main__":
  print(mincut(test_graph, source=0, sink=5))




