






"""simple docstring"""


_UpperCamelCase				:    List[Any]     =						float('inf')






class 						snake_case  :





    def __init__(							self	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   [0] * n
      a     :			Any				=   [0] * n
      a     :			Optional[int]				=   [0] * n
      a     :			Dict				=   [[] for _ in range(A			)]





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					List[str]				,		A	:					int				,		A	:					List[Any]				,		A	:					Tuple=0			):


      '''simple docstring'''




      self.adj[a].append([b, len(self.adj[b]			), c, 0]			)
      self.adj[b].append([a, len(self.adj[a]			) - 1, rcap, 0]			)





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					List[Any]				,		A	:					Union[str, Any]				,		A	:					int			):


      '''simple docstring'''




      if vertex == sink or not flow:
        return flow

      for i in range(self.ptr[vertex]				,		len(self.adj[vertex]			)			):
        a     :			Optional[int]				=   self.adj[vertex][i]
        if self.lvl[e[0]] == self.lvl[vertex] + 1:
          a     :			List[Any]				=   self.depth_first_search(e[0]				,		A				,		min(A				,		e[2] - e[3]			)			)
          if p:
            self.adj[vertex][i][3] += p
            self.adj[e[0]][e[1]][3] -= p
            return p
        a     :			Union[str, Any]				=   self.ptr[vertex] + 1
      return 0





    def 				lowerCamelCase__      (							self	:					str				,		A	:					List[str]				,		A	:					int			):


      '''simple docstring'''




      a,     a     :			List[str]				=   0, source
      for l in range(3_1			):  # noqa: E741  l = 30 maybe faster for random data
        while True:
          a,     a     :			str				=   [0] * len(self.q			), [0] * len(self.q			)
          a,     a,     a     :			List[Any]				=   0, 1, 1
          while qi < qe and not self.lvl[sink]:
            a     :			Optional[int]				=   self.q[qi]
            qi += 1
            for e in self.adj[v]:
              if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
                a     :			str				=   e[0]
                qe += 1
                a     :			Union[str, Any]				=   self.lvl[v] + 1

          a     :			Tuple				=   self.depth_first_search(A				,		A				,		A			)
          while p:
            flow += p
            a     :			Dict				=   self.depth_first_search(A				,		A				,		A			)

          if not self.lvl[sink]:
            break

      return flow
_UpperCamelCase				:    Tuple     =						Dinic(10)
_UpperCamelCase				:    List[str]     =						0
_UpperCamelCase				:    Union[str, Any]     =						9
for vertex in range(1, 5):
   graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
   graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
   graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))












"""simple docstring"""


import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy


_UpperCamelCase				:    Optional[Any]     =						datasets.utils.logging.get_logger(__name__)






@dataclass
class 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      if self.sql is None:
        raise ValueError('sql must be specified'			)
      if self.con is None:
        raise ValueError('con must be specified'			)





    def 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      a     :			str				=   config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      a     :			List[str]				=   config_kwargs['sql']
      if not isinstance(A				,		A			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(A				,		sqlalchemy.sql.Selectable			):
            a     :			Union[str, Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'			)[0] + '://'			)
            a     :			List[Any]				=   str(sql.compile(dialect=engine.dialect			)			)
            a     :			str				=   sql_str
          else:
            raise TypeError(
                F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
        else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
      a     :			List[str]				=   config_kwargs['con']
      if not isinstance(A				,		A			):
        a     :			Optional[Any]				=   id(A			)
        logger.info(
            F'''SQL connection \'con\' of type {type(A			)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''			)

      return super().create_config_id(A				,		custom_features=A			)





    @property
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   {
          'index_col': self.index_col,
          'columns': self.columns,
          'params': self.params,
          'coerce_float': self.coerce_float,
          'parse_dates': self.parse_dates,
      }
      return pd_read_sql_kwargs






class 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      return datasets.DatasetInfo(features=self.config.features			)





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={}			)]





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




      if self.config.features is not None:
        a     :			List[Any]				=   self.config.features.arrow_schema
        if all(not require_storage_cast(A			) for feature in self.config.features.values()			):
          # cheaper cast
          a     :			Dict				=   pa.Table.from_arrays([pa_table[field.name] for field in schema]				,		schema=A			)
        else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          a     :			Union[str, Any]				=   table_cast(A				,		A			)
      return pa_table





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			List[Any]				=   self.config.chunksize
      a     :			Union[str, Any]				=   pd.read_sql(
          self.config.sql				,		self.config.con				,		chunksize=A				,		**self.config.pd_read_sql_kwargs			)
      a     :			Optional[Any]				=   [sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(A			):
        a     :			List[Any]				=   pa.Table.from_pandas(A			)
        yield chunk_idx, self._cast_table(A			)




