






class      UpperCamelCase :







  """simple docstring"""



  def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


      '''simple docstring'''
      A	 =      None
      A	 =      None
      A	 =      graph

      self._normalize_graph(__UpperCamelCase						,__UpperCamelCase		)
      A	 =      len(__UpperCamelCase		)
      A	 =      None
  def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[Any]:


      '''simple docstring'''
      if sources is int:
          A	 =      [sources]
      if sinks is int:
          A	 =      [sinks]

      if len(__UpperCamelCase		) == 0 or len(__UpperCamelCase		) == 0:
          return

      A	 =      sources[0]
      A	 =      sinks[0]

      # make fake vertex if there are more
      # than one source or sink
      if len(__UpperCamelCase		) > 1 or len(__UpperCamelCase		) > 1:
          A	 =      0
          for i in sources:
              max_input_flow += sum(self.graph[i]		)

          A	 =      len(self.graph		) + 1
          for room in self.graph:
              room.insert(0						,0		)
          self.graph.insert(0						,[0] * size		)
          for i in sources:
              A	 =      max_input_flow
          A	 =      0

          A	 =      len(self.graph		) + 1
          for room in self.graph:
              room.append(0		)
          self.graph.append([0] * size		)
          for i in sinks:
              A	 =      max_input_flow
          A	 =      size - 1
  def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


      '''simple docstring'''
      if self.maximum_flow_algorithm is None:
          raise Exception('You need to set maximum flow algorithm before.'		)
      if self.source_index is None or self.sink_index is None:
          return 0

      self.maximum_flow_algorithm.execute()
      return self.maximum_flow_algorithm.getMaximumFlow()







  def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


      '''simple docstring'''
      A	 =      algorithm(self		)


class      UpperCamelCase :







  """simple docstring"""



  def __init__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


      '''simple docstring'''
      A	 =      flow_network
      A	 =      flow_network.verticesCount
      A	 =      flow_network.sourceIndex
      A	 =      flow_network.sinkIndex
      # it's just a reference, so you shouldn't change
      # it in your algorithms, use deep copy before doing that
      A	 =      flow_network.graph
      A	 =      False
  def       A(  self		:       int		)    ->   int:


      '''simple docstring'''
      if not self.executed:
          self._algorithm()
          A	 =      True







  def       A(  self		:       int		)    ->   Optional[Any]:


      '''simple docstring'''
      pass


class      UpperCamelCase (				snake_case__ ):







  """simple docstring"""



  def __init__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   int:


      '''simple docstring'''
      super().__init__(__UpperCamelCase		)
      # use this to save your result
      A	 =      -1







  def       A(  self		:       List[str]		)    ->   Union[str, Any]:


      '''simple docstring'''
      if not self.executed:
          raise Exception('You should execute algorithm before using its result!'		)

      return self.maximum_flow


class      UpperCamelCase (				snake_case__ ):







  """simple docstring"""



  def __init__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Tuple:


      '''simple docstring'''
      super().__init__(__UpperCamelCase		)

      A	 =      [[0] * self.verticies_count for i in range(self.verticies_count		)]

      A	 =      [0] * self.verticies_count
      A	 =      [0] * self.verticies_count
  def       A(  self		:       Tuple		)    ->   Union[str, Any]:


      '''simple docstring'''
      A	 =      self.verticies_count

      # push some substance to graph
      for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]		):
          self.preflow[self.source_index][nextvertex_index] += bandwidth
          self.preflow[nextvertex_index][self.source_index] -= bandwidth
          self.excesses[nextvertex_index] += bandwidth

      # Relabel-to-front selection rule
      A	 =      [
          i
          for i in range(self.verticies_count		)
          if i != self.source_index and i != self.sink_index
      ]

      # move through list
      A	 =      0
      while i < len(__UpperCamelCase		):
          A	 =      vertices_list[i]
          A	 =      self.heights[vertex_index]
          self.process_vertex(__UpperCamelCase		)
          if self.heights[vertex_index] > previous_height:
              # if it was relabeled, swap elements
              # and start from 0 index
              vertices_list.insert(0						,vertices_list.pop(__UpperCamelCase		)		)
              A	 =      0
          else:
              i += 1

      A	 =      sum(self.preflow[self.source_index]		)
  def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   int:


      '''simple docstring'''
      while self.excesses[vertex_index] > 0:
          for neighbour_index in range(self.verticies_count		):
              # if it's neighbour and current vertex is higher
              if (
                  self.graph[vertex_index][neighbour_index]
                  - self.preflow[vertex_index][neighbour_index]
                  > 0
                  and self.heights[vertex_index] > self.heights[neighbour_index]
              ):
                  self.push(__UpperCamelCase						,__UpperCamelCase		)

          self.relabel(__UpperCamelCase		)
  def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Optional[int]:


      '''simple docstring'''
      A	 =      min(
          self.excesses[from_index]						,self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,)
      self.preflow[from_index][to_index] += preflow_delta
      self.preflow[to_index][from_index] -= preflow_delta
      self.excesses[from_index] -= preflow_delta
      self.excesses[to_index] += preflow_delta







  def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   str:


      '''simple docstring'''
      A	 =      None
      for to_index in range(self.verticies_count		):
          if (
              self.graph[vertex_index][to_index]
              - self.preflow[vertex_index][to_index]
              > 0
          ) and (min_height is None or self.heights[to_index] < min_height):
              A	 =      self.heights[to_index]

      if min_height is not None:
          A	 =      min_height + 1


if __name__ == "__main__":
     lowerCAmelCase_           = [0]
     lowerCAmelCase_           = [3]
     # graph = [
     #     [0, 0, 4, 6, 0, 0],
     #     [0, 0, 5, 2, 0, 0],
     #     [0, 0, 0, 0, 4, 4],
     #     [0, 0, 0, 0, 6, 6],
     #     [0, 0, 0, 0, 0, 0],
     #     [0, 0, 0, 0, 0, 0],
     # ]
     lowerCAmelCase_           = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

     # prepare our network
     lowerCAmelCase_           = FlowNetwork(graph, entrances, exits)
     # set algorithm
     flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
     # and calculate
     lowerCAmelCase_           = flow_network.find_maximum_flow()

     print(f'''maximum flow is {maximum_flow}''')













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

lowerCAmelCase_           = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='







def      snake_case		(							UpperCAmelCase	:       str = "mumbai"   ):
				A	 =      BeautifulSoup(requests.get(url + location   ).content,						'html.parser'   )
				# This attribute finds out all the specifics listed in a job
				for job in soup.find_all('div',						attrs={'data-tn-component': 'organicJob'}   ):
								A	 =      job.find('a',						attrs={'data-tn-element': 'jobTitle'}   ).text.strip()
								A	 =      job.find('span',						{'class': 'company'}   ).text.strip()
								yield job_title, company_name


if __name__ == "__main__":
					for i, job in enumerate(fetch_jobs('Bangalore'), 1):
										print(f'''Job {i:>2} is {job[0]} at {job[1]}''')





