
'''simple docstring'''




import logging
import os

from .state import PartialState





class  A				(							logging.LoggerAdapter						):





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         A       :   List[str]					=						PartialState()
         return not main_process_only or (main_process_only and state.is_main_process)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         if PartialState._shared_state == {}:
             raise RuntimeError(
                 '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''	)
         A       :   Dict					=						kwargs.pop('''main_process_only'''						,			SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						kwargs.pop('''in_order'''						,			SCREAMING_SNAKE_CASE	)

         if self.isEnabledFor(SCREAMING_SNAKE_CASE	):
             if self._should_log(SCREAMING_SNAKE_CASE	):
                 A,  A       :   Optional[Any]					=						self.process(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
                 self.logger.log(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

             elif in_order:
                 A       :   str					=						PartialState()
                 for i in range(state.num_processes	):
                     if i == state.process_index:
                         A,  A       :   List[Any]					=						self.process(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
                         self.logger.log(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
                     state.wait_for_everyone()







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = None						):




    '''simple docstring'''



    if log_level is None:
        A       :   str					=						os.environ.get('''ACCELERATE_LOG_LEVEL'''		,				snake_case__						)
    A       :   Dict					=						logging.getLogger(snake_case__						)
    if log_level is not None:
        logger.setLevel(log_level.upper()						)
        logger.root.setLevel(log_level.upper()						)
    return MultiProcessAdapter(snake_case__		,				{}						)






'''simple docstring'''




class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   Any					=						None
         A       :   Optional[Any]					=						None
         A       :   Tuple					=						graph

         self._normalize_graph(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						len(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						None





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         if sources is int:
             A       :   Dict					=						[sources]
         if sinks is int:
             A       :   str					=						[sinks]

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

         A       :   Optional[int]					=						sources[0]
         A       :   Union[str, Any]					=						sinks[0]

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

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

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





     def 				__lowerCAmelCase							(							self	) ->				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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						algorithm(self	)





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						flow_network
         A       :   Optional[Any]					=						flow_network.verticesCount
         A       :   Tuple					=						flow_network.sourceIndex
         A       :   Dict					=						flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         A       :   str					=						flow_network.graph
         A       :   Optional[Any]					=						False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         if not self.executed:
             self._algorithm()
             A       :   Optional[int]					=						True





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)
         # use this to save your result
         A       :   List[str]					=						-1





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






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

         return self.maximum_flow





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)

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

         A       :   Union[str, Any]					=						[0] * self.verticies_count
         A       :   List[Any]					=						[0] * self.verticies_count





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Tuple					=						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       :   Optional[Any]					=						[
             i
             for i in range(self.verticies_count	)
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         A       :   Union[str, Any]					=						0
         while i < len(SCREAMING_SNAKE_CASE	):
             A       :   str					=						vertices_list[i]
             A       :   List[str]					=						self.heights[vertex_index]
             self.process_vertex(SCREAMING_SNAKE_CASE	)
             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(SCREAMING_SNAKE_CASE	)	)
                 A       :   int					=						0
             else:
                 i += 1

         A       :   Optional[Any]					=						sum(self.preflow[self.source_index]	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """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(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

             self.relabel(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Dict					=						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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   Dict					=						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       :   Dict					=						self.heights[to_index]

         if min_height is not None:
             A       :   Dict					=						min_height + 1


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			[0]
      lowercase			:				List[Any]    		=			[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],
      # ]
      lowercase			:				int    		=			[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

      # prepare our network
      lowercase			:				List[str]    		=			FlowNetwork(graph, entrances, exits)
      # set algorithm
      flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
      # and calculate
      lowercase			:				List[str]    		=			flow_network.find_maximum_flow()

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




