





class 					A__   :





      def __init__( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   None
       UpperCamelCase	:					int       	=   None
       UpperCamelCase	:					Optional[int]       	=   graph

       self._normalize_graph(A_       ,							A_   )
       UpperCamelCase	:					Tuple       	=   len(A_   )
       UpperCamelCase	:					str       	=   None





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       if sources is int:
        UpperCamelCase	:					int       	=   [sources]
       if sinks is int:
        UpperCamelCase	:					Union[str, Any]       	=   [sinks]

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

       UpperCamelCase	:					Any       	=   sources[0]
       UpperCamelCase	:					Union[str, Any]       	=   sinks[0]

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

        UpperCamelCase	:					List[Any]       	=   len(self.graph   ) + 1
        for room in self.graph:
         room.insert(0       ,							0   )
        self.graph.insert(0       ,							[0] * size   )
        for i in sources:
         UpperCamelCase	:					Union[str, Any]       	=   max_input_flow
        UpperCamelCase	:					str       	=   0

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





      def        __UpperCamelCase( self   ):
       '''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        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   algorithm(self   )



class 					A__   :





      def __init__( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if not self.executed:
        self._algorithm()
        UpperCamelCase	:					Dict       	=   True





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )
       # use this to save your result
       UpperCamelCase	:					List[Any]       	=   -1





      def        __UpperCamelCase( self   ):
       '''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       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )

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

       UpperCamelCase	:					str       	=   [0] * self.verticies_count
       UpperCamelCase	:					Dict       	=   [0] * self.verticies_count





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   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
       UpperCamelCase	:					int       	=   [
           i
           for i in range(self.verticies_count   )
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       UpperCamelCase	:					int       	=   0
       while i < len(A_   ):
        UpperCamelCase	:					List[str]       	=   vertices_list[i]
        UpperCamelCase	:					str       	=   self.heights[vertex_index]
        self.process_vertex(A_   )
        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(A_   )   )
         UpperCamelCase	:					Optional[int]       	=   0
        else:
         i += 1

       UpperCamelCase	:					Dict       	=   sum(self.preflow[self.source_index]   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''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(A_       ,							A_   )

        self.relabel(A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					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        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   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):
         UpperCamelCase	:					Tuple       	=   self.heights[to_index]

       if min_height is not None:
        UpperCamelCase	:					Optional[Any]       	=   min_height + 1


if __name__ == "__main__":
    __lowerCamelCase       :     Optional[Any]   =     [0]
    __lowerCamelCase       :     str   =     [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       :     Union[str, Any]   =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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







"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_ =			50000
a_ =			5000

a_ ,			a_ =			os.path.split(__file__)
a_ =			os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Tuple						)   ->Tuple:



    '''simple docstring'''







    for i in range(snake_case_						):
        __A    :						int						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Optional[Any]				,snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    for i in range(0				,len(snake_case_						)				,snake_case_						):
        __A    :						List[str]						 =     dataset[i : i + batch_size]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  List[Any]				,snake_case_  :  Any						)   ->int:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(snake_case_						):
            __A    :						Union[str, Any]						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Any				,snake_case_  :  Union[str, Any]				,snake_case_  :  Optional[int]						)   ->Union[str, Any]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(0				,snake_case_				,snake_case_						):
            __A    :						Dict						 =     dataset[i : i + batch_size]







def 		__lowercase     (   )   ->Optional[int]:



    '''simple docstring'''







    __A    :						int						 =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A    :						Optional[int]						 =     [
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]

    __A    :						int						 =     [
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''						)
        __A    :						Any						 =     datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A    :						List[Any]						 =     generate_example_dataset(
            os.path.join(snake_case_				,'''dataset.arrow'''						)				,snake_case_				,num_examples=snake_case_				,seq_shapes={'''list''': (100,)}				,)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__				,str(snake_case_						)						)
            __A    :						Dict						 =     func(snake_case_				,**snake_case_						)

        print('''shuffling dataset'''						)
        __A    :						int						 =     dataset.shuffle()
        print('''Second set of iterations (after shuffling'''						)
        for func, kwargs in functions_shuffled:
            print('''shuffled '''				,func.__name__				,str(snake_case_						)						)
            __A    :						Optional[Any]						 =     func(
                snake_case_				,**snake_case_						)

    with open(snake_case_				,'''wb'''						) as f:
        f.write(json.dumps(snake_case_						).encode('''utf-8'''						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_iterating()




