





class   A							:



      def __init__(						self:    List[str]		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    str							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =None
         UpperCAmelCase_           =graph

         self._normalize_graph(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =len(_lowerCAmelCase							)
         UpperCAmelCase_           =None



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Dict							)    ->     str:
         '''simple docstring'''

         if sources is int:
            UpperCAmelCase_           =[sources]
         if sinks is int:
            UpperCAmelCase_           =[sinks]

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

         UpperCAmelCase_           =sources[0]
         UpperCAmelCase_           =sinks[0]

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

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

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



      def 						lowerCAmelCase__     (						self:    str							)    ->     Tuple:
         '''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:    int		,	_lowerCAmelCase:    Tuple							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =algorithm(self							)

class   A							:



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Union[str, Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =flow_network
         UpperCAmelCase_           =flow_network.verticesCount
         UpperCAmelCase_           =flow_network.sourceIndex
         UpperCAmelCase_           =flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         UpperCAmelCase_           =flow_network.graph
         UpperCAmelCase_           =False



      def 						lowerCAmelCase__     (						self:    int							)    ->     List[str]:
         '''simple docstring'''

         if not self.executed:
            self._algorithm()
            UpperCAmelCase_           =True






      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     int:
         '''simple docstring'''

         pass

class   A							(						__lowercase    ):



      def __init__(						self:    Optional[int]		,	_lowerCAmelCase:    Optional[int]							)    ->     List[str]:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)
         # use this to save your result
         UpperCAmelCase_           =-1






      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Tuple:
         '''simple docstring'''

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

         return self.maximum_flow

class   A							(						__lowercase    ):



      def __init__(						self:    str		,	_lowerCAmelCase:    List[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)

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

         UpperCAmelCase_           =[0] * self.verticies_count
         UpperCAmelCase_           =[0] * self.verticies_count



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =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_           =[
             i
             for i in range(self.verticies_count							)
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         UpperCAmelCase_           =0
         while i < len(_lowerCAmelCase							):
            UpperCAmelCase_           =vertices_list[i]
            UpperCAmelCase_           =self.heights[vertex_index]
            self.process_vertex(_lowerCAmelCase							)
            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(_lowerCAmelCase							)							)
               UpperCAmelCase_           =0
            else:
               i += 1

         UpperCAmelCase_           =sum(self.preflow[self.source_index]							)



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Optional[Any]							)    ->     List[str]:
         '''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(_lowerCAmelCase		,	_lowerCAmelCase							)

            self.relabel(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    str		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    int							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =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:    str		,	_lowerCAmelCase:    Any							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =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_           =self.heights[to_index]

         if min_height is not None:
            UpperCAmelCase_           =min_height + 1


if __name__ == "__main__":
     __lowercase	: int       =[0]
     __lowercase	: Optional[int]       =[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	: str       =[[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	: Optional[int]       =flow_network.find_maximum_flow()

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











'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      _lowerCAmelCase					=				np.zeros((image.shape[0], image.shape[1])    )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,      image.shape[0]    ):
            for j in range(0				,      image.shape[1]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






