class      A							:


   def __init__(self     :						str       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  graph

         self._normalize_graph(_UpperCAmelCase       ,  _UpperCAmelCase		)
         UpperCAmelCase__								=  len(_UpperCAmelCase		)
         UpperCAmelCase__								=  None


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						int		)						->				Dict:
         """simple docstring"""



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

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

         UpperCAmelCase__								=  sources[0]
         UpperCAmelCase__								=  sinks[0]

         # make fake vertex if there are more
         # than one source or sink
         if len(_UpperCAmelCase		) > 1 or len(_UpperCAmelCase		) > 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 			lowercase_      (self     :						Optional[Any]		)						->				Dict:
         """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 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  algorithm(self		)





class      A							:


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple		)						->				int:
         """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 			lowercase_      (self     :						Optional[int]		)						->				Dict:
         """simple docstring"""



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





   def 			lowercase_      (self     :						List[Any]		)						->				Optional[Any]:
         """simple docstring"""



         pass





class      A							(				_UpperCAmelCase     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[int]		)						->				Dict:
         """simple docstring"""



         super().__init__(_UpperCAmelCase		)
         # use this to save your result
         UpperCAmelCase__								=  -1





   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



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

         return self.maximum_flow





class      A							(				_UpperCAmelCase     ):


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						int		)						->				Optional[Any]:
         """simple docstring"""



         super().__init__(_UpperCAmelCase		)

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

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


   def 			lowercase_      (self     :						Dict		)						->				Tuple:
         """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(_UpperCAmelCase		):
               UpperCAmelCase__								=  vertices_list[i]
               UpperCAmelCase__								=  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		)		)
                     UpperCAmelCase__								=  0
               else:
                     i += 1

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


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						List[Any]		)						->				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 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Optional[int]:
         """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 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Any:
         """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__":
      UpperCamelCase__											=					[0]
      UpperCamelCase__											=					[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],
      # ]
      UpperCamelCase__											=					[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
