

'''simple docstring'''

class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Union[str, Any]	)						->     List[Any]:
      UpperCAmelCase	:     str						 =			None
      UpperCAmelCase	:     List[Any]						 =			None
      UpperCAmelCase	:     Tuple						 =			graph

      self._normalize_graph(lowercase_					,      lowercase_	)
      UpperCAmelCase	:     Any						 =			len(lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			None


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Dict	)						->     Optional[int]:
      if sources is int:
       UpperCAmelCase	:     int						 =			[sources]
      if sinks is int:
       UpperCAmelCase	:     List[Any]						 =			[sinks]

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

      UpperCAmelCase	:     Tuple						 =			sources[0]
      UpperCAmelCase	:     int						 =			sinks[0]

      # make fake vertex if there are more
      # than one source or sink
      if len(lowercase_	) > 1 or len(lowercase_	) > 1:
       UpperCAmelCase	:     Union[str, 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	:     Optional[int]						 =			max_input_flow
       UpperCAmelCase	:     str						 =			0

       UpperCAmelCase	:     Dict						 =			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	:     int						 =			size - 1


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[str]:
      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			:					Dict					,      lowercase_			:					Tuple	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[Any]						 =			algorithm(self	)




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Optional[Any]					,      lowercase_			:					str	)						->     str:
      UpperCAmelCase	:     Optional[Any]						 =			flow_network
      UpperCAmelCase	:     int						 =			flow_network.verticesCount
      UpperCAmelCase	:     Optional[int]						 =			flow_network.sourceIndex
      UpperCAmelCase	:     Tuple						 =			flow_network.sinkIndex
      # it's just a reference, so you shouldn't change
      # it in your algorithms, use deep copy before doing that
      UpperCAmelCase	:     Optional[int]						 =			flow_network.graph
      UpperCAmelCase	:     Tuple						 =			False


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict:
      if not self.executed:
       self._algorithm()
       UpperCAmelCase	:     Optional[int]						 =			True




     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      pass




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[str]					,      lowercase_			:					Tuple	)						->     int:
      super().__init__(lowercase_	)
      # use this to save your result
      UpperCAmelCase	:     int						 =			-1




     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Dict:
      if not self.executed:
       raise Exception('You should execute algorithm before using its result!'	)

      return self.maximum_flow




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					Tuple					,      lowercase_			:					Any	)						->     Tuple:
      super().__init__(lowercase_	)

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

      UpperCAmelCase	:     Optional[int]						 =			[0] * self.verticies_count
      UpperCAmelCase	:     Optional[int]						 =			[0] * self.verticies_count


     def        UpperCAmelCase_	(		self			:					str	)						->     List[str]:
      UpperCAmelCase	:     Optional[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	:     Tuple						 =			[
          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(lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			vertices_list[i]
       UpperCAmelCase	:     str						 =			self.heights[vertex_index]
       self.process_vertex(lowercase_	)
       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(lowercase_	)	)
        UpperCAmelCase	:     Optional[Any]						 =			0
       else:
        i += 1

      UpperCAmelCase	:     Union[str, Any]						 =			sum(self.preflow[self.source_index]	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					List[Any]	)						->     int:
      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(lowercase_					,      lowercase_	)

       self.relabel(lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					int	)						->     Union[str, Any]:
      UpperCAmelCase	:     List[str]						 =			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			:					Dict					,      lowercase_			:					Any	)						->     str:
      UpperCAmelCase	:     Union[str, 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	:     List[str]						 =			min_height + 1


if __name__ == "__main__":
 lowercase__         =      [0]
 lowercase__         =      [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__         =      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




