


'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							None
      _a			      =							None
      _a			      =							graph

      self._normalize_graph(__magic_name__      ,				__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							None
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if sources is int:
           _a			      =							[sources]
      if sinks is int:
           _a			      =							[sinks]

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

      _a			      =							sources[0]
      _a			      =							sinks[0]

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

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

           _a			      =							len(self.graph						) + 1
           for room in self.graph:
                room.append(0						)
           self.graph.append([0] * size						)
           for i in sinks:
                _a			      =							max_input_flow
           _a			      =							size - 1
 def 							__UpperCAmelCase		( self						)					->							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      ,				__magic_name__						)					->							Tuple:
      _a			      =							algorithm(self						)





class  a     :
 def __init__( self      ,				__magic_name__						)					->							Any:
      _a			      =							flow_network
      _a			      =							flow_network.verticesCount
      _a			      =							flow_network.sourceIndex
      _a			      =							flow_network.sinkIndex
      # it's just a reference, so you shouldn't change
      # it in your algorithms, use deep copy before doing that
      _a			      =							flow_network.graph
      _a			      =							False
 def 							__UpperCAmelCase		( self						)					->							Any:
      if not self.executed:
           self._algorithm()
           _a			      =							True




 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      pass





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__						)					->							Tuple:
      super().__init__(__magic_name__						)
      # use this to save your result
      _a			      =							-1




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

      return self.maximum_flow





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__						)					->							Tuple:
      super().__init__(__magic_name__						)

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

      _a			      =							[0] * self.verticies_count
      _a			      =							[0] * self.verticies_count
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							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			      =							[
          i
          for i in range(self.verticies_count						)
          if i != self.source_index and i != self.sink_index
      ]

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

      _a			      =							sum(self.preflow[self.source_index]						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      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(__magic_name__      ,				__magic_name__						)

           self.relabel(__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Any:
      _a			      =							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      ,				__magic_name__						)					->							int:
      _a			      =							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			      =							self.heights[to_index]

      if min_height is not None:
           _a			      =							min_height + 1


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

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

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





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

