





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}""")







import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase       :     str   =     """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase       :     Tuple   =     direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCamelCase       :     List[str]   =     transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCamelCase       :     Optional[Any]   =     re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


__lowerCamelCase       :     List[str]   =     {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 		A_				(     _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					Optional[Any]       	=   None

 # source code of `config_class`
 UpperCamelCase	:					Tuple       	=   inspect.getsource(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[Any]       	=   _re_checkpoint.findall(_lowerCAmelCase     )

 # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
 # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
 for ckpt_name, ckpt_link in checkpoints:
  # allow the link to end with `/`
  if ckpt_link.endswith("/"     ):
   UpperCamelCase	:					Dict       	=   ckpt_link[:-1]

  # verify the checkpoint name corresponds to the checkpoint link
  UpperCamelCase	:					Any       	=   F"""https://huggingface.co/{ckpt_name}"""
  if ckpt_link == ckpt_link_from_name:
   UpperCamelCase	:					List[Any]       	=   ckpt_name
   break

 return checkpoint



def 		A_				(     )						-> List[str]:
 UpperCamelCase	:					Optional[int]       	=   []

 for config_class in list(CONFIG_MAPPING.values()     ):
  # Skip deprecated models
  if "models.deprecated" in config_class.__module__:
   continue
  UpperCamelCase	:					Union[str, Any]       	=   get_checkpoint_from_config_class(_lowerCAmelCase     )

  UpperCamelCase	:					Optional[int]       	=   config_class.__name__
  if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
   configs_without_checkpoint.append(_lowerCAmelCase     )

 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Any       	=   "\n".join(sorted(_lowerCAmelCase     )     )
  raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""     )


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()
