


class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Tuple        =       None
        lowercase__:       int        =       None
        lowercase__:       Dict        =       graph

        self._normalize_graph(__snake_case    ,			__snake_case			)
        lowercase__:       Tuple        =       len(__snake_case			)
        lowercase__:       List[Any]        =       None
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        if sources is int:
               lowercase__:       Any        =       [sources]
        if sinks is int:
               lowercase__:       Dict        =       [sinks]

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

        lowercase__:       Optional[Any]        =       sources[0]
        lowercase__:       Tuple        =       sinks[0]

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

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

               lowercase__:       Dict        =       len(self.graph			) + 1
               for room in self.graph:
                      room.append(0			)
               self.graph.append([0] * size			)
               for i in sinks:
                      lowercase__:       Union[str, Any]        =       max_input_flow
               lowercase__:       Dict        =       size - 1
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       algorithm(self			)





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       flow_network
        lowercase__:       Any        =       flow_network.verticesCount
        lowercase__:       Optional[int]        =       flow_network.sourceIndex
        lowercase__:       int        =       flow_network.sinkIndex
        # it's just a reference, so you shouldn't change
        # it in your algorithms, use deep copy before doing that
        lowercase__:       Optional[Any]        =       flow_network.graph
        lowercase__:       Union[str, Any]        =       False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        if not self.executed:
               self._algorithm()
               lowercase__:       List[str]        =       True
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        pass





class   __a      (   A__				):
 def __init__(       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        super().__init__(__snake_case			)
        # use this to save your result
        lowercase__:       List[str]        =       -1
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




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

        return self.maximum_flow





class   __a      (   A__				):
 def __init__(       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        super().__init__(__snake_case			)

        lowercase__:       str        =       [[0] * self.verticies_count for i in range(self.verticies_count			)]

        lowercase__:       str        =       [0] * self.verticies_count
        lowercase__:       Tuple        =       [0] * self.verticies_count
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       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
        lowercase__:       str        =       [
            i
            for i in range(self.verticies_count			)
            if i != self.source_index and i != self.sink_index
        ]

        # move through list
        lowercase__:       str        =       0
        while i < len(__snake_case			):
               lowercase__:       Optional[int]        =       vertices_list[i]
               lowercase__:       List[Any]        =       self.heights[vertex_index]
               self.process_vertex(__snake_case			)
               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(__snake_case			)			)
                      lowercase__:       Optional[int]        =       0
               else:
                      i += 1

        lowercase__:       List[str]        =       sum(self.preflow[self.source_index]			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    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(__snake_case    ,			__snake_case			)

               self.relabel(__snake_case			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       str        =       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):
                      lowercase__:       Optional[int]        =       self.heights[to_index]

        if min_height is not None:
               lowercase__:       Tuple        =       min_height + 1


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

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


