



'''simple docstring'''





class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A       )    ->     Any:


      """simple docstring"""



      _a      =  None
      _a      =  None
      _a      =  graph

      self._normalize_graph(A ,   A       )
      _a      =  len(A       )
      _a      =  None



 def 					a__  (self ,   A ,   A       )    ->     Any:


      """simple docstring"""



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

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

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

      # make fake vertex if there are more
      # than one source or sink
      if len(A       ) > 1 or len(A       ) > 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 					a__  (self       )    ->     int:


      """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 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  algorithm(self       )






class __A			:



 '''simple docstring'''







 def __init__(self ,   A       )    ->     Any:


      """simple docstring"""



      _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 					a__  (self       )    ->     int:


      """simple docstring"""



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



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      pass






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A       )    ->     List[str]:


      """simple docstring"""



      super().__init__(A       )
      # use this to save your result
      _a      =  -1



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



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

      return self.maximum_flow






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      super().__init__(A       )

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

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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _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(A       ):
           _a      =  vertices_list[i]
           _a      =  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       )       )
                _a      =  0
           else:
                i += 1

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



 def 					a__  (self ,   A       )    ->     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(A ,   A       )

           self.relabel(A       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      _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 					a__  (self ,   A       )    ->     Any:


      """simple docstring"""



      _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__":
  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'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



