


"""simple docstring"""




class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							=None
         __UpperCamelCase   							=None
         __UpperCamelCase   							=graph

         self._normalize_graph(A_ ,       A_   )
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=None






    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         if sources is int:
              __UpperCamelCase   							=[sources]
         if sinks is int:
              __UpperCamelCase   							=[sinks]

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

         __UpperCamelCase   							=sources[0]
         __UpperCamelCase   							=sinks[0]

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

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

              __UpperCamelCase   							=len(self.graph   ) + 1
              for room in self.graph:
                   room.append(0   )
              self.graph.append([0] * size   )
              for i in sinks:
                   __UpperCamelCase   							=max_input_flow
              __UpperCamelCase   							=size - 1






    def _a (      self   )  ->	Optional[Any]:
         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]:
         __UpperCamelCase   							=algorithm(self   )






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Any:
         __UpperCamelCase   							=flow_network
         __UpperCamelCase   							=flow_network.verticesCount
         __UpperCamelCase   							=flow_network.sourceIndex
         __UpperCamelCase   							=flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         __UpperCamelCase   							=flow_network.graph
         __UpperCamelCase   							=False






    def _a (      self   )  ->	Optional[int]:
         if not self.executed:
              self._algorithm()
              __UpperCamelCase   							=True






    def _a (      self   )  ->	Tuple:
         pass






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Optional[int]:
         super().__init__(A_   )
         # use this to save your result
         __UpperCamelCase   							=-1






    def _a (      self   )  ->	Optional[Any]:
         if not self.executed:
              raise Exception('You should execute algorithm before using its result!'   )

         return self.maximum_flow






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	int:
         super().__init__(A_   )

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

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






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=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   							=[
             i
             for i in range(self.verticies_count   )
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         __UpperCamelCase   							=0
         while i < len(A_   ):
              __UpperCamelCase   							=vertices_list[i]
              __UpperCamelCase   							=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   							=0
              else:
                   i += 1

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






    def _a (      self ,       A_   )  ->	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(A_ ,       A_   )

              self.relabel(A_   )






    def _a (      self ,       A_ ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=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_   )  ->	str:
         __UpperCamelCase   							=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   							=self.heights[to_index]

         if min_height is not None:
              __UpperCamelCase   							=min_height + 1


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

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

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









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
         pl_module.logger.log_metrics(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}   )
         # Log results
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=od / 'test_generations.txt'
         else:
              # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
              # If people want this it will be easy enough to add back.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
              results_file.parent.mkdir(exist_ok=A_   )
              generations_file.parent.mkdir(exist_ok=A_   )
         with open(A_ ,       'a+'   ) as writer:
              for key in sorted(A_   ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

         if "preds" in metrics:
              __UpperCamelCase   							='\n'.join(metrics['preds']   )
              generations_file.open('w+'   ).write(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=count_trainable_parameters(A_   )
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





