






'''simple docstring'''





class  snake_case__      :




					def __init__(  self  :		Optional[Any]				, _A  :		Dict				, _A  :		str				, _A  :		Any      )    ->							Tuple:
								UpperCAmelCase_      :       Optional[int]          =       None
								UpperCAmelCase_      :       Tuple          =       None
								UpperCAmelCase_      :       int          =       graph

								self._normalize_graph(A_				, A_      )
								UpperCAmelCase_      :       Dict          =       len(A_      )
								UpperCAmelCase_      :       Optional[int]          =       None




					def 		A      (  self  :		Dict				, _A  :		str				, _A  :		List[Any]      )    ->							int:


								if sources is int:
											UpperCAmelCase_      :       Union[str, Any]          =       [sources]
								if sinks is int:
											UpperCAmelCase_      :       Optional[int]          =       [sinks]

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

								UpperCAmelCase_      :       str          =       sources[0]
								UpperCAmelCase_      :       str          =       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_      :       Union[str, 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_      :       Any          =       max_input_flow
											UpperCAmelCase_      :       Optional[int]          =       0

											UpperCAmelCase_      :       Tuple          =       len(self.graph      ) + 1
											for room in self.graph:
														room.append(0      )
											self.graph.append([0] * size      )
											for i in sinks:
														UpperCAmelCase_      :       Tuple          =       max_input_flow
											UpperCAmelCase_      :       Union[str, Any]          =       size - 1




					def 		A      (  self  :		Dict      )    ->							List[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  :		List[str]				, _A  :		Dict      )    ->							str:
								UpperCAmelCase_      :       Any          =       algorithm(self      )






class  snake_case__      :




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




					def 		A      (  self  :		Dict      )    ->							Union[str, Any]:


								if not self.executed:
											self._algorithm()
											UpperCAmelCase_      :       Optional[Any]          =       True





					def 		A      (  self  :		Union[str, Any]      )    ->							int:
								pass






class  snake_case__      (					_a):




					def __init__(  self  :		Union[str, Any]				, _A  :		int      )    ->							Union[str, Any]:
								super().__init__(A_      )
								# use this to save your result
								UpperCAmelCase_      :       Tuple          =       -1





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:


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

								return self.maximum_flow






class  snake_case__      (					_a):




					def __init__(  self  :		List[str]				, _A  :		Tuple      )    ->							Any:
								super().__init__(A_      )

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

								UpperCAmelCase_      :       Optional[int]          =       [0] * self.verticies_count
								UpperCAmelCase_      :       Optional[Any]          =       [0] * self.verticies_count




					def 		A      (  self  :		Dict      )    ->							List[Any]:
								UpperCAmelCase_      :       str          =       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_      :       Any          =       [
								    i
								    for i in range(self.verticies_count      )
								    if i != self.source_index and i != self.sink_index
								]

								# move through list
								UpperCAmelCase_      :       List[Any]          =       0
								while i < len(A_      ):
											UpperCAmelCase_      :       Union[str, Any]          =       vertices_list[i]
											UpperCAmelCase_      :       Any          =       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_      :       str          =       0
											else:
														i += 1

								UpperCAmelCase_      :       Any          =       sum(self.preflow[self.source_index]      )




					def 		A      (  self  :		Optional[int]				, _A  :		Optional[int]      )    ->							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  :		List[Any]				, _A  :		Union[str, Any]				, _A  :		List[str]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Tuple          =       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  :		Optional[int]				, _A  :		Optional[Any]      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[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_      :       str          =       self.heights[to_index]

								if min_height is not None:
											UpperCAmelCase_      :       Union[str, Any]          =       min_height + 1


if __name__ == "__main__":
						_UpperCamelCase						:						List[Any]  =					[0]
						_UpperCamelCase						:						Optional[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],
						# ]
						_UpperCamelCase						:						List[Any]  =					[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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






"""simple docstring"""

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  int           =		field(
          default=1_28      , metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''simple docstring'''


       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						os.path.join(
           cache_dir if cache_dir is not None else args.data_dir   ,							F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''   ,							)
       __lowercase						=						self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       __lowercase						=						cached_features_file + """.lock"""
       with FileLock(A_				):
        if os.path.exists(A_				) and not args.overwrite_cache:
         __lowercase						=						time.time()
         __lowercase						=						torch.load(A_				)
         logger.info(
             F'''Loading features from cached file {cached_features_file} [took %.3f s]'''   ,							time.time() - start				)
        else:
         logger.info(F'''Creating features from dataset file at {args.data_dir}'''				)

         if mode == Split.dev:
          __lowercase						=						self.processor.get_dev_examples(args.data_dir				)
         elif mode == Split.test:
          __lowercase						=						self.processor.get_test_examples(args.data_dir				)
         else:
          __lowercase						=						self.processor.get_train_examples(args.data_dir				)
         if limit_length is not None:
          __lowercase						=						examples[:limit_length]
         __lowercase						=						glue_convert_examples_to_features(
             A_   ,							A_   ,							max_length=args.max_seq_length   ,							label_list=A_   ,							output_mode=self.output_mode   ,							)
         __lowercase						=						time.time()
         torch.save(self.features   ,							A_				)
         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
         logger.info(
             F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)
      def __len__(		self						:						int				):






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list
