class __snake_case       :




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   graph

     self._normalize_graph(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   len(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   None




   def 			__a		(					self    :       Any ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__    							=   sources[0]
     SCREAMING_SNAKE_CASE__    							=   sinks[0]

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

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

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




   def 			__a		(					self    :       Dict   ):



     """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    :       List[Any] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   algorithm(self   )







class __snake_case       :




   def __init__(					self    :       List[Any] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   flow_network
     SCREAMING_SNAKE_CASE__    							=   flow_network.verticesCount
     SCREAMING_SNAKE_CASE__    							=   flow_network.sourceIndex
     SCREAMING_SNAKE_CASE__    							=   flow_network.sinkIndex
     # it's just a reference, so you shouldn't change
     # it in your algorithms, use deep copy before doing that
     SCREAMING_SNAKE_CASE__    							=   flow_network.graph
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     pass







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Tuple ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase   )
     # use this to save your result
     SCREAMING_SNAKE_CASE__    							=   -1




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






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

     return self.maximum_flow







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     super().__init__(_lowercase   )

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

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




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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
     SCREAMING_SNAKE_CASE__    							=   [
         i
         for i in range(self.verticies_count   )
         if i != self.source_index and i != self.sink_index
     ]

     # move through list
     SCREAMING_SNAKE_CASE__    							=   0
     while i < len(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   vertices_list[i]
       SCREAMING_SNAKE_CASE__    							=   self.heights[vertex_index]
       self.process_vertex(_lowercase   )
       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(_lowercase   )   )
         SCREAMING_SNAKE_CASE__    							=   0
       else:
         i += 1

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




   def 			__a		(					self    :       str ,			_lowercase    :       List[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(_lowercase ,			_lowercase   )

       self.relabel(_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Dict ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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    :       Tuple ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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):
         SCREAMING_SNAKE_CASE__    							=   self.heights[to_index]

     if min_height is not None:
       SCREAMING_SNAKE_CASE__    							=   min_height + 1


if __name__ == "__main__":
   __lowerCamelCase    :				Union[str, Any]							     =	[0]
   __lowerCamelCase    :				List[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],
   # ]
   __lowerCamelCase    :				int							     =	[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Optional[Any]							     =	{'''vocab_file''': '''vocab.txt'''}

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__lowerCamelCase    :				Optional[Any]							     =	{
    '''openbmb/cpm-ant-10b''': 1024,
}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict()
  with open(__UpperCamelCase				,  """r"""				,  encoding="""utf-8"""			) as reader:
    SCREAMING_SNAKE_CASE__    							=   reader.readlines()
  for index, token in enumerate(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   token.rstrip("""\n"""			)
    SCREAMING_SNAKE_CASE__    							=   index
  return vocab







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int="<unk>" ,			_lowercase    :       int=2_00   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab
     SCREAMING_SNAKE_CASE__    							=   unk_token
     SCREAMING_SNAKE_CASE__    							=   max_input_chars_per_word




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )
     if len(_lowercase   ) > self.max_input_chars_per_word:
       return [self.unk_token]

     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   []
     while start < len(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   len(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   None
       while start < end:
         SCREAMING_SNAKE_CASE__    							=   """""".join(chars[start:end]   )
         if substr in self.vocab:
           SCREAMING_SNAKE_CASE__    							=   substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token   )
         start += 1
       else:
         sub_tokens.append(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   end

     return sub_tokens







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							False




   def __init__(					self    :       int ,			_lowercase    :       str ,			_lowercase    :       List[Any]="<d>" ,			_lowercase    :       List[Any]="</d>" ,			_lowercase    :       Union[str, Any]="<s>" ,			_lowercase    :       List[str]="</s>" ,			_lowercase    :       str="<pad>" ,			_lowercase    :       int="<unk>" ,			_lowercase    :       List[str]="</n>" ,			_lowercase    :       Tuple="</_>" ,			_lowercase    :       Any="left" ,			**_lowercase    :       Any ,			):



     """simple docstring"""






     requires_backends(self ,			["""jieba"""]   )
     super().__init__(
         bod_token=_lowercase ,			eod_token=_lowercase ,			bos_token=_lowercase ,			eos_token=_lowercase ,			pad_token=_lowercase ,			unk_token=_lowercase ,			line_token=_lowercase ,			space_token=_lowercase ,			padding_side=_lowercase ,			**_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   bod_token
     SCREAMING_SNAKE_CASE__    							=   eod_token
     SCREAMING_SNAKE_CASE__    							=   load_vocab(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.encoder[space_token]
     SCREAMING_SNAKE_CASE__    							=   self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in self.encoder.items()}

     SCREAMING_SNAKE_CASE__    							=   WordpieceTokenizer(vocab=self.encoder ,			unk_token=self.unk_token   )




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return self.encoder[self.bod_token]




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return self.encoder[self.eod_token]




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return self.encoder["\n"]




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     return len(self.encoder   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     return dict(self.encoder ,			**self.added_tokens_encoder   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     for x in jieba.cut(_lowercase ,			cut_all=_lowercase   ):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase   )   )
     return output_tokens




   def 			__a		(					self    :       int ,			_lowercase    :       Any ,			**_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [i for i in token_ids if i >= 0]
     SCREAMING_SNAKE_CASE__    							=   [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return token in self.encoder




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     return "".join(_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Any   ):



     """simple docstring"""






     return self.encoder.get(_lowercase ,			self.encoder.get(self.unk_token   )   )




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return self.decoder.get(_lowercase ,			self.unk_token   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if os.path.isdir(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   os.path.join(
           _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
     else:
       SCREAMING_SNAKE_CASE__    							=   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
     SCREAMING_SNAKE_CASE__    							=   0
     if " " in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder[""" """]
       del self.encoder[" "]
     if "\n" in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder["""\n"""]
       del self.encoder["\n"]
     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     with open(_lowercase ,			"""w""" ,			encoding="""utf-8"""   ) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               """ Please check that the vocabulary is not corrupted!"""   )
           SCREAMING_SNAKE_CASE__    							=   token_index
         writer.write(token + """\n"""   )
         index += 1
     return (vocab_file,)




   def 			__a		(					self    :       int ,			_lowercase    :       List[int] ,			_lowercase    :       List[int] = None   ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowercase ,			token_ids_a=_lowercase ,			already_has_special_tokens=_lowercase   )

     if token_ids_a is not None:
       return [1] + ([0] * len(_lowercase   )) + [1] + ([0] * len(_lowercase   ))
     return [1] + ([0] * len(_lowercase   ))
