




"""simple docstring"""

import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a							      =     logging.get_logger(__name__)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      ['''input_ids''', '''attention_mask''']

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]="</s>"       ,				_UpperCAmelCase			:			Union[str, Any]="<unk>"       ,				_UpperCAmelCase			:			Optional[Any]="<pad>"       ,				_UpperCAmelCase			:			Any=125       ,				_UpperCAmelCase			:			Union[str, Any]=None       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
             _A     =  [F'''<extra_id_{i}>''' for i in range(_UpperCAmelCase						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra_id special tokens
             _A     =  len(set(filter(lambda _UpperCAmelCase						: bool('extra_id' in str(_UpperCAmelCase						)						)       ,				_UpperCAmelCase						)						)						)
             if extra_tokens != extra_ids:
                   raise ValueError(
                       F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                       ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                       ' extra_ids tokens'						)

       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else pad_token
       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else eos_token
       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else unk_token

       super().__init__(
           eos_token=_UpperCAmelCase       ,				unk_token=_UpperCAmelCase       ,				pad_token=_UpperCAmelCase       ,				extra_ids=_UpperCAmelCase       ,				additional_special_tokens=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

       _A     =  extra_ids

       _A     =  2**8  # utf is 8 bits

       # define special tokens dict
       _A     =  {
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       _A     =  len(self.special_tokens_encoder						)
       _A     =  len(_UpperCAmelCase						)
       for i, token in enumerate(_UpperCAmelCase						):
             _A     =  self.vocab_size + i - n
       _A     =  {v: k for k, v in self.special_tokens_encoder.items()}

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None       ,				_UpperCAmelCase			:			bool = False						):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase       ,				token_ids_a=_UpperCAmelCase       ,				already_has_special_tokens=_UpperCAmelCase						)

       # normal case: some special tokens
       if token_ids_a is None:
             return ([0] * len(_UpperCAmelCase						)) + [1]
       return ([0] * len(_UpperCAmelCase						)) + [1] + ([0] * len(_UpperCAmelCase						)) + [1]

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			List[int]						):
       if len(_UpperCAmelCase						) > 0 and token_ids[-1] == self.eos_token_id:
             warnings.warn(
                 F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
                 ' eos tokens being added.'						)
             return token_ids
       else:
             return token_ids + [self.eos_token_id]

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.eos_token_id]

       if token_ids_a is None:
             return len(token_ids_a + eos						) * [0]
       return len(token_ids_a + eos + token_ids_a + eos						) * [0]

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  self._add_eos_if_not_present(_UpperCAmelCase						)
       if token_ids_a is None:
             return token_ids_a
       else:
             _A     =  self._add_eos_if_not_present(_UpperCAmelCase						)
             return token_ids_a + token_ids_a

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  [chr(_UpperCAmelCase						) for i in text.encode('utf-8'						)]
       return tokens

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]						):
       if token in self.special_tokens_encoder:
             _A     =  self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             _A     =  self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase						) != 1:
             _A     =  self.unk_token_id
       else:
             _A     =  ord(_UpperCAmelCase						) + self._num_special_tokens
       return token_id

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]						):
       if index in self.special_tokens_decoder:
             _A     =  self.special_tokens_decoder[index]
       else:
             _A     =  chr(index - self._num_special_tokens						)
       return token

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  b''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   _A     =  self.special_tokens_decoder[token].encode('utf-8'						)
             elif token in self.added_tokens_decoder:
                   _A     =  self.special_tokens_decoder[token].encode('utf-8'						)
             elif token in self.special_tokens_encoder:
                   _A     =  token.encode('utf-8'						)
             elif token in self.added_tokens_encoder:
                   _A     =  token.encode('utf-8'						)
             else:
                   _A     =  bytes([ord(_UpperCAmelCase						)]						)
             bstring += tok_string
       _A     =  bstring.decode('utf-8'       ,				errors='ignore'						)
       return string



 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str] = None						):
       return ()







"""simple docstring"""

class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       _A     =  None
       _A     =  graph

       self._normalize_graph(_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  len(_UpperCAmelCase						)
       _A     =  None

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Dict						):
       if sources is int:
             _A     =  [sources]
       if sinks is int:
             _A     =  [sinks]

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

       _A     =  sources[0]
       _A     =  sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(_UpperCAmelCase						) > 1 or len(_UpperCAmelCase						) > 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      lowerCAmelCase_				(					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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  algorithm(self						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _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      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             self._algorithm()
             _A     =  True



 def      lowerCAmelCase_				(					self			:			int						):
       pass





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       super().__init__(_UpperCAmelCase						)
       # use this to save your result
       _A     =  -1



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

       return self.maximum_flow





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]						):
       super().__init__(_UpperCAmelCase						)

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

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

 def      lowerCAmelCase_				(					self			:			Dict						):
       _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(_UpperCAmelCase						):
             _A     =  vertices_list[i]
             _A     =  self.heights[vertex_index]
             self.process_vertex(_UpperCAmelCase						)
             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(_UpperCAmelCase						)						)
                   _A     =  0
             else:
                   i += 1

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

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       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(_UpperCAmelCase       ,				_UpperCAmelCase						)

             self.relabel(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple						):
       _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      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			int						):
       _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__":
       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}''')

