



'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {'''vocab_file''': '''spiece.model'''}

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Optional[int]=False	,_UpperCAmelCase					:       Dict="<s>"	,_UpperCAmelCase					:       Tuple="</s>"	,_UpperCAmelCase					:       Dict="<unk>"	,_UpperCAmelCase					:       Dict="<sep>"	,_UpperCAmelCase					:       List[str]="<pad>"	,_UpperCAmelCase					:       List[Any]="<cls>"	,_UpperCAmelCase					:       Union[str, Any]="<mask>"	,_UpperCAmelCase					:       Optional[Any]=["<eop>", "<eod>"]	,_UpperCAmelCase					:       Optional[Dict[str, Any]] = None	,**_UpperCAmelCase					:       Union[str, Any]	,):
									_a     :      List[Any]        =							AddedToken(_UpperCAmelCase	,lstrip=_UpperCAmelCase	,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase	,_UpperCAmelCase		) else mask_token

									_a     :      int        =							{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    do_lower_case=_UpperCAmelCase	,remove_space=_UpperCAmelCase	,keep_accents=_UpperCAmelCase	,bos_token=_UpperCAmelCase	,eos_token=_UpperCAmelCase	,unk_token=_UpperCAmelCase	,sep_token=_UpperCAmelCase	,pad_token=_UpperCAmelCase	,cls_token=_UpperCAmelCase	,mask_token=_UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,sp_model_kwargs=self.sp_model_kwargs	,**_UpperCAmelCase	,)

									_a     :      List[Any]        =							3

									_a     :      Optional[int]        =							do_lower_case
									_a     :      Dict        =							remove_space
									_a     :      Union[str, Any]        =							keep_accents
									_a     :      Any        =							vocab_file

									_a     :      Optional[int]        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_UpperCAmelCase		)

									try:
											import jieba
									except ModuleNotFoundError as error:
											raise error.__class__(
											    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
											    'See https://pypi.org/project/jieba/ for installation.'		)
									_a     :      Optional[int]        =							jieba
									_a     :      Union[str, Any]        =							str.maketrans(' \n'	,'\u2582\u2583'		)







							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def 							__lowercase			(							self					:       Dict		):
									return len(self.sp_model		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab







							def __getstate__(							self					:       int		):
									_a     :      Tuple        =							self.__dict__.copy()
									_a     :      Any        =							None
									return state







							def __setstate__(							self					:       Any	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Any        =							d

									# for backward compatibility
									if not hasattr(self	,'sp_model_kwargs'		):
											_a     :      Optional[int]        =							{}

									_a     :      Tuple        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       str		):
									if self.remove_space:
											_a     :      List[str]        =							' '.join(inputs.strip().split()		)
									else:
											_a     :      int        =							inputs
									_a     :      str        =							outputs.replace('``'	,'"'		).replace('\'\''	,'"'		)

									if not self.keep_accents:
											_a     :      Dict        =							unicodedata.normalize('NFKD'	,_UpperCAmelCase		)
											_a     :      List[str]        =							''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase		)]		)
									if self.do_lower_case:
											_a     :      Dict        =							outputs.lower()

									return outputs







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       str		):
									_a     :      int        =							self.preprocess_text(_UpperCAmelCase		)
									_a     :      Dict        =							self.sp_model.encode(_UpperCAmelCase	,out_type=_UpperCAmelCase		)
									_a     :      Optional[Any]        =							[]
									for piece in pieces:
											if len(_UpperCAmelCase		) > 1 and piece[-1] == str(','		) and piece[-2].isdigit():
													_a     :      Dict        =							self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase	,''		)		)
													if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
															if len(cur_pieces[0]		) == 1:
																	_a     :      Optional[int]        =							cur_pieces[1:]
															else:
																	_a     :      Optional[int]        =							cur_pieces[0][1:]
													cur_pieces.append(piece[-1]		)
													new_pieces.extend(_UpperCAmelCase		)
											else:
													new_pieces.append(_UpperCAmelCase		)

									return new_pieces







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, Any]		):
									return self.sp_model.PieceToId(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]		):
									return self.sp_model.IdToPiece(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Dict        =							''.join(_UpperCAmelCase		).replace(_UpperCAmelCase	,' '		).strip()
									return out_string







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      List[str]        =							[self.sep_token_id]
									_a     :      int        =							[self.cls_token_id]
									if token_ids_a is None:
											return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







							def 							__lowercase			(							self					:       Optional[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		)

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







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      Union[str, Any]        =							[self.sep_token_id]
									_a     :      List[str]        =							[2]

									if token_ids_a is None:
											return len(token_ids_a + sep		) * [0] + cls_segment_id
									return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									if not os.path.isdir(_UpperCAmelCase		):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
											return
									_a     :      List[Any]        =							os.path.join(
									    _UpperCAmelCase	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file	,_UpperCAmelCase		)
									elif not os.path.isfile(self.vocab_file		):
											with open(_UpperCAmelCase	,'wb'		) as fi:
													_a     :      Optional[Any]        =							self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase		)

									return (out_vocab_file,)







							def 							__lowercase			(							self					:       int	,*_UpperCAmelCase					:       Dict	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      List[str]        =							super()._decode(*_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[str]        =							text.replace(' '	,''		).replace('\u2582'	,' '		).replace('\u2583'	,'\n'		)
									return text






'''simple docstring'''




class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   Any					=						None
         A       :   Optional[Any]					=						None
         A       :   Tuple					=						graph

         self._normalize_graph(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						len(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						None





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         if sources is int:
             A       :   Dict					=						[sources]
         if sinks is int:
             A       :   str					=						[sinks]

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

         A       :   Optional[int]					=						sources[0]
         A       :   Union[str, Any]					=						sinks[0]

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

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

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





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						algorithm(self	)





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						flow_network
         A       :   Optional[Any]					=						flow_network.verticesCount
         A       :   Tuple					=						flow_network.sourceIndex
         A       :   Dict					=						flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         A       :   str					=						flow_network.graph
         A       :   Optional[Any]					=						False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         if not self.executed:
             self._algorithm()
             A       :   Optional[int]					=						True





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)
         # use this to save your result
         A       :   List[str]					=						-1





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






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

         return self.maximum_flow





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						[[0] * self.verticies_count for i in range(self.verticies_count	)]

         A       :   Union[str, Any]					=						[0] * self.verticies_count
         A       :   List[Any]					=						[0] * self.verticies_count





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Tuple					=						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       :   Optional[Any]					=						[
             i
             for i in range(self.verticies_count	)
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         A       :   Union[str, Any]					=						0
         while i < len(SCREAMING_SNAKE_CASE	):
             A       :   str					=						vertices_list[i]
             A       :   List[str]					=						self.heights[vertex_index]
             self.process_vertex(SCREAMING_SNAKE_CASE	)
             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(SCREAMING_SNAKE_CASE	)	)
                 A       :   int					=						0
             else:
                 i += 1

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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """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(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

             self.relabel(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Dict					=						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						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   Dict					=						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       :   Dict					=						self.heights[to_index]

         if min_height is not None:
             A       :   Dict					=						min_height + 1


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			[0]
      lowercase			:				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],
      # ]
      lowercase			:				int    		=			[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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




