



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

import sentencepiece as spm

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


__snake_case		:Any   	=  logging.get_logger(__name__)


__snake_case		:Dict   	=  '''▁'''

__snake_case		:Optional[Any]   	=  {'''vocab_file''': '''spiece.model'''}

__snake_case		:str   	=  {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case		:Optional[int]   	=  {
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					List[Any]		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Optional[Any]		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					Tuple		      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					Tuple		      =		['''input_ids''', '''attention_mask''']



       def __init__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  List[str]=[]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Dict[str, Any]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE  ,	unk_token=__SCREAMING_SNAKE_CASE  ,	additional_special_tokens=__SCREAMING_SNAKE_CASE  ,	sp_model_kwargs=self.sp_model_kwargs  ,	**__SCREAMING_SNAKE_CASE  ,	)

           __a		 =       vocab_file
           __a		 =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)



       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       self.__dict__.copy()
           __a		 =       None
           return state



       def __setstate__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       d

           # for backward compatibility
           if not hasattr(self  ,	'''sp_model_kwargs'''):
               __a		 =       {}

           __a		 =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,	out_type=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a		 =       self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           __a		 =       []
           __a		 =       ''''''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
                   __a		 =       []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a		 =       os.path.join(
               __SCREAMING_SNAKE_CASE  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file  ,	__SCREAMING_SNAKE_CASE)
           elif not os.path.isfile(self.vocab_file):
               with open(__SCREAMING_SNAKE_CASE  ,	'''wb''') as fi:
                   __a		 =       self.sp_model.serialized_model_proto()
                   fi.write(__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)










__snake_case		:str   	=  [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Return True if there is node that has not iterated.
    __a		 =       [False] * len(_UpperCAmelCase       )
    __a		 =       [s]
    __a		 =       True

    while queue:
        __a		 =       queue.pop(0       )
        for ind in range(len(graph[u]       )       ):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_UpperCAmelCase       )
                __a		 =       True
                __a		 =       u

    return visited[t]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [-1] * (len(_UpperCAmelCase       ))
    __a		 =       0
    __a		 =       []
    __a		 =       [i[:] for i in graph]  # Record original cut, copy.
    while bfs(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       float('''Inf'''       )
        __a		 =       sink

        while s != source:
            # Find the minimum value in select path
            __a		 =       min(_UpperCAmelCase     ,				graph[parent[s]][s]       )
            __a		 =       parent[s]

        max_flow += path_flow
        __a		 =       sink

        while v != source:
            __a		 =       parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            __a		 =       parent[v]

    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(graph[0]       )       ):
            if graph[i][j] == 0 and temp[i][j] > 0:
                res.append((i, j)       )

    return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))





