




"""simple docstring"""

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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       a							      =     None


a							      =     logging.get_logger(__name__)


a							      =     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a							      =     {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

a							      =     {
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
a							      =     ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      VOCAB_FILES_NAMES
 UpperCAmelCase       :	Any     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase       :	Union[str, Any]     =      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase       :	List[str]     =      ['''input_ids''', '''attention_mask''']
 UpperCAmelCase       :	Tuple     =      MBartTokenizer

 UpperCAmelCase       :	List[int]     =      []
 UpperCAmelCase       :	List[int]     =      []

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[str]="<s>"       ,				_UpperCAmelCase			:			Union[str, Any]="</s>"       ,				_UpperCAmelCase			:			Any="</s>"       ,				_UpperCAmelCase			:			Any="<s>"       ,				_UpperCAmelCase			:			List[str]="<unk>"       ,				_UpperCAmelCase			:			Tuple="<pad>"       ,				_UpperCAmelCase			:			str="<mask>"       ,				_UpperCAmelCase			:			str=None       ,				_UpperCAmelCase			:			str=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Dict       ,				):
       # Mask token behave like a normal word, i.e. include the space before it
       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else mask_token

       super().__init__(
           vocab_file=_UpperCAmelCase       ,				tokenizer_file=_UpperCAmelCase       ,				bos_token=_UpperCAmelCase       ,				eos_token=_UpperCAmelCase       ,				sep_token=_UpperCAmelCase       ,				cls_token=_UpperCAmelCase       ,				unk_token=_UpperCAmelCase       ,				pad_token=_UpperCAmelCase       ,				mask_token=_UpperCAmelCase       ,				src_lang=_UpperCAmelCase       ,				tgt_lang=_UpperCAmelCase       ,				additional_special_tokens=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

       _A     =  vocab_file
       _A     =  False if not self.vocab_file else True

       _A     =  FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

       self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}						)
       _A     =  {
           lang_code: self.convert_tokens_to_ids(_UpperCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _A     =  src_lang if src_lang is not None else 'en_XX'
       _A     =  self.convert_tokens_to_ids(self._src_lang						)
       _A     =  tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return self._src_lang

 @src_lang.setter
 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			str						):
       _A     =  new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.sep_token_id]
       _A     =  [self.cls_token_id]

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

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str]       ,				_UpperCAmelCase			:			Optional[str]       ,				**_UpperCAmelCase			:			Dict						):
       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
       _A     =  src_lang
       _A     =  self(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  tgt_lang_id
       return inputs

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str = "en_XX"       ,				_UpperCAmelCase			:			Optional[List[str]] = None       ,				_UpperCAmelCase			:			str = "ro_RO"       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  src_lang
       _A     =  tgt_lang
       return super().prepare_seqaseq_batch(_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_src_lang_special_tokens(self.src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_tgt_lang_special_tokens(self.tgt_lang						)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

       _A     =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _A     =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _A     =  processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens						)						)       ,				)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

       _A     =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _A     =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _A     =  processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens						)						)       ,				)



 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str] = None						):
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'						)

       if not os.path.isdir(_UpperCAmelCase						):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
             return
       _A     =  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						):
             copyfile(self.vocab_file       ,				_UpperCAmelCase						)

       return (out_vocab_file,)







"""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}''')

