

'''simple docstring'''







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

import sentencepiece as spm

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


__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    '''▁'''

__lowerCamelCase		       =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowerCamelCase		       =    {
    '''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'''
        ),
    }
}

__lowerCamelCase		       =    {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__lowerCamelCase		       =    ['''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 					A__      (      __UpperCAmelCase			):
    lowercase						=		VOCAB_FILES_NAMES
    lowercase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase						=		PRETRAINED_VOCAB_FILES_MAP
    lowercase						=		["input_ids", "attention_mask"]

    lowercase						=		[]
    lowercase						=		[]



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__="<s>"		,				UpperCamelCase__="</s>"		,				UpperCamelCase__="</s>"		,				UpperCamelCase__="<s>"		,				UpperCamelCase__="<unk>"		,				UpperCamelCase__="<pad>"		,				UpperCamelCase__="<mask>"		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__ = None		,				UpperCamelCase__=None		,				**UpperCamelCase__		,				)						->      List[str]:





           '''simple docstring'''
           # Mask token behave like a normal word, i.e. include the space before it
           A_						=      AddedToken(_lowerCamelCase		,				lstrip=_lowerCamelCase		,				rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase		,				_lowerCamelCase       ) else mask_token

           A_						=      {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=_lowerCamelCase		,				eos_token=_lowerCamelCase		,				unk_token=_lowerCamelCase		,				sep_token=_lowerCamelCase		,				cls_token=_lowerCamelCase		,				pad_token=_lowerCamelCase		,				mask_token=_lowerCamelCase		,				tokenizer_file=_lowerCamelCase		,				src_lang=_lowerCamelCase		,				tgt_lang=_lowerCamelCase		,				additional_special_tokens=_lowerCamelCase		,				sp_model_kwargs=self.sp_model_kwargs		,				**_lowerCamelCase		,				)

           A_						=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(_lowerCamelCase       )       )
           A_						=      vocab_file

           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
           # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
           # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

           # Mimic fairseq token-to-id alignment for the first 4 token
           A_						=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           A_						=      1

           A_						=      len(self.sp_model       )
           A_						=      {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowerCamelCase       )
           }
           A_						=      {v: k for k, v in self.lang_code_to_id.items()}
           A_						=      len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
           A_						=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
           A_						=      list(self.lang_code_to_id.keys()       )

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

           A_						=      src_lang if src_lang is not None else """en_XX"""
           A_						=      self.lang_code_to_id[self._src_lang]
           A_						=      tgt_lang
           self.set_src_lang_special_tokens(self._src_lang       )



    def __getstate__(					self       )						->      List[Any]:





           '''simple docstring'''
           A_						=      self.__dict__.copy()
           A_						=      None
           A_						=      self.sp_model.serialized_model_proto()
           return state



    def __setstate__(					self		,				UpperCamelCase__       )						->      Union[str, 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.LoadFromSerializedProto(self.sp_model_proto       )



    @property
    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token



    @property
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           return self._src_lang



    @src_lang.setter
    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      new_src_lang
           self.set_src_lang_special_tokens(self._src_lang       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = False       )						->      Optional[Any]:





           '''simple docstring'''

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

           A_						=      [1] * len(self.prefix_tokens       )
           A_						=      [1] * len(self.suffix_tokens       )
           if token_ids_a is None:
                  return prefix_ones + ([0] * len(_lowerCamelCase       )) + suffix_ones
           return prefix_ones + ([0] * len(_lowerCamelCase       )) + ([0] * len(_lowerCamelCase       )) + suffix_ones



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      Optional[Any]:





           '''simple docstring'''
           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 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      Dict:





           '''simple docstring'''
           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 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           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(_lowerCamelCase		,				add_special_tokens=_lowerCamelCase		,				return_tensors=_lowerCamelCase		,				**_lowerCamelCase       )
           A_						=      self.convert_tokens_to_ids(_lowerCamelCase       )
           A_						=      tgt_lang_id
           return inputs



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      {self.convert_ids_to_tokens(_lowerCamelCase       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           return self.sp_model.encode(_lowerCamelCase		,				out_type=_lowerCamelCase       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           A_						=      self.sp_model.PieceToId(_lowerCamelCase       )

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      """""".join(_lowerCamelCase       ).replace(_lowerCamelCase		,				""" """       ).strip()
           return out_string



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      Optional[int]:





           '''simple docstring'''
           if not os.path.isdir(_lowerCamelCase       ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
                  return
           A_						=      os.path.join(
               _lowerCamelCase		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

           return (out_vocab_file,)



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = "en_XX"		,				UpperCamelCase__ = None		,				UpperCamelCase__ = "ro_RO"		,				**UpperCamelCase__		,				)						->      Dict:





           '''simple docstring'''
           A_						=      src_lang
           A_						=      tgt_lang
           return super().prepare_seqaseq_batch(_lowerCamelCase		,				_lowerCamelCase		,				**_lowerCamelCase       )



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           return self.set_src_lang_special_tokens(self.src_lang       )



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           return self.set_tgt_lang_special_tokens(self.tgt_lang       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      self.lang_code_to_id[src_lang]
           A_						=      []
           A_						=      [self.eos_token_id, self.cur_lang_code]







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      self.lang_code_to_id[lang]
           A_						=      []
           A_						=      [self.eos_token_id, self.cur_lang_code]



'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )			->			float | int:
       for nxt, d in graph[v]:
              if nxt in visited_forward:
                     continue
              A_						=      cst_fwd.get(UpperCAmelCase__,       np.inf				)
              A_						=      cst_fwd[v] + d
              if new_cost_f < old_cost_f:
                     queue.put((new_cost_f, nxt)				)
                     A_						=      new_cost_f
                     A_						=      v
              if nxt in visited_backward:
                     if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                            A_						=      cst_fwd[v] + d + cst_bwd[nxt]
       return shortest_distance
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
       A_						=      -1

       A_						=      set()
       A_						=      set()
       A_						=      {source: 0}
       A_						=      {destination: 0}
       A_						=      {source: None}
       A_						=      {destination: None}
       A_						=      PriorityQueue()
       A_						=      PriorityQueue()

       A_						=      np.inf

       queue_forward.put((0, source)				)
       queue_backward.put((0, destination)				)

       if source == destination:
              return 0

       while not queue_forward.empty() and not queue_backward.empty():
              A_					,			A_						=      queue_forward.get()
              visited_forward.add(UpperCAmelCase__				)

              A_					,			A_						=      queue_backward.get()
              visited_backward.add(UpperCAmelCase__				)

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
                     break

       if shortest_distance != np.inf:
              A_						=      shortest_distance
       return shortest_path_distance


__lowerCamelCase		       =    {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
__lowerCamelCase		       =    {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
