



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__lowerCamelCase						=       logging.get_logger(__name__)


__lowerCamelCase						=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCamelCase						=       {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase						=       {
    """RUCAIBox/mvp""": 1_0_2_4,
}



class 		_snake_case   (      _lowerCAmelCase					):





 """simple docstring"""



 lowerCamelCase_        =      VOCAB_FILES_NAMES
 lowerCamelCase_        =      PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_        =      ['''input_ids''', '''attention_mask''']
 lowerCamelCase_        =      MvpTokenizer
 def __init__(    self						,     a=None						,     a=None						,     a=None						,     a="replace"						,     a="<s>"						,     a="</s>"						,     a="</s>"						,     a="<s>"						,     a="<unk>"						,     a="<pad>"						,     a="<mask>"						,     a=False						,     a=True						,     **a						,     )						->				Dict:




   """simple docstring"""







   super().__init__(
       a						,     a						,     tokenizer_file=a						,     errors=a						,     bos_token=a						,     eos_token=a						,     sep_token=a						,     cls_token=a						,     unk_token=a						,     pad_token=a						,     mask_token=a						,     add_prefix_space=a						,     trim_offsets=a						,     **a						,     )

   _A							     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
   if pre_tok_state.get('''add_prefix_space'''						,     a       ) != add_prefix_space:
     _A							     =     getattr(a						,     pre_tok_state.pop('''type'''       )       )
     _A							     =     add_prefix_space
     _A							     =     pre_tok_class(**a       )

   _A							     =     add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   _A							     =     '''post_processor'''
   _A							     =     getattr(self.backend_tokenizer						,     a						,     a       )
   if tokenizer_component_instance:
     _A							     =     json.loads(tokenizer_component_instance.__getstate__()       )

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       _A							     =     tuple(state['''sep''']       )
     if "cls" in state:
       _A							     =     tuple(state['''cls''']       )

     _A							     =     False

     if state.get('''add_prefix_space'''						,     a       ) != add_prefix_space:
       _A							     =     add_prefix_space
       _A							     =     True

     if state.get('''trim_offsets'''						,     a       ) != trim_offsets:
       _A							     =     trim_offsets
       _A							     =     True

     if changes_to_apply:
       _A							     =     getattr(a						,     state.pop('''type'''       )       )
       _A							     =     component_class(**a       )
       setattr(self.backend_tokenizer						,     a						,     a       )
 @property
 def lowercase_		(    self       )						->				str:




   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error('''Using mask_token, but it is not set yet.'''       )
     return None
   return str(self._mask_token       )
 @mask_token.setter
 def lowercase_		(    self						,     a       )						->				Dict:




   """simple docstring"""







   _A							     =     AddedToken(a						,     lstrip=a						,     rstrip=a       ) if isinstance(a						,     a       ) else value
   _A							     =     value
 def lowercase_		(    self						,     *a						,     **a       )						->				BatchEncoding:




   """simple docstring"""







   _A							     =     kwargs.get('''is_split_into_words'''						,     a       )

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         '''to use it with pretokenized inputs.'''       )

   return super()._batch_encode_plus(*a						,     **a       )
 def lowercase_		(    self						,     *a						,     **a       )						->				BatchEncoding:




   """simple docstring"""







   _A							     =     kwargs.get('''is_split_into_words'''						,     a       )

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         '''to use it with pretokenized inputs.'''       )

   return super()._encode_plus(*a						,     **a       )
 def lowercase_		(    self						,     a						,     a = None       )						->				Tuple[str]:




   """simple docstring"""







   _A							     =     self._tokenizer.model.save(a						,     name=a       )
   return tuple(a       )
 def lowercase_		(    self						,     a						,     a=None       )						->				Dict:




   """simple docstring"""







   _A							     =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def lowercase_		(    self						,     a						,     a = None       )						->				List[int]:




   """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]







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       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						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __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(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

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

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


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """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()