"""simple docstring"""






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__A			   = TypeVar("T")



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[str]:
   return (position - 1) // 2



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   return (2 * position) + 1



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[Any]:
   return (2 * position) + 2


class     UpperCAmelCase						(Generic[T]  ):



    """simple docstring"""

    def __init__(   self							):
       lowercase__:   list[tuple[T, int]]					=      []
       lowercase__:   dict[T, int]					=      {}
       lowercase__:   int					=      0






    def __len__(   self							):
       return self.elements






    def __repr__(   self							):
       return str(self.heap							)






    def _snake_case			(   self							):
       # Check if the priority queue is empty
       return self.elements == 0






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       # Add an element with given priority to the queue
       self.heap.append((elem, weight)							)
       lowercase__:   int					=      self.elements
       self.elements += 1
       self._bubble_up(__lowerCamelCase							)






    def _snake_case			(   self							):
       # Remove and return the element with lowest weight (highest priority)
       if self.elements > 1:
          self._swap_nodes(0    ,			self.elements - 1							)
       lowercase__:   Optional[Any]					=      self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
          lowercase__:   str					=      self.heap[0]
          self._bubble_down(__lowerCamelCase							)
       return elem






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       # Update the weight of the given key
       lowercase__:   Optional[Any]					=      self.position_map[elem]
       lowercase__:   Any					=      (elem, weight)
       if position > 0:
          lowercase__:   Any					=      get_parent_position(__lowerCamelCase							)
          lowercase__:   Tuple					=      self.heap[parent_position]
          if parent_weight > weight:
             self._bubble_up(__lowerCamelCase							)
          else:
             self._bubble_down(__lowerCamelCase							)
       else:
          self._bubble_down(__lowerCamelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       # Place a node at the proper position (upward movement) [to be used internally
       # only]
       lowercase__:   Optional[int]					=      self.position_map[elem]
       if curr_pos == 0:
          return None
       lowercase__:   Optional[Any]					=      get_parent_position(__lowerCamelCase							)
       lowercase__:   Any					=      self.heap[curr_pos]
       lowercase__:   str					=      self.heap[parent_position]
       if parent_weight > weight:
          self._swap_nodes(__lowerCamelCase    ,			__lowerCamelCase							)
          return self._bubble_up(__lowerCamelCase							)
       return None






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       # Place a node at the proper position (downward movement) [to be used
       # internally only]
       lowercase__:   Any					=      self.position_map[elem]
       lowercase__:   Optional[int]					=      self.heap[curr_pos]
       lowercase__:   Optional[int]					=      get_child_left_position(__lowerCamelCase							)
       lowercase__:   List[str]					=      get_child_right_position(__lowerCamelCase							)
       if child_left_position < self.elements and child_right_position < self.elements:
          lowercase__:   str					=      self.heap[child_left_position]
          lowercase__:   List[str]					=      self.heap[child_right_position]
          if child_right_weight < child_left_weight and child_right_weight < weight:
             self._swap_nodes(__lowerCamelCase    ,			__lowerCamelCase							)
             return self._bubble_down(__lowerCamelCase							)
       if child_left_position < self.elements:
          lowercase__:   int					=      self.heap[child_left_position]
          if child_left_weight < weight:
             self._swap_nodes(__lowerCamelCase    ,			__lowerCamelCase							)
             return self._bubble_down(__lowerCamelCase							)
       else:
          return None
       if child_right_position < self.elements:
          lowercase__:   int					=      self.heap[child_right_position]
          if child_right_weight < weight:
             self._swap_nodes(__lowerCamelCase    ,			__lowerCamelCase							)
             return self._bubble_down(__lowerCamelCase							)
       return None






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       # Swap the nodes at the given positions
       lowercase__:   str					=      self.heap[nodea_pos][0]
       lowercase__:   List[Any]					=      self.heap[nodea_pos][0]
       lowercase__:   Dict					=      (
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       lowercase__:   Tuple					=      nodea_pos
       lowercase__:   int					=      nodea_pos



class     UpperCAmelCase						(Generic[T]  ):



    """simple docstring"""

    def __init__(   self							):
       lowercase__:   dict[T, dict[T, int]]					=      {}
       lowercase__:   int					=      0






    def __repr__(   self							):
       return str(self.connections							)






    def __len__(   self							):
       return self.nodes






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       # Add a node in the graph if it is not in the graph
       if node not in self.connections:
          lowercase__:   Optional[int]					=      {}
          self.nodes += 1






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       # Add an edge between 2 nodes in the graph
       self.add_node(__lowerCamelCase							)
       self.add_node(__lowerCamelCase							)
       lowercase__:   Optional[int]					=      weight
       lowercase__:   str					=      weight



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    )  ->  Any:
   lowercase__:   dict[T, int]					=      {node: maxsize for node in graph.connections}
   lowercase__:   dict[T, T | None]					=      {node: None for node in graph.connections}

   lowercase__:   MinPriorityQueue[T]					=      MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(UpperCamelCase__       ,    UpperCamelCase__ )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   lowercase__:   List[str]					=      priority_queue.extract_min()
   lowercase__:   Tuple					=      0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         lowercase__:   Tuple					=      dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(UpperCamelCase__       ,    dist[neighbour] )
         lowercase__:   int					=      node

    # running prim's algorithm
   while not priority_queue.is_empty():
      lowercase__:   Optional[Any]					=      priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            lowercase__:   Optional[Any]					=      dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(UpperCamelCase__       ,    dist[neighbour] )
            lowercase__:   Any					=      node
   return dist, parent




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:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


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

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'
        ),
    },
    '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',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# 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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


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

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

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = 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      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            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 ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				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      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            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 ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = 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 ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       str				    = 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 ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            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(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = 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):
                   copyfile(self.vocab_file ,						__lowerCamelCase)

            return (out_vocab_file,)
