



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:
    __UpperCAmelCase			  =			None


__UpperCAmelCase			  =			logging.get_logger(__name__)


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

__UpperCAmelCase			  =			{
    '''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''',
    },
}

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

# fmt: off
__UpperCAmelCase			  =			['''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__				):
       UpperCAmelCase__							:   List[Any]  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   List[str]  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   Any  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   Tuple  	=    ["input_ids", "attention_mask"]
       UpperCAmelCase__							:   Any  	=    MBartTokenizer

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

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

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

          UpperCamelCase       :    Optional[Any]		      =      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}			)
          UpperCamelCase       :    Tuple		      =      {
              lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          UpperCamelCase       :    Any		      =      src_lang if src_lang is not None else 'en_XX'
          UpperCamelCase       :    Union[str, Any]		      =      self.convert_tokens_to_ids(self._src_lang			)
          UpperCamelCase       :    List[Any]		      =      tgt_lang
          self.set_src_lang_special_tokens(self._src_lang			)
       @property
       def    snake_case_			(     self			)	->						str:
          return self._src_lang
       @src_lang.setter
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    List[str]		      =      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    Union[str, Any]		      =      [self.sep_token_id]
          UpperCamelCase       :    Union[str, Any]		      =      [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,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
          UpperCamelCase       :    List[Any]		      =      src_lang
          UpperCamelCase       :    List[str]		      =      self(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Any		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Any		      =      tgt_lang_id
          return inputs
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = "en_XX",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = "ro_RO",     **SCREAMING_SNAKE_CASE_,     )	->						BatchEncoding:
          UpperCamelCase       :    Tuple		      =      src_lang
          UpperCamelCase       :    Union[str, Any]		      =      tgt_lang
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Any:
          return self.set_src_lang_special_tokens(self.src_lang			)
       def    snake_case_			(     self			)	->						Dict:
          return self.set_tgt_lang_special_tokens(self.tgt_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    str		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      []
          UpperCamelCase       :    str		      =      [self.eos_token_id, self.cur_lang_code]

          UpperCamelCase       :    Dict		      =      self.convert_ids_to_tokens(self.prefix_tokens			)
          UpperCamelCase       :    List[str]		      =      self.convert_ids_to_tokens(self.suffix_tokens			)

          UpperCamelCase       :    Union[str, 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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    Optional[Any]		      =      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[str]		      =      []
          UpperCamelCase       :    List[str]		      =      [self.eos_token_id, self.cur_lang_code]

          UpperCamelCase       :    List[str]		      =      self.convert_ids_to_tokens(self.prefix_tokens			)
          UpperCamelCase       :    Optional[int]		      =      self.convert_ids_to_tokens(self.suffix_tokens			)

          UpperCamelCase       :    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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 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(SCREAMING_SNAKE_CASE_			):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""			)
             return
          UpperCamelCase       :    Optional[Any]		      =      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_			):
             copyfile(self.vocab_file,     SCREAMING_SNAKE_CASE_			)

          return (out_vocab_file,)










'''simple docstring'''

import numpy as np







class __a			:





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							(0, 0)
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0





   def __eq__(       self					:      int					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return self.position == cell.position





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

      '''simple docstring'''

      print(self.position     )







class __a			:





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=(5, 5)     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.zeros(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							world_size[0]
      __SCREAMING_SNAKE_CASE   				=							world_size[1]





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      print(self.w     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE   				=							cell.position[0]
      __SCREAMING_SNAKE_CASE   				=							cell.position[1]
      __SCREAMING_SNAKE_CASE   				=							[]
      for n in neughbour_cord:
         __SCREAMING_SNAKE_CASE   				=							current_x + n[0]
         __SCREAMING_SNAKE_CASE   				=							current_y + n[1]
         if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            __SCREAMING_SNAKE_CASE   				=							Cell()
            __SCREAMING_SNAKE_CASE   				=							(x, y)
            __SCREAMING_SNAKE_CASE   				=							cell
            neighbours.append(lowerCamelCase     )
      return neighbours





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							[]
   _open.append(__UpperCAmelCase   )

   while _open:
      __SCREAMING_SNAKE_CASE   				=							np.argmin([n.f for n in _open]   )
      __SCREAMING_SNAKE_CASE   				=							_open[min_f]
      _closed.append(_open.pop(__UpperCAmelCase   )   )
      if current == goal:
         break
      for n in world.get_neigbours(__UpperCAmelCase   ):
         for c in _closed:
            if c == n:
               continue
         __SCREAMING_SNAKE_CASE   				=							current.g + 1
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							n.position
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							goal.position
         __SCREAMING_SNAKE_CASE   				=							(ya - ya) ** 2 + (xa - xa) ** 2
         __SCREAMING_SNAKE_CASE   				=							n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							[]
   while current.parent is not None:
      path.append(current.position   )
      __SCREAMING_SNAKE_CASE   				=							current.parent
   path.append(current.position   )
   return path[::-1]


if __name__ == "__main__":
    a   	=    Gridworld()
    # Start position and goal
    a   	=    Cell()
    a   	=    (0, 0)
    a   	=    Cell()
    a   	=    (4, 4)
    print(F'''path from {start.position} to {goal.position}''')
    a   	=    astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        a   	=    1
    print(world.w)





