





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)


_lowerCAmelCase				:   Tuple						       =						'''▁'''

_lowerCAmelCase				:   Union[str, Any]						       =						{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase				:   Dict						       =						{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

_lowerCAmelCase				:   Union[str, Any]						       =						{
    '''google/reformer-crime-and-punishment''': 524_288,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']



     def __init__(				self							:str      ,   snake_case							:int      ,   snake_case							:int="</s>"      ,   snake_case							:List[Any]="<unk>"      ,   snake_case							:Dict=[]      ,   snake_case							:Optional[Dict[str, Any]] = None      ,   **snake_case							:Tuple      ,   ):





        '''simple docstring'''




        A_       :					int        =      {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=snake_case      ,   unk_token=snake_case      ,   additional_special_tokens=snake_case      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **snake_case      ,   )

        A_       :					str        =      vocab_file
        A_       :					List[Any]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return self.sp_model.get_piece_size()



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      {self.convert_ids_to_tokens(snake_case	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



     def __getstate__(				self							:int	):





        '''simple docstring'''




        A_       :					int        =      self.__dict__.copy()
        A_       :					Union[str, Any]        =      None
        return state



     def __setstate__(				self							:List[str]      ,   snake_case							:Optional[int]	):





        '''simple docstring'''




        A_       :					List[str]        =      d

        # for backward compatibility
        if not hasattr(self      ,   "sp_model_kwargs"	):
           A_       :					List[Any]        =      {}

        A_       :					Optional[int]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:str	):





        '''simple docstring'''




        return self.sp_model.encode(snake_case      ,   out_type=snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str	):





        '''simple docstring'''




        return self.sp_model.piece_to_id(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Optional[int]	):





        '''simple docstring'''




        if index < self.sp_model.get_piece_size():
           A_       :					List[Any]        =      self.sp_model.IdToPiece(snake_case	)
        return token



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:Tuple	):





        '''simple docstring'''




        A_       :					Any        =      []
        A_       :					Dict        =      ""
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              out_string += self.sp_model.decode(snake_case	) + token
              A_       :					Tuple        =      []
           else:
              current_sub_tokens.append(snake_case	)
        out_string += self.sp_model.decode(snake_case	)
        return out_string.strip()





     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




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

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

        return (out_vocab_file,)







import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      (0, 0)
        A_       :					Dict        =      None
        A_       :					Optional[Any]        =      0
        A_       :					Optional[Any]        =      0
        A_       :					List[Any]        =      0



     def __eq__(				self							:int      ,   snake_case							:str	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Optional[Any]=(5, 5)	):





        '''simple docstring'''




        A_       :					Optional[int]        =      np.zeros(snake_case	)
        A_       :					Tuple        =      world_size[0]
        A_       :					Optional[Any]        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A_       :					Union[str, Any]        =      cell.position[0]
        A_       :					int        =      cell.position[1]
        A_       :					Optional[int]        =      []
        for n in neughbour_cord:
           A_       :					List[str]        =      current_x + n[0]
           A_       :					Optional[Any]        =      current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
              A_       :					List[Any]        =      Cell()
              A_       :					Tuple        =      (x, y)
              A_       :					List[Any]        =      cell
              neighbours.append(snake_case	)
        return neighbours




def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			str						)			->    str:
   A_       :					Optional[Any]        =      []
   A_       :					Tuple        =      []
   _open.append(_lowerCAmelCase						)

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

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(_lowerCAmelCase						)
   A_       :					Optional[int]        =      []
   while current.parent is not None:
      path.append(current.position						)
      A_       :					List[Any]        =      current.parent
   path.append(current.position						)
   return path[::-1]


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