






from __future__ import annotations

lowerCamelCase__       		=  list[tuple[int, int]]

lowerCamelCase__       		=  [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase__       		=  ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   List[str]:






            '''simple docstring'''





            __snake_case      :int 							=    pos_x
            __snake_case      :Optional[int] 							=    pos_y
            __snake_case      :Optional[Any] 							=    (pos_y, pos_x)
            __snake_case      :Any 							=    goal_x
            __snake_case      :List[Any] 							=    goal_y
            __snake_case      :Optional[Any] 							=    g_cost
            __snake_case      :int 							=    parent
            __snake_case      :Tuple 							=    self.calculate_heuristic()



       def        __lowercase    (				self     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    abs(self.pos_x - self.goal_x     )
            __snake_case      :Tuple 							=    abs(self.pos_y - self.goal_y     )
            return dx + dy



       def __lt__(				self      ,    a__     )			->   bool:






            '''simple docstring'''





            return self.f_cost < other.f_cost




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    Node(start[1]      ,    start[0]      ,    goal[1]      ,    goal[0]      ,    0      ,    a__     )
            __snake_case      :Dict 							=    Node(goal[1]      ,    goal[0]      ,    goal[1]      ,    goal[0]      ,    9_99_99      ,    a__     )

            __snake_case      :str 							=    [self.start]
            __snake_case      :list[Node] 							=    []

            __snake_case      :int 							=    False



       def        __lowercase    (				self     )			->   Path | None:






            '''simple docstring'''





            while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 __snake_case      :int 							=    self.open_nodes.pop(0     )

                 if current_node.pos == self.target.pos:
                      __snake_case      :Tuple 							=    True
                      return self.retrace_path(a__     )

                 self.closed_nodes.append(a__     )
                 __snake_case      :Dict 							=    self.get_successors(a__     )

                 for child_node in successors:
                      if child_node in self.closed_nodes:
                           continue

                      if child_node not in self.open_nodes:
                           self.open_nodes.append(a__     )
                      else:
                           # retrieve the best current path
                           __snake_case      :Dict 							=    self.open_nodes.pop(self.open_nodes.index(a__     )     )

                           if child_node.g_cost < better_node.g_cost:
                                self.open_nodes.append(a__     )
                           else:
                                self.open_nodes.append(a__     )

            if not self.reached:
                 return [self.start.pos]
            return None



       def        __lowercase    (				self      ,    a__     )			->   list[Node]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    []
            for action in delta:
                 __snake_case      :List[str] 							=    parent.pos_x + action[1]
                 __snake_case      :str 							=    parent.pos_y + action[0]

                 if not (0 <= pos_x <= len(grid[0]     ) - 1 and 0 <= pos_y <= len(a__     ) - 1):
                      continue

                 if grid[pos_y][pos_x] != 0:
                      continue

                 successors.append(
                     Node(
                         a__      ,    a__      ,    self.target.pos_y      ,    self.target.pos_x      ,    parent.g_cost + 1      ,    a__      ,    )     )
            return successors



       def        __lowercase    (				self      ,    a__     )			->   Path:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    node
            __snake_case      :List[Any] 							=    []
            while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)     )
                 __snake_case      :Dict 							=    current_node.parent
            path.reverse()
            return path


if __name__ == "__main__":
     lowerCamelCase__       		=  (0, 0)
     lowerCamelCase__       		=  (len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     print("""------""")

     lowerCamelCase__       		=  GreedyBestFirst(init, goal)
     lowerCamelCase__       		=  greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               lowerCamelCase__       		=  2

          for elem in grid:
               print(elem)










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

import sentencepiece as spm

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


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
            vocab.update(self.added_tokens_encoder     )
            return vocab



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [self.cls_token_id]
            if token_ids_a is None:
                 return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :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     ) * [0] + len(token_ids_a + sep     ) * [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    os.path.join(
                a__      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


