


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

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


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						'''▁'''

__snake_case   							=						{'''vocab_file''': '''prophetnet.tokenizer'''}

__snake_case   							=						{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

__snake_case   							=						{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

__snake_case   							=						{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}






def 		_A		(		_lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			collections.OrderedDict()
     with open(_lowercase					,  'r'					,  encoding='utf-8' ) as reader:
          __UpperCamelCase =			reader.readlines()
     for index, token in enumerate(_lowercase ):
          __UpperCamelCase =			token.rstrip('\n' )
          __UpperCamelCase =			index
     return vocab



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     ["""input_ids""", """attention_mask"""]







       def __init__(			self: str,A_: int,A_: str="[SEP]",A_: List[Any]="[SEP]",A_: str="[SEP]",A_: Any="[UNK]",A_: Optional[int]="[PAD]",A_: List[str]="[CLS]",A_: Dict="[MASK]",A_: Optional[Dict[str, Any]] = None,**A_: str,):






            '''simple docstring'''




            __UpperCamelCase =			{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=A_,eos_token=A_,sep_token=A_,unk_token=A_,pad_token=A_,cls_token=A_,mask_token=A_,sp_model_kwargs=self.sp_model_kwargs,**A_,)

            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                     ' pip install sentencepiece'					)
                 raise

            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(str(A_					)					)
            __UpperCamelCase =			vocab_file

            # Original fairseq vocab and spm vocab must be "aligned":
            # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
            # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
            # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # put special tokens and [unused] tokens into the vocab
            __UpperCamelCase =			{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

            for i in range(10					):
                 __UpperCamelCase =			F'''[unused{i}]'''
                 __UpperCamelCase =			5 + i

            # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
            __UpperCamelCase =			12
            __UpperCamelCase =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
            for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(A_					)







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






            '''simple docstring'''




            __UpperCamelCase =			self.__dict__.copy()
            __UpperCamelCase =			None
            return state







       def __setstate__(			self: List[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			d
            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                     ' pip install sentencepiece'					)
                 raise

            # for backward compatibility
            if not hasattr(self,'sp_model_kwargs'					):
                 __UpperCamelCase =			{}

            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)







       def 		snake_case_	(			self: Any,A_: List[int],A_: Optional[List[int]] = None,A_: bool = False					):






            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A_,token_ids_a=A_,already_has_special_tokens=A_					)

            if token_ids_a is None:
                 return ([0] * len(A_					)) + [1]
            return ([0] * len(A_					)) + [1] + ([0] * len(A_					)) + [1]







       def 		snake_case_	(			self: Optional[int],A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.sep_token_id]

            if token_ids_a is None:
                 return len(token_ids_a + sep					) * [0]
            return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return len(self.sp_model					) + self.fairseq_offset







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab







       def 		snake_case_	(			self: List[Any],A_: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any,A_: Optional[int]					):






            '''simple docstring'''




            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            __UpperCamelCase =			self.sp_model.PieceToId(A_					)

            # Need to return unknown token if the SP model returned 0
            return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







       def 		snake_case_	(			self: str,A_: int					):






            '''simple docstring'''




            if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(index - self.fairseq_offset					)







       def 		snake_case_	(			self: Tuple,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			''.join(A_					).replace(A_,' '					).strip()
            return out_string







       def 		snake_case_	(			self: Optional[int],A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            if not os.path.isdir(A_					):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                 return
            __UpperCamelCase =			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_					) and os.path.isfile(self.vocab_file					):
                 copyfile(self.vocab_file,A_					)
            elif not os.path.isfile(self.vocab_file					):
                 with open(A_,'wb'					) as fi:
                      __UpperCamelCase =			self.sp_model.serialized_model_proto()
                      fi.write(A_					)

            return (out_vocab_file,)







       def 		snake_case_	(			self: Tuple,A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
            __UpperCamelCase =			[self.sep_token_id]
            return token_ids_a + sep + token_ids_a + sep






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__snake_case   							=						0

__snake_case   							=						[
    [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],
]

__snake_case   							=						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__snake_case   							=						tuple[int, int]



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: int,A_: int,A_: int,A_: int,A_: int,A_: Node | None,):






            '''simple docstring'''




            __UpperCamelCase =			pos_x
            __UpperCamelCase =			pos_y
            __UpperCamelCase =			(pos_y, pos_x)
            __UpperCamelCase =			goal_x
            __UpperCamelCase =			goal_y
            __UpperCamelCase =			g_cost
            __UpperCamelCase =			parent
            __UpperCamelCase =			self.calculate_heuristic()
            __UpperCamelCase =			self.g_cost + self.h_cost







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.pos_x - self.goal_x
            __UpperCamelCase =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(A_					) + abs(A_					)
            else:
                 return sqrt(dy**2 + dx**2					)







       def __lt__(			self: int,A_: Node					):






            '''simple docstring'''




            return self.f_cost < other.f_cost



class 	__lowerCamelCase			:







       def __init__(			self: Any,A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			Node(start[1],start[0],goal[1],goal[0],0,A_					)
            __UpperCamelCase =			Node(goal[1],goal[0],goal[1],goal[0],9_9999,A_					)

            __UpperCamelCase =			[self.start]
            __UpperCamelCase =			[]

            __UpperCamelCase =			False







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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

                 if current_node.pos == self.target.pos:
                      return self.retrace_path(A_					)

                 self.closed_nodes.append(A_					)
                 __UpperCamelCase =			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
                           __UpperCamelCase =			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_					)

            return [self.start.pos]







       def 		snake_case_	(			self: int,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            for action in delta:
                 __UpperCamelCase =			parent.pos_x + action[1]
                 __UpperCamelCase =			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 		snake_case_	(			self: Any,A_: Node | None					):






            '''simple docstring'''




            __UpperCamelCase =			node
            __UpperCamelCase =			[]
            while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)					)
                 __UpperCamelCase =			current_node.parent
            path.reverse()
            return path



class 	__lowerCamelCase			:







       def __init__(			self: List[Any],A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			False







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






            '''simple docstring'''




            while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 __UpperCamelCase =			self.fwd_astar.open_nodes.pop(0					)
                 __UpperCamelCase =			self.bwd_astar.open_nodes.pop(0					)

                 if current_bwd_node.pos == current_fwd_node.pos:
                      return self.retrace_bidirectional_path(
                          A_,A_					)

                 self.fwd_astar.closed_nodes.append(A_					)
                 self.bwd_astar.closed_nodes.append(A_					)

                 __UpperCamelCase =			current_bwd_node
                 __UpperCamelCase =			current_fwd_node

                 __UpperCamelCase =			{
                     self.fwd_astar: self.fwd_astar.get_successors(A_					),
                     self.bwd_astar: self.bwd_astar.get_successors(A_					),
                 }

                 for astar in [self.fwd_astar, self.bwd_astar]:
                      for child_node in successors[astar]:
                           if child_node in astar.closed_nodes:
                                continue

                           if child_node not in astar.open_nodes:
                                astar.open_nodes.append(A_					)
                           else:
                                # retrieve the best current path
                                __UpperCamelCase =			astar.open_nodes.pop(
                                    astar.open_nodes.index(A_					)					)

                                if child_node.g_cost < better_node.g_cost:
                                     astar.open_nodes.append(A_					)
                                else:
                                     astar.open_nodes.append(A_					)

            return [self.fwd_astar.start.pos]







       def 		snake_case_	(			self: List[str],A_: Node,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			self.fwd_astar.retrace_path(A_					)
            __UpperCamelCase =			self.bwd_astar.retrace_path(A_					)
            bwd_path.pop()
            bwd_path.reverse()
            __UpperCamelCase =			fwd_path + bwd_path
            return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    __snake_case   							=						(0, 0)
    __snake_case   							=						(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    __snake_case   							=						time.time()
    __snake_case   							=						AStar(init, goal)
    __snake_case   							=						a_star.search()
    __snake_case   							=						time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __snake_case   							=						time.time()
    __snake_case   							=						BidirectionalAStar(init, goal)
    __snake_case   							=						time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")


