


import heapq
import sys

import numpy as np

_A			=						tuple[int, int]






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self   )  ->	List[Any]:
         __UpperCamelCase   							=[]
         __UpperCamelCase   							=set()






    def _a (      self   )  ->	List[Any]:
         if not self.empty():
              return self.elements[0][0]
         else:
              return float('inf'   )






    def _a (      self   )  ->	str:
         return len(self.elements   ) == 0






    def _a (      self ,       A_ ,       A_   )  ->	Tuple:
         if item not in self.set:
              heapq.heappush(self.elements ,       (priority, item)   )
              self.set.add(A_   )
         else:
              # update
              # print("update", item)
              __UpperCamelCase   							=[]
              ((__UpperCamelCase)							,				(__UpperCamelCase))   							=heapq.heappop(self.elements   )
              while x != item:
                   temp.append((pri, x)   )
                   ((__UpperCamelCase)							,				(__UpperCamelCase))   							=heapq.heappop(self.elements   )
              temp.append((priority, item)   )
              for pro, xxx in temp:
                   heapq.heappush(self.elements ,       (pro, xxx)   )






    def _a (      self ,       A_   )  ->	int:
         if item in self.set:
              self.set.remove(A_   )
              __UpperCamelCase   							=[]
              ((__UpperCamelCase)							,				(__UpperCamelCase))   							=heapq.heappop(self.elements   )
              while x != item:
                   temp.append((pro, x)   )
                   ((__UpperCamelCase)							,				(__UpperCamelCase))   							=heapq.heappop(self.elements   )
              for prito, yyy in temp:
                   heapq.heappush(self.elements ,       (prito, yyy)   )






    def _a (      self   )  ->	List[Any]:
         return self.elements[0][1]






    def _a (      self   )  ->	int:
         ((__UpperCamelCase)							,				(__UpperCamelCase))   							=heapq.heappop(self.elements   )
         self.set.remove(A_   )
         return (priority, item)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : TPos    ):
     # euclidean distance
     __UpperCamelCase   							=np.array(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=np.array(SCREAMING_SNAKE_CASE__    )
     return np.linalg.norm(a - b    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : TPos    ):
     # integer division by time variable
     return consistent_heuristic(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) // t

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : TPos    ):
     # manhattan distance
     return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : dict[TPos, float]    ):
     __UpperCamelCase   							=g_function[start] + Wa * heuristics[i](SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     return ans

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=np.chararray((n, n)    )
     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							='*'

     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               if (j, (n - 1) - i) in blocks:
                    __UpperCamelCase   							='#'

     __UpperCamelCase   							='-'
     __UpperCamelCase   							=back_pointer[goal]
     while x != start:
          ((__UpperCamelCase)							,				(__UpperCamelCase))   							=x
          # print(x)
          __UpperCamelCase   							='-'
          __UpperCamelCase   							=back_pointer[x]
     __UpperCamelCase   							='-'

     for i in range(SCREAMING_SNAKE_CASE__    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               if (i, j) == (0, n - 1):
                    print(grid[i][j]      ,					end=' '    )
                    print('<-- End position'      ,					end=' '    )
               else:
                    print(grid[i][j]      ,					end=' '    )
          print()
     print('^'    )
     print('Start position'    )
     print()
     print('# is an obstacle'    )
     print('- is the path taken by algorithm'    )
     print('PATH TAKEN BY THE ALGORITHM IS:-'    )
     __UpperCamelCase   							=back_pointer[goal]
     while x != start:
          print(SCREAMING_SNAKE_CASE__      ,					end=' '    )
          __UpperCamelCase   							=back_pointer[x]
     print(SCREAMING_SNAKE_CASE__    )
     sys.exit()

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos    ):
     if p[0] < 0 or p[0] > n - 1:
          return False
     if p[1] < 0 or p[1] > n - 1:
          return False
     return True

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Any      ,					):
     for itera in range(SCREAMING_SNAKE_CASE__    ):
          open_list[itera].remove_element(SCREAMING_SNAKE_CASE__    )
     # print("s", s)
     # print("j", j)
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=s
     __UpperCamelCase   							=(x - 1, y)
     __UpperCamelCase   							=(x + 1, y)
     __UpperCamelCase   							=(x, y + 1)
     __UpperCamelCase   							=(x, y - 1)

     for neighbours in [left, right, up, down]:
          if neighbours not in blocks:
               if valid(SCREAMING_SNAKE_CASE__    ) and neighbours not in visited:
                    # print("neighbour", neighbours)
                    visited.add(SCREAMING_SNAKE_CASE__    )
                    __UpperCamelCase   							=-1
                    __UpperCamelCase   							=float('inf'    )

               if valid(SCREAMING_SNAKE_CASE__    ) and g_function[neighbours] > g_function[s] + 1:
                    __UpperCamelCase   							=g_function[s] + 1
                    __UpperCamelCase   							=s
                    if neighbours not in close_list_anchor:
                         open_list[0].put(SCREAMING_SNAKE_CASE__      ,					key(SCREAMING_SNAKE_CASE__      ,					0      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    )
                         if neighbours not in close_list_inad:
                              for var in range(1      ,					SCREAMING_SNAKE_CASE__    ):
                                   if key(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) <= Wa * key(
                                       SCREAMING_SNAKE_CASE__      ,					0      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
                                        open_list[j].put(
                                            SCREAMING_SNAKE_CASE__      ,					key(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    )

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							=[]
     for x in range(1      ,					5    ):
          for y in range(1      ,					6    ):
               some_list.append((x, y)    )

     for x in range(15      ,					20    ):
          some_list.append((x, 17)    )

     for x in range(10      ,					19    ):
          for y in range(1      ,					15    ):
               some_list.append((x, y)    )

    # L block
     for x in range(1      ,					4    ):
          for y in range(12      ,					19    ):
               some_list.append((x, y)    )
     for x in range(3      ,					13    ):
          for y in range(16      ,					19    ):
               some_list.append((x, y)    )
     return some_list


_A			=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_A			=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
_A			=						make_common_ground()


_A			=						blocks_blk
# hyper parameters
_A			=						1
_A			=						1
_A			=						20
_A			=						3  # one consistent and two other inconsistent

# start and end destination
_A			=						(0, 0)
_A			=						(n - 1, n - 1)

_A			=						1

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : TPos      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							={start: 0, goal: float('inf'    )}
     __UpperCamelCase   							={start: -1, goal: -1}
     __UpperCamelCase   							=[]
     __UpperCamelCase   							=set()

     for i in range(SCREAMING_SNAKE_CASE__    ):
          open_list.append(PriorityQueue()    )
          open_list[i].put(SCREAMING_SNAKE_CASE__      ,					key(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    )

     __UpperCamelCase   							=[]
     __UpperCamelCase   							=[]
     while open_list[0].minkey() < float('inf'    ):
          for i in range(1      ,					SCREAMING_SNAKE_CASE__    ):
               # print(open_list[0].minkey(), open_list[i].minkey())
               if open_list[i].minkey() <= Wa * open_list[0].minkey():
                    global t
                    t += 1
                    if g_function[goal] <= open_list[i].minkey():
                         if g_function[goal] < float('inf'    ):
                              do_something(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
                    else:
                         __UpperCamelCase							,				__UpperCamelCase   							=open_list[i].top_show()
                         visited.add(SCREAMING_SNAKE_CASE__    )
                         expand_state(
                             SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					)
                         close_list_inad.append(SCREAMING_SNAKE_CASE__    )
               else:
                    if g_function[goal] <= open_list[0].minkey():
                         if g_function[goal] < float('inf'    ):
                              do_something(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
                    else:
                         __UpperCamelCase   							=open_list[0].top_show()
                         visited.add(SCREAMING_SNAKE_CASE__    )
                         expand_state(
                             SCREAMING_SNAKE_CASE__      ,					0      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					)
                         close_list_anchor.append(SCREAMING_SNAKE_CASE__    )
     print('No path found to goal'    )
     print()
     for i in range(n - 1      ,					-1      ,					-1    ):
          for j in range(SCREAMING_SNAKE_CASE__    ):
               if (j, i) in blocks:
                    print('#'      ,					end=' '    )
               elif (j, i) in back_pointer:
                    if (j, i) == (n - 1, n - 1):
                         print('*'      ,					end=' '    )
                    else:
                         print('-'      ,					end=' '    )
               else:
                    print('*'      ,					end=' '    )
               if (j, i) == (n - 1, n - 1):
                    print('<-- End position'      ,					end=' '    )
          print()
     print('^'    )
     print('Start position'    )
     print()
     print('# is an obstacle'    )
     print('- is the path taken by algorithm'    )


if __name__ == "__main__":
   multi_a_star(start, goal, n_heuristic)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_A			=						logging.get_logger(__name__)

_A			=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_A			=						{
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

_A			=						{
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

_A			=						{
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		List[str]              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Optional[int]              =  PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__						:		List[str]              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Dict              =  BertTokenizer






    def __init__(      self ,       A_=None ,       A_=None ,       A_=True ,       A_="[UNK]" ,       A_="[SEP]" ,       A_="[PAD]" ,       A_="[CLS]" ,       A_="[MASK]" ,       A_=True ,       A_=None ,       **A_ ,       )  ->	Any:
         super().__init__(
             A_ ,       tokenizer_file=A_ ,       do_lower_case=A_ ,       unk_token=A_ ,       sep_token=A_ ,       pad_token=A_ ,       cls_token=A_ ,       mask_token=A_ ,       tokenize_chinese_chars=A_ ,       strip_accents=A_ ,       **A_ ,       )

         __UpperCamelCase   							=json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
         if (
             normalizer_state.get('lowercase' ,       A_   ) != do_lower_case
             or normalizer_state.get('strip_accents' ,       A_   ) != strip_accents
             or normalizer_state.get('handle_chinese_chars' ,       A_   ) != tokenize_chinese_chars
         ):
              __UpperCamelCase   							=getattr(A_ ,       normalizer_state.pop('type'   )   )
              __UpperCamelCase   							=do_lower_case
              __UpperCamelCase   							=strip_accents
              __UpperCamelCase   							=tokenize_chinese_chars
              __UpperCamelCase   							=normalizer_class(**A_   )

         __UpperCamelCase   							=do_lower_case






    def _a (      self ,       A_ ,       A_=None   )  ->	List[str]:
         __UpperCamelCase   							=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]
         __UpperCamelCase   							=[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 _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         __UpperCamelCase   							=self._tokenizer.model.save(A_ ,       name=A_   )
         return tuple(A_   )





