



from heapq import heappop, heappush

import numpy as np

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:np.ndarray	,   _snake_case	:tuple[int, int]	,   _snake_case	:tuple[int, int]	,   _snake_case	:bool	,   )  ->     tuple[float | int, list[tuple[int, int]]]:
   _A  ,    _A      =       grid.shape
   _A      =       [-1, 1, 0, 0]
   _A      =       [0, 0, -1, 1]
   if allow_diagonal:
      dx += [-1, -1, 1, 1]
      dy += [-1, 1, -1, 1]

   _A  ,    _A      =       [(0, source)], set()
   _A      =       np.full((rows, cols)	,   np.inf				)
   _A      =       0
   _A      =       np.empty((rows, cols)	,   dtype=_snake_case				)
   _A      =       None

   while queue:
      ((_A)  ,    (_A))      =       heappop(_snake_case				)
      if (x, y) in visited:
         continue
      visited.add((x, y)				)

      if (x, y) == destination:
         _A      =       []
         while (x, y) != source:
            path.append((x, y)				)
            _A  ,    _A      =       predecessors[x, y]
         path.append(_snake_case				)  # add the source manually
         path.reverse()
         return matrix[destination], path

      for i in range(len(_snake_case				)				):
         _A  ,    _A      =       x + dx[i], y + dy[i]
         if 0 <= nx < rows and 0 <= ny < cols:
            _A      =       grid[nx][ny]
            if next_node == 1 and matrix[nx, ny] > dist + 1:
               heappush(_snake_case	,   (dist + 1, (nx, ny))				)
               _A      =       dist + 1
               _A      =       (x, y)

   return np.inf, []


if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






