



from heapq import heappop, heappush

import numpy as np
def 		UpperCamelCase     (       snake_case__	:   np.ndarray	,					snake_case__	:   tuple[int, int]	,					snake_case__	:   tuple[int, int]	,					snake_case__	:   bool	,					)     ->					tuple[float | int, list[tuple[int, int]]]:
   UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     grid.shape
   UpperCamelCase   :  Tuple						     =     [-1, 1, 0, 0]
   UpperCamelCase   :  List[str]						     =     [0, 0, -1, 1]
   if allow_diagonal:
      dx += [-1, -1, 1, 1]
      dy += [-1, 1, -1, 1]

   UpperCamelCase     ,					UpperCamelCase   :  List[str]						     =     [(0, source)], set()
   UpperCamelCase   :  Optional[Any]						     =     np.full((rows, cols)	,					np.inf     )
   UpperCamelCase   :  Union[str, Any]						     =     0
   UpperCamelCase   :  Optional[Any]						     =     np.empty((rows, cols)	,					dtype=snake_case__     )
   UpperCamelCase   :  List[str]						     =     None

   while queue:
      ((UpperCamelCase)     ,					(UpperCamelCase))   :  Optional[int]						     =     heappop(snake_case__     )
      if (x, y) in visited:
         continue
      visited.add((x, y)     )

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

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

   return np.inf, []


if __name__ == "__main__":
    import doctest

    doctest.testmod()








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  "data2vec-text"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  List[str]						     =     type_vocab_size
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[str]						     =     position_embedding_type
          UpperCamelCase   :  Any						     =     use_cache
          UpperCamelCase   :  Any						     =     classifier_dropout






class     lowerCAmelCase_		(  a__							):
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             UpperCamelCase   :  List[Any]						     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
             UpperCamelCase   :  int						     =     {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]	)



