





"""simple docstring"""
from heapq import heappop, heappush

import numpy as np



def 					__lowerCamelCase       (							a_					:		np.ndarray			,       a_					:		tuple[int, int]			,       a_					:		tuple[int, int]			,       a_					:		bool			,       )      ->				tuple[float | int, list[tuple[int, int]]]:
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  grid.shape
      __SCREAMING_SNAKE_CASE       :Dict		    =  [-1, 1, 0, 0]
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[str]		    =  [(0, source)], set()
      __SCREAMING_SNAKE_CASE       :int		    =  np.full((rows, cols)			,       np.inf							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  0
      __SCREAMING_SNAKE_CASE       :Any		    =  np.empty((rows, cols)			,       dtype=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  None

      while queue:
            ((__SCREAMING_SNAKE_CASE)    ,(__SCREAMING_SNAKE_CASE))       :List[str]		    =  heappop(a_							)
            if (x, y) in visited:
                  continue
            visited.add((x, y)							)

            if (x, y) == destination:
                  __SCREAMING_SNAKE_CASE       :int		    =  []
                  while (x, y) != source:
                        path.append((x, y)							)
                        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  predecessors[x, y]
                  path.append(a_							)  # add the source manually
                  path.reverse()
                  return matrix[destination], path

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

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()






"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''xlm-prophetnet'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  {
           '''num_attention_heads''': '''num_encoder_attention_heads''',
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = "gelu"		,SCREAMING_SNAKE_CASE__ = 3_05_22		,SCREAMING_SNAKE_CASE__ = 10_24		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 5_12		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  init_std  # Normal(0, this parameter)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  activation_function

             # parameters for xlmprophetnet
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ngram
             __SCREAMING_SNAKE_CASE       :int		    =  num_buckets
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  relative_max_distance
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  disable_ngram_loss
             __SCREAMING_SNAKE_CASE       :Dict		    =  eps

             # 3 Types of Dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dropout

             __SCREAMING_SNAKE_CASE       :int		    =  use_cache

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,add_cross_attention=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.num_encoder_layers + self.num_decoder_layers




       @num_hidden_layers.setter
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''							)