



"""simple docstring"""




from heapq import heappop, heappush

import numpy as np


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					)   ->	Tuple:






      '''simple docstring'''


      lowercase     :					Optional[int] 		=      grid.shape
      lowercase     :					int 		=      [-1, 1, 0, 0]
      lowercase     :					Optional[Any] 		=      [0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      lowercase     :					Tuple 		=      [(0, source)], set()
      lowercase     :					int 		=      np.full((rows, cols)		,					np.inf )
      lowercase     :					List[str] 		=      0
      lowercase     :					Tuple 		=      np.empty((rows, cols)		,					dtype=__UpperCamelCase )
      lowercase     :					Any 		=      None

      while queue:
            (lowercase)     :					Dict 		=      heappop(__UpperCamelCase )
            if (x, y) in visited:
                  continue
            visited.add((x, y) )

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

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


A__  :  Union[str, Any]								=					logging.get_logger(__name__)

A__  :  Tuple								=					{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''xlm-roberta-xl'''




							def __init__(       self     :       int				,				A_     :       List[str]=2_5_0_8_8_0				,				A_     :       List[str]=2_5_6_0				,				A_     :       Optional[int]=3_6				,				A_     :       List[Any]=3_2				,				A_     :       Optional[int]=1_0_2_4_0				,				A_     :       Dict="gelu"				,				A_     :       int=0.1				,				A_     :       Optional[Any]=0.1				,				A_     :       int=5_1_4				,				A_     :       Any=1				,				A_     :       Optional[Any]=0.02				,				A_     :       str=1e-05				,				A_     :       Dict=1				,				A_     :       Any=0				,				A_     :       Tuple=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[str]=None				,				**A_     :       Dict				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)
									lowerCAmelCase_			:     Tuple									= vocab_size
									lowerCAmelCase_			:     List[str]									= hidden_size
									lowerCAmelCase_			:     int									= num_hidden_layers
									lowerCAmelCase_			:     int									= num_attention_heads
									lowerCAmelCase_			:     Dict									= hidden_act
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= type_vocab_size
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     str									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




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





