



'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowercase__     :  List[Any]    					=			logging.get_logger(__name__)

lowercase__     :  List[Any]    					=			{
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     str				=	"deberta"


   def __init__(  self     ,						__UpperCamelCase=5_02_65     ,						__UpperCamelCase=7_68     ,						__UpperCamelCase=12     ,						__UpperCamelCase=12     ,						__UpperCamelCase=30_72     ,						__UpperCamelCase="gelu"     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=5_12     ,						__UpperCamelCase=0     ,						__UpperCamelCase=0.02     ,						__UpperCamelCase=1e-7     ,						__UpperCamelCase=False     ,						__UpperCamelCase=-1     ,						__UpperCamelCase=0     ,						__UpperCamelCase=True     ,						__UpperCamelCase=None     ,						__UpperCamelCase=0     ,						__UpperCamelCase="gelu"     ,						**__UpperCamelCase     ,						):
       super().__init__(**__UpperCamelCase )

       UpperCamelCase__      =			hidden_size
       UpperCamelCase__      =			num_hidden_layers
       UpperCamelCase__      =			num_attention_heads
       UpperCamelCase__      =			intermediate_size
       UpperCamelCase__      =			hidden_act
       UpperCamelCase__      =			hidden_dropout_prob
       UpperCamelCase__      =			attention_probs_dropout_prob
       UpperCamelCase__      =			max_position_embeddings
       UpperCamelCase__      =			type_vocab_size
       UpperCamelCase__      =			initializer_range
       UpperCamelCase__      =			relative_attention
       UpperCamelCase__      =			max_relative_positions
       UpperCamelCase__      =			pad_token_id
       UpperCamelCase__      =			position_biased_input

       # Backwards compatibility
       if type(__UpperCamelCase ) == str:
           UpperCamelCase__      =			[x.strip() for x in pos_att_type.lower().split("|" )]

       UpperCamelCase__      =			pos_att_type
       UpperCamelCase__      =			vocab_size
       UpperCamelCase__      =			layer_norm_eps

       UpperCamelCase__      =			kwargs.get("pooler_hidden_size"     ,						__UpperCamelCase )
       UpperCamelCase__      =			pooler_dropout
       UpperCamelCase__      =			pooler_hidden_act

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   @property
   def 	_UpperCamelCase       (  self ):
       if self.task == "multiple-choice":
           UpperCamelCase__      =			{0: "batch", 1: "choice", 2: "sequence"}
       else:
           UpperCamelCase__      =			{0: "batch", 1: "sequence"}
       if self._config.type_vocab_size > 0:
           return OrderedDict(
               [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
       else:
           return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )


   @property
   def 	_UpperCamelCase       (  self ):
       return 12







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = -1     ,						__UpperCamelCase = -1     ,						__UpperCamelCase = -1     ,						__UpperCamelCase = False     ,						__UpperCamelCase = None     ,						__UpperCamelCase = 3     ,						__UpperCamelCase = 40     ,						__UpperCamelCase = 40     ,						__UpperCamelCase = None     ,						):
       UpperCamelCase__      =			super().generate_dummy_inputs(preprocessor=__UpperCamelCase     ,						framework=__UpperCamelCase )
       if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
       return dummy_inputs









'''simple docstring'''







import heapq





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			[(0, start)]  # cost from start node,end node
    UpperCamelCase__      =			set()
    while heap:
        ((UpperCamelCase__)    ,  (UpperCamelCase__))      =			heapq.heappop(lowerCAmelCase_	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase_	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCamelCase__      =			cost + c
            heapq.heappush(lowerCAmelCase_					,  (next_item, v)	)
    return -1


lowercase__     :  Any    					=			{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
lowercase__     :  Optional[int]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
lowercase__     :  Union[str, Any]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

lowercase__     :  Union[str, Any]    					=			dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowercase__     :  Optional[int]    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

lowercase__     :  Any    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()




