






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


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

a_						:     Tuple	     =		{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}





class    lowerCamelCase__  (						_UpperCAmelCase):
     """simple docstring"""



     _A       =			'deberta-v2'







     def __init__(self				,			__a=12_81_00				,			__a=15_36				,			__a=24				,			__a=24				,			__a=61_44				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=5_12				,			__a=0				,			__a=0.02				,			__a=1E-7				,			__a=False				,			__a=-1				,			__a=0				,			__a=True				,			__a=None				,			__a=0				,			__a="gelu"				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__UpperCamelCase    )

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

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

           lowerCamelCase								=		pos_att_type
           lowerCamelCase								=		vocab_size
           lowerCamelCase								=		layer_norm_eps

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





class    lowerCamelCase__  (						_UpperCAmelCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           if self.task == "multiple-choice":
                 lowerCamelCase								=		{0: "batch", 1: "choice", 2: "sequence"}
           else:
                 lowerCamelCase								=		{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 						_a      (self    ):






           '''simple docstring'''



           return 12







     def 						_a      (self				,			__a				,			__a = -1				,			__a = -1				,			__a = -1				,			__a = False				,			__a = None				,			__a = 3				,			__a = 40				,			__a = 40				,			__a = None				,			):






           '''simple docstring'''



           lowerCamelCase								=		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




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if weight not in (0, 1):
                 raise ValueError("Edge weight must be either 0 or 1."    )

           if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("Vertex indexes must be in [0; size)."    )

           self._graph[from_vertex].append(Edge(__a				,			__a    )    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

           while queue:
                 lowerCamelCase								=		queue.popleft()
                 lowerCamelCase								=		distances[current_vertex]
                 if current_distance is None:
                       continue

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		new_distance
                       if edge.weight == 0:
                             queue.appendleft(edge.destination_vertex    )
                       else:
                             queue.append(edge.destination_vertex    )

           if distances[finish_vertex] is None:
                 raise ValueError("No path from start_vertex to finish_vertex."    )

           return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()