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

_lowercase							:      Union[str, Any]      							={
    '''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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Union[str, Any]            =				"deberta-v2"







  def __init__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Any=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=24      ,	SCREAMING_SNAKE_CASE__    :					int=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Dict=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					Dict=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Tuple      ,	)    ->				Any:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     int         =hidden_size
         A						:     List[str]         =num_hidden_layers
         A						:     List[Any]         =num_attention_heads
         A						:     Any         =intermediate_size
         A						:     str         =hidden_act
         A						:     List[Any]         =hidden_dropout_prob
         A						:     List[str]         =attention_probs_dropout_prob
         A						:     List[str]         =max_position_embeddings
         A						:     Union[str, Any]         =type_vocab_size
         A						:     Optional[Any]         =initializer_range
         A						:     List[Any]         =relative_attention
         A						:     Optional[Any]         =max_relative_positions
         A						:     Union[str, Any]         =pad_token_id
         A						:     str         =position_biased_input

         # Backwards compatibility
         if type(SCREAMING_SNAKE_CASE__     ) == str:
                A						:     Optional[Any]         =[x.strip() for x in pos_att_type.lower().split('|'     )]

         A						:     Dict         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Dict         =layer_norm_eps

         A						:     str         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Any         =pooler_dropout
         A						:     Dict         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				List[str]:







         if self.task == "multiple-choice":
                A						:     Optional[Any]         ={0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                A						:     int         ={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 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional["TensorType"] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					"PreTrainedTokenizerBase" = None      ,	)    ->				Optional[Any]:
         A						:     List[str]         =super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__      ,	framework=SCREAMING_SNAKE_CASE__     )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                del dummy_inputs["token_type_ids"]
         return dummy_inputs







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     list[list]         =[]

       # for each node and his adjacency list add them and the rank of the node to queue
       # using heapq module the queue will be filled like a Priority Queue
       # heapq works with a min priority queue, so I used -1*len(v) to build it
       for key, value in graph.items():
              # O(log(n))
              heapq.heappush(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =set()

       # while queue isn't empty and there are still edges
       #   (queue[0][0] is the rank of the node with max rank)
       while queue and queue[0][0] != 0:
              # extract vertex with max rank from queue and add it to chosen_vertices
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # Remove all arcs adjacent to argmax
              for elem in queue:
                     # if v haven't adjacent node, skip
                     if elem[0] == 0:
                            continue
                     # if argmax is reachable from elem
                     # remove argmax from elem's adjacent list and update his rank
                     if argmax in elem[1][1]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')






