"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
    """distilbert-base-uncased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
    """distilbert-base-cased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
    """distilbert-base-multilingual-cased""": (
        """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
    ),
    """distilbert-base-uncased-finetuned-sst-2-english""": (
        """https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
    ),
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Tuple  				=				'distilbert'
    _snake_case  :	List[Any]  				=				{
        'hidden_size': 'dim',
        'num_attention_heads': 'n_heads',
        'num_hidden_layers': 'n_layers',
    }


    def __init__(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[Any]=3_0522 ,					__lowerCAmelCase						:	List[Any]=512 ,					__lowerCAmelCase						:	List[str]=False ,					__lowerCAmelCase						:	str=6 ,					__lowerCAmelCase						:	Optional[Any]=12 ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	Tuple=4 * 768 ,					__lowerCAmelCase						:	Dict=0.1 ,					__lowerCAmelCase						:	Tuple=0.1 ,					__lowerCAmelCase						:	int="gelu" ,					__lowerCAmelCase						:	Union[str, Any]=0.02 ,					__lowerCAmelCase						:	Tuple=0.1 ,					__lowerCAmelCase						:	List[Any]=0.2 ,					__lowerCAmelCase						:	Union[str, Any]=0 ,					**__lowerCAmelCase						:	Dict ,					):
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   max_position_embeddings
      _UpperCAmelCase    			=   sinusoidal_pos_embds
      _UpperCAmelCase    			=   n_layers
      _UpperCAmelCase    			=   n_heads
      _UpperCAmelCase    			=   dim
      _UpperCAmelCase    			=   hidden_dim
      _UpperCAmelCase    			=   dropout
      _UpperCAmelCase    			=   attention_dropout
      _UpperCAmelCase    			=   activation
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   qa_dropout
      _UpperCAmelCase    			=   seq_classif_dropout
      super().__init__(**__lowerCAmelCase ,					pad_token_id=__lowerCAmelCase       )



class 							a					(		lowerCAmelCase_ ):


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

"""simple docstring"""


import heapq






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # 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
  _UpperCAmelCase    			=   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
    _UpperCAmelCase    			=   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]:
        _UpperCAmelCase    			=   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()

    UpperCAmelCase__			=    {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)}''')
